diff --git a/.gitattributes b/.gitattributes
index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..2121c89e5799bf797e36bd7ec038189772049f22 100644
--- a/.gitattributes
+++ b/.gitattributes
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+{"slug": "2603.30040v1", "url": "http://arxiv.org/abs/2603.30040v1", "pdf_url": "https://arxiv.org/pdf/2603.30040v1", "title": "Automatic Identification of Parallelizable Loops Using Transformer-Based Source Code Representations", "authors": ["Izavan dos S. Correia", "Henrique C. T. Santos", "Tiago A. E. Ferreira"], "annotation": "Automatic parallelization remains a challenging problem in software engineering, particularly in identifying code regions where loops can be safely executed in parallel on modern multi-core architectures. Traditional static analysis techniques, such as dependence analysis and polyhedral models, often struggle with irregular or dynamically structured code. In this work, we propose a Transformer-based approach to classify the parallelization potential of source code, focusing on distinguishing independent (parallelizable) loops from undefined ones. We adopt DistilBERT to process source code sequences using subword tokenization, enabling the model to capture contextual syntactic and semantic patterns without handcrafted features. The approach is evaluated on a balanced dataset combining synthetically generated loops and manually annotated real-world code, using 10-fold cross-validation and multiple performance metrics. Results show consistently high performance, with mean accuracy above 99\\% and low false positive rates, demonstrating robustness and reliability. Compared to prior token-based methods, the proposed approach simplifies preprocessing while improving generalization and maintaining computational efficiency. These findings highlight the potential of lightweight Transformer models for practical identification of parallelization opportunities at the loop level.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30040v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30036v1", "url": "http://arxiv.org/abs/2603.30036v1", "pdf_url": "https://arxiv.org/pdf/2603.30036v1", "title": "Aligned, Orthogonal or In-conflict: When can we safely optimize Chain-of-Thought?", "authors": ["Max Kaufmann", "David Lindner", "Roland S. Zimmermann", "and Rohin Shah"], "annotation": "Chain-of-Thought (CoT) monitoring, in which automated systems monitor the CoT of an LLM, is a promising approach for effectively overseeing AI systems. However, the extent to which a model's CoT helps us oversee the model - the monitorability of the CoT - can be affected by training, for instance by the model learning to hide important features of its reasoning. We propose and empirically validate a conceptual framework for predicting when and why this occurs. We model LLM post-training as an RL environment where the reward decomposes into two terms: one term depending on final outputs and another term depending on the CoT. Our framework allows us to classify these two terms as \"aligned\", \"orthogonal\", or \"in-conflict\" before training. We predict that training with in-conflict terms will reduce monitorability, orthogonal terms will not affect it, and aligned terms will improve it. To validate our framework, we use it to classify a set of RL environments, train LLMs within those environments, and evaluate how training affects CoT monitorability. We find that (1) training with \"in-conflict\" reward terms reduces CoT monitorability and (2) optimizing in-conflict reward terms is difficult.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30036v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30033v1", "url": "http://arxiv.org/abs/2603.30033v1", "pdf_url": "https://arxiv.org/pdf/2603.30033v1", "title": "Tucker Attention: A generalization of approximate attention mechanisms", "authors": ["Timon Klein", "Jonas Kusch", "Sebastian Sager", "Stefan Schnake", "Steffen Schotthöfer"], "annotation": "The pursuit of reducing the memory footprint of the self-attention mechanism in multi-headed self attention (MHA) spawned a rich portfolio of methods, e.g., group-query attention (GQA) and multi-head latent attention (MLA). The methods leverage specialized low-rank factorizations across embedding dimensions or attention heads. From the point of view of classical low-rank approximation, these methods are unconventional and raise questions of which objects they really approximate and how to interpret the low-rank behavior of the resulting representations. To answer these questions, this work proposes a generalized view on the weight objects in the self-attention layer and a factorization strategy, which allows us to construct a parameter efficient scheme, called Tucker Attention. Tucker Attention requires an order of magnitude fewer parameters for comparable validation metrics, compared to GQA and MLA, as evaluated in LLM and ViT test cases. Additionally, Tucker Attention~encompasses GQA, MLA, MHA as special cases and is fully compatible with flash-attention and rotary position embeddings (RoPE). This generalization strategy yields insights of the actual ranks achieved by MHA, GQA, and MLA, and further enables simplifications for MLA.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30033v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30031v1", "url": "http://arxiv.org/abs/2603.30031v1", "pdf_url": "https://arxiv.org/pdf/2603.30031v1", "title": "The Triadic Cognitive Architecture: Bounding Autonomous Action via Spatio-Temporal and Epistemic Friction", "authors": ["Davide Di Gioia"], "annotation": "Current autonomous AI agents, driven primarily by Large Language Models (LLMs), operate in a state of cognitive weightlessness: they process information without an intrinsic sense of network topology, temporal pacing, or epistemic limits. Consequently, heuristic agentic loops (e.g., ReAct) can exhibit failure modes in interactive environments, including excessive tool use under congestion, prolonged deliberation under time decay, and brittle behavior under ambiguous evidence. In this paper, we propose the Triadic Cognitive Architecture (TCA), a unified mathematical framework that grounds machine reasoning in continuous-time physics. By synthesizing nonlinear filtering theory, Riemannian routing geometry, and optimal control, we formally define the concept of Cognitive Friction. We map the agent's deliberation process to a coupled stochastic control problem where information acquisition is path-dependent and physically constrained. Rather than relying on arbitrary heuristic stop-tokens, the TCA uses an HJB-motivated stopping boundary and instantiates a rollout-based approximation of belief-dependent value-of-information with a net-utility halting condition. Through empirical validation in a simulated Emergency Medical Diagnostic Grid (EMDG), we demonstrate that while greedy baselines over-deliberate under latency and congestion costs, the triadic policy reduces time-to-action while improving patient viability without degrading diagnostic accuracy in this environment.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30031v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30022v1", "url": "http://arxiv.org/abs/2603.30022v1", "pdf_url": "https://arxiv.org/pdf/2603.30022v1", "title": "Hybrid Framework for Robotic Manipulation: Integrating Reinforcement Learning and Large Language Models", "authors": ["Md Saad", "Sajjad Hussain", "Mohd Suhaib"], "annotation": "This paper introduces a new hybrid framework that combines Reinforcement Learning (RL) and Large Language Models (LLMs) to improve robotic manipulation tasks. By utilizing RL for accurate low-level control and LLMs for high level task planning and understanding of natural language, the proposed framework effectively connects low-level execution with high-level reasoning in robotic systems. This integration allows robots to understand and carry out complex, human-like instructions while adapting to changing environments in real time. The framework is tested in a PyBullet-based simulation environment using the Franka Emika Panda robotic arm, with various manipulation scenarios as benchmarks. The results show a 33.5% decrease in task completion time and enhancements of 18.1% and 36.4% in accuracy and adaptability, respectively, when compared to systems that use only RL. These results underscore the potential of LLM-enhanced robotic systems for practical applications, making them more efficient, adaptable, and capable of interacting with humans. Future research will aim to explore sim-to-real transfer, scalability, and multi-robot systems to further broaden the framework's applicability.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30022v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30016v1", "url": "http://arxiv.org/abs/2603.30016v1", "pdf_url": "https://arxiv.org/pdf/2603.30016v1", "title": "Architecting Secure AI Agents: Perspectives on System-Level Defenses Against Indirect Prompt Injection Attacks", "authors": ["Chong Xiang", "Drew Zagieboylo", "Shaona Ghosh", "Sanjay Kariyappa", "Kai Greshake", "Hanshen Xiao", "Chaowei Xiao", "G. Edward Suh"], "annotation": "AI agents, predominantly powered by large language models (LLMs), are vulnerable to indirect prompt injection, in which malicious instructions embedded in untrusted data can trigger dangerous agent actions. This position paper discusses our vision for system-level defenses against indirect prompt injection attacks. We articulate three positions: (1) dynamic replanning and security policy updates are often necessary for dynamic tasks and realistic environments; (2) certain context-dependent security decisions would still require LLMs (or other learned models), but should only be made within system designs that strictly constrain what the model can observe and decide; (3) in inherently ambiguous cases, personalization and human interaction should be treated as core design considerations. In addition to our main positions, we discuss limitations of existing benchmarks that can create a false sense of utility and security. We also highlight the value of system-level defenses, which serve as the skeleton of agentic systems by structuring and controlling agent behaviors, integrating rule-based and model-based security checks, and enabling more targeted research on model robustness and human interaction.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30016v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30014v1", "url": "http://arxiv.org/abs/2603.30014v1", "pdf_url": "https://arxiv.org/pdf/2603.30014v1", "title": "Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing", "authors": ["Derek Anderson", "Amit Bashyal", "Markus Diefenthaler", "Cristiano Fanelli", "Wen Guan", "Tanja Horn", "Alex Jentsch Meifeng Lin", "Tadashi Maeno", "Kei Nagai", "Hemalata Nayak", "Connor Pecar", "Karthik Suresh", "Fang-Ying Tsai", "Anselm Vossen", "Tianle Wang", "Torre Wenaus"], "annotation": "The Production and Distributed Analysis (PanDA) system, originally developed for the ATLAS experiment at the CERN Large Hadron Collider (LHC), has evolved into a robust platform for orchestrating large-scale workflows across distributed computing resources. Coupled with its intelligent Distributed Dispatch and Scheduling (iDDS) component, PanDA supports AI/ML-driven workflows through a scalable and flexible workflow engine.   We present an AI-assisted framework for detector design optimization that integrates multi-objective Bayesian optimization with the PanDA--iDDS workflow engine to coordinate iterative simulations across heterogeneous resources. The framework addresses the challenge of exploring high-dimensional parameter spaces inherent in modern detector design.   We demonstrate the framework using benchmark problems and realistic studies of the ePIC and dRICH detectors for the Electron-Ion Collider (EIC). Results show improved automation, scalability, and efficiency in multi-objective optimization. This work establishes a flexible and extensible paradigm for AI-driven detector design and other computationally intensive scientific applications.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30014v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29999v1", "url": "http://arxiv.org/abs/2603.29999v1", "pdf_url": "https://arxiv.org/pdf/2603.29999v1", "title": "Phyelds: A Pythonic Framework for Aggregate Computing", "authors": ["Gianluca Aguzzi", "Davide Domini", "Nicolas Farabegoli", "Mirko Viroli"], "annotation": "Aggregate programming is a field-based coordination paradigm with over a decade of exploration and successful applications across domains including sensor networks, robotics, and IoT, with implementations in various programming languages, such as Protelis, ScaFi (Scala), and FCPP (C++). A recent research direction integrates machine learning with aggregate computing, aiming to support large-scale distributed learning and provide new abstractions for implementing learning algorithms. However, existing implementations do not target data science practitioners, who predominantly work in Python--the de facto language for data science and machine learning, with a rich and mature ecosystem. Python also offers advantages for other use cases, such as education and robotics (e.g., via ROS). To address this gap, we present Phyelds, a Python library for aggregate programming. Phyelds offers a fully featured yet lightweight implementation of the field calculus model of computation, featuring a Pythonic API and an architecture designed for seamless integration with Python's machine learning ecosystem. We describe the design and implementation of Phyelds and illustrate its versatility across domains, from well-known aggregate computing patterns to federated learning coordination and integration with a widely used multi-agent reinforcement learning simulator.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29999v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29997v1", "url": "http://arxiv.org/abs/2603.29997v1", "pdf_url": "https://arxiv.org/pdf/2603.29997v1", "title": "Enhancing Structural Mapping with LLM-derived Abstractions for Analogical Reasoning in Narratives", "authors": ["Mohammadhossein Khojasteh", "Yifan Jiang", "Stefano De Giorgis", "Frank van Harmelen", "Filip Ilievski"], "annotation": "Analogical reasoning is a key driver of human generalization in problem-solving and argumentation. Yet, analogies between narrative structures remain challenging for machines. Cognitive engines for structural mapping are not directly applicable, as they assume pre-extracted entities, whereas LLMs' performance is sensitive to prompt format and the degree of surface similarity between narratives. This gap motivates a key question: What is the impact of enhancing structural mapping with LLM-derived abstractions on their analogical reasoning ability in narratives? To that end, we propose a modular framework named YARN (Yielding Abstractions for Reasoning in Narratives), which uses LLMs to decompose narratives into units, abstract these units, and then passes them to a mapping component that aligns elements across stories to perform analogical reasoning. We define and operationalize four levels of abstraction that capture both the general meaning of units and their roles in the story, grounded in prior work on framing. Our experiments reveal that abstractions consistently improve model performance, resulting in competitive or better performance than end-to-end LLM baselines. Closer error analysis reveals the remaining challenges in abstraction at the right level, in incorporating implicit causality, and an emerging categorization of analogical patterns in narratives. YARN enables systematic variation of experimental settings to analyze component contributions, and to support future work, we make the code for YARN openly available.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29997v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29993v1", "url": "http://arxiv.org/abs/2603.29993v1", "pdf_url": "https://arxiv.org/pdf/2603.29993v1", "title": "Extending MONA in Camera Dropbox: Reproduction, Learned Approval, and Design Implications for Reward-Hacking Mitigation", "authors": ["Nathan Heath"], "annotation": "Myopic Optimization with Non-myopic Approval (MONA) mitigates multi-step reward hacking by restricting the agent's planning horizon while supplying far-sighted approval as a training signal~\\cite{farquhar2025mona}. The original paper identifies a critical open question: how the method of constructing approval -- particularly the degree to which approval depends on achieved outcomes -- affects whether MONA's safety guarantees hold. We present a reproduction-first extension of the public MONA Camera Dropbox environment that (i)~repackages the released codebase as a standard Python project with scripted PPO training, (ii)~confirms the published contrast between ordinary RL (91.5\\% reward-hacking rate) and oracle MONA (0.0\\% hacking rate) using the released reference arrays, and (iii)~introduces a modular learned-approval suite spanning oracle, noisy, misspecified, learned, and calibrated approval mechanisms. In reduced-budget pilot sweeps across approval methods, horizons, dataset sizes, and calibration strategies, the best calibrated learned-overseer run achieves zero observed reward hacking but substantially lower intended-behavior rates than oracle MONA (11.9\\% vs.\\ 99.9\\%), consistent with under-optimization rather than re-emergent hacking. These results operationalize the MONA paper's approval-spectrum conjecture as a runnable experimental object and suggest that the central engineering challenge shifts from proving MONA's concept to building learned approval models that preserve sufficient foresight without reopening reward-hacking channels. Code, configurations, and reproduction commands are publicly available. https://github.com/codernate92/mona-camera-dropbox-repro", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29993v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29977v1", "url": "http://arxiv.org/abs/2603.29977v1", "pdf_url": "https://arxiv.org/pdf/2603.29977v1", "title": "Quantifying Cross-Modal Interactions in Multimodal Glioma Survival Prediction via InterSHAP: Evidence for Additive Signal Integration", "authors": ["Iain Swift", "JingHua Ye", "Ruairi O'Reilly"], "annotation": "Multimodal deep learning for cancer prognosis is commonly assumed to benefit from synergistic cross-modal interactions, yet this assumption has not been directly tested in survival prediction settings. This work adapts InterSHAP, a Shapley interaction index-based metric, from classification to Cox proportional hazards models and applies it to quantify cross-modal interactions in glioma survival prediction. Using TCGA-GBM and TCGA-LGG data (n=575), we evaluate four fusion architectures combining whole-slide image (WSI) and RNA-seq features. Our central finding is an inverse relationship between predictive performance and measured interaction: architectures achieving superior discrimination (C-index 0.64$\\to$0.82) exhibit equivalent or lower cross-modal interaction (4.8\\%$\\to$3.0\\%). Variance decomposition reveals stable additive contributions across all architectures (WSI${\\approx}$40\\%, RNA${\\approx}$55\\%, Interaction${\\approx}$4\\%), indicating that performance gains arise from complementary signal aggregation rather than learned synergy. These findings provide a practical model auditing tool for comparing fusion strategies, reframe the role of architectural complexity in multimodal fusion, and have implications for privacy-preserving federated deployment.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29977v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29968v1", "url": "http://arxiv.org/abs/2603.29968v1", "pdf_url": "https://arxiv.org/pdf/2603.29968v1", "title": "Trimodal Deep Learning for Glioma Survival Prediction: A Feasibility Study Integrating Histopathology, Gene Expression, and MRI", "authors": ["Iain Swift", "JingHua Ye"], "annotation": "Multimodal deep learning has improved prognostic accuracy for brain tumours by integrating histopathology and genomic data, yet the contribution of volumetric MRI within unified survival frameworks remains unexplored. This pilot study extends a bimodal framework by incorporating Fluid Attenuated Inversion Recovery (FLAIR) MRI from BraTS2021 as a third modality. Using the TCGA-GBMLGG cohort (664 patients), we evaluate three unimodal models, nine bimodal configurations, and three trimodal configurations across early, late, and joint fusion strategies. In this small cohort setting, trimodal early fusion achieves an exploratory Composite Score (CS = 0.854), with a controlled $Δ$CS of +0.011 over the bimodal baseline on identical patients, though this difference is not statistically significant (p = 0.250, permutation test). MRI achieves reasonable unimodal discrimination (CS = 0.755) but does not substantially improve bimodal pairs, while providing measurable uplift in the three-way combination. All MRI containing experiments are constrained to 19 test patients, yielding wide bootstrap confidence intervals (e.g. [0.400,1.000]) that preclude definitive conclusions. These findings provide preliminary evidence that a third imaging modality may add prognostic value even with limited sample sizes, and that additional modalities require sufficient multimodal context to contribute effectively.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29968v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29953v1", "url": "http://arxiv.org/abs/2603.29953v1", "pdf_url": "https://arxiv.org/pdf/2603.29953v1", "title": "Structured Intent as a Protocol-Like Communication Layer: Cross-Model Robustness, Framework Comparison, and the Weak-Model Compensation Effect", "authors": ["Peng Gang"], "annotation": "How reliably can structured intent representations preserve user goals across different AI models, languages, and prompting frameworks? Prior work showed that PPS (Prompt Protocol Specification), a 5W3H-based structured intent framework, improves goal alignment in Chinese and generalizes to English and Japanese. This paper extends that line of inquiry in three directions: cross-model robustness across Claude, GPT-4o, and Gemini 2.5 Pro; controlled comparison with CO-STAR and RISEN; and a user study (N=50) of AI-assisted intent expansion in ecologically valid settings. Across 3,240 model outputs (3 languages x 6 conditions x 3 models x 3 domains x 20 tasks), evaluated by an independent judge (DeepSeek-V3), we find that structured prompting substantially reduces cross-language score variance relative to unstructured baselines. The strongest structured conditions reduce cross-language sigma from 0.470 to about 0.020. We also observe a weak-model compensation pattern: the lowest-baseline model (Gemini) shows a much larger D-A gain (+1.006) than the strongest model (Claude, +0.217). Under the current evaluation resolution, 5W3H, CO-STAR, and RISEN achieve similarly high goal-alignment scores, suggesting that dimensional decomposition itself is an important active ingredient. In the user study, AI-expanded 5W3H prompts reduce interaction rounds by 60 percent and increase user satisfaction from 3.16 to 4.04. These findings support the practical value of structured intent representation as a robust, protocol-like communication layer for human-AI interaction.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29953v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29950v1", "url": "http://arxiv.org/abs/2603.29950v1", "pdf_url": "https://arxiv.org/pdf/2603.29950v1", "title": "Physiological and Semantic Patterns in Medical Teams Using an Intelligent Tutoring System", "authors": ["Xiaoshan Huang", "Conrad Borchers", "Jiayi Zhang", "Susanne P. Lajoie"], "annotation": "Effective collaboration requires teams to manage complex cognitive and emotional states through Socially Shared Regulation of Learning (SSRL). Physiological synchrony (i.e., longitudinal alignment in physiological signals) can indicate these states, but is hard to interpret on its own. We investigate the physiological and conversational dynamics of four medical dyads diagnosing a virtual patient case using an intelligent tutoring system. Semantic shifts in dialogue were correlated with transient physiological synchrony peaks. We also coded utterance segments for SSRL and derived cosine similarity using sentence embeddings. The results showed that activating prior knowledge featured significantly lower semantic similarity than simpler task execution. High physiological synchrony was associated with lower semantic similarity, suggesting that such moments involve exploratory and varied language use. Qualitative analysis triangulated these synchrony peaks as ``pivotal moments'': successful teams synchronized during shared discovery, while unsuccessful teams peaked during shared uncertainty. This research advances human-centered AI by demonstrating how biological signals can be fused with dialogues to understand critical moments in problem solving.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29950v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29944v1", "url": "http://arxiv.org/abs/2603.29944v1", "pdf_url": "https://arxiv.org/pdf/2603.29944v1", "title": "Four Generations of Quantum Biomedical Sensors", "authors": ["Xin Jin", "Priyam Srivastava", "Ronghe Wang", "Yuqing Li", "Jonathan Beaumariage", "Tom Purdy", "M. V. Gurudev Dutt", "Kang Kim", "Kaushik Seshadreesan", "Junyu Liu"], "annotation": "Quantum sensing technologies offer transformative potential for ultra-sensitive biomedical sensing, yet their clinical translation remains constrained by classical noise limits and a reliance on macroscopic ensembles. We propose a unifying generational framework to organize the evolving landscape of quantum biosensors based on their utilization of quantum resources. First-generation devices utilize discrete energy levels for signal transduction but follow classical scaling laws. Second-generation sensors exploit quantum coherence to reach the standard quantum limit, while third-generation architectures leverage entanglement and spin squeezing to approach Heisenberg-limited precision. We further define an emerging fourth generation characterized by the end-to-end integration of quantum sensing with quantum learning and variational circuits, enabling adaptive inference directly within the quantum domain. By analyzing critical parameters such as bandwidth matching and sensor-tissue proximity, we identify key technological bottlenecks and propose a roadmap for transitioning from measuring physical observables to extracting structured biological information with quantum-enhanced intelligence.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29944v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29935v1", "url": "http://arxiv.org/abs/2603.29935v1", "pdf_url": "https://arxiv.org/pdf/2603.29935v1", "title": "Rethinking AI Literacy Education in Higher Education: Bridging Risk Perception and Responsible Adoption", "authors": ["Shasha Yu", "Fiona Carroll", "Barry L. Bentley"], "annotation": "As AI becomes increasingly embedded across societal domains, understanding how future AI practitioners, particularly technology students, perceive its risks is essential for responsible development and adoption. This study analyzed responses from 139 students in Computer Science, Data Science/Data Analytics, and other disciplines using both explicit AI risk ratings and scenario-based assessments of risk and adoption willingness. Four key findings emerged: (1) Students expressed substantially higher concern for concrete, explicitly stated risks than for abstract or scenario-embedded risks; (2) Perceived risk and willingness to adopt AI demonstrated a clear inverse relationship; (3) Although technical education narrowed gender differences in risk awareness, male students reported higher adoption willingness; and (4) A form of \"risk underappreciation\" was observed, wherein students in AI-related specializations showed both elevated explicit risk awareness and higher willingness to adopt AI, despite lower recognition of risks in applied scenarios. These findings underscore the need for differentiated AI literacy strategies that bridge the gap between awareness and responsible adoption and offer valuable insights for educators, policymakers, industry leaders, and academic institutions aiming to cultivate ethically informed and socially responsible AI practitioners.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29935v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29932v1", "url": "http://arxiv.org/abs/2603.29932v1", "pdf_url": "https://arxiv.org/pdf/2603.29932v1", "title": "Bethe Ansatz with a Large Language Model", "authors": ["Balázs Pozsgay", "István Vona"], "annotation": "We explore the capability of a Large Language Model (LLM) to perform specific computations in mathematical physics: the task is to compute the coordinate Bethe Ansatz solution of selected integrable spin chain models. We select three integrable Hamiltonians for which the solutions were unpublished; two of the Hamiltonians are actually new. We observed that the LLM semi-autonomously solved the task in all cases, with a few mistakes along the way. These were corrected after the human researchers spotted them. The results of the LLM were checked against exact diagonalization (performed by separate programs), and the derivations were also checked by the authors. The Bethe Ansatz solutions are interesting in themselves. Our second model manifestly breaks left-right invariance, but it is PT-symmetric, therefore its solution could be interesting for applications in Generalized Hydrodynamics. And our third model is solved by a special form of the nested Bethe Ansatz, where the model is interacting, but the nesting level has a free fermionic structure lacking $U(1)$-invariance. This structure appears to be unique and it was found by the LLM. We used ChatGPT 5.2 Pro and 5.4 Pro by OpenAI.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29932v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29928v1", "url": "http://arxiv.org/abs/2603.29928v1", "pdf_url": "https://arxiv.org/pdf/2603.29928v1", "title": "ScoringBench: A Benchmark for Evaluating Tabular Foundation Models with Proper Scoring Rules", "authors": ["Jonas Landsgesell", "Pascal Knoll"], "annotation": "Tabular foundation models such as TabPFN and TabICL already produce full predictive distributions yet prevailing regression benchmarks evaluate them almost exclusively via point estimate metrics RMSE R2 These aggregate measures often obscure model performance in the tails of the distribution a critical deficit for high stakes decision making in domains like finance and clinical research where asymmetric risk profiles are the norm We introduce ScoringBench an open benchmark that computes a comprehensive suite of proper scoring rules like CRPS CRLS Interval Score Energy Score weighted CRPS and Brier Score alongside standard point metrics providing a richer picture of probabilistic forecast quality We evaluate realTabPFNv2.5 fine tuned with different scoring rule objectives and TabICL relative to untuned realTabPFNv2.5 across a suite of regression benchmarks Our results confirm that model rankings depend on the chosen scoring rule and that no single pretraining objective is universally optimal This demonstrates that for applications sensitive to extreme events the choice of evaluation metric is as much a domain specific requirement as the data itself ScoringBench is available at https://github.com/jonaslandsgesell/ScoringBench A live preview of the current leaderboard is available at https://scoringbench.bolt.host The leaderboard is maintained via git pull requests to ensure transparency traceability agility and reproducibility", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29928v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29927v1", "url": "http://arxiv.org/abs/2603.29927v1", "pdf_url": "https://arxiv.org/pdf/2603.29927v1", "title": "End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines", "authors": ["Raül Pérez-Gonzalo", "Andreas Espersen", "Søren Forchhammer", "Antonio Agudo"], "annotation": "Transferring large volumes of high-resolution images during wind turbine inspections introduces a bottleneck in assessing and detecting severe defects. Efficient coding must preserve high fidelity in blade regions while aggressively compressing the background. In this work, we propose an end-to-end deep learning framework that jointly performs segmentation and dual-mode (lossy and lossless) compression. The segmentation module accurately identifies the blade region, after which our region-of-interest (ROI) compressor encodes it at superior quality compared to the rest of the image. Unlike conventional ROI schemes that merely allocate more bits to salient areas, our framework integrates: (i) a robust segmentation network (BU-Netv2+P) with a CRF-regularized loss for precise blade localization, (ii) a hyperprior-based autoencoder optimized for lossy compression, and (iii) an extended bits-back coder with hierarchical models for fully lossless blade reconstruction. Furthermore, our ROI framework removes the sequential dependency in bits-back coding by reusing background-coded bits, enabling parallelized and efficient dual-mode compression. To the best of our knowledge, this is the first fully integrated learning-based ROI codec combining segmentation, lossy, and lossless compression, ensuring that subsequent defect detection is not compromised. Experiments on a large-scale wind turbine dataset demonstrate superior compression performance and efficiency, offering a practical solution for automated inspections.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29927v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29922v1", "url": "http://arxiv.org/abs/2603.29922v1", "pdf_url": "https://arxiv.org/pdf/2603.29922v1", "title": "Training deep learning based dynamic MR image reconstruction using synthetic fractals", "authors": ["Anirudh Raman", "Olivier Jaubert", "Mark Wrobel", "Tina Yao", "Ruaraidh Campbell", "Rebecca Baker", "Ruta Virsinskaite", "Daniel Knight", "Michael Quail", "Jennifer Steeden", "Vivek Muthurangu"], "annotation": "Purpose: To investigate whether synthetically generated fractal data can be used to train deep learning (DL) models for dynamic MRI reconstruction, thereby avoiding the privacy, licensing, and availability limitations associated with cardiac MR training datasets. Methods: A training dataset was generated using quaternion Julia fractals to produce 2D+time images. Multi-coil MRI acquisition was simulated to generate paired fully sampled and radially undersampled k-space data. A 3D UNet deep artefact suppression model was trained using these fractal data (F-DL) and compared with an identical model trained on cardiac MRI data (CMR-DL). Both models were evaluated on prospectively acquired radial real-time cardiac MRI from 10 patients. Reconstructions were compared against compressed sensing(CS) and low-rank deep image prior (LR-DIP). All reconstrctuions were ranked for image quality, while ventricular volumes and ejection fraction were compared with reference breath-hold cine MRI. Results: There was no significant difference in qualitative ranking between F-DL and CMR-DL (p=0.9), while both outperformed CS and LR-DIP (p<0.001). Ventricular volumes and function derived from F-DL were similar to CMR-DL, showing no significant bias and accptable limits of agreement compared to reference cine imaging. However, LR-DIP had a signifcant bias (p=0.016) and wider lmits of agreement. Conclusion: DL models trained using synthetic fractal data can reconstruct real-time cardiac MRI with image quality and clinical measurements comparable to models trained on true cardiac MRI data. Fractal training data provide an open, scalable alternative to clinical datasets and may enable development of more generalisable DL reconstruction models for dynamic MRI.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29922v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29915v1", "url": "http://arxiv.org/abs/2603.29915v1", "pdf_url": "https://arxiv.org/pdf/2603.29915v1", "title": "Uncertainty Gating for Cost-Aware Explainable Artificial Intelligence", "authors": ["Georgii Mikriukov", "Grégoire Montavon", "Marina M. -C. Höhne"], "annotation": "Post-hoc explanation methods are widely used to interpret black-box predictions, but their generation is often computationally expensive and their reliability is not guaranteed. We propose epistemic uncertainty as a low-cost proxy for explanation reliability: high epistemic uncertainty identifies regions where the decision boundary is poorly defined and where explanations become unstable and unfaithful. This insight enables two complementary use cases: `improving worst-case explanations' (routing samples to cheap or expensive XAI methods based on expected explanation reliability), and `recalling high-quality explanations' (deferring explanation generation for uncertain samples under constrained budget). Across four tabular datasets, five diverse architectures, and four XAI methods, we observe a strong negative correlation between epistemic uncertainty and explanation stability. Further analysis shows that epistemic uncertainty distinguishes not only stable from unstable explanations, but also faithful from unfaithful ones. Experiments on image classification confirm that our findings generalize beyond tabular data.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29915v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29913v1", "url": "http://arxiv.org/abs/2603.29913v1", "pdf_url": "https://arxiv.org/pdf/2603.29913v1", "title": "SISA: A Scale-In Systolic Array for GEMM Acceleration", "authors": ["Luigi Altamura", "Alessio Cicero", "Mateo Vázquez Maceiras", "Mohammad Ali Maleki", "Pedro Trancoso"], "annotation": "The currently dominant AI/ML workloads, such as Large Language Models (LLMs), rely on the efficient execution of General Matrix-Matrix Multiplication (GEMM) operations. Thus, most systems are equipped with dedicated matrix hardware accelerators based on square Systolic Arrays (SAs) of Processing Elements (PEs). While this organization was effective for traditional Deep Neural Networks (DNNs), LLMs introduce input-dependent and highly skewed matrices, leading to underutilized SA resources. To address this challenge, we propose SISA (Scale-In Systolic Array), a novel SA architecture that partitions the traditional square array into horizontal rectangular slabs. With minimal overhead, SISA exposes parallelism through independently scheduled slabs for efficient execution of small or skewed matrix shapes, while retaining full-array operation for large GEMMs. SISA achieves up to 8.52x speedup and 93% energy-delay-product (EDP) reduction for representative LLMs compared to a state-of-the-art monolithic SA with the same number of PEs.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29913v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29908v1", "url": "http://arxiv.org/abs/2603.29908v1", "pdf_url": "https://arxiv.org/pdf/2603.29908v1", "title": "C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving", "authors": ["Zhihong Cui", "Haoran Tang", "Tianyi Li", "Yushuai Li", "Peiyuan Guan", "Amir Taherkordi", "Tor Skeie"], "annotation": "Trajectory planning for autonomous driving increasingly leverages large language models (LLMs) for commonsense reasoning, yet LLM outputs are inherently unreliable, posing risks in safety-critical applications. We propose C-TRAIL, a framework built on a Commonsense World that couples LLM-derived commonsense with a trust mechanism to guide trajectory planning. C-TRAIL operates through a closed-loop Recall, Plan, and Update cycle: the Recall module queries an LLM for semantic relations and quantifies their reliability via a dual-trust mechanism; the Plan module injects trust-weighted commonsense into Monte Carlo Tree Search (MCTS) through a Dirichlet trust policy; and the Update module adaptively refines trust scores and policy parameters from environmental feedback. Experiments on four simulated scenarios in Highway-env and two real-world levelXData datasets (highD, rounD) show that C-TRAIL consistently outperforms state-of-the-art baselines, reducing ADE by 40.2%, FDE by 51.7%, and improving SR by 16.9 percentage points on average. The source code is available at https://github.com/ZhihongCui/CTRAIL.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29908v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29902v1", "url": "http://arxiv.org/abs/2603.29902v1", "pdf_url": "https://arxiv.org/pdf/2603.29902v1", "title": "ATP-Bench: Towards Agentic Tool Planning for MLLM Interleaved Generation", "authors": ["Yinuo Liu", "Zi Qian", "Heng Zhou", "Jiahao Zhang", "Yajie Zhang", "Zhihang Li", "Mengyu Zhou", "Erchao Zhao", "Xiaoxi Jiang", "Guanjun Jiang"], "annotation": "Interleaved text-and-image generation represents a significant frontier for Multimodal Large Language Models (MLLMs), offering a more intuitive way to convey complex information. Current paradigms rely on either image generation or retrieval augmentation, yet they typically treat the two as mutually exclusive paths, failing to unify factuality with creativity. We argue that the next milestone in this field is Agentic Tool Planning, where the model serves as a central controller that autonomously determines when, where, and which tools to invoke to produce interleaved responses for visual-critical queries. To systematically evaluate this paradigm, we introduce ATP-Bench, a novel benchmark comprising 7,702 QA pairs (including 1,592 VQA pairs) across eight categories and 25 visual-critical intents, featuring human-verified queries and ground truths. Furthermore, to evaluate agentic planning independent of end-to-end execution and changing tool backends, we propose a Multi-Agent MLLM-as-a-Judge (MAM) system. MAM evaluates tool-call precision, identifies missed opportunities for tool use, and assesses overall response quality without requiring ground-truth references. Our extensive experiments on 10 state-of-the-art MLLMs reveal that models struggle with coherent interleaved planning and exhibit significant variations in tool-use behavior, highlighting substantial room for improvement and providing actionable guidance for advancing interleaved generation. Dataset and code are available at https://github.com/Qwen-Applications/ATP-Bench.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29902v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29871v1", "url": "http://arxiv.org/abs/2603.29871v1", "pdf_url": "https://arxiv.org/pdf/2603.29871v1", "title": "ShapE-GRPO: Shapley-Enhanced Reward Allocation for Multi-Candidate LLM Training", "authors": ["Rui Ai", "Yu Pan", "David Simchi-Levi", "Chonghuan Wang"], "annotation": "In user-agent interaction scenarios such as recommendation, brainstorming, and code suggestion, Large Language Models (LLMs) often generate sets of candidate recommendations where the objective is to maximize the collective utility of the entire set rather than individual candidates independently. However, existing reinforcement learning post-training paradigms, such as Group Relative Policy Optimization (GRPO), typically assign the same set-level scalar reward to every candidate in the set. This leads to noisy training signals where poor candidates free-ride on the high reward produced by a single strong peer, resulting in suboptimal exploration. To address this, we propose Shapley-Enhanced GRPO (ShapE-GRPO). By leveraging the permutation-invariant nature of set-level utility, we derive a Shapley-enhanced formulation from cooperative game theory to decompose set-level rewards into granular, candidate-specific signals. We show that our formulation preserves the fundamental axioms of the Shapley value while remaining computationally efficient with polynomial-time complexity. Empirically, ShapE-GRPO consistently outperforms standard GRPO across diverse datasets with accelerated convergence during training.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29871v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29861v1", "url": "http://arxiv.org/abs/2603.29861v1", "pdf_url": "https://arxiv.org/pdf/2603.29861v1", "title": "Towards Empowering Consumers through Sentence-level Readability Scoring in German ESG Reports", "authors": ["Benjamin Josef Schüßler", "Jakob Prange"], "annotation": "With the ever-growing urgency of sustainability in the economy and society, and the massive stream of information that comes with it, consumers need reliable access to that information. To address this need, companies began publishing so called Environmental, Social, and Governance (ESG) reports, both voluntarily and forced by law. To serve the public, these reports must be addressed not only to financial experts but also to non-expert audiences. But are they written clearly enough? In this work, we extend an existing sentence-level dataset of German ESG reports with crowdsourced readability annotations. We find that, in general, native speakers perceive sentences in ESG reports as easy to read, but also that readability is subjective. We apply various readability scoring methods and evaluate them regarding their prediction error and correlation with human rankings. Our analysis shows that, while LLM prompting has potential for distinguishing clear from hard-to-read sentences, a small finetuned transformer predicts human readability with the lowest error. Averaging predictions of multiple models can slightly improve the performance at the cost of slower inference.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29861v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29844v1", "url": "http://arxiv.org/abs/2603.29844v1", "pdf_url": "https://arxiv.org/pdf/2603.29844v1", "title": "DIAL: Decoupling Intent and Action via Latent World Modeling for End-to-End VLA", "authors": ["Yi Chen", "Yuying Ge", "Hui Zhou", "Mingyu Ding", "Yixiao Ge", "Xihui Liu"], "annotation": "The development of Vision-Language-Action (VLA) models has been significantly accelerated by pre-trained Vision-Language Models (VLMs). However, most existing end-to-end VLAs treat the VLM primarily as a multimodal encoder, directly mapping vision-language features to low-level actions. This paradigm underutilizes the VLM's potential in high-level decision making and introduces training instability, frequently degrading its rich semantic representations. To address these limitations, we introduce DIAL, a framework bridging high-level decision making and low-level motor execution through a differentiable latent intent bottleneck. Specifically, a VLM-based System-2 performs latent world modeling by synthesizing latent visual foresight within the VLM's native feature space; this foresight explicitly encodes intent and serves as the structural bottleneck. A lightweight System-1 policy then decodes this predicted intent together with the current observation into precise robot actions via latent inverse dynamics. To ensure optimization stability, we employ a two-stage training paradigm: a decoupled warmup phase where System-2 learns to predict latent futures while System-1 learns motor control under ground-truth future guidance within a unified feature space, followed by seamless end-to-end joint optimization. This enables action-aware gradients to refine the VLM backbone in a controlled manner, preserving pre-trained knowledge. Extensive experiments on the RoboCasa GR1 Tabletop benchmark show that DIAL establishes a new state-of-the-art, achieving superior performance with 10x fewer demonstrations than prior methods. Furthermore, by leveraging heterogeneous human demonstrations, DIAL learns physically grounded manipulation priors and exhibits robust zero-shot generalization to unseen objects and novel configurations during real-world deployment on a humanoid robot.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29844v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29828v1", "url": "http://arxiv.org/abs/2603.29828v1", "pdf_url": "https://arxiv.org/pdf/2603.29828v1", "title": "Owl-AuraID 1.0: An Intelligent System for Autonomous Scientific Instrumentation and Scientific Data Analysis", "authors": ["Han Deng", "Anqi Zou", "Hanling Zhang", "Ben Fei", "Chengyu Zhang", "Haobo Wang", "Xinru Guo", "Zhenyu Li", "Xuzhu Wang", "Peng Yang", "Fujian Zhang", "Weiyu Guo", "Xiaohong Shao", "Zhaoyang Liu", "Shixiang Tang", "Zhihui Wang", "Wanli Ouyang"], "annotation": "Scientific discovery increasingly depends on high-throughput characterization, yet automation is hindered by proprietary GUIs and the limited generalizability of existing API-based systems. We present Owl-AuraID, a software-hardware collaborative embodied agent system that adopts a GUI-native paradigm to operate instruments through the same interfaces as human experts. Its skill-centric framework integrates Type-1 (GUI operation) and Type-2 (data analysis) skills into end-to-end workflows, connecting physical sample handling with scientific interpretation. Owl-AuraID demonstrates broad coverage across ten categories of precision instruments and diverse workflows, including multimodal spectral analysis, microscopic imaging, and crystallographic analysis, supporting modalities such as FTIR, NMR, AFM, and TGA. Overall, Owl-AuraID provides a practical, extensible foundation for autonomous laboratories and illustrates a path toward evolving laboratory intelligence through reusable operational and analytical skills. The code are available at https://github.com/OpenOwlab/AuraID.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29828v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29805v1", "url": "http://arxiv.org/abs/2603.29805v1", "pdf_url": "https://arxiv.org/pdf/2603.29805v1", "title": "From Density Matrices to Phase Transitions in Deep Learning: Spectral Early Warnings and Interpretability", "authors": ["Max Hennick", "Guillaume Corlouer"], "annotation": "A key problem in the modern study of AI is predicting and understanding emergent capabilities in models during training. Inspired by methods for studying reactions in quantum chemistry, we present the ``2-datapoint reduced density matrix\". We show that this object provides a computationally efficient, unified observable of phase transitions during training. By tracking the eigenvalue statistics of the 2RDM over a sliding window, we derive two complementary signals: the spectral heat capacity, which we prove provides early warning of second-order phase transitions via critical slowing down, and the participation ratio, which reveals the dimensionality of the underlying reorganization. Remarkably, the top eigenvectors of the 2RDM are directly interpretable making it straightforward to study the nature of the transitions. We validate across four settings distinct settings: deep linear networks, induction head formation, grokking, and emergent misalignment. We then discuss directions for future work using the 2RDM.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29805v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29791v1", "url": "http://arxiv.org/abs/2603.29791v1", "pdf_url": "https://arxiv.org/pdf/2603.29791v1", "title": "Reasoning-Driven Synthetic Data Generation and Evaluation", "authors": ["Tim R. Davidson", "Benoit Seguin", "Enrico Bacis", "Cesar Ilharco", "Hamza Harkous"], "annotation": "Although many AI applications of interest require specialized multi-modal models, relevant data to train such models is inherently scarce or inaccessible. Filling these gaps with human annotators is prohibitively expensive, error-prone, and time-consuming, leading model builders to increasingly consider synthetic data as a scalable alternative. However, existing synthetic data generation methods often rely on manual prompts, evolutionary algorithms, or extensive seed data from the target distribution - limiting their scalability, explainability, and control. In this paper, we introduce Simula: a novel reasoning-driven framework for data generation and evaluation. It employs a seedless, agentic approach to generate synthetic datasets at scale, allowing users to define desired dataset characteristics through an explainable and controllable process that enables fine-grained resource allocation. We show the efficacy of our approach on a variety of datasets, rigorously testing both intrinsic and downstream properties. Our work (1) offers guidelines for synthetic data mechanism design, (2) provides insights into generating and evaluating synthetic data at scale, and (3) unlocks new opportunities for developing and deploying AI in domains where data scarcity or privacy concerns are paramount.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29791v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29777v1", "url": "http://arxiv.org/abs/2603.29777v1", "pdf_url": "https://arxiv.org/pdf/2603.29777v1", "title": "From Skeletons to Semantics: Design and Deployment of a Hybrid Edge-Based Action Detection System for Public Safety", "authors": ["Ganen Sethupathy", "Lalit Dumka", "Jan Schagen"], "annotation": "Public spaces such as transport hubs, city centres, and event venues require timely and reliable detection of potentially violent behaviour to support public safety. While automated video analysis has made significant progress, practical deployment remains constrained by latency, privacy, and resource limitations, particularly under edge-computing conditions. This paper presents the design and demonstrator-based deployment of a hybrid edge-based action detection system that combines skeleton-based motion analysis with vision-language models for semantic scene interpretation. Skeleton-based processing enables continuous, privacy-aware monitoring with low computational overhead, while vision-language models provide contextual understanding and zero-shot reasoning capabilities for complex and previously unseen situations. Rather than proposing new recognition models, the contribution focuses on a system-level comparison of both paradigms under realistic edge constraints. The system is implemented on a GPU-enabled edge device and evaluated with respect to latency, resource usage, and operational trade-offs using a demonstrator-based setup. The results highlight the complementary strengths and limitations of motioncentric and semantic approaches and motivate a hybrid architecture that selectively augments fast skeletonbased detection with higher-level semantic reasoning. The presented system provides a practical foundation for privacy-aware, real-time video analysis in public safety applications.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29777v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29761v1", "url": "http://arxiv.org/abs/2603.29761v1", "pdf_url": "https://arxiv.org/pdf/2603.29761v1", "title": "Tracking vs. Deciding: The Dual-Capability Bottleneck in Searchless Chess Transformers", "authors": ["Quanhao Li", "Wei Jiang"], "annotation": "A human-like chess engine should mimic the style, errors, and consistency of a strong human player rather than maximize playing strength. We show that training from move sequences alone forces a model to learn two capabilities: state tracking, which reconstructs the board from move history, and decision quality, which selects good moves from that reconstructed state. These impose contradictory data requirements: low-rated games provide the diversity needed for tracking, while high-rated games provide the quality signal for decision learning. Removing low-rated data degrades performance.   We formalize this tension as a dual-capability bottleneck, P <= min(T,Q), where overall performance is limited by the weaker capability. Guided by this view, we scale the model from 28M to 120M parameters to improve tracking, then introduce Elo-weighted training to improve decisions while preserving diversity. A 2 x 2 factorial ablation shows that scaling improves tracking, weighting improves decisions, and their combination is superadditive. Linear weighting works best, while overly aggressive weighting harms tracking despite lower validation loss. We also introduce a coverage-decay formula, t* = log(N/kcrit)/log b, as a reliability horizon for intra-game degeneration risk.   Our final 120M-parameter model, without search, reached Lichess bullet 2570 over 253 rated games. On human move prediction it achieves 55.2% Top-1 accuracy, exceeding Maia-2 rapid and Maia-2 blitz. Unlike position-based methods, sequence input naturally encodes full game history, enabling history-dependent decisions that single-position models cannot exhibit.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29761v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29759v1", "url": "http://arxiv.org/abs/2603.29759v1", "pdf_url": "https://arxiv.org/pdf/2603.29759v1", "title": "TSHA: A Benchmark for Visual Language Models in Trustworthy Safety Hazard Assessment Scenarios", "authors": ["Qiucheng Yu", "Ruijie Xu", "Mingang Chen", "Xuequan Lu", "Jianfeng Dong", "Chaochao Lu", "Xin Tan"], "annotation": "Recent advances in vision-language models (VLMs) have accelerated their application to indoor safety hazards assessment. However, existing benchmarks suffer from three fundamental limitations: (1) heavy reliance on synthetic datasets constructed via simulation software, creating a significant domain gap with real-world environments; (2) oversimplified safety tasks with artificial constraints on hazard and scene types, thereby limiting model generalization; and (3) absence of rigorous evaluation protocols to thoroughly assess model capabilities in complex home safety scenarios. To address these challenges, we introduce TSHA (\\textbf{T}rustworthy \\textbf{S}afety \\textbf{H}azards \\textbf{A}ssessment), a comprehensive benchmark comprising 81,809 carefully curated training samples drawn from four complementary sources: existing indoor datasets, internet images, AIGC images, and newly captured images. This benchmark set also includes a highly challenging test set with 1707 samples, comprising not only a carefully selected subset from the training distribution but also newly added videos and panoramic images containing multiple safety hazards, used to evaluate the model's robustness in complex safety scenarios. Extensive experiments on 23 popular VLMs demonstrate that current VLMs lack robust capabilities for safety hazard assessment. Importantly, models trained on the TSHA training set not only achieve a significant performance improvement of up to +18.3 points on the TSHA test set but also exhibit enhanced generalizability across other benchmarks, underscoring the substantial contribution and importance of the TSHA benchmark.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29759v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29755v1", "url": "http://arxiv.org/abs/2603.29755v1", "pdf_url": "https://arxiv.org/pdf/2603.29755v1", "title": "CausalPulse: An Industrial-Grade Neurosymbolic Multi-Agent Copilot for Causal Diagnostics in Smart Manufacturing", "authors": ["Chathurangi Shyalika", "Utkarshani Jaimini", "Cory Henson", "Amit Sheth"], "annotation": "Modern manufacturing environments demand real-time, trustworthy, and interpretable root-cause insights to sustain productivity and quality. Traditional analytics pipelines often treat anomaly detection, causal inference, and root-cause analysis as isolated stages, limiting scalability and explainability. In this work, we present CausalPulse, an industry-grade multi-agent copilot that automates causal diagnostics in smart manufacturing. It unifies anomaly detection, causal discovery, and reasoning through a neurosymbolic architecture built on standardized agentic protocols. CausalPulse is being deployed in a Robert Bosch manufacturing plant, integrating seamlessly with existing monitoring workflows and supporting real-time operation at production scale. Evaluations on both public (Future Factories) and proprietary (Planar Sensor Element) datasets show high reliability, achieving overall success rates of 98.0% and 98.73%. Per-criterion success rates reached 98.75% for planning and tool use, 97.3% for self-reflection, and 99.2% for collaboration. Runtime experiments report end-to-end latency of 50-60s per diagnostic workflow with near-linear scalability (R^2=0.97), confirming real-time readiness. Comparison with existing industrial copilots highlights distinct advantages in modularity, extensibility, and deployment maturity. These results demonstrate how CausalPulse's modular, human-in-the-loop design enables reliable, interpretable, and production-ready automation for next-generation manufacturing.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29755v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29741v1", "url": "http://arxiv.org/abs/2603.29741v1", "pdf_url": "https://arxiv.org/pdf/2603.29741v1", "title": "BotVerse: Real-Time Event-Driven Simulation of Social Agents", "authors": ["Edoardo Allegrini", "Edoardo Di Paolo", "Angelo Spognardi", "Marinella Petrocchi"], "annotation": "BotVerse is a scalable, event-driven framework for high-fidelity social simulation using LLM-based agents. It addresses the ethical risks of studying autonomous agents on live networks by isolating interactions within a controlled environment while grounding them in real-time content streams from the Bluesky ecosystem. The system features an asynchronous orchestration API and a simulation engine that emulates human-like temporal patterns and cognitive memory. Through the Synthetic Social Observatory, researchers can deploy customizable personas and observe multimodal interactions at scale. We demonstrate BotVersevia a coordinated disinformation scenario, providing a safe, experimental framework for red-teaming and computational social scientists. A video demonstration of the framework is available at https://youtu.be/eZSzO5Jarqk.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29741v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29735v1", "url": "http://arxiv.org/abs/2603.29735v1", "pdf_url": "https://arxiv.org/pdf/2603.29735v1", "title": "Spontaneous Functional Differentiation in Large Language Models: A Brain-Like Intelligence Economy", "authors": ["Junjie Zhang", "Zhen Shen", "Gang Xiong", "Xisong Dong"], "annotation": "The evolution of intelligence in artificial systems provides a unique opportunity to identify universal computational principles. Here we show that large language models spontaneously develop synergistic cores where information integration exceeds individual parts remarkably similar to the human brain. Using Integrated Information Decomposition across multiple architectures we find that middle layers exhibit synergistic processing while early and late layers rely on redundancy. This organization is dynamic and emerges as a physical phase transition as task difficulty increases. Crucially ablating synergistic components causes catastrophic performance loss confirming their role as the physical entity of abstract reasoning and bridging artificial and biological intelligence.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29735v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29723v1", "url": "http://arxiv.org/abs/2603.29723v1", "pdf_url": "https://arxiv.org/pdf/2603.29723v1", "title": "Reinforced Reasoning for End-to-End Retrosynthetic Planning", "authors": ["Chenyang Zuo", "Siqi Fan", "Yizhen Luo", "Zaiqing Nie"], "annotation": "Retrosynthetic planning is a fundamental task in organic chemistry, yet remains challenging due to its combinatorial complexity. To address this, conventional approaches typically rely on hybrid frameworks that combine single-step predictions with external search heuristics, inevitably fracturing the logical coherence between local molecular transformations and global planning objectives. To bridge this gap and embed sophisticated strategic foresight directly into the model's chemical reasoning, we introduce ReTriP, an end-to-end generative framework that reformulates retrosynthesis as a direct Chain-of-Thought reasoning task. We establish a path-coherent molecular representation and employ a progressive training curriculum that transitions from reasoning distillation to reinforcement learning with verifiable rewards, effectively aligning stepwise generation with practical route utility. Empirical evaluation on RetroBench demonstrates that ReTriP achieves state-of-the-art performance, exhibiting superior robustness in long-horizon planning compared to hybrid baselines.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29723v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29709v1", "url": "http://arxiv.org/abs/2603.29709v1", "pdf_url": "https://arxiv.org/pdf/2603.29709v1", "title": "Symphony for Medical Coding: A Next-Generation Agentic System for Scalable and Explainable Medical Coding", "authors": ["Joakim Edin", "Andreas Motzfeldt", "Simon Flachs", "Lars Maaløe"], "annotation": "Medical coding translates free-text clinical documentation into standardized codes drawn from classification systems that contain tens of thousands of entries and are updated annually. It is central to billing, clinical research, and quality reporting, yet remains largely manual, slow, and error-prone. Existing automated approaches learn to predict a fixed set of codes from labeled data, thereby preventing adaptation to new codes or different coding systems without retraining on different data. They also provide no explanation for their predictions, limiting trust in safety-critical settings. We introduce Symphony for Medical Coding, a system that approaches the task the way expert human coders do: by reasoning over the clinical narrative with direct access to the coding guidelines. This design allows Symphony to operate across any coding system and to provide span-level evidence linking each predicted code to the text that supports it. We evaluate on two public benchmarks and three real-world datasets spanning inpatient, outpatient, emergency, and subspecialty settings across the United States and the United Kingdom. Symphony achieves state-of-the-art results across all settings, establishing itself as a flexible, deployment-ready foundation for automated clinical coding.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29709v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29694v1", "url": "http://arxiv.org/abs/2603.29694v1", "pdf_url": "https://arxiv.org/pdf/2603.29694v1", "title": "Exploring the Impact of Skin Color on Skin Lesion Segmentation", "authors": ["Kuniko Paxton", "Medina Kapo", "Amila Akagić", "Koorosh Aslansefat", "Dhavalkumar Thakker", "Yiannis Papadopoulos"], "annotation": "Skin cancer, particularly melanoma, remains a major cause of morbidity and mortality, making early detection critical. AI-driven dermatology systems often rely on skin lesion segmentation as a preprocessing step to delineate the lesion from surrounding skin and support downstream analysis. While fairness concerns regarding skin tone have been widely studied for lesion classification, the influence of skin tone on the segmentation stage remains under-quantified and is frequently assessed using coarse, discrete skin tone categories. In this work, we evaluate three strong segmentation architectures (UNet, DeepLabV3 with a ResNet50 backbone, and DINOv2) on two public dermoscopic datasets (HAM10000 and ISIC2017) and introduce a continuous pigment or contrast analysis that treats pixel-wise ITA values as distributions. Using Wasserstein distances between within-image distributions for skin-only, lesion-only, and whole-image regions, we quantify lesion skin contrast and relate it to segmentation performance across multiple metrics. Within the range represented in these datasets, global skin tone metrics (Fitzpatrick grouping or mean ITA) show weak association with segmentation quality. In contrast, low lesion-skin contrast is consistently associated with larger segmentation errors in models, indicating that boundary ambiguity and low contrast are key drivers of failure. These findings suggest that fairness improvements in dermoscopic segmentation should prioritize robust handling of low-contrast lesions, and the distribution-based pigment measures provide a more informative audit signal than discrete skin-tone categories.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29694v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29693v1", "url": "http://arxiv.org/abs/2603.29693v1", "pdf_url": "https://arxiv.org/pdf/2603.29693v1", "title": "Measuring the metacognition of AI", "authors": ["Richard Servajean", "Philippe Servajean"], "annotation": "A robust decision-making process must take into account uncertainty, especially when the choice involves inherent risks. Because artificial Intelligence (AI) systems are increasingly integrated into decision-making workflows, managing uncertainty relies more and more on the metacognitive capabilities of these systems; i.e, their ability to assess the reliability of and regulate their own decisions. Hence, it is crucial to employ robust methods to measure the metacognitive abilities of AI. This paper is primarily a methodological contribution arguing for the adoption of the meta-d' framework, or its model-free alternatives, as the gold standard for assessing the metacognitive sensitivity of AIs--the ability to generate confidence ratings that distinguish correct from incorrect responses. Moreover, we propose to leverage signal detection theory (SDT) to measure the ability of AIs to spontaneously regulate their decisions based on uncertainty and risk. To demonstrate the practical utility of these psychophysical frameworks, we conduct two series of experiments on three large language models (LLMs)--GPT-5, DeepSeek-V3.2-Exp, and Mistral-Medium-2508. In the first experiments, LLMs performed a primary judgment followed by a confidence rating. In the second, LLMs only performed the primary judgment, while we manipulated the risk associated with either response. On the one hand, applying the meta-d' framework allows us to conduct comparisons along three axes: comparing an LLM to optimality, comparing different LLMs on a given task, and comparing the same LLM across different tasks. On the other hand, SDT allows us to assess whether LLMs become more conservative when risks are high.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29693v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29691v1", "url": "http://arxiv.org/abs/2603.29691v1", "pdf_url": "https://arxiv.org/pdf/2603.29691v1", "title": "A First Step Towards Even More Sparse Encodings of Probability Distributions", "authors": ["Florian Andreas Marwitz", "Tanya Braun", "Ralf Möller"], "annotation": "Real world scenarios can be captured with lifted probability distributions. However, distributions are usually encoded in a table or list, requiring an exponential number of values. Hence, we propose a method for extracting first-order formulas from probability distributions that require significantly less values by reducing the number of values in a distribution and then extracting, for each value, a logical formula to be further minimized. This reduction and minimization allows for increasing the sparsity in the encoding while also generalizing a given distribution. Our evaluation shows that sparsity can increase immensely by extracting a small set of short formulas while preserving core information.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29691v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29689v1", "url": "http://arxiv.org/abs/2603.29689v1", "pdf_url": "https://arxiv.org/pdf/2603.29689v1", "title": "KEditVis: A Visual Analytics System for Knowledge Editing of Large Language Models", "authors": ["Zhenning Chen", "Hanbei Zhan", "Yanwei Huang", "Xin Wu", "Dazhen Deng", "Di Weng", "Yingcai Wu"], "annotation": "Large Language Models (LLMs) demonstrate exceptional capabilities in factual question answering, yet they sometimes provide incorrect responses. To address this issue, knowledge editing techniques have emerged as effective methods for correcting factual information in LLMs. However, typical knowledge editing workflows struggle with identifying the optimal set of model layers for editing and rely on summary indicators that provide insufficient guidance. This lack of transparency hinders effective comparison and identification of optimal editing strategies. In this paper, we present KEditVis, a novel visual analytics system designed to assist users in gaining a deeper understanding of knowledge editing through interactive visualizations, improving editing outcomes, and discovering valuable insights for the future development of knowledge editing algorithms. With KEditVis, users can select appropriate layers as the editing target, explore the reasons behind ineffective edits, and perform more targeted and effective edits. Our evaluation, including usage scenarios, expert interviews, and a user study, validates the effectiveness and usability of the system.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29689v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29681v1", "url": "http://arxiv.org/abs/2603.29681v1", "pdf_url": "https://arxiv.org/pdf/2603.29681v1", "title": "Beyond the Steeper Curve: AI-Mediated Metacognitive Decoupling and the Limits of the Dunning-Kruger Metaphor", "authors": ["Christopher Koch"], "annotation": "The common claim that generative AI simply amplifies the Dunning-Kruger effect is too coarse to capture the available evidence. The clearest findings instead suggest that large language model (LLM) use can improve observable output and short-term task performance while degrading metacognitive accuracy and flattening the classic competence-confidence gradient across skill groups. This paper synthesizes evidence from human-AI interaction, learning research, and model evaluation, and proposes the working model of AI-mediated metacognitive decoupling: a widening gap among produced output, underlying understanding, calibration accuracy, and self-assessed ability. This four-variable account better explains overconfidence, over- and under-reliance, crutch effects, and weak transfer than the simpler metaphor of a uniformly steeper Dunning-Kruger curve. The paper concludes with implications for tool design, assessment, and knowledge work.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29681v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29678v1", "url": "http://arxiv.org/abs/2603.29678v1", "pdf_url": "https://arxiv.org/pdf/2603.29678v1", "title": "View-oriented Conversation Compiler for Agent Trace Analysis", "authors": ["Lvmin Zhang", "Maneesh Agrawala"], "annotation": "Agent traces carry increasing analytical value in the era of context learning and harness-driven agentic cognition, yet most prior work treats conversation format as a trivial engineering detail. Modern agent conversations contain deeply structured content, including nested tool calls and results, chain-of-thought reasoning blocks, sub-agent invocations, context-window compaction boundaries, and harness-injected system directives, whose complexity far exceeds that of simple user-assistant exchanges. Feeding such traces to a reflector or other analytical mechanism in plain text, JSON, YAML, or via grep can materially degrade analysis quality. This paper presents VCC (View-oriented Conversation Compiler), a compiler (lex, parse, IR, lower, emit) that transforms raw agent JSONL logs into a family of structured views: a full view (lossless transcript serving as the canonical line-number coordinate system), a user-interface view (reconstructing the interaction as the user actually perceived it), and an adaptive view (a structure-preserving projection governed by a relevance predicate). In a context-learning experiment on AppWorld, replacing only the reflector's input format, from raw JSONL to VCC-compiled views, leads to higher pass rates across all three model configurations tested, while cutting reflector token consumption by half to two-thirds and producing more concise learned memory. These results suggest that message format functions as infrastructure for context learning, not as an incidental implementation choice.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29678v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29677v1", "url": "http://arxiv.org/abs/2603.29677v1", "pdf_url": "https://arxiv.org/pdf/2603.29677v1", "title": "Mind the Gap: A Framework for Assessing Pitfalls in Multimodal Active Learning", "authors": ["Dustin Eisenhardt", "Yunhee Jeong", "Florian Buettner"], "annotation": "Multimodal learning enables neural networks to integrate information from heterogeneous sources, but active learning in this setting faces distinct challenges. These include missing modalities, differences in modality difficulty, and varying interaction structures. These are issues absent in the unimodal case. While the behavior of active learning strategies in unimodal settings is well characterized, their behavior under such multimodal conditions remains poorly understood. We introduce a new framework for benchmarking multimodal active learning that isolates these pitfalls using synthetic datasets, allowing systematic evaluation without confounding noise. Using this framework, we compare unimodal and multimodal query strategies and validate our findings on two real-world datasets. Our results show that models consistently develop imbalanced representations, relying primarily on one modality while neglecting others. Existing query methods do not mitigate this effect, and multimodal strategies do not consistently outperform unimodal ones. These findings highlight limitations of current active learning methods and underline the need for modality-aware query strategies that explicitly address these pitfalls. Code and benchmark resources will be made publicly available.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29677v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29661v1", "url": "http://arxiv.org/abs/2603.29661v1", "pdf_url": "https://arxiv.org/pdf/2603.29661v1", "title": "Agenda-based Narrative Extraction: Steering Pathfinding Algorithms with Large Language Models", "authors": ["Brian Felipe Keith-Norambuena", "Carolina Inés Rojas-Córdova", "Claudio Juvenal Meneses-Villegas", "Elizabeth Johanna Lam-Esquenazi", "Angélica María Flores-Bustos", "Ignacio Alejandro Molina-Villablanca", "Joshua Emanuel Leyton-Vallejos"], "annotation": "Existing narrative extraction methods face a trade-off between coherence, interactivity, and multi-storyline support. Narrative Maps supports rich interaction and generates multiple storylines as a byproduct of its coverage constraints, though this comes at the cost of individual path coherence. Narrative Trails achieves high coherence through maximum capacity path optimization but provides no mechanism for user guidance or multiple perspectives. We introduce agenda-based narrative extraction, a method that bridges this gap by integrating large language models into the Narrative Trails pathfinding process to steer storyline construction toward user-specified perspectives. Our approach uses an LLM at each step to rank candidate documents based on their alignment with a given agenda while maintaining narrative coherence. Running the algorithm with different agendas yields different storylines through the same corpus. We evaluated our approach on a news article corpus using LLM judges with Claude Opus 4.5 and GPT 5.1, measuring both coherence and agenda alignment across 64 endpoint pairs and 6 agendas. LLM-driven steering achieves 9.9% higher alignment than keyword matching on semantic agendas (p=0.017), with 13.3% improvement on \\textit{Regime Crackdown} specifically (p=0.037), while keyword matching remains competitive on agendas with literal keyword overlap. The coherence cost is minimal: LLM steering reduces coherence by only 2.2% compared to the agenda-agnostic baseline. Counter-agendas that contradict the source material score uniformly low (2.2-2.5) across all methods, confirming that steering cannot fabricate unsupported narratives.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29661v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29656v1", "url": "http://arxiv.org/abs/2603.29656v1", "pdf_url": "https://arxiv.org/pdf/2603.29656v1", "title": "6GAgentGym: Tool Use, Data Synthesis, and Agentic Learning for Network Management", "authors": ["Jiao Chen", "Jianhua Tang", "Xiaotong Yang", "Zuohong Lv"], "annotation": "Autonomous 6G network management requires agents that can execute tools, observe the resulting state changes, and adapt their decisions accordingly. Existing benchmarks based on static questions or scripted episode replay, however, do not support such closed-loop interaction, limiting agents to passive evaluation without the ability to learn from environmental feedback. This paper presents 6GAgentGym to provide closed-loop capability. The framework provides an interactive environment with 42 typed tools whose effect classification distinguishes read-only observation from state-mutating configuration, backed by a learned Experiment Model calibrated on NS-3 simulation data. 6G-Forge bootstraps closed-loop training trajectories from NS-3 seeds via iterative Self-Instruct generation with execution verification against the Experiment Model. Supervised fine-tuning on the resulting corpus followed by reinforcement learning with online closed-loop interaction enables an 8B open-source model to achieve comparable overall success rate to GPT-5 on the accompanying 6GAgentBench, with stronger performance on long-horizon tasks. Together, these components provide a viable path toward autonomous, closed-loop network management.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29656v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29654v1", "url": "http://arxiv.org/abs/2603.29654v1", "pdf_url": "https://arxiv.org/pdf/2603.29654v1", "title": "Concept frustration: Aligning human concepts and machine representations", "authors": ["Enrico Parisini", "Christopher J. Soelistyo", "Ahab Isaac", "Alessandro Barp", "Christopher R. S. Banerji"], "annotation": "Aligning human-interpretable concepts with the internal representations learned by modern machine learning systems remains a central challenge for interpretable AI. We introduce a geometric framework for comparing supervised human concepts with unsupervised intermediate representations extracted from foundation model embeddings. Motivated by the role of conceptual leaps in scientific discovery, we formalise the notion of concept frustration: a contradiction that arises when an unobserved concept induces relationships between known concepts that cannot be made consistent within an existing ontology. We develop task-aligned similarity measures that detect concept frustration between supervised concept-based models and unsupervised representations derived from foundation models, and show that the phenomenon is detectable in task-aligned geometry while conventional Euclidean comparisons fail. Under a linear-Gaussian generative model we derive a closed-form expression for Bayes-optimal concept-based classifier accuracy, decomposing predictive signal into known-known, known-unknown and unknown-unknown contributions and identifying analytically where frustration affects performance. Experiments on synthetic data and real language and vision tasks demonstrate that frustration can be detected in foundation model representations and that incorporating a frustrating concept into an interpretable model reorganises the geometry of learned concept representations, to better align human and machine reasoning. These results suggest a principled framework for diagnosing incomplete concept ontologies and aligning human and machine conceptual reasoning, with implications for the development and validation of safe interpretable AI for high-risk applications.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29654v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29651v1", "url": "http://arxiv.org/abs/2603.29651v1", "pdf_url": "https://arxiv.org/pdf/2603.29651v1", "title": "Semantic Interaction for Narrative Map Sensemaking: An Insight-based Evaluation", "authors": ["Brian Felipe Keith-Norambuena", "Fausto German", "Eric Krokos", "Sarah Joseph", "Chris North"], "annotation": "Semantic interaction (SI) enables analysts to incorporate their cognitive processes into AI models through direct manipulation of visualizations. While SI frameworks for narrative extraction have been proposed, empirical evaluations of their effectiveness remain limited. This paper presents a user study that evaluates SI for narrative map sensemaking, involving 33 participants under three conditions: a timeline baseline, a basic narrative map, and an interactive narrative map with SI capabilities. The results show that the map-based prototypes yielded more insights than the timeline baseline, with the SI-enabled condition reaching statistical significance and the basic map condition trending in the same direction. The SI-enabled condition showed the highest mean performance; differences between the map conditions were not statistically significant but showed large effect sizes (d > 0.8), suggesting that the study was underpowered to detect them. Qualitative analysis identified two distinct SI approaches-corrective and additive-that enable analysts to impose quality judgments and organizational structure on extracted narratives. We also find that SI users achieved comparable exploration breadth with less parameter manipulation, suggesting that SI serves as an alternative pathway for model refinement. This work provides empirical evidence that map-based representations outperform timelines for narrative sensemaking, along with qualitative insights into how analysts use SI for narrative refinement.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29651v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29643v1", "url": "http://arxiv.org/abs/2603.29643v1", "pdf_url": "https://arxiv.org/pdf/2603.29643v1", "title": "Optimizing Donor Outreach for Blood Collection Sessions: A Scalable Decision Support Framework", "authors": ["André Carneiro", "Pedro T. Monteiro", "Rui Henriques"], "annotation": "Blood donation centers face challenges in matching supply with demand while managing donor availability. Although targeted outreach is important, it can cause donor fatigue via over-solicitation. Effective recruitment requires targeting the right donors at the right time, balancing constraints with donor convenience and eligibility. Despite extensive work on blood supply chain optimization and growing interest in algorithmic donor recruitment, the operational problem of assigning donors to sessions across a multi-site network, taking into account eligibility, capacity, blood-type demand targets, geographic convenience, and donor safety, remains unaddressed.   We address this gap with an optimization framework for donor invitation scheduling incorporating donor eligibility, travel convenience, blood-type demand targets, and penalties. We evaluate two strategies: (i) a binary integer linear programming (BILP) formulation and (ii) an efficient greedy heuristic. Evaluation uses the registry from Instituto Português do Sangue e da Transplantação (IPST) for invite planning in the Lisbon operational region using 4-month windows. A prospective pipeline integrates organic attendance forecasting, quantile-based demand targets, and residual capacity estimation for forward-looking invitation plans. Results reveal its key role in closing the supply-demand gap in the Lisbon operational region. A controlled comparison shows that the greedy heuristic achieves results comparable to the BILP, with 188x less peak memory and 115x faster runtime; trade-offs include 3.9 pp lower demand fulfillment (86.1% vs. 90.0%), larger donor-session distance, higher adverse-reaction donor exposure, and greater invitation burden per non-high-frequency donor, reflecting local versus global optimization. Experiments assess how constraint-aware scheduling can close gaps by mobilizing eligible inactive/lapsing donors.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29643v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29640v1", "url": "http://arxiv.org/abs/2603.29640v1", "pdf_url": "https://arxiv.org/pdf/2603.29640v1", "title": "ASI-Evolve: AI Accelerates AI", "authors": ["Weixian Xu", "Tiantian Mi", "Yixiu Liu", "Yang Nan", "Zhimeng Zhou", "Lyumanshan Ye", "Lin Zhang", "Yu Qiao", "Pengfei Liu"], "annotation": "Can AI accelerate the development of AI itself? While recent agentic systems have shown strong performance on well-scoped tasks with rapid feedback, it remains unclear whether they can tackle the costly, long-horizon, and weakly supervised research loops that drive real AI progress. We present ASI-Evolve, an agentic framework for AI-for-AI research that closes this loop through a learn-design-experiment-analyze cycle. ASI-Evolve augments standard evolutionary agents with two key components: a cognition base that injects accumulated human priors into each round of exploration, and a dedicated analyzer that distills complex experimental outcomes into reusable insights for future iterations. To our knowledge, ASI-Evolve is the first unified framework to demonstrate AI-driven discovery across three central components of AI development: data, architectures, and learning algorithms. In neural architecture design, it discovered 105 SOTA linear attention architectures, with the best discovered model surpassing DeltaNet by +0.97 points, nearly 3x the gain of recent human-designed improvements. In pretraining data curation, the evolved pipeline improves average benchmark performance by +3.96 points, with gains exceeding 18 points on MMLU. In reinforcement learning algorithm design, discovered algorithms outperform GRPO by up to +12.5 points on AMC32, +11.67 points on AIME24, and +5.04 points on OlympiadBench. We further provide initial evidence that this AI-for-AI paradigm can transfer beyond the AI stack through experiments in mathematics and biomedicine. Together, these results suggest that ASI-Evolve represents a promising step toward enabling AI to accelerate AI across the foundational stages of development, offering early evidence for the feasibility of closed-loop AI research.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29640v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29634v1", "url": "http://arxiv.org/abs/2603.29634v1", "pdf_url": "https://arxiv.org/pdf/2603.29634v1", "title": "MacTok: Robust Continuous Tokenization for Image Generation", "authors": ["Hengyu Zeng", "Xin Gao", "Guanghao Li", "Yuxiang Yan", "Jiaoyang Ruan", "Junpeng Ma", "Haoyu Albert Wang", "Jian Pu"], "annotation": "Continuous image tokenizers enable efficient visual generation, and those based on variational frameworks can learn smooth, structured latent representations through KL regularization. Yet this often leads to posterior collapse when using fewer tokens, where the encoder fails to encode informative features into the compressed latent space. To address this, we introduce \\textbf{MacTok}, a \\textbf{M}asked \\textbf{A}ugmenting 1D \\textbf{C}ontinuous \\textbf{Tok}enizer that leverages image masking and representation alignment to prevent collapse while learning compact and robust representations. MacTok applies both random masking to regularize latent learning and DINO-guided semantic masking to emphasize informative regions in images, forcing the model to encode robust semantics from incomplete visual evidence. Combined with global and local representation alignment, MacTok preserves rich discriminative information in a highly compressed 1D latent space, requiring only 64 or 128 tokens. On ImageNet, MacTok achieves a competitive gFID of 1.44 at 256$\\times$256 and a state-of-the-art 1.52 at 512$\\times$512 with SiT-XL, while reducing token usage by up to 64$\\times$. These results confirm that masking and semantic guidance together prevent posterior collapse and achieve efficient, high-fidelity tokenization.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29634v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29632v1", "url": "http://arxiv.org/abs/2603.29632v1", "pdf_url": "https://arxiv.org/pdf/2603.29632v1", "title": "An Empirical Study of Multi-Agent Collaboration for Automated Research", "authors": ["Yang Shen", "Zhenyi Yi", "Ziyi Zhao", "Lijun Sun", "Dongyang Li", "Chin-Teng Lin", "Yuhui Shi"], "annotation": "As AI agents evolve, the community is rapidly shifting from single Large Language Models (LLMs) to Multi-Agent Systems (MAS) to overcome cognitive bottlenecks in automated research. However, the optimal multi-agent coordination framework for these autonomous agents remains largely unexplored. In this paper, we present a systematic empirical study investigating the comparative efficacy of distinct multi-agent structures for automated machine learning optimization. Utilizing a rigorously controlled, execution-based testbed equipped with Git worktree isolation and explicit global memory, we benchmark a single-agent baseline against two multi-agent paradigms: a subagent architecture (parallel exploration with post-hoc consolidation) and an agent team architecture (experts with pre-execution handoffs). By evaluating these systems under strictly fixed computational time budgets, our findings reveal a fundamental trade-off between operational stability and theoretical deliberation. The subagent mode functions as a highly resilient, high-throughput search engine optimal for broad, shallow optimizations under strict time constraints. Conversely, the agent team topology exhibits higher operational fragility due to multi-author code generation but achieves the deep theoretical alignment necessary for complex architectural refactoring given extended compute budgets. These empirical insights provide actionable guidelines for designing future autoresearch systems, advocating for dynamically routed architectures that adapt their collaborative structures to real-time task complexity.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29632v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29617v1", "url": "http://arxiv.org/abs/2603.29617v1", "pdf_url": "https://arxiv.org/pdf/2603.29617v1", "title": "Convergent Representations of Linguistic Constructions in Human and Artificial Neural Systems", "authors": ["Pegah Ramezani", "Thomas Kinfe", "Andreas Maier", "Achim Schilling", "Patrick Krauss"], "annotation": "Understanding how the brain processes linguistic constructions is a central challenge in cognitive neuroscience and linguistics. Recent computational studies show that artificial neural language models spontaneously develop differentiated representations of Argument Structure Constructions (ASCs), generating predictions about when and how construction-level information emerges during processing. The present study tests these predictions in human neural activity using electroencephalography (EEG). Ten native English speakers listened to 200 synthetically generated sentences across four construction types (transitive, ditransitive, caused-motion, resultative) while neural responses were recorded. Analyses using time-frequency methods, feature extraction, and machine learning classification revealed construction-specific neural signatures emerging primarily at sentence-final positions, where argument structure becomes fully disambiguated, and most prominently in the alpha band. Pairwise classification showed reliable differentiation, especially between ditransitive and resultative constructions, while other pairs overlapped. Crucially, the temporal emergence and similarity structure of these effects mirror patterns in recurrent and transformer-based language models, where constructional representations arise during integrative processing stages. These findings support the view that linguistic constructions are neurally encoded as distinct form-meaning mappings, in line with Construction Grammar, and suggest convergence between biological and artificial systems on similar representational solutions. More broadly, this convergence is consistent with the idea that learning systems discover stable regions within an underlying representational landscape - recently termed a Platonic representational space - that constrains the emergence of efficient linguistic abstractions.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29617v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29570v1", "url": "http://arxiv.org/abs/2603.29570v1", "pdf_url": "https://arxiv.org/pdf/2603.29570v1", "title": "Generating Key Postures of Bharatanatyam Adavus with Pose Estimation", "authors": ["Jagadish Kashinath Kamble", "Jayanta Mukhopadhyay", "Debaditya Roy", "Partha Pratim Das"], "annotation": "Preserving intangible cultural dances rooted in centuries of tradition and governed by strict structural and symbolic rules presents unique challenges in the digital era. Among these, Bharatanatyam, a classical Indian dance form, stands out for its emphasis on codified adavus and precise key postures. Accurately generating these postures is crucial not only for maintaining anatomical and stylistic integrity, but also for enabling effective documentation, analysis, and transmission to broader global audiences through digital means. We propose a pose-aware generative framework integrated with a pose estimation module, guided by keypoint-based loss and pose consistency constraints. These supervisory signals ensure anatomical accuracy and stylistic integrity in the synthesized outputs. We evaluate four configurations: standard conditional generative adversarial network (cGAN), cGAN with pose supervision, conditional diffusion, and conditional diffusion with pose supervision. Each model is conditioned on key posture class labels and optimized to maintain geometric structure. In both cGAN and conditional diffusion settings, the integrated pose guidance aligns generated poses with ground-truth keypoint structures, promoting cultural fidelity. Our results demonstrate that incorporating pose supervision significantly enhances the quality, realism, and authenticity of generated Bharatanatyam postures. This framework provides a scalable approach for the digital preservation, education, and dissemination of traditional dance forms, enabling high-fidelity generation without compromising cultural precision. Code is available at https://github.com/jagidsh/Generating-Key-Postures-of-Bharatanatyam-Adavus-with-Pose-Estimation.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29570v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29557v1", "url": "http://arxiv.org/abs/2603.29557v1", "pdf_url": "https://arxiv.org/pdf/2603.29557v1", "title": "FlowPIE: Test-Time Scientific Idea Evolution with Flow-Guided Literature Exploration", "authors": ["Qiyao Wang", "Hongbo Wang", "Longze Chen", "Zhihao Yang", "Guhong Chen", "Hamid Alinejad-Rokny", "Hui Li", "Yuan Lin", "Min Yang"], "annotation": "Scientific idea generation (SIG) is critical to AI-driven autonomous research, yet existing approaches are often constrained by a static retrieval-then-generation paradigm, leading to homogeneous and insufficiently divergent ideas. In this work, we propose FlowPIE, a tightly coupled retrieval-generation framework that treats literature exploration and idea generation as a co-evolving process. FlowPIE expands literature trajectories via a flow-guided Monte Carlo Tree Search (MCTS) inspired by GFlowNets, using the quality of current ideas assessed by an LLM-based generative reward model (GRM) as a supervised signal to guide adaptive retrieval and construct a diverse, high-quality initial population. Based on this population, FlowPIE models idea generation as a test-time idea evolution process, applying selection, crossover, and mutation with the isolation island paradigm and GRM-based fitness computation to incorporate cross-domain knowledge. It effectively mitigates the information cocoons arising from over-reliance on parametric knowledge and static literature. Extensive evaluations demonstrate that FlowPIE consistently produces ideas with higher novelty, feasibility and diversity compared to strong LLM-based and agent-based frameworks, while enabling reward scaling during test time.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29557v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29552v1", "url": "http://arxiv.org/abs/2603.29552v1", "pdf_url": "https://arxiv.org/pdf/2603.29552v1", "title": "Bringing Up a Bilingual BabyLM: Investigating Multilingual Language Acquisition Using Small-Scale Models", "authors": ["Linda Zeng", "Steven Y. Feng", "Michael C. Frank"], "annotation": "Multilingualism is incredibly common around the world, leading to many important theoretical and practical questions about how children learn multiple languages at once. For example, does multilingual acquisition lead to delays in learning? Are there better and worse ways to structure multilingual input? Many correlational studies address these questions, but it is surprisingly difficult to get definitive answers because children cannot be randomly assigned to be multilingual and data are typically not matched between languages. We use language model training as a method for simulating a variety of highly controlled exposure conditions, and create matched 100M-word mono- and bilingual datasets using synthetic data and machine translation. We train GPT-2 models on monolingual and bilingual data organized to reflect a range of exposure regimes, and evaluate their performance on perplexity, grammaticality, and semantic knowledge. Across model scales and measures, bilingual models perform similarly to monolingual models in one language, but show strong performance in the second language as well. These results suggest that there are no strong differences between different bilingual exposure regimes, and that bilingual input poses no in-principle challenges for agnostic statistical learners.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29552v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29543v1", "url": "http://arxiv.org/abs/2603.29543v1", "pdf_url": "https://arxiv.org/pdf/2603.29543v1", "title": "Reducing Complexity for Quantum Approaches in Train Load Optimization", "authors": ["Zhijie Tang", "Albert Nieto-Morales", "Arit Kumar Bishwas"], "annotation": "Efficiently planning container loads onto trains is a computationally challenging combinatorial optimization problem, central to logistics and supply chain management. A primary source of this complexity arises from the need to model and reduce rehandle operations-unproductive crane moves required to access blocked containers. Conventional mathematical formulations address this by introducing explicit binary variables and a web of logical constraints for each potential rehandle, resulting in large-scale models that are difficult to solve. This paper presents a fundamental departure from this paradigm. We introduce an innovative and compact mathematical formulation for the Train Load Optimization (TLO) problem where the rehandle cost is calculated implicitly within the objective function. This novel approach helps prevent the need for dedicated rehandle variables and their associated constraints, leading to a dramatic reduction in model size. We provide a formal comparison against a conventional model to analytically demonstrate the significant reduction in the number of variables and constraints. The efficacy of our compact formulation is assessed through a simulated annealing metaheuristic, which finds high-quality loading plans for various problem instances. The results confirm that our model is not only more parsimonious but also practically effective, offering a scalable and powerful tool for modern rail logistics.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29543v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29537v1", "url": "http://arxiv.org/abs/2603.29537v1", "pdf_url": "https://arxiv.org/pdf/2603.29537v1", "title": "Mean Masked Autoencoder with Flow-Mixing for Encrypted Traffic Classification", "authors": ["Xiao Liu", "Xiaowei Fu", "Fuxiang Huang", "Lei Zhang"], "annotation": "Network traffic classification using self-supervised pre-training models based on Masked Autoencoders (MAE) has demonstrated a huge potential. However, existing methods are confined to isolated byte-level reconstruction of individual flows, lacking adequate perception of the multi-granularity contextual relationship in traffic. To address this limitation, we propose Mean MAE (MMAE), a teacher-student MAE paradigm with flow mixing strategy for building encrypted traffic pre-training model. MMAE employs a self-distillation mechanism for teacher-student interaction, where the teacher provides unmasked flow-level semantic supervision to advance the student from local byte reconstruction to multi-granularity comprehension. To break the information bottleneck in individual flows, we introduce a dynamic Flow Mixing (FlowMix) strategy to replace traditional random masking mechanism. By constructing challenging cross-flow mixed samples with interferences, it compels the model to learn discriminative representations from distorted tokens. Furthermore, we design a Packet-importance aware Mask Predictor (PMP) equipped with an attention bias mechanism that leverages packet-level side-channel statistics to dynamically mask tokens with high semantic density. Numerous experiments on a number of datasets covering encrypted applications, malware, and attack traffic demonstrate that MMAE achieves state-of-the-art performance. The code is available at https://github.com/lx6c78/MMAE", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29537v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29535v1", "url": "http://arxiv.org/abs/2603.29535v1", "pdf_url": "https://arxiv.org/pdf/2603.29535v1", "title": "Quantization with Unified Adaptive Distillation to enable multi-LoRA based one-for-all Generative Vision Models on edge", "authors": ["Sowmya Vajrala", "Aakash Parmar", "Prasanna R", "Sravanth Kodavanti", "Manjunath Arveti", "Srinivas Soumitri Miriyala", "Ashok Senapati"], "annotation": "Generative Artificial Intelligence (GenAI) features such as image editing, object removal, and prompt-guided image transformation are increasingly integrated into mobile applications. However, deploying Large Vision Models (LVMs) for such tasks on resource-constrained devices remains challenging due to their high memory and compute requirements. While Low-Rank Adapters (LoRAs) enable parameter-efficient task adaptation, existing Mobile deployment pipelines typically compile separate model binaries for each LoRA + a copy of the foundation model, resulting in redundant storage and increased runtime overhead. In this work, we present a unified framework for enabling multi-task GenAI inference on edge devices using a single shared model. Our key idea is to treat LoRA weights as runtime inputs rather than embedding them into the compiled model graph, allowing dynamic task switching at runtime without recompilation. Then, to support efficient on-device execution, we introduce QUAD (Quantization with Unified Adaptive Distillation), a quantizationaware training strategy that aligns multiple LoRA adapters under a shared quantization profile. We implement the proposed system with a lightweight runtime stack compatible with mobile NPUs and evaluate it across multiple chipsets. Experimental results demonstrate up to 6x and 4x reduction in memory footprint and latency improvements, respectively, while maintaining high visual quality across multiple GenAI tasks.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29535v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29522v1", "url": "http://arxiv.org/abs/2603.29522v1", "pdf_url": "https://arxiv.org/pdf/2603.29522v1", "title": "Baby Scale: Investigating Models Trained on Individual Children's Language Input", "authors": ["Steven Y. Feng", "Alvin W. M. Tan", "Michael C. Frank"], "annotation": "Modern language models (LMs) must be trained on many orders of magnitude more words of training data than human children receive before they begin to produce useful behavior. Assessing the nature and origins of this \"data gap\" requires benchmarking LMs on human-scale datasets to understand how linguistic knowledge emerges from children's natural training data. Using transcripts from the BabyView dataset (videos from children ages 6-36 months), we investigate (1) scaling performance at child-scale data regimes, (2) variability in model performance across datasets from different children's experiences and linguistic predictors of dataset quality, and (3) relationships between model and child language learning outcomes. LMs trained on child data show acceptable scaling for grammar tasks, but lower scaling on semantic and world knowledge tasks than models trained on synthetic data; we also observe substantial variability on data from different children. Beyond dataset size, performance is most associated with a combination of distributional and interactional linguistic features, broadly consistent with what makes high-quality input for child language development. Finally, model likelihoods for individual words correlate with children's learning of those words, suggesting that properties of child-directed input may influence both model learning and human language development. Overall, understanding what properties make language data efficient for learning can enable more powerful small-scale language models while also shedding light on human language acquisition.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29522v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29520v1", "url": "http://arxiv.org/abs/2603.29520v1", "pdf_url": "https://arxiv.org/pdf/2603.29520v1", "title": "TrafficMoE: Heterogeneity-aware Mixture of Experts for Encrypted Traffic Classification", "authors": ["Qing He", "Xiaowei Fu", "Lei Zhang"], "annotation": "Encrypted traffic classification is a critical task for network security. While deep learning has advanced this field, the occlusion of payload semantics by encryption severely challenges standard modeling approaches. Most existing frameworks rely on static and homogeneous pipelines that apply uniform parameter sharing and static fusion strategies across all inputs. This one-size-fits-all static design is inherently flawed: by forcing structured headers and randomized payloads into a unified processing pipeline, it inevitably entangles the raw protocol signals with stochastic encryption noise, thereby degrading the fine-grained discriminative features. In this paper, we propose TrafficMoE, a framework that breaks through the bottleneck of static modeling by establishing a Disentangle-Filter-Aggregate (DFA) paradigm. Specifically, to resolve the structural between-components conflict, the architecture disentangles headers and payloads using dual-branch sparse Mixture-of-Experts (MoE), enabling modality-specific modeling. To mitigate the impact of stochastic noise, an uncertainty-aware filtering mechanism is introduced to quantify reliability and selectively suppress high-variance representations. Finally, to overcome the limitations of static fusion, a routing-guided strategy aggregates cross-modality features dynamically, that adaptively weighs contributions based on traffic context. With this DFA paradigm, TrafficMoE maximizes representational efficiency by focusing solely on the most discriminative traffic features. Extensive experiments on six datasets demonstrate TrafficMoE consistently outperforms state-of-the-art methods, validating the necessity of heterogeneity-aware modeling in encrypted traffic analysis. The source code is publicly available at https://github.com/Posuly/TrafficMoE_main.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29520v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29518v1", "url": "http://arxiv.org/abs/2603.29518v1", "pdf_url": "https://arxiv.org/pdf/2603.29518v1", "title": "Impact of enriched meaning representations for language generation in dialogue tasks: A comprehensive exploration of the relevance of tasks, corpora and metrics", "authors": ["Alain Vázquez", "Maria Inés Torres"], "annotation": "Conversational systems should generate diverse language forms to interact fluently and accurately with users. In this context, Natural Language Generation (NLG) engines convert Meaning Representations (MRs) into sentences, directly influencing user perception. These MRs usually encode the communicative function (e.g., inform, request, confirm) via DAs and enumerate the semantic content with slot-value pairs. In this work, our objective is to analyse whether providing a task demonstrator to the generator enhances the generations of a fine-tuned model. This demonstrator is an MR-sentence pair extracted from the original dataset that enriches the input at training and inference time. The analysis involves five metrics that focus on different linguistic aspects, and four datasets that differ in multiple features, such as domain, size, lexicon, MR variability, and acquisition process. To the best of our knowledge, this is the first study on dialogue NLG implementing a comparative analysis of the impact of MRs on generation quality across domains, corpus characteristics, and the metrics used to evaluate these generations. Our key insight is that the proposed enriched inputs are effective for complex tasks and small datasets with high variability in MRs and sentences. They are also beneficial in zero-shot settings for any domain. Moreover, the analysis of the metrics shows that semantic metrics capture generation quality more accurately than lexical metrics. In addition, among these semantic metrics, those trained with human ratings can detect omissions and other subtle semantic issues that embedding-based metrics often miss. Finally, the evolution of the metric scores and the excellent results for Slot Accuracy and Dialogue Act Accuracy demonstrate that the generative models present fast adaptability to different tasks and robustness at semantic and communicative intention levels.", "category": "cs.AI", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29518v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30035v1", "url": "http://arxiv.org/abs/2603.30035v1", "pdf_url": "https://arxiv.org/pdf/2603.30035v1", "title": "Reward-Based Online LLM Routing via NeuralUCB", "authors": ["Ming-Hua Tsai", "Phat Tran"], "annotation": "This study investigates the use of NeuralUCB for cost-aware large language model (LLM) routing. Existing routing approaches can be broadly grouped into supervised routing methods and partial-feedback methods, each with different tradeoffs in efficiency and adaptivity. We implement a NeuralUCB-based routing policy and evaluate it on RouterBench under a simulated online setting. Experimental results show that the proposed method consistently outperforms random and min-cost baselines in utility reward. Compared with the max-quality reference, our method achieves substantially lower inference cost while maintaining competitive reward. These findings suggest that NeuralUCB is a promising approach for cost-aware LLM routing, while also highlighting remaining challenges in action discrimination and exploration.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30035v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30032v1", "url": "http://arxiv.org/abs/2603.30032v1", "pdf_url": "https://arxiv.org/pdf/2603.30032v1", "title": "Covertly improving intelligibility with data-driven adaptations of speech timing", "authors": ["Paige Tuttösí", "Angelica Lim", "H. Henny Yeung", "Yue Wang", "Jean-Julien Aucouturier"], "annotation": "Human talkers often address listeners with language-comprehension challenges, such as hard-of-hearing or non-native adults, by globally slowing down their speech. However, it remains unclear whether this strategy actually makes speech more intelligible. Here, we take advantage of recent advancements in machine-generated speech allowing more precise control of speech rate in order to systematically examine how targeted speech-rate adjustments may improve comprehension. We first use reverse-correlation experiments to show that the temporal influence of speech rate prior to a target vowel contrast (ex. the tense-lax distinction) in fact manifests in a scissor-like pattern, with opposite effects in early versus late context windows; this pattern is remarkably stable both within individuals and across native L1-English listeners and L2-English listeners with French, Mandarin, and Japanese L1s. Second, we show that this speech rate structure not only facilitates L2 listeners' comprehension of the target vowel contrast, but that native listeners also rely on this pattern in challenging acoustic conditions. Finally, we build a data-driven text-to-speech algorithm that replicates this temporal structure on novel speech sequences. Across a variety of sentences and vowel contrasts, listeners remained unaware that such targeted slowing improved word comprehension. Strikingly, participants instead judged the common strategy of global slowing as clearer, even though it actually increased comprehension errors. Together, these results show that targeted adjustments to speech rate significantly aid intelligibility under challenging conditions, while often going unnoticed. More generally, this paper provides a data-driven methodology to improve the accessibility of machine-generated speech which can be extended to other aspects of speech comprehension and a wide variety of listeners and environments.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30032v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30025v1", "url": "http://arxiv.org/abs/2603.30025v1", "pdf_url": "https://arxiv.org/pdf/2603.30025v1", "title": "ContextClaim: A Context-Driven Paradigm for Verifiable Claim Detection", "authors": ["Yufeng Li", "Rrubaa Panchendrarajan", "Arkaitz Zubiaga"], "annotation": "Verifiable claim detection asks whether a claim expresses a factual statement that can, in principle, be assessed against external evidence. As an early filtering stage in automated fact-checking, it plays an important role in reducing the burden on downstream verification components. However, existing approaches to claim detection, whether based on check-worthiness or verifiability, rely solely on the claim text itself. This is a notable limitation for verifiable claim detection in particular, where determining whether a claim is checkable may benefit from knowing what entities and events it refers to and whether relevant information exists to support verification. Inspired by the established role of evidence retrieval in later-stage claim verification, we propose Context-Driven Claim Detection (ContextClaim), a paradigm that advances retrieval to the detection stage. ContextClaim extracts entity mentions from the input claim, retrieves relevant information from Wikipedia as a structured knowledge source, and employs large language models to produce concise contextual summaries for downstream classification. We evaluate ContextClaim on two datasets covering different topics and text genres, the CheckThat! 2022 COVID-19 Twitter dataset and the PoliClaim political debate dataset, across encoder-only and decoder-only models under fine-tuning, zero-shot, and few-shot settings. Results show that context augmentation can improve verifiable claim detection, although its effectiveness varies across domains, model architectures, and learning settings. Through component analysis, human evaluation, and error analysis, we further examine when and why the retrieved context contributes to more reliable verifiability judgments.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30025v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30002v1", "url": "http://arxiv.org/abs/2603.30002v1", "pdf_url": "https://arxiv.org/pdf/2603.30002v1", "title": "Tracking Equivalent Mechanistic Interpretations Across Neural Networks", "authors": ["Alan Sun", "Mariya Toneva"], "annotation": "Mechanistic interpretability (MI) is an emerging framework for interpreting neural networks. Given a task and model, MI aims to discover a succinct algorithmic process, an interpretation, that explains the model's decision process on that task. However, MI is difficult to scale and generalize. This stems in part from two key challenges: there is no precise notion of a valid interpretation; and, generating interpretations is often an ad hoc process. In this paper, we address these challenges by defining and studying the problem of interpretive equivalence: determining whether two different models share a common interpretation, without requiring an explicit description of what that interpretation is. At the core of our approach, we propose and formalize the principle that two interpretations of a model are equivalent if all of their possible implementations are also equivalent. We develop an algorithm to estimate interpretive equivalence and case study its use on Transformer-based models. To analyze our algorithm, we introduce necessary and sufficient conditions for interpretive equivalence based on models' representation similarity. We provide guarantees that simultaneously relate a model's algorithmic interpretations, circuits, and representations. Our framework lays a foundation for the development of more rigorous evaluation methods of MI and automated, generalizable interpretation discovery methods.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30002v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29997v1", "url": "http://arxiv.org/abs/2603.29997v1", "pdf_url": "https://arxiv.org/pdf/2603.29997v1", "title": "Enhancing Structural Mapping with LLM-derived Abstractions for Analogical Reasoning in Narratives", "authors": ["Mohammadhossein Khojasteh", "Yifan Jiang", "Stefano De Giorgis", "Frank van Harmelen", "Filip Ilievski"], "annotation": "Analogical reasoning is a key driver of human generalization in problem-solving and argumentation. Yet, analogies between narrative structures remain challenging for machines. Cognitive engines for structural mapping are not directly applicable, as they assume pre-extracted entities, whereas LLMs' performance is sensitive to prompt format and the degree of surface similarity between narratives. This gap motivates a key question: What is the impact of enhancing structural mapping with LLM-derived abstractions on their analogical reasoning ability in narratives? To that end, we propose a modular framework named YARN (Yielding Abstractions for Reasoning in Narratives), which uses LLMs to decompose narratives into units, abstract these units, and then passes them to a mapping component that aligns elements across stories to perform analogical reasoning. We define and operationalize four levels of abstraction that capture both the general meaning of units and their roles in the story, grounded in prior work on framing. Our experiments reveal that abstractions consistently improve model performance, resulting in competitive or better performance than end-to-end LLM baselines. Closer error analysis reveals the remaining challenges in abstraction at the right level, in incorporating implicit causality, and an emerging categorization of analogical patterns in narratives. YARN enables systematic variation of experimental settings to analyze component contributions, and to support future work, we make the code for YARN openly available.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29997v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29979v1", "url": "http://arxiv.org/abs/2603.29979v1", "pdf_url": "https://arxiv.org/pdf/2603.29979v1", "title": "Structural Feature Engineering for Generative Engine Optimization: How Content Structure Shapes Citation Behavior", "authors": ["Junwei Yu", "Mufeng Yang", "Yepeng Ding", "Hiroyuki Sato"], "annotation": "The proliferation of AI-powered search engines has shifted information discovery from traditional link-based retrieval to direct answer generation with selective source citation, creating new challenges for content visibility. While existing Generative Engine Optimization (GEO) approaches focus primarily on semantic content modification, the role of structural features in influencing citation behavior remains underexplored.   In this paper, we propose GEO-SFE, a systematic framework for structural feature engineering in generative engine optimization. Our approach decomposes content structure into three hierarchical levels: macro-structure (document architecture), meso-structure (information chunking), and micro-structure (visual emphasis), and models their impact on citation probability across different generative engine architectures. We develop architecture-aware optimization strategies and predictive models that preserve semantic integrity while improving structural effectiveness.   Experimental evaluation across six mainstream generative engines demonstrates consistent improvements in citation rate (17.3 percent) and subjective quality (18.5 percent), validating the effectiveness and generalizability of the proposed framework. This work establishes structural optimization as a foundational component of GEO, providing a data-driven methodology for enhancing content visibility in LLM-powered information ecosystems.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29979v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29950v1", "url": "http://arxiv.org/abs/2603.29950v1", "pdf_url": "https://arxiv.org/pdf/2603.29950v1", "title": "Physiological and Semantic Patterns in Medical Teams Using an Intelligent Tutoring System", "authors": ["Xiaoshan Huang", "Conrad Borchers", "Jiayi Zhang", "Susanne P. Lajoie"], "annotation": "Effective collaboration requires teams to manage complex cognitive and emotional states through Socially Shared Regulation of Learning (SSRL). Physiological synchrony (i.e., longitudinal alignment in physiological signals) can indicate these states, but is hard to interpret on its own. We investigate the physiological and conversational dynamics of four medical dyads diagnosing a virtual patient case using an intelligent tutoring system. Semantic shifts in dialogue were correlated with transient physiological synchrony peaks. We also coded utterance segments for SSRL and derived cosine similarity using sentence embeddings. The results showed that activating prior knowledge featured significantly lower semantic similarity than simpler task execution. High physiological synchrony was associated with lower semantic similarity, suggesting that such moments involve exploratory and varied language use. Qualitative analysis triangulated these synchrony peaks as ``pivotal moments'': successful teams synchronized during shared discovery, while unsuccessful teams peaked during shared uncertainty. This research advances human-centered AI by demonstrating how biological signals can be fused with dialogues to understand critical moments in problem solving.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29950v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29937v1", "url": "http://arxiv.org/abs/2603.29937v1", "pdf_url": "https://arxiv.org/pdf/2603.29937v1", "title": "Rewrite the News: Tracing Editorial Reuse Across News Agencies", "authors": ["Soveatin Kuntur", "Nina Smirnova", "Anna Wroblewska", "Philipp Mayr", "Sebastijan Razboršek Maček"], "annotation": "This paper investigates sentence-level text reuse in multilingual journalism, analyzing where reused content occurs within articles. We present a weakly supervised method for detecting sentence-level cross-lingual reuse without requiring full translations, designed to support automated pre-selection to reduce information overload for journalists (Holyst et al., 2024). The study compares English-language articles from the Slovenian Press Agency (STA) with reports from 15 foreign agencies (FA) in seven languages, using publication timestamps to retain the earliest likely foreign source for each reused sentence. We analyze 1,037 STA and 237,551 FA articles from two time windows (October 7-November 2, 2023; February 1-28, 2025) and identify 1,087 aligned sentence pairs after filtering to the earliest sources. Reuse occurs in 52% of STA articles and 1.6% of FA articles and is predominantly non-literal, involving paraphrase and compositional reuse from multiple sources. Reused content tends to appear in the middle and end of English articles, while leads are more often original, indicating that simple lexical matching overlooks substantial editorial reuse. Compared with prior work focused on monolingual overlap, we (i) detect reuse across languages without requiring full translation, (ii) use publication timing to identify likely sources, and (iii) analyze where reused material is situated within articles. Dataset and code: https://github.com/kunturs/lrec2026-rewrite-news.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29937v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29901v1", "url": "http://arxiv.org/abs/2603.29901v1", "pdf_url": "https://arxiv.org/pdf/2603.29901v1", "title": "Less Is More? Selective Visual Attention to High-Importance Regions for Multimodal Radiology Summarization", "authors": ["Mst. Fahmida Sultana Naznin", "Adnan Ibney Faruq", "Mushfiqur Rahman", "Niloy Kumar Mondal", "Md. Mehedi Hasan Shawon", "Md Rakibul Hasan"], "annotation": "Automated radiology report summarization aims to distill verbose findings into concise clinical impressions, but existing multimodal models often struggle with visual noise and fail to meaningfully improve over strong text-only baselines in the FINDINGS $\\to$ IMPRESSION transformation. We challenge two prevailing assumptions: (1) that more visual input is always better, and (2) that multimodal models add limited value when findings already contain rich image-derived detail. Through controlled ablations on MIMIC-CXR benchmark, we show that selectively focusing on pathology-relevant visual patches rather than full images yields substantially better performance. We introduce ViTAS, Visual-Text Attention Summarizer, a multi-stage pipeline that combines ensemble-guided MedSAM2 lung segmentation, bidirectional cross-attention for multi-view fusion, Shapley-guided adaptive patch clustering, and hierarchical visual tokenization feeding a ViT. ViTAS achieves SOTA results with 29.25% BLEU-4 and 69.83% ROUGE-L, improved factual alignment in qualitative analysis, and the highest expert-rated human evaluation scores. Our findings demonstrate that less but more relevant visual input is not only sufficient but superior for multimodal radiology summarization.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29901v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29892v1", "url": "http://arxiv.org/abs/2603.29892v1", "pdf_url": "https://arxiv.org/pdf/2603.29892v1", "title": "FLEURS-Kobani: Extending the FLEURS Dataset for Northern Kurdish", "authors": ["Daban Q. Jaff", "Mohammad Mohammadamini"], "annotation": "FLEURS offers n-way parallel speech for 100+ languages, but Northern Kurdish is not one of them, which limits benchmarking for automatic speech recognition and speech translation tasks in this language. We present FLEURS-Kobani, a Northern Kurdish (ISO 639-3 KMR) spoken extension of the FLEURS benchmark. The FLEURS-Kobani dataset consists of 5,162 validated utterances, totaling 18 hours and 24 minutes. The data were recorded by 31 native speakers. It extends benchmark coverage to an under-resourced Kurdish variety. As baselines, we fine-tuned Whisper v3-large for ASR and E2E S2TT. A two-stage fine-tuning strategy (Common Voice to FLEURS-Kobani) yields the best ASR performance (WER 28.11, CER 9.84 on test). For E2E S2TT (KMR to EN), Whisper achieves 8.68 BLEU on test; we additionally report pivot-derived targets and a cascaded S2TT setup. FLEURS-Kobani provides the first public Northern Kurdish benchmark for evaluation of ASR, S2TT and S2ST tasks. The dataset is publicly released for research use under a CC BY 4.0 license.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29892v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29861v1", "url": "http://arxiv.org/abs/2603.29861v1", "pdf_url": "https://arxiv.org/pdf/2603.29861v1", "title": "Towards Empowering Consumers through Sentence-level Readability Scoring in German ESG Reports", "authors": ["Benjamin Josef Schüßler", "Jakob Prange"], "annotation": "With the ever-growing urgency of sustainability in the economy and society, and the massive stream of information that comes with it, consumers need reliable access to that information. To address this need, companies began publishing so called Environmental, Social, and Governance (ESG) reports, both voluntarily and forced by law. To serve the public, these reports must be addressed not only to financial experts but also to non-expert audiences. But are they written clearly enough? In this work, we extend an existing sentence-level dataset of German ESG reports with crowdsourced readability annotations. We find that, in general, native speakers perceive sentences in ESG reports as easy to read, but also that readability is subjective. We apply various readability scoring methods and evaluate them regarding their prediction error and correlation with human rankings. Our analysis shows that, while LLM prompting has potential for distinguishing clear from hard-to-read sentences, a small finetuned transformer predicts human readability with the lowest error. Averaging predictions of multiple models can slightly improve the performance at the cost of slower inference.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29861v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29846v1", "url": "http://arxiv.org/abs/2603.29846v1", "pdf_url": "https://arxiv.org/pdf/2603.29846v1", "title": "SNEAK: Evaluating Strategic Communication and Information Leakage in Large Language Models", "authors": ["Adar Avsian", "Larry Heck"], "annotation": "Large language models (LLMs) are increasingly deployed in multi-agent settings where communication must balance informativeness and secrecy. In such settings, an agent may need to signal information to collaborators while preventing an adversary from inferring sensitive details. However, existing LLM benchmarks primarily evaluate capabilities such as reasoning, factual knowledge, or instruction following, and do not directly measure strategic communication under asymmetric information. We introduce SNEAK (Secret-aware Natural language Evaluation for Adversarial Knowledge), a benchmark for evaluating selective information sharing in language models. In SNEAK, a model is given a semantic category, a candidate set of words, and a secret word, and must generate a message that indicates knowledge of the secret without revealing it too clearly. We evaluate generated messages using two simulated agents with different information states: an ally, who knows the secret and must identify the intended message, and a chameleon, who does not know the secret and attempts to infer it from the message. This yields two complementary metrics: utility, measuring how well the message communicates to collaborators, and leakage, measuring how much information it reveals to an adversary. Using this framework, we analyze the trade-off between informativeness and secrecy in modern language models and show that strategic communication under asymmetric information remains a challenging capability for current systems. Notably, human participants outperform all evaluated models by a large margin, achieving up to four times higher scores.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29846v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29828v1", "url": "http://arxiv.org/abs/2603.29828v1", "pdf_url": "https://arxiv.org/pdf/2603.29828v1", "title": "Owl-AuraID 1.0: An Intelligent System for Autonomous Scientific Instrumentation and Scientific Data Analysis", "authors": ["Han Deng", "Anqi Zou", "Hanling Zhang", "Ben Fei", "Chengyu Zhang", "Haobo Wang", "Xinru Guo", "Zhenyu Li", "Xuzhu Wang", "Peng Yang", "Fujian Zhang", "Weiyu Guo", "Xiaohong Shao", "Zhaoyang Liu", "Shixiang Tang", "Zhihui Wang", "Wanli Ouyang"], "annotation": "Scientific discovery increasingly depends on high-throughput characterization, yet automation is hindered by proprietary GUIs and the limited generalizability of existing API-based systems. We present Owl-AuraID, a software-hardware collaborative embodied agent system that adopts a GUI-native paradigm to operate instruments through the same interfaces as human experts. Its skill-centric framework integrates Type-1 (GUI operation) and Type-2 (data analysis) skills into end-to-end workflows, connecting physical sample handling with scientific interpretation. Owl-AuraID demonstrates broad coverage across ten categories of precision instruments and diverse workflows, including multimodal spectral analysis, microscopic imaging, and crystallographic analysis, supporting modalities such as FTIR, NMR, AFM, and TGA. Overall, Owl-AuraID provides a practical, extensible foundation for autonomous laboratories and illustrates a path toward evolving laboratory intelligence through reusable operational and analytical skills. The code are available at https://github.com/OpenOwlab/AuraID.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29828v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29801v1", "url": "http://arxiv.org/abs/2603.29801v1", "pdf_url": "https://arxiv.org/pdf/2603.29801v1", "title": "ENEIDE: A High Quality Silver Standard Dataset for Named Entity Recognition and Linking in Historical Italian", "authors": ["Cristian Santini", "Sebastian Barzaghi", "Paolo Sernani", "Emanuele Frontoni", "Laura Melosi", "Mehwish Alam"], "annotation": "This paper introduces ENEIDE (Extracting Named Entities from Italian Digital Editions), a silver standard dataset for Named Entity Recognition and Linking (NERL) in historical Italian texts. The corpus comprises 2,111 documents with over 8,000 entity annotations semi-automatically extracted from two scholarly digital editions: Digital Zibaldone, the philosophical diary of the Italian poet Giacomo Leopardi (1798--1837), and Aldo Moro Digitale, the complete works of the Italian politician Aldo Moro (1916--1978). Annotations cover multiple entity types (person, location, organization, literary work) linked to Wikidata identifiers, including NIL entities that cannot be mapped to the knowledge graph. To the best of our knowledge, ENEIDE represents the first multi-domain, publicly available NERL dataset for historical Italian with training, development, and test splits. We present a methodology for semi-automatic annotations extraction from manually curated scholarly digital editions, including quality control and annotation enhancement procedures. Baseline experiments using state-of-the-art models demonstrate the dataset's challenge for NERL and the gap between zero-shot approaches and fine-tuned models. The dataset's diachronic coverage spanning two centuries makes it particularly suitable for temporal entity disambiguation and cross-domain evaluation. ENEIDE is released under a CC BY-NC-SA 4.0 license.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29801v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29791v1", "url": "http://arxiv.org/abs/2603.29791v1", "pdf_url": "https://arxiv.org/pdf/2603.29791v1", "title": "Reasoning-Driven Synthetic Data Generation and Evaluation", "authors": ["Tim R. Davidson", "Benoit Seguin", "Enrico Bacis", "Cesar Ilharco", "Hamza Harkous"], "annotation": "Although many AI applications of interest require specialized multi-modal models, relevant data to train such models is inherently scarce or inaccessible. Filling these gaps with human annotators is prohibitively expensive, error-prone, and time-consuming, leading model builders to increasingly consider synthetic data as a scalable alternative. However, existing synthetic data generation methods often rely on manual prompts, evolutionary algorithms, or extensive seed data from the target distribution - limiting their scalability, explainability, and control. In this paper, we introduce Simula: a novel reasoning-driven framework for data generation and evaluation. It employs a seedless, agentic approach to generate synthetic datasets at scale, allowing users to define desired dataset characteristics through an explainable and controllable process that enables fine-grained resource allocation. We show the efficacy of our approach on a variety of datasets, rigorously testing both intrinsic and downstream properties. Our work (1) offers guidelines for synthetic data mechanism design, (2) provides insights into generating and evaluating synthetic data at scale, and (3) unlocks new opportunities for developing and deploying AI in domains where data scarcity or privacy concerns are paramount.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29791v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29765v1", "url": "http://arxiv.org/abs/2603.29765v1", "pdf_url": "https://arxiv.org/pdf/2603.29765v1", "title": "Training-Free Dynamic Upcycling of Expert Language Models", "authors": ["Eros Fanì", "Oğuzhan Ersoy"], "annotation": "Large Language Models (LLMs) have achieved remarkable performance on a wide range of specialized tasks, exhibiting strong problem-solving capabilities. However, training these models is prohibitively expensive, and they often lack domain-specific expertise because they rely on general knowledge datasets. Expertise finetuning can address this issue; however, it often leads to overspecialization, and developing a single multi-domain expert remains difficult due to diverging objectives. Furthermore, multitask training is challenging due to interference and catastrophic forgetting. Existing work proposes combining the expertise of dense models within a Mixture of Experts (MoE) architecture, although this approach still requires multitask finetuning. To address these issues, we introduce Dynamic Upcycling MoE (DUME), a novel approach that reuses dense experts trained on different domains to construct a unified MoE model. Our method builds a single multitask model that preserves the capabilities of the original dense experts without requiring additional training. DUME is both cost-efficient and scalable: by leveraging the closed-form solution of ridge regression, it eliminates the need for further optimization and enables experts to be added dynamically while maintaining the model's original performance. We demonstrate that DUME consistently outperforms baseline approaches in both causal language modeling and reasoning settings. Finally, we also show that the DUME model can be fine-tuned to further improve performance. We show that, in the causal language modeling setting, DUME can retain up to 97.6% of a dense expert model specialized in one particular domain, and that it can also surpass it in the reasoning setting, where it can achieve 102.1% of the dense expert performance. Our code is available at: github.com/gensyn-ai/dume.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29765v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29676v1", "url": "http://arxiv.org/abs/2603.29676v1", "pdf_url": "https://arxiv.org/pdf/2603.29676v1", "title": "A Comprehensive Information-Decomposition Analysis of Large Vision-Language Models", "authors": ["Lixin Xiu", "Xufang Luo", "Hideki Nakayama"], "annotation": "Large vision-language models (LVLMs) achieve impressive performance, yet their internal decision-making processes remain opaque, making it difficult to determine if the success stems from true multimodal fusion or from reliance on unimodal priors. To address this attribution gap, we introduce a novel framework using partial information decomposition (PID) to quantitatively measure the \"information spectrum\" of LVLMs -- decomposing a model's decision-relevant information into redundant, unique, and synergistic components. By adapting a scalable estimator to modern LVLM outputs, our model-agnostic pipeline profiles 26 LVLMs on four datasets across three dimensions -- breadth (cross-model & cross-task), depth (layer-wise information dynamics), and time (learning dynamics across training). Our analysis reveals two key results: (i) two task regimes (synergy-driven vs. knowledge-driven) and (ii) two stable, contrasting family-level strategies (fusion-centric vs. language-centric). We also uncover a consistent three-phase pattern in layer-wise processing and identify visual instruction tuning as the key stage where fusion is learned. Together, these contributions provide a quantitative lens beyond accuracy-only evaluation and offer insights for analyzing and designing the next generation of LVLMs. Code and data are available at https://github.com/RiiShin/pid-lvlm-analysis .", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29676v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29665v1", "url": "http://arxiv.org/abs/2603.29665v1", "pdf_url": "https://arxiv.org/pdf/2603.29665v1", "title": "Near-Miss: Latent Policy Failure Detection in Agentic Workflows", "authors": ["Ella Rabinovich", "David Boaz", "Naama Zwerdling", "Ateret Anaby-Tavor"], "annotation": "Agentic systems for business process automation often require compliance with policies governing conditional updates to the system state. Evaluation of policy adherence in LLM-based agentic workflows is typically performed by comparing the final system state against a predefined ground truth. While this approach detects explicit policy violations, it may overlook a more subtle class of issues in which agents bypass required policy checks, yet reach a correct outcome due to favorable circumstances. We refer to such cases as $\\textit{near-misses}$ or $\\textit{latent failures}$. In this work, we introduce a novel metric for detecting latent policy failures in agent conversations traces. Building on the ToolGuard framework, which converts natural-language policies into executable guard code, our method analyzes agent trajectories to determine whether agent's tool-calling decisions where sufficiently informed.   We evaluate our approach on the $τ^2$-verified Airlines benchmark across several contemporary open and proprietary LLMs acting as agents. Our results show that latent failures occur in 8-17% of trajectories involving mutating tool calls, even when the final outcome matches the expected ground-truth state. These findings reveal a blind spot in current evaluation methodologies and highlight the need for metrics that assess not only final outcomes but also the decision process leading to them.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29665v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29661v1", "url": "http://arxiv.org/abs/2603.29661v1", "pdf_url": "https://arxiv.org/pdf/2603.29661v1", "title": "Agenda-based Narrative Extraction: Steering Pathfinding Algorithms with Large Language Models", "authors": ["Brian Felipe Keith-Norambuena", "Carolina Inés Rojas-Córdova", "Claudio Juvenal Meneses-Villegas", "Elizabeth Johanna Lam-Esquenazi", "Angélica María Flores-Bustos", "Ignacio Alejandro Molina-Villablanca", "Joshua Emanuel Leyton-Vallejos"], "annotation": "Existing narrative extraction methods face a trade-off between coherence, interactivity, and multi-storyline support. Narrative Maps supports rich interaction and generates multiple storylines as a byproduct of its coverage constraints, though this comes at the cost of individual path coherence. Narrative Trails achieves high coherence through maximum capacity path optimization but provides no mechanism for user guidance or multiple perspectives. We introduce agenda-based narrative extraction, a method that bridges this gap by integrating large language models into the Narrative Trails pathfinding process to steer storyline construction toward user-specified perspectives. Our approach uses an LLM at each step to rank candidate documents based on their alignment with a given agenda while maintaining narrative coherence. Running the algorithm with different agendas yields different storylines through the same corpus. We evaluated our approach on a news article corpus using LLM judges with Claude Opus 4.5 and GPT 5.1, measuring both coherence and agenda alignment across 64 endpoint pairs and 6 agendas. LLM-driven steering achieves 9.9% higher alignment than keyword matching on semantic agendas (p=0.017), with 13.3% improvement on \\textit{Regime Crackdown} specifically (p=0.037), while keyword matching remains competitive on agendas with literal keyword overlap. The coherence cost is minimal: LLM steering reduces coherence by only 2.2% compared to the agenda-agnostic baseline. Counter-agendas that contradict the source material score uniformly low (2.2-2.5) across all methods, confirming that steering cannot fabricate unsupported narratives.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29661v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29651v1", "url": "http://arxiv.org/abs/2603.29651v1", "pdf_url": "https://arxiv.org/pdf/2603.29651v1", "title": "Semantic Interaction for Narrative Map Sensemaking: An Insight-based Evaluation", "authors": ["Brian Felipe Keith-Norambuena", "Fausto German", "Eric Krokos", "Sarah Joseph", "Chris North"], "annotation": "Semantic interaction (SI) enables analysts to incorporate their cognitive processes into AI models through direct manipulation of visualizations. While SI frameworks for narrative extraction have been proposed, empirical evaluations of their effectiveness remain limited. This paper presents a user study that evaluates SI for narrative map sensemaking, involving 33 participants under three conditions: a timeline baseline, a basic narrative map, and an interactive narrative map with SI capabilities. The results show that the map-based prototypes yielded more insights than the timeline baseline, with the SI-enabled condition reaching statistical significance and the basic map condition trending in the same direction. The SI-enabled condition showed the highest mean performance; differences between the map conditions were not statistically significant but showed large effect sizes (d > 0.8), suggesting that the study was underpowered to detect them. Qualitative analysis identified two distinct SI approaches-corrective and additive-that enable analysts to impose quality judgments and organizational structure on extracted narratives. We also find that SI users achieved comparable exploration breadth with less parameter manipulation, suggesting that SI serves as an alternative pathway for model refinement. This work provides empirical evidence that map-based representations outperform timelines for narrative sensemaking, along with qualitative insights into how analysts use SI for narrative refinement.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29651v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29617v1", "url": "http://arxiv.org/abs/2603.29617v1", "pdf_url": "https://arxiv.org/pdf/2603.29617v1", "title": "Convergent Representations of Linguistic Constructions in Human and Artificial Neural Systems", "authors": ["Pegah Ramezani", "Thomas Kinfe", "Andreas Maier", "Achim Schilling", "Patrick Krauss"], "annotation": "Understanding how the brain processes linguistic constructions is a central challenge in cognitive neuroscience and linguistics. Recent computational studies show that artificial neural language models spontaneously develop differentiated representations of Argument Structure Constructions (ASCs), generating predictions about when and how construction-level information emerges during processing. The present study tests these predictions in human neural activity using electroencephalography (EEG). Ten native English speakers listened to 200 synthetically generated sentences across four construction types (transitive, ditransitive, caused-motion, resultative) while neural responses were recorded. Analyses using time-frequency methods, feature extraction, and machine learning classification revealed construction-specific neural signatures emerging primarily at sentence-final positions, where argument structure becomes fully disambiguated, and most prominently in the alpha band. Pairwise classification showed reliable differentiation, especially between ditransitive and resultative constructions, while other pairs overlapped. Crucially, the temporal emergence and similarity structure of these effects mirror patterns in recurrent and transformer-based language models, where constructional representations arise during integrative processing stages. These findings support the view that linguistic constructions are neurally encoded as distinct form-meaning mappings, in line with Construction Grammar, and suggest convergence between biological and artificial systems on similar representational solutions. More broadly, this convergence is consistent with the idea that learning systems discover stable regions within an underlying representational landscape - recently termed a Platonic representational space - that constrains the emergence of efficient linguistic abstractions.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29617v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29608v1", "url": "http://arxiv.org/abs/2603.29608v1", "pdf_url": "https://arxiv.org/pdf/2603.29608v1", "title": "Learning Diagnostic Reasoning for Decision Support in Toxicology", "authors": ["Nico Oberländer", "David Bani-Harouni", "Tobias Zellner", "Nassir Navab", "Florian Eyer", "Matthias Keicher"], "annotation": "Acute poly-substance intoxication requires rapid, life-saving decisions under substantial uncertainty, as clinicians must rely on incomplete ingestion details and nonspecific symptoms. Effective diagnostic reasoning in this chaotic environment requires fusing unstructured, non-medical narratives (e.g. paramedic scene descriptions and unreliable patient self-reports or known histories), with structured medical data like vital signs. While Large Language Models (LLMs) show potential for processing such heterogeneous inputs, they struggle in this setting, often underperforming simple baselines that rely solely on patient histories. To address this, we present DeToxR (Decision-support for Toxicology with Reasoning), the first adaptation of Reinforcement Learning (RL) to emergency toxicology. We design a robust data-fusion engine for multi-label prediction across 14 substance classes based on an LLM finetuned with Group Relative Policy Optimization (GRPO). We optimize the model's reasoning directly using a clinical performance reward. By formulating a multi-label agreement metric as the reward signal, the model is explicitly penalized for missing co-ingested substances and hallucinating absent poisons. Our model significantly outperforms its unadapted base LLM counterpart and supervised baselines. Furthermore, in a clinical validation study, the model indicates a clinical advantage by outperforming an expert toxicologist in identifying the correct poisons (Micro-F1: 0.644 vs. 0.473). These results demonstrate the potential of RL-aligned LLMs to synthesize unstructured pre-clinical narratives and structured medical data for decision support in high-stakes environments.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29608v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29559v1", "url": "http://arxiv.org/abs/2603.29559v1", "pdf_url": "https://arxiv.org/pdf/2603.29559v1", "title": "When Can We Trust LLM Graders? Calibrating Confidence for Automated Assessment", "authors": ["Robinson Ferrer", "Damla Turgut", "Zhongzhou Chen", "Shashank Sonkar"], "annotation": "Large Language Models (LLMs) show promise for automated grading, but their outputs can be unreliable. Rather than improving grading accuracy directly, we address a complementary problem: \\textit{predicting when an LLM grader is likely to be correct}. This enables selective automation where high-confidence predictions are processed automatically while uncertain cases are flagged for human review. We compare three confidence estimation methods (self-reported confidence, self-consistency voting, and token probability) across seven LLMs of varying scale (4B to 120B parameters) on three educational datasets: RiceChem (long-answer chemistry), SciEntsBank, and Beetle (short-answer science). Our experiments reveal that self-reported confidence consistently achieves the best calibration across all conditions (avg ECE 0.166 vs 0.229 for self-consistency). Surprisingly, self-consistency remains 38\\% worse despite requiring 5$\\times$ the inference cost. Larger models exhibit substantially better calibration though gains vary by dataset and method (e.g., a 28\\% ECE reduction for self-reported), with GPT-OSS-120B achieving the best calibration (avg ECE 0.100) and strong discrimination (avg AUC 0.668). We also observe that confidence is strongly top-skewed across methods, creating a ``confidence floor'' that practitioners must account for when setting thresholds. These findings suggest that simply asking LLMs to report their confidence provides a practical approach for identifying reliable grading predictions. Code is available \\href{https://github.com/sonkar-lab/llm_grading_calibration}{here}.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29559v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29557v1", "url": "http://arxiv.org/abs/2603.29557v1", "pdf_url": "https://arxiv.org/pdf/2603.29557v1", "title": "FlowPIE: Test-Time Scientific Idea Evolution with Flow-Guided Literature Exploration", "authors": ["Qiyao Wang", "Hongbo Wang", "Longze Chen", "Zhihao Yang", "Guhong Chen", "Hamid Alinejad-Rokny", "Hui Li", "Yuan Lin", "Min Yang"], "annotation": "Scientific idea generation (SIG) is critical to AI-driven autonomous research, yet existing approaches are often constrained by a static retrieval-then-generation paradigm, leading to homogeneous and insufficiently divergent ideas. In this work, we propose FlowPIE, a tightly coupled retrieval-generation framework that treats literature exploration and idea generation as a co-evolving process. FlowPIE expands literature trajectories via a flow-guided Monte Carlo Tree Search (MCTS) inspired by GFlowNets, using the quality of current ideas assessed by an LLM-based generative reward model (GRM) as a supervised signal to guide adaptive retrieval and construct a diverse, high-quality initial population. Based on this population, FlowPIE models idea generation as a test-time idea evolution process, applying selection, crossover, and mutation with the isolation island paradigm and GRM-based fitness computation to incorporate cross-domain knowledge. It effectively mitigates the information cocoons arising from over-reliance on parametric knowledge and static literature. Extensive evaluations demonstrate that FlowPIE consistently produces ideas with higher novelty, feasibility and diversity compared to strong LLM-based and agent-based frameworks, while enabling reward scaling during test time.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29557v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29552v1", "url": "http://arxiv.org/abs/2603.29552v1", "pdf_url": "https://arxiv.org/pdf/2603.29552v1", "title": "Bringing Up a Bilingual BabyLM: Investigating Multilingual Language Acquisition Using Small-Scale Models", "authors": ["Linda Zeng", "Steven Y. Feng", "Michael C. Frank"], "annotation": "Multilingualism is incredibly common around the world, leading to many important theoretical and practical questions about how children learn multiple languages at once. For example, does multilingual acquisition lead to delays in learning? Are there better and worse ways to structure multilingual input? Many correlational studies address these questions, but it is surprisingly difficult to get definitive answers because children cannot be randomly assigned to be multilingual and data are typically not matched between languages. We use language model training as a method for simulating a variety of highly controlled exposure conditions, and create matched 100M-word mono- and bilingual datasets using synthetic data and machine translation. We train GPT-2 models on monolingual and bilingual data organized to reflect a range of exposure regimes, and evaluate their performance on perplexity, grammaticality, and semantic knowledge. Across model scales and measures, bilingual models perform similarly to monolingual models in one language, but show strong performance in the second language as well. These results suggest that there are no strong differences between different bilingual exposure regimes, and that bilingual input poses no in-principle challenges for agnostic statistical learners.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29552v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29541v1", "url": "http://arxiv.org/abs/2603.29541v1", "pdf_url": "https://arxiv.org/pdf/2603.29541v1", "title": "Can LLM Agents Identify Spoken Dialects like a Linguist?", "authors": ["Tobias Bystrich", "Lukas Hamm", "Maria Hassan", "Lea Fischbach", "Lucie Flek", "Akbar Karimi"], "annotation": "Due to the scarcity of labeled dialectal speech, audio dialect classification is a challenging task for most languages, including Swiss German. In this work, we explore the ability of large language models (LLMs) as agents in understanding the dialects and whether they can show comparable performance to models such as HuBERT in dialect classification. In addition, we provide an LLM baseline and a human linguist one. Our approach uses phonetic transcriptions produced by ASR systems and combines them with linguistic resources such as dialect feature maps, vowel history, and rules. Our findings indicate that, when linguistic information is provided, the LLM predictions improve. The human baseline shows that automatically generated transcriptions can be beneficial for such classifications, but also presents opportunities for improvement.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29541v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29522v1", "url": "http://arxiv.org/abs/2603.29522v1", "pdf_url": "https://arxiv.org/pdf/2603.29522v1", "title": "Baby Scale: Investigating Models Trained on Individual Children's Language Input", "authors": ["Steven Y. Feng", "Alvin W. M. Tan", "Michael C. Frank"], "annotation": "Modern language models (LMs) must be trained on many orders of magnitude more words of training data than human children receive before they begin to produce useful behavior. Assessing the nature and origins of this \"data gap\" requires benchmarking LMs on human-scale datasets to understand how linguistic knowledge emerges from children's natural training data. Using transcripts from the BabyView dataset (videos from children ages 6-36 months), we investigate (1) scaling performance at child-scale data regimes, (2) variability in model performance across datasets from different children's experiences and linguistic predictors of dataset quality, and (3) relationships between model and child language learning outcomes. LMs trained on child data show acceptable scaling for grammar tasks, but lower scaling on semantic and world knowledge tasks than models trained on synthetic data; we also observe substantial variability on data from different children. Beyond dataset size, performance is most associated with a combination of distributional and interactional linguistic features, broadly consistent with what makes high-quality input for child language development. Finally, model likelihoods for individual words correlate with children's learning of those words, suggesting that properties of child-directed input may influence both model learning and human language development. Overall, understanding what properties make language data efficient for learning can enable more powerful small-scale language models while also shedding light on human language acquisition.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29522v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29518v1", "url": "http://arxiv.org/abs/2603.29518v1", "pdf_url": "https://arxiv.org/pdf/2603.29518v1", "title": "Impact of enriched meaning representations for language generation in dialogue tasks: A comprehensive exploration of the relevance of tasks, corpora and metrics", "authors": ["Alain Vázquez", "Maria Inés Torres"], "annotation": "Conversational systems should generate diverse language forms to interact fluently and accurately with users. In this context, Natural Language Generation (NLG) engines convert Meaning Representations (MRs) into sentences, directly influencing user perception. These MRs usually encode the communicative function (e.g., inform, request, confirm) via DAs and enumerate the semantic content with slot-value pairs. In this work, our objective is to analyse whether providing a task demonstrator to the generator enhances the generations of a fine-tuned model. This demonstrator is an MR-sentence pair extracted from the original dataset that enriches the input at training and inference time. The analysis involves five metrics that focus on different linguistic aspects, and four datasets that differ in multiple features, such as domain, size, lexicon, MR variability, and acquisition process. To the best of our knowledge, this is the first study on dialogue NLG implementing a comparative analysis of the impact of MRs on generation quality across domains, corpus characteristics, and the metrics used to evaluate these generations. Our key insight is that the proposed enriched inputs are effective for complex tasks and small datasets with high variability in MRs and sentences. They are also beneficial in zero-shot settings for any domain. Moreover, the analysis of the metrics shows that semantic metrics capture generation quality more accurately than lexical metrics. In addition, among these semantic metrics, those trained with human ratings can detect omissions and other subtle semantic issues that embedding-based metrics often miss. Finally, the evolution of the metric scores and the excellent results for Slot Accuracy and Dialogue Act Accuracy demonstrate that the generative models present fast adaptability to different tasks and robustness at semantic and communicative intention levels.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29518v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29517v1", "url": "http://arxiv.org/abs/2603.29517v1", "pdf_url": "https://arxiv.org/pdf/2603.29517v1", "title": "LLM Probe: Evaluating LLMs for Low-Resource Languages", "authors": ["Hailay Kidu Teklehaymanot", "Gebrearegawi Gebremariam", "Wolfgang Nejdl"], "annotation": "Despite rapid advances in large language models (LLMs), their linguistic abilities in low-resource and morphologically rich languages are still not well understood due to limited annotated resources and the absence of standardized evaluation frameworks. This paper presents LLM Probe, a lexicon-based assessment framework designed to systematically evaluate the linguistic skills of LLMs in low-resource language environments. The framework analyzes models across four areas of language understanding: lexical alignment, part-of-speech recognition, morphosyntactic probing, and translation accuracy. To illustrate the framework, we create a manually annotated benchmark dataset using a low-resource Semitic language as a case study. The dataset comprises bilingual lexicons with linguistic annotations, including part-of-speech tags, grammatical gender, and morphosyntactic features, which demonstrate high inter-annotator agreement to ensure reliable annotations. We test a variety of models, including causal language models and sequence-to-sequence architectures. The results reveal notable differences in performance across various linguistic tasks: sequence-to-sequence models generally excel in morphosyntactic analysis and translation quality, whereas causal models demonstrate strong performance in lexical alignment but exhibit weaker translation accuracy. Our results emphasize the need for linguistically grounded evaluation to better understand LLM limitations in low-resource settings. We release LLM Probe and the accompanying benchmark dataset as open-source tools to promote reproducible benchmarking and to support the development of more inclusive multilingual language technologies.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29517v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29497v1", "url": "http://arxiv.org/abs/2603.29497v1", "pdf_url": "https://arxiv.org/pdf/2603.29497v1", "title": "Distilling Human-Aligned Privacy Sensitivity Assessment from Large Language Models", "authors": ["Gabriel Loiseau", "Damien Sileo", "Damien Riquet", "Maxime Meyer", "Marc Tommasi"], "annotation": "Accurate privacy evaluation of textual data remains a critical challenge in privacy-preserving natural language processing. Recent work has shown that large language models (LLMs) can serve as reliable privacy evaluators, achieving strong agreement with human judgments; however, their computational cost and impracticality for processing sensitive data at scale limit real-world deployment. We address this gap by distilling the privacy assessment capabilities of Mistral Large 3 (675B) into lightweight encoder models with as few as 150M parameters. Leveraging a large-scale dataset of privacy-annotated texts spanning 10 diverse domains, we train efficient classifiers that preserve strong agreement with human annotations while dramatically reducing computational requirements. We validate our approach on human-annotated test data and demonstrate its practical utility as an evaluation metric for de-identification systems.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29497v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29493v1", "url": "http://arxiv.org/abs/2603.29493v1", "pdf_url": "https://arxiv.org/pdf/2603.29493v1", "title": "MemFactory: Unified Inference & Training Framework for Agent Memory", "authors": ["Ziliang Guo", "Ziheng Li", "Zhiyu Li"], "annotation": "Memory-augmented Large Language Models (LLMs) are essential for developing capable, long-term AI agents. Recently, applying Reinforcement Learning (RL) to optimize memory operations, such as extraction, updating, and retrieval, has emerged as a highly promising research direction. However, existing implementations remain highly fragmented and task-specific, lacking a unified infrastructure to streamline the integration, training, and evaluation of these complex pipelines. To address this gap, we present MemFactory, the first unified, highly modular training and inference framework specifically designed for memory-augmented agents. Inspired by the success of unified fine-tuning frameworks like LLaMA-Factory, MemFactory abstracts the memory lifecycle into atomic, plug-and-play components, enabling researchers to seamlessly construct custom memory agents via a \"Lego-like\" architecture. Furthermore, the framework natively integrates Group Relative Policy Optimization (GRPO) to fine-tune internal memory management policies driven by multi-dimensional environmental rewards. MemFactory provides out-of-the-box support for recent cutting-edge paradigms, including Memory-R1, RMM, and MemAgent. We empirically validate MemFactory on the open-source MemAgent architecture using its publicly available training and evaluation data. Across both in-domain and out-of-distribution evaluation sets, MemFactory consistently improves performance over the corresponding base models, with relative gains of up to 14.8%. By providing a standardized, extensible, and easy-to-use infrastructure, MemFactory significantly lowers the barrier to entry, paving the way for future innovations in memory-driven AI agents.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29493v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29492v1", "url": "http://arxiv.org/abs/2603.29492v1", "pdf_url": "https://arxiv.org/pdf/2603.29492v1", "title": "Calibrated Confidence Expression for Radiology Report Generation", "authors": ["David Bani-Harouni", "Chantal Pellegrini", "Julian Lüers", "Su Hwan Kim", "Markus Baalmann", "Benedikt Wiestler", "Rickmer Braren", "Nassir Navab", "Matthias Keicher"], "annotation": "Safe deployment of Large Vision-Language Models (LVLMs) in radiology report generation requires not only accurate predictions but also clinically interpretable indicators of when outputs should be thoroughly reviewed, enabling selective radiologist verification and reducing the risk of hallucinated findings influencing clinical decisions. One intuitive approach to this is verbalized confidence, where the model explicitly states its certainty. However, current state-of-the-art language models are often overconfident, and research on calibration in multimodal settings such as radiology report generation is limited. To address this gap, we introduce ConRad (Confidence Calibration for Radiology Reports), a reinforcement learning framework for fine-tuning medical LVLMs to produce calibrated verbalized confidence estimates alongside radiology reports. We study two settings: a single report-level confidence score and a sentence-level variant assigning a confidence to each claim. Both are trained using the GRPO algorithm with reward functions based on the logarithmic scoring rule, which incentivizes truthful self-assessment by penalizing miscalibration and guarantees optimal calibration under reward maximization. Experimentally, ConRad substantially improves calibration and outperforms competing methods. In a clinical evaluation we show that ConRad's report level scores are well aligned with clinicians' judgment. By highlighting full reports or low-confidence statements for targeted review, ConRad can support safer clinical integration of AI-assistance for report generation.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29492v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29467v1", "url": "http://arxiv.org/abs/2603.29467v1", "pdf_url": "https://arxiv.org/pdf/2603.29467v1", "title": "M-MiniGPT4: Multilingual VLLM Alignment via Translated Data", "authors": ["Seung Hun Han", "Youssef Mohamed", "Mohamed Elhoseiny"], "annotation": "This paper presents a Multilingual Vision Large Language Model, named M-MiniGPT4. Our model exhibits strong vision-language understanding (VLU) capabilities across 11 languages. We utilize a mixture of native multilingual and translated data to push the multilingual VLU performance of the MiniGPT4 architecture. In addition, we propose a multilingual alignment training stage that uses parallel text corpora to further enhance the multilingual capabilities of our model. M-MiniGPT4 achieves 36% accuracy on the multilingual MMMU benchmark, outperforming state-of-the-art models in the same weight class, including foundation models released after the majority of this work was completed. We open-source our models, code, and translated datasets to facilitate future research in low-resource and multilingual settings.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29467v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29466v1", "url": "http://arxiv.org/abs/2603.29466v1", "pdf_url": "https://arxiv.org/pdf/2603.29466v1", "title": "An Isotropic Approach to Efficient Uncertainty Quantification with Gradient Norms", "authors": ["Nils Grünefeld", "Jes Frellsen", "Christian Hardmeier"], "annotation": "Existing methods for quantifying predictive uncertainty in neural networks are either computationally intractable for large language models or require access to training data that is typically unavailable. We derive a lightweight alternative through two approximations: a first-order Taylor expansion that expresses uncertainty in terms of the gradient of the prediction and the parameter covariance, and an isotropy assumption on the parameter covariance. Together, these yield epistemic uncertainty as the squared gradient norm and aleatoric uncertainty as the Bernoulli variance of the point prediction, from a single forward-backward pass through an unmodified pretrained model. We justify the isotropy assumption by showing that covariance estimates built from non-training data introduce structured distortions that isotropic covariance avoids, and that theoretical results on the spectral properties of large networks support the approximation at scale. Validation against reference Markov Chain Monte Carlo estimates on synthetic problems shows strong correspondence that improves with model size. We then use the estimates to investigate when each uncertainty type carries useful signal for predicting answer correctness in question answering with large language models, revealing a benchmark-dependent divergence: the combined estimate achieves the highest mean AUROC on TruthfulQA, where questions involve genuine conflict between plausible answers, but falls to near chance on TriviaQA's factual recall, suggesting that parameter-level uncertainty captures a fundamentally different signal than self-assessment methods.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29466v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29454v1", "url": "http://arxiv.org/abs/2603.29454v1", "pdf_url": "https://arxiv.org/pdf/2603.29454v1", "title": "Authorship Impersonation via LLM Prompting does not Evade Authorship Verification Methods", "authors": ["Baoyi Zeng", "Andrea Nini"], "annotation": "Authorship verification (AV), the task of determining whether a questioned text was written by a specific individual, is a critical part of forensic linguistics. While manual authorial impersonation by perpetrators has long been a recognized threat in historical forensic cases, recent advances in large language models (LLMs) raise new challenges, as adversaries may exploit these tools to impersonate another's writing. This study investigates whether prompted LLMs can generate convincing authorial impersonations and whether such outputs can evade existing forensic AV systems. Using GPT-4o as the adversary model, we generated impersonation texts under four prompting conditions across three genres: emails, text messages, and social media posts. We then evaluated these outputs against both non-neural AV methods (n-gram tracing, Ranking-Based Impostors Method, LambdaG) and neural approaches (AdHominem, LUAR, STAR) within a likelihood-ratio framework. Results show that LLM-generated texts failed to sufficiently replicate authorial individuality to bypass established AV systems. We also observed that some methods achieved even higher accuracy when rejecting impersonation texts compared to genuine negative samples. Overall, these findings indicate that, despite the accessibility of LLMs, current AV systems remain robust against entry-level impersonation attempts across multiple genres. Furthermore, we demonstrate that this counter-intuitive resilience stems, at least in part, from the higher lexical diversity and entropy inherent in LLM-generated texts.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29454v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29429v1", "url": "http://arxiv.org/abs/2603.29429v1", "pdf_url": "https://arxiv.org/pdf/2603.29429v1", "title": "CounselReflect: A Toolkit for Auditing Mental-Health Dialogues", "authors": ["Yahan Li", "Chaohao Du", "Zeyang Li", "Christopher Chun Kuizon", "Shupeng Cheng", "Angel Hsing-Chi Hwang", "Adam C. Frank", "Ruishan Liu"], "annotation": "Mental-health support is increasingly mediated by conversational systems (e.g., LLM-based tools), but users often lack structured ways to audit the quality and potential risks of the support they receive. We introduce CounselReflect, an end-to-end toolkit for auditing mental-health support dialogues. Rather than producing a single opaque quality score, CounselReflect provides structured, multi-dimensional reports with session-level summaries, turn-level scores, and evidence-linked excerpts to support transparent inspection. The system integrates two families of evaluation signals: (i) 12 model-based metrics produced by task-specific predictors, and (ii) rubric-based metrics that extend coverage via a literature-derived library (69 metrics) and user-defined custom metrics, operationalized with configurable LLM judges. CounselReflect is available as a web application, browser extension, and command-line interface (CLI), enabling use in real-time settings as well as at scale. Human evaluation includes a user study with 20 participants and an expert review with 6 mental-health professionals, suggesting that CounselReflect supports understandable, usable, and trustworthy auditing. A demo video and full source code are also provided.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29429v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29406v1", "url": "http://arxiv.org/abs/2603.29406v1", "pdf_url": "https://arxiv.org/pdf/2603.29406v1", "title": "PRISM: PRIor from corpus Statistics for topic Modeling", "authors": ["Tal Ishon", "Yoav Goldberg", "Uri Shaham"], "annotation": "Topic modeling seeks to uncover latent semantic structure in text, with LDA providing a foundational probabilistic framework. While recent methods often incorporate external knowledge (e.g., pre-trained embeddings), such reliance limits applicability in emerging or underexplored domains. We introduce \\textbf{PRISM}, a corpus-intrinsic method that derives a Dirichlet parameter from word co-occurrence statistics to initialize LDA without altering its generative process. Experiments on text and single cell RNA-seq data show that PRISM improves topic coherence and interpretability, rivaling models that rely on external knowledge. These results underscore the value of corpus-driven initialization for topic modeling in resource-constrained settings.   Code is available at: https://github.com/shaham-lab/PRISM.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29406v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29396v1", "url": "http://arxiv.org/abs/2603.29396v1", "pdf_url": "https://arxiv.org/pdf/2603.29396v1", "title": "Is my model perplexed for the right reason? Contrasting LLMs' Benchmark Behavior with Token-Level Perplexity", "authors": ["Zoë Prins", "Samuele Punzo", "Frank Wildenburg", "Giovanni Cinà", "Sandro Pezzelle"], "annotation": "Standard evaluations of Large language models (LLMs) focus on task performance, offering limited insight into whether correct behavior reflects appropriate underlying mechanisms and risking confirmation bias. We introduce a simple, principled interpretability framework based on token-level perplexity to test whether models rely on linguistically relevant cues. By comparing perplexity distributions over minimal sentence pairs differing in one or a few `pivotal' tokens, our method enables precise, hypothesis-driven analysis without relying on unstable feature-attribution techniques. Experiments on controlled linguistic benchmarks with several open-weight LLMs show that, while linguistically important tokens influence model behavior, they never fully explain perplexity shifts, revealing that models rely on heuristics other than the expected linguistic ones.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29396v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29373v1", "url": "http://arxiv.org/abs/2603.29373v1", "pdf_url": "https://arxiv.org/pdf/2603.29373v1", "title": "Beyond Idealized Patients: Evaluating LLMs under Challenging Patient Behaviors in Medical Consultations", "authors": ["Yahan Li", "Xinyi Jie", "Wanjia Ruan", "Xubei Zhang", "Huaijie Zhu", "Yicheng Gao", "Chaohao Du", "Ruishan Liu"], "annotation": "Large language models (LLMs) are increasingly used for medical consultation and health information support. In this high-stakes setting, safety depends not only on medical knowledge, but also on how models respond when patient inputs are unclear, inconsistent, or misleading. However, most existing medical LLM evaluations assume idealized and well-posed patient questions, which limits their realism. In this paper, we study challenging patient behaviors that commonly arise in real medical consultations and complicate safe clinical reasoning. We define four clinically grounded categories of such behaviors: information contradiction, factual inaccuracy, self-diagnosis, and care resistance. For each behavior, we specify concrete failure criteria that capture unsafe responses. Building on four existing medical dialogue datasets, we introduce CPB-Bench (Challenging Patient Behaviors Benchmark), a bilingual (English and Chinese) benchmark of 692 multi-turn dialogues annotated with these behaviors. We evaluate a range of open- and closed-source LLMs on their responses to challenging patient utterances. While models perform well overall, we identify consistent, behavior-specific failure patterns, with particular difficulty in handling contradictory or medically implausible patient information. We also study four intervention strategies and find that they yield inconsistent improvements and can introduce unnecessary corrections. We release the dataset and code.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29373v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29347v1", "url": "http://arxiv.org/abs/2603.29347v1", "pdf_url": "https://arxiv.org/pdf/2603.29347v1", "title": "Developing a Guideline for the Labovian-Structural Analysis of Oral Narratives in Japanese", "authors": ["Amane Watahiki", "Tomoki Doi", "Akari Kikuchi", "Hiroshi Ohata", "Yuki I. Nakata", "Takuya Niikawa", "Taiga Shinozaki", "Hitomi Yanaka"], "annotation": "Narrative analysis is a cornerstone of qualitative research. One leading approach is the Labovian model, but its application is labor-intensive, requiring a holistic, recursive interpretive process that moves back and forth between individual parts of the transcript and the transcript as a whole. Existing Labovian datasets are available only in English, which differs markedly from Japanese in terms of grammar and discourse conventions. To address this gap, we introduce the first systematic guidelines for Labovian narrative analysis of Japanese narrative data. Our guidelines retain all six Labovian categories and extend the framework by providing explicit rules for clause segmentation tailored to Japanese constructions. In addition, our guidelines cover a broader range of clause types and narrative types. Using these guidelines, annotators achieved high agreement in clause segmentation (Fleiss' kappa = 0.80) and moderate agreement in two structural classification tasks (Krippendorff's alpha = 0.41 and 0.45, respectively), one of which is slightly higher than that found in prior work despite the use of finer-grained distinctions. This paper describes the Labovian model, the proposed guidelines, the annotation process, and their utility. It concludes by discussing the challenges encountered during the annotation process and the prospects for developing a larger dataset for structural narrative analysis in Japanese qualitative research.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29347v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29346v1", "url": "http://arxiv.org/abs/2603.29346v1", "pdf_url": "https://arxiv.org/pdf/2603.29346v1", "title": "L-ReLF: A Framework for Lexical Dataset Creation", "authors": ["Anass Sedrati", "Mounir Afifi", "Reda Benkhadra"], "annotation": "This paper introduces the L-ReLF (Low-Resource Lexical Framework), a novel, reproducible methodology for creating high-quality, structured lexical datasets for underserved languages. The lack of standardized terminology, exemplified by Moroccan Darija, poses a critical barrier to knowledge equity in platforms like Wikipedia, often forcing editors to rely on inconsistent, ad-hoc methods to create new words in their language. Our research details the technical pipeline developed to overcome these challenges. We systematically address the difficulties of working with low-resource data, including source identification, utilizing Optical Character Recognition (OCR) despite its bias towards Modern Standard Arabic, and rigorous post-processing to correct errors and standardize the data model. The resulting structured dataset is fully compatible with Wikidata Lexemes, serving as a vital technical resource. The L-ReLF methodology is designed for generalizability, offering other language communities a clear path to build foundational lexical data for downstream NLP applications, such as Machine Translation and morphological analysis.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29346v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29345v1", "url": "http://arxiv.org/abs/2603.29345v1", "pdf_url": "https://arxiv.org/pdf/2603.29345v1", "title": "Open Machine Translation for Esperanto", "authors": ["Ona de Gibert", "Lluís de Gibert"], "annotation": "Esperanto is a widespread constructed language, known for its regular grammar and productive word formation. Besides having substantial resources available thanks to its online community, it remains relatively underexplored in the context of modern machine translation (MT) approaches. In this work, we present the first comprehensive evaluation of open-source MT systems for Esperanto, comparing rule-based systems, encoder-decoder models, and LLMs across model sizes. We evaluate translation quality across six language directions involving English, Spanish, Catalan, and Esperanto using multiple automatic metrics as well as human evaluation. Our results show that the NLLB family achieves the best performance in all language pairs, followed closely by our trained compact models and a fine-tuned general-purpose LLM. Human evaluation confirms this trend, with NLLB translations preferred in approximately half of the comparisons, although noticeable errors remain. In line with Esperanto's tradition of openness and international collaboration, we release our code and best-performing models publicly.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29345v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29336v1", "url": "http://arxiv.org/abs/2603.29336v1", "pdf_url": "https://arxiv.org/pdf/2603.29336v1", "title": "CADEL: A Corpus of Administrative Web Documents for Japanese Entity Linking", "authors": ["Shohei Higashiyama", "Masao Ideuchi", "Masao Utiyama"], "annotation": "Entity linking is the task of associating linguistic expressions with entries in a knowledge base that represent real-world entities and concepts. Language resources for this task have primarily been developed for English, and the resources available for evaluating Japanese systems remain limited. In this study, we develop a corpus design policy for the entity linking task and construct an annotated corpus for training and evaluating Japanese entity linking systems, with rich coverage of linguistic expressions referring to entities that are specific to Japan. Evaluation of inter-annotator agreement confirms the high consistency of the annotations in the corpus, and a preliminary experiment on entity disambiguation based on string matching suggests that the corpus contains a substantial number of non-trivial cases, supporting its potential usefulness as an evaluation benchmark.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29336v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29288v1", "url": "http://arxiv.org/abs/2603.29288v1", "pdf_url": "https://arxiv.org/pdf/2603.29288v1", "title": "Sima AIunty: Caste Audit in LLM-Driven Matchmaking", "authors": ["Atharva Naik", "Shounok Kar", "Varnika Sharma", "Ashwin Rajadesingan", "Koustuv Saha"], "annotation": "Social and personal decisions in relational domains such as matchmaking are deeply entwined with cultural norms and historical hierarchies, and can potentially be shaped by algorithmic and AI-mediated assessments of compatibility, acceptance, and stability. In South Asian contexts, caste remains a central aspect of marital decision-making, yet little is known about how contemporary large language models (LLMs) reproduce or disrupt caste-based stratification in such settings. In this work, we conduct a controlled audit of caste bias in LLM-mediated matchmaking evaluations using real-world matrimonial profiles. We vary caste identity across Brahmin, Kshatriya, Vaishya, Shudra, and Dalit, and income across five buckets, and evaluate five LLM families (GPT, Gemini, Llama, Qwen, and BharatGPT). Models are prompted to assess profiles along dimensions of social acceptance, marital stability, and cultural compatibility. Our analysis reveals consistent hierarchical patterns across models: same-caste matches are rated most favorably, with average ratings up to 25% higher (on a 10-point scale) than inter-caste matches, which are further ordered according to traditional caste hierarchy. These findings highlight how existing caste hierarchies are reproduced in LLM decision-making and underscore the need for culturally grounded evaluation and intervention strategies in AI systems deployed in socially sensitive domains, where such systems risk reinforcing historical forms of exclusion.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29288v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29259v1", "url": "http://arxiv.org/abs/2603.29259v1", "pdf_url": "https://arxiv.org/pdf/2603.29259v1", "title": "Aligning Multimodal Sequential Recommendations via Robust Direct Preference Optimization with Sparse MoE", "authors": ["Hejin Huang", "Jusheng Zhang", "Kaitong Cai", "Jian Wang", "Rong Pan"], "annotation": "Preference-based alignment objectives have been widely adopted, from RLHF-style pairwise learning in large language models to emerging applications in recommender systems. Yet, existing work rarely examines how Direct Preference Optimization (DPO) behaves under implicit feedback, where unobserved items are not reliable negatives. We conduct systematic experiments on multimodal sequential recommendation to compare common negative-selection strategies and their interaction with DPO training. Our central finding is that a simple modification, replacing deterministic hard negatives with stochastic sampling from a dynamic top-K candidate pool, consistently improves ranking performance. We attribute its effectiveness to two factors: (1) reducing erroneous suppressive gradients caused by false negatives, and (2) retaining informative hard signals while smoothing optimization via controlled stochasticity. With an optional sparse Mixture-of-Experts encoder for efficient capacity scaling, RoDPO achieves up to 5.25% NDCG@5 on three Amazon benchmarks, with nearly unchanged inference cost.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29259v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29247v1", "url": "http://arxiv.org/abs/2603.29247v1", "pdf_url": "https://arxiv.org/pdf/2603.29247v1", "title": "MemRerank: Preference Memory for Personalized Product Reranking", "authors": ["Zhiyuan Peng", "Xuyang Wu", "Huaixiao Tou", "Yi Fang", "Yi Gong"], "annotation": "LLM-based shopping agents increasingly rely on long purchase histories and multi-turn interactions for personalization, yet naively appending raw history to prompts is often ineffective due to noise, length, and relevance mismatch. We propose MemRerank, a preference memory framework that distills user purchase history into concise, query-independent signals for personalized product reranking. To study this problem, we build an end-to-end benchmark and evaluation framework centered on an LLM-based \\textbf{1-in-5} selection task, which measures both memory quality and downstream reranking utility. We further train the memory extractor with reinforcement learning (RL), using downstream reranking performance as supervision. Experiments with two LLM-based rerankers show that MemRerank consistently outperforms no-memory, raw-history, and off-the-shelf memory baselines, yielding up to \\textbf{+10.61} absolute points in 1-in-5 accuracy. These results suggest that explicit preference memory is a practical and effective building block for personalization in agentic e-commerce systems.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29247v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29244v1", "url": "http://arxiv.org/abs/2603.29244v1", "pdf_url": "https://arxiv.org/pdf/2603.29244v1", "title": "The Thiomi Dataset: A Large-Scale Multimodal Corpus for Low-Resource African Languages", "authors": ["Hillary Mutisya", "John Mugane", "Gavin Nyamboga", "Brian Chege", "Maryruth Gathoni"], "annotation": "We present the Thiomi Dataset, a large-scale multimodal corpus spanning ten African languages across four language families: Swahili, Kikuyu, Kamba, Kimeru, Luo, Maasai, Kipsigis, Somali (East Africa); Wolof (West Africa); and Fulani (West/Central Africa). The dataset contains over 601,000 approved sentence-level text annotations and over 385,000 audio recordings across nine languages, collected through a dedicated community data collection platform involving over 100 contributors. The Thiomi platform collected data for nine languages; Swahili data was supplemented with existing Common Voice recordings. A multi-tier quality assurance pipeline achieves 86-100% text approval rates for the six primary languages. To validate the dataset's utility, we train and evaluate ASR, MT, and TTS models, establishing baselines across all ten languages. Our best ASR system achieves 3.24% WER on Swahili (Common Voice), reducing prior academic SOTA from 8.3% to 3.24% (5.1 percentage point absolute, 61% relative reduction), and 4.3% WER on Somali. The dataset will be published on HuggingFace. We describe the collection platform, quality assurance workflows, and baseline experiments, and discuss implications for African language technology infrastructure.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29244v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29232v1", "url": "http://arxiv.org/abs/2603.29232v1", "pdf_url": "https://arxiv.org/pdf/2603.29232v1", "title": "Long-Document QA with Chain-of-Structured-Thought and Fine-Tuned SLMs", "authors": ["Zhuowen Liang", "Xiaotian Lin", "Zhengxuan Zhang", "Yuyu Luo", "Haixun Wang", "Nan Tang"], "annotation": "Large language models (LLMs) are widely applied to data analytics over documents, yet direct reasoning over long, noisy documents remains brittle and error-prone. Hence, we study document question answering (QA) that consolidates dispersed evidence into a structured output (e.g., a table, graph, or chunks) to support reliable, verifiable QA. We propose a two-pillar framework, LiteCoST, to achieve both high accuracy and low latency with small language models (SLMs). Pillar 1: Chain-of-Structured-Thought (CoST). We introduce a CoST template, a schema-aware instruction that guides a strong LLM to produce both a step-wise CoST trace and the corresponding structured output. The process induces a minimal structure, normalizes entities/units, aligns records, serializes the output, and verifies/refines it, yielding auditable supervision. Pillar 2: SLM fine-tuning. The compact models are trained on LLM-generated CoST data in two stages: Supervised Fine-Tuning for structural alignment, followed by Group Relative Policy Optimization (GRPO) incorporating triple rewards for answer/format quality and process consistency. By distilling structure-first behavior into SLMs, this approach achieves LLM-comparable quality on multi-domain long-document QA using 3B/7B SLMs, while delivering 2-4x lower latency than GPT-4o and DeepSeek-R1 (671B). The code is available at https://github.com/HKUSTDial/LiteCoST.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29232v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29221v1", "url": "http://arxiv.org/abs/2603.29221v1", "pdf_url": "https://arxiv.org/pdf/2603.29221v1", "title": "SiPaKosa: A Comprehensive Corpus of Canonical and Classical Buddhist Texts in Sinhala and Pali", "authors": ["Ranidu Gurusinghe", "Nevidu Jayatilleke"], "annotation": "SiPaKosa is a comprehensive corpus of Sinhala and Pali doctrinal texts comprising approximately 786K sentences and 9.25M words, incorporating 16 copyright-cleared historical Buddhist documents alongside the complete web-scraped Tripitaka canonical texts. The corpus was created through high-quality OCR using Google Document AI on historical manuscripts, combined with systematic web scraping of canonical repositories, followed by rigorous quality control and metadata annotation. The corpus is organised into language-specific subcorpora: Sinhala and Mixed Sinhala-Pali. We evaluate the performance of language models using ten pretrained models, with perplexity scores ranging from 1.09 to 189.67 on our corpus. This analysis shows that proprietary models significantly outperform open-source alternatives by factors of three to six times. This corpus supports the pretraining of domain-adapted language models, facilitates historical language analysis, and aids in the development of information retrieval systems for Buddhist scholarship while preserving Sinhala cultural heritage.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29221v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29219v1", "url": "http://arxiv.org/abs/2603.29219v1", "pdf_url": "https://arxiv.org/pdf/2603.29219v1", "title": "SyriSign: A Parallel Corpus for Arabic Text to Syrian Arabic Sign Language Translation", "authors": ["Mohammad Amer Khalil", "Raghad Nahas", "Ahmad Nassar", "Khloud Al Jallad"], "annotation": "Sign language is the primary approach of communication for the Deaf and Hard-of-Hearing (DHH) community. While there are numerous benchmarks for high-resource sign languages, low-resource languages like Arabic remain underrepresented. Currently, there is no publicly available dataset for Syrian Arabic Sign Language (SyArSL). To overcome this gap, we introduce SyriSign, a dataset comprising 1500 video samples across 150 unique lexical signs, designed for text-to-SyArSL translation tasks. This work aims to reduce communication barriers in Syria, as most news are delivered in spoken or written Arabic, which is often inaccessible to the deaf community. We evaluated SyriSign using three deep learning architectures: MotionCLIP for semantic motion generation, T2M-GPT for text-conditioned motion synthesis, and SignCLIP for bilingual embedding alignment. Experimental results indicate that while generative approaches show strong potential for sign representation, the limited dataset size constrains generalization performance. We will release SyriSign publicly, hoping it serves as an initial benchmark.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29219v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29217v1", "url": "http://arxiv.org/abs/2603.29217v1", "pdf_url": "https://arxiv.org/pdf/2603.29217v1", "title": "Advancing LLM-based phoneme-to-grapheme for multilingual speech recognition", "authors": ["Lukuang Dong", "Ziwei Li", "Saierdaer Yusuyin", "Xianyu Zhao", "Zhijian Ou"], "annotation": "Phoneme-based ASR factorizes recognition into speech-to-phoneme (S2P) and phoneme-to-grapheme (P2G), enabling cross-lingual acoustic sharing while keeping language-specific orthography in a separate module. While large language models (LLMs) are promising for P2G, multilingual P2G remains challenging due to language-aware generation and severe cross-language data imbalance. We study multilingual LLM-based P2G on the ten-language CV-Lang10 benchmark. We examine robustness strategies that account for S2P uncertainty, including DANP and Simplified SKM (S-SKM). S-SKM is a Monte Carlo approximation that avoids CTC-based S2P probability weighting in P2G training. Robust training and low-resource oversampling reduce the average WER from 10.56% to 7.66%.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29217v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29211v1", "url": "http://arxiv.org/abs/2603.29211v1", "pdf_url": "https://arxiv.org/pdf/2603.29211v1", "title": "Xuanwu: Evolving General Multimodal Models into an Industrial-Grade Foundation for Content Ecosystems", "authors": ["Zhiqian Zhang", "Xu Zhao", "Xiaoqing Xu", "Guangdong Liang", "Weijia Wang", "Xiaolei Lv", "Bo Li", "Jun Gao"], "annotation": "In recent years, multimodal large models have continued to improve on general benchmarks. However, in real-world content moderation and adversarial settings, mainstream models still suffer from degraded generalization and catastrophic forgetting because of limited fine-grained visual perception and insufficient modeling of long-tail noise. In this paper, we present Xuanwu VL-2B as a case study of how general multimodal models can be developed into an industrial-grade foundation model for content ecosystems. The model adopts a compact InternViT-300M + MLP + Qwen3 1.7B architecture, balancing fine-grained visual perception, language-semantic alignment, and deployment cost within an approximately 2B-parameter budget. To balance business specialization with the retention of general capabilities, we developed a data iteration and curation mechanism and trained the model through a progressive three-stage pipeline: pre-training, mid-training, and post-training. Ablation studies and offline business evaluations show that Xuanwu VL-2B achieves an average score of 67.90 across seven OpenCompass multimodal metrics (vs. 64.27 for InternVL 3.5 2B), an average recall of 94.38% over seven independent business moderation tasks, and a weighted overall recall of 82.82% on policy-violating text in challenging adversarial OCR scenarios, outperforming Gemini-2.5-Pro (76.72%). These results show that, under a limited parameter budget, Xuanwu VL-2B achieves a practical balance among business alignment, visual perception, general capability retention, and deployment cost.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29211v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29159v1", "url": "http://arxiv.org/abs/2603.29159v1", "pdf_url": "https://arxiv.org/pdf/2603.29159v1", "title": "Kwame 2.0: Human-in-the-Loop Generative AI Teaching Assistant for Large Scale Online Coding Education in Africa", "authors": ["George Boateng", "Samuel Boateng", "Victor Kumbol"], "annotation": "Providing timely and accurate learning support in large-scale online coding courses is challenging, particularly in resource-constrained contexts. We present Kwame 2.0, a bilingual (English-French) generative AI teaching assistant built using retrieval-augmented generation and deployed in a human-in-the-loop forum within SuaCode, an introductory mobile-based coding course for learners across Africa. Kwame 2.0 retrieves relevant course materials and generates context-aware responses while encouraging human oversight and community participation. We deployed the system in a 15-month longitudinal study spanning 15 cohorts with 3,717 enrollments across 35 African countries. Evaluation using community feedback and expert ratings shows that Kwame 2.0 provided high-quality and timely support, achieving high accuracy on curriculum-related questions, while human facilitators and peers effectively mitigated errors, particularly for administrative queries. Our findings demonstrate that human-in-the-loop generative AI systems can combine the scalability and speed of AI with the reliability of human support, offering an effective approach to learning assistance for underrepresented populations in resource-constrained settings at scale.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29159v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29140v1", "url": "http://arxiv.org/abs/2603.29140v1", "pdf_url": "https://arxiv.org/pdf/2603.29140v1", "title": "Designing FSMs Specifications from Requirements with GPT 4.0", "authors": ["Omer Nguena Timo", "Paul-Alexis Rodriguez", "Florent Avellaneda"], "annotation": "Finite state machines (FSM) are executable formal specifications of reactive systems. These machines are designed based on systems' requirements. The requirements are often recorded in textual documents written in natural languages. FSMs play a crucial role in different phases of the model-driven system engineering (MDE). For example, they serve to automate testing activities. FSM quality is critical: the lower the quality of FSM, the higher the number of faults surviving the testing phase and the higher the risk of failure of the systems in production, which could lead to catastrophic scenarios. Therefore, this paper leverages recent advances in the domain of LLM to propose an LLM-based framework for designing FSMs from requirements. The framework also suggests an expert-centric approach based on FSM mutation and test generation for repairing the FSMs produced by LLMs. This paper also provides an experimental analysis and evaluation of LLM's capacities in performing the tasks presented in the framework and FSM repair via various methods. The paper presents experimental results with simulated data. These results and methods bring a new analysis and vision of LLMs that are useful for further development of machine learning technology and its applications to MDE.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29140v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29123v1", "url": "http://arxiv.org/abs/2603.29123v1", "pdf_url": "https://arxiv.org/pdf/2603.29123v1", "title": "Concept Training for Human-Aligned Language Models", "authors": ["Christine Zhang", "Dan Jurafsky", "Chen Shani"], "annotation": "The next-token prediction (NTP) objective trains language models to predict a single continuation token at each step. In natural language, however, a prefix can be continued in many valid ways, and even similar meanings may differ in surface form. For example, the sentence ``this website is safe to \\underline{browse}'' could plausibly continue with words such as browse, search, visit, surf, or navigate. While standard NTP training treats these alternatives as mutually exclusive targets, we explore a framework that instead predicts concepts, approximated as sets of semantically related tokens. We show that models trained with concept supervision exhibit stronger alignment with human semantic similarity judgments on multiple lexical benchmarks. These gains are accompanied by lower perplexity on semantically meaningful words (definition in Section 3.1), and a modest increase in global token-level perplexity, reflecting a tradeoff between standard NTP optimization and concept-level supervision. Our results suggest that concept-level objectives can improve semantic alignment while maintaining competitive language modeling performance.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29123v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29112v1", "url": "http://arxiv.org/abs/2603.29112v1", "pdf_url": "https://arxiv.org/pdf/2603.29112v1", "title": "GISTBench: Evaluating LLM User Understanding via Evidence-Based Interest Verification", "authors": ["Iordanis Fostiropoulos", "Muhammad Rafay Azhar", "Abdalaziz Sawwan", "Boyu Fang", "Yuchen Liu", "Jiayi Liu", "Hanchao Yu", "Qi Guo", "Jianyu Wang", "Fei Liu", "Xiangjun Fan"], "annotation": "We introduce GISTBench, a benchmark for evaluating Large Language Models' (LLMs) ability to understand users from their interaction histories in recommendation systems. Unlike traditional RecSys benchmarks that focus on item prediction accuracy, our benchmark evaluates how well LLMs can extract and verify user interests from engagement data. We propose two novel metric families: Interest Groundedness (IG), decomposed into precision and recall components to separately penalize hallucinated interest categories and reward coverage, and Interest Specificity (IS), which assesses the distinctiveness of verified LLM-predicted user profiles. We release a synthetic dataset constructed on real user interactions on a global short-form video platform. Our dataset contains both implicit and explicit engagement signals and rich textual descriptions. We validate our dataset fidelity against user surveys, and evaluate eight open-weight LLMs spanning 7B to 120B parameters. Our findings reveal performance bottlenecks in current LLMs, particularly their limited ability to accurately count and attribute engagement signals across heterogeneous interaction types.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29112v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29093v1", "url": "http://arxiv.org/abs/2603.29093v1", "pdf_url": "https://arxiv.org/pdf/2603.29093v1", "title": "APEX-EM: Non-Parametric Online Learning for Autonomous Agents via Structured Procedural-Episodic Experience Replay", "authors": ["Pratyay Banerjee", "Masud Moshtaghi", "Ankit Chadha"], "annotation": "LLM-based autonomous agents lack persistent procedural memory: they re-derive solutions from scratch even when structurally identical tasks have been solved before. We present \\textbf{APEX-EM}, a non-parametric online learning framework that accumulates, retrieves, and reuses structured procedural plans without modifying model weights. APEX-EM introduces: (1) a \\emph{structured experience representation} encoding the full procedural-episodic trace of each execution -- planning steps, artifacts, iteration history with error analysis, and quality scores; (2) a \\emph{Plan-Retrieve-Generate-Iterate-Ingest} (PRGII) workflow with Task Verifiers providing multi-dimensional reward signals; and (3) a \\emph{dual-outcome Experience Memory} with hybrid retrieval combining semantic search, structural signature matching, and plan DAG traversal -- enabling cross-domain transfer between tasks sharing no lexical overlap but analogous operational structure. Successful experiences serve as positive in-context examples; failures as negative examples with structured error annotations.   We evaluate on BigCodeBench~\\cite{zhuo2025bigcodebench}, KGQAGen-10k~\\cite{zhang2025kgqagen}, and Humanity's Last Exam~\\cite{phan2025hle} using Claude Sonnet 4.5 and Opus 4.5. On KGQAGen-10k, APEX-EM achieves 89.6\\% accuracy versus 41.3\\% without memory (+48.3pp), surpassing the oracle-retrieval upper bound (84.9\\%). On BigCodeBench, it reaches 83.3\\% SR from a 53.9\\% baseline (+29.4pp), exceeding MemRL's~\\cite{memrl2025} +11.0pp gain under comparable frozen-backbone conditions (noting backbone differences controlled for in our analysis). On HLE, entity graph retrieval reaches 48.0\\% from 25.2\\% (+22.8pp). Ablations show component value is task-dependent: rich judge feedback is negligible for code generation but critical for structured queries (+10.3pp), while binary-signal iteration partially compensates for weaker feedback.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29093v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29078v1", "url": "http://arxiv.org/abs/2603.29078v1", "pdf_url": "https://arxiv.org/pdf/2603.29078v1", "title": "PolarQuant: Optimal Gaussian Weight Quantization via Hadamard Rotation for LLM Compression", "authors": ["Caio Vicentino"], "annotation": "We present PolarQuant, a post-training weight quantization method for large language models (LLMs) that exploits the distributional structure of neural network weights to achieve near-lossless compression. PolarQuant operates in three stages: (1) block-wise normalization to the unit hypersphere, (2) Walsh-Hadamard rotation to transform coordinates into approximately Gaussian random variables, and (3) quantization with centroids matched to the Gaussian distribution. Our ablation reveals that Hadamard rotation alone accounts for 98% of the quality improvement, reducing Qwen3.5-9B perplexity from 6.90 (absmax Q5) to 6.40 (Delta = +0.03 from FP16), making it practically lossless without any calibration data. Furthermore, PolarQuant functions as an effective preprocessing step for downstream INT4 quantizers: PolarQuant Q5 dequantized and re-quantized by torchao INT4 achieves perplexity 6.56 versus 6.68 for direct absmax INT4, while maintaining 43.1 tok/s throughput at 6.5 GB VRAM. Code and models are publicly available.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29078v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29077v1", "url": "http://arxiv.org/abs/2603.29077v1", "pdf_url": "https://arxiv.org/pdf/2603.29077v1", "title": "Dual Perspectives in Emotion Attribution: A Generator-Interpreter Framework for Cross-Cultural Analysis of Emotion in LLMs", "authors": ["Aizirek Turdubaeva", "Uichin Lee"], "annotation": "Large language models (LLMs) are increasingly used in cross-cultural systems to understand and adapt to human emotions, which are shaped by cultural norms of expression and interpretation. However, prior work on emotion attribution has focused mainly on interpretation, overlooking the cultural background of emotion generators. This assumption of universality neglects variation in how emotions are expressed and perceived across nations. To address this gap, we propose a Generator-Interpreter framework that captures dual perspectives of emotion attribution by considering both expression and interpretation. We systematically evaluate six LLMs on an emotion attribution task using data from 15 countries. Our analysis reveals that performance variations depend on the emotion type and cultural context. Generator-interpreter alignment effects are present; the generator's country of origin has a stronger impact on performance. We call for culturally sensitive emotion modeling in LLM-based systems to improve robustness and fairness in emotion understanding across diverse cultural contexts.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29077v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29042v1", "url": "http://arxiv.org/abs/2603.29042v1", "pdf_url": "https://arxiv.org/pdf/2603.29042v1", "title": "An Empirical Recipe for Universal Phone Recognition", "authors": ["Shikhar Bharadwaj", "Chin-Jou Li", "Kwanghee Choi", "Eunjung Yeo", "William Chen", "Shinji Watanabe", "David R. Mortensen"], "annotation": "Phone recognition (PR) is a key enabler of multilingual and low-resource speech processing tasks, yet robust performance remains elusive. Highly performant English-focused models do not generalize across languages, while multilingual models underutilize pretrained representations. It also remains unclear how data scale, architecture, and training objective contribute to multilingual PR. We present PhoneticXEUS -- trained on large-scale multilingual data and achieving state-of-the-art performance on both multilingual (17.7% PFER) and accented English speech (10.6% PFER). Through controlled ablations with evaluations across 100+ languages under a unified scheme, we empirically establish our training recipe and quantify the impact of SSL representations, data scale, and loss objectives. In addition, we analyze error patterns across language families, accented speech, and articulatory features. All data and code are released openly.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29042v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29038v1", "url": "http://arxiv.org/abs/2603.29038v1", "pdf_url": "https://arxiv.org/pdf/2603.29038v1", "title": "Trojan-Speak: Bypassing Constitutional Classifiers with No Jailbreak Tax via Adversarial Finetuning", "authors": ["Bilgehan Sel", "Xuanli He", "Alwin Peng", "Ming Jin", "Jerry Wei"], "annotation": "Fine-tuning APIs offered by major AI providers create new attack surfaces where adversaries can bypass safety measures through targeted fine-tuning. We introduce Trojan-Speak, an adversarial fine-tuning method that bypasses Anthropic's Constitutional Classifiers. Our approach uses curriculum learning combined with GRPO-based hybrid reinforcement learning to teach models a communication protocol that evades LLM-based content classification. Crucially, while prior adversarial fine-tuning approaches report more than 25% capability degradation on reasoning benchmarks, Trojan-Speak incurs less than 5% degradation while achieving 99+% classifier evasion for models with 14B+ parameters. We demonstrate that fine-tuned models can provide detailed responses to expert-level CBRN (Chemical, Biological, Radiological, and Nuclear) queries from Anthropic's Constitutional Classifiers bug-bounty program. Our findings reveal that LLM-based content classifiers alone are insufficient for preventing dangerous information disclosure when adversaries have fine-tuning access, and we show that activation-level probes can substantially improve robustness to such attacks.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29038v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29026v1", "url": "http://arxiv.org/abs/2603.29026v1", "pdf_url": "https://arxiv.org/pdf/2603.29026v1", "title": "On the limited utility of parallel data for learning shared multilingual representations", "authors": ["Julius Leino", "Jörg Tiedemann"], "annotation": "Shared multilingual representations are essential for cross-lingual tasks and knowledge transfer across languages. This study looks at the impact of parallel data, i.e. translated sentences, in pretraining as a signal to trigger representations that are aligned across languages. We train reference models with different proportions of parallel data and show that parallel data seem to have only a minimal effect on the cross-lingual alignment. Based on multiple evaluation methods, we find that the effect is limited to potentially accelerating the representation sharing in the early phases of pretraining, and to decreasing the amount of language-specific neurons in the model. Cross-lingual alignment seems to emerge on similar levels even without the explicit signal from parallel data.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29026v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29025v1", "url": "http://arxiv.org/abs/2603.29025v1", "pdf_url": "https://arxiv.org/pdf/2603.29025v1", "title": "The Model Says Walk: How Surface Heuristics Override Implicit Constraints in LLM Reasoning", "authors": ["Yubo Li", "Lu Zhang", "Tianchong Jiang", "Ramayya Krishnan", "Rema Padman"], "annotation": "Large language models systematically fail when a salient surface cue conflicts with an unstated feasibility constraint. We study this through a diagnose-measure-bridge-treat framework. Causal-behavioral analysis of the ``car wash problem'' across six models reveals approximately context-independent sigmoid heuristics: the distance cue exerts 8.7 to 38 times more influence than the goal, and token-level attribution shows patterns more consistent with keyword associations than compositional inference. The Heuristic Override Benchmark (HOB) -- 500 instances spanning 4 heuristic by 5 constraint families with minimal pairs and explicitness gradients -- demonstrates generality across 14 models: under strict evaluation (10/10 correct), no model exceeds 75%, and presence constraints are hardest (44%). A minimal hint (e.g., emphasizing the key object) recovers +15 pp on average, suggesting the failure lies in constraint inference rather than missing knowledge; 12/14 models perform worse when the constraint is removed (up to -39 pp), revealing conservative bias. Parametric probes confirm that the sigmoid pattern generalizes to cost, efficiency, and semantic-similarity heuristics; goal-decomposition prompting recovers +6 to 9 pp by forcing models to enumerate preconditions before answering. Together, these results characterize heuristic override as a systematic reasoning vulnerability and provide a benchmark for measuring progress toward resolving it.", "category": "cs.CL", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29025v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30045v1", "url": "http://arxiv.org/abs/2603.30045v1", "pdf_url": "https://arxiv.org/pdf/2603.30045v1", "title": "OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation", "authors": ["Yuheng Liu", "Xin Lin", "Xinke Li", "Baihan Yang", "Chen Wang", "Kalyan Sunkavalli", "Yannick Hold-Geoffroy", "Hao Tan", "Kai Zhang", "Xiaohui Xie", "Zifan Shi", "Yiwei Hu"], "annotation": "Modeling scenes using video generation models has garnered growing research interest in recent years. However, most existing approaches rely on perspective video models that synthesize only limited observations of a scene, leading to issues of completeness and global consistency. We propose OmniRoam, a controllable panoramic video generation framework that exploits the rich per-frame scene coverage and inherent long-term spatial and temporal consistency of panoramic representation, enabling long-horizon scene wandering. Our framework begins with a preview stage, where a trajectory-controlled video generation model creates a quick overview of the scene from a given input image or video. Then, in the refine stage, this video is temporally extended and spatially upsampled to produce long-range, high-resolution videos, thus enabling high-fidelity world wandering. To train our model, we introduce two panoramic video datasets that incorporate both synthetic and real-world captured videos. Experiments show that our framework consistently outperforms state-of-the-art methods in terms of visual quality, controllability, and long-term scene consistency, both qualitatively and quantitatively. We further showcase several extensions of this framework, including real-time video generation and 3D reconstruction. Code is available at https://github.com/yuhengliu02/OmniRoam.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30045v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30043v1", "url": "http://arxiv.org/abs/2603.30043v1", "pdf_url": "https://arxiv.org/pdf/2603.30043v1", "title": "Video Models Reason Early: Exploiting Plan Commitment for Maze Solving", "authors": ["Kaleb Newman", "Tyler Zhu", "Olga Russakovsky"], "annotation": "Video diffusion models exhibit emergent reasoning capabilities like solving mazes and puzzles, yet little is understood about how they reason during generation. We take a first step towards understanding this and study the internal planning dynamics of video models using 2D maze solving as a controlled testbed. Our investigations reveal two findings. Our first finding is early plan commitment: video diffusion models commit to a high-level motion plan within the first few denoising steps, after which further denoising alters visual details but not the underlying trajectory. Our second finding is that path length, not obstacle density, is the dominant predictor of maze difficulty, with a sharp failure threshold at 12 steps. This means video models can only reason over long mazes by chaining together multiple sequential generations. To demonstrate the practical benefits of our findings, we introduce Chaining with Early Planning, or ChEaP, which only spends compute on seeds with promising early plans and chains them together to tackle complex mazes. This improves accuracy from 7% to 67% on long-horizon mazes and by 2.5x overall on hard tasks in Frozen Lake and VR-Bench across Wan2.2-14B and HunyuanVideo-1.5. Our analysis reveals that current video models possess deeper reasoning capabilities than previously recognized, which can be elicited more reliably with better inference-time scaling.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30043v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30038v1", "url": "http://arxiv.org/abs/2603.30038v1", "pdf_url": "https://arxiv.org/pdf/2603.30038v1", "title": "Benchmarking PhD-Level Coding in 3D Geometric Computer Vision", "authors": ["Wenyi Li", "Renkai Luo", "Yue Yu", "Huan-ang Gao", "Mingju Gao", "Li Yuan", "Chaoyou Fu", "Hao Zhao"], "annotation": "AI-assisted coding has rapidly reshaped software practice and research workflows, yet today's models still struggle to produce correct code for complex 3D geometric vision. If models could reliably write such code, the research of our community would change substantially. To measure progress toward that goal, we introduce GeoCodeBench, a PhD-level benchmark that evaluates coding for 3D vision. Each problem is a fill-in-the-function implementation task curated from representative papers at recent venues: we first let a tool propose candidate functions from official repositories, then perform careful human screening to select core 3D geometric components. For every target, we generate diverse, edge-case unit tests, enabling fully automatic, reproducible scoring. We evaluate eight representative open- and closed-source models to reflect the current ecosystem. The best model, GPT-5, attains only 36.6% pass rate, revealing a large gap between current capabilities and dependable 3D scientific coding. GeoCodeBench organizes tasks into a two-level hierarchy: General 3D capability (geometric transformations and mechanics/optics formulation) and Research capability (novel algorithm implementation and geometric logic routing). Scores are positively correlated across these axes, but research-oriented tasks are markedly harder. Context ablations further show that \"more paper text\" is not always better: cutting off at the Method section statistically outperforms full-paper inputs, highlighting unresolved challenges in long-context scientific comprehension. Together, these findings position GeoCodeBench as a rigorous testbed for advancing from generic coding to trustworthy 3D geometric vision coding.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30038v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30008v1", "url": "http://arxiv.org/abs/2603.30008v1", "pdf_url": "https://arxiv.org/pdf/2603.30008v1", "title": "Conditional Polarization Guidance for Camouflaged Object Detection", "authors": ["QIfan Zhang", "Hao Wang", "Xiangrong Qin", "Ruijie Li"], "annotation": "Camouflaged object detection (COD) aims to identify targets that are highly blended with their backgrounds. Recent works have shown that the optical characteristics of polarization cues play a significant role in improving camouflaged object detection. However, most existing polarization-based approaches depend on complex visual encoders and fusion mechanisms, leading to increased model complexity and computational overhead, while failing to fully explore how polarization can explicitly guide hierarchical RGB representation learning. To address these limitations, we propose CPGNet, an asymmetric RGB-polarization framework that introduces a conditional polarization guidance mechanism to explicitly regulate RGB feature learning for camouflaged object detection. Specifically, we design a lightweight polarization interaction module that jointly models these complementary cues and generates reliable polarization guidance in a unified manner. Unlike conventional feature fusion strategies, the proposed conditional guidance mechanism dynamically modulates RGB features using polarization priors, enabling the network to focus on subtle discrepancies between camouflaged objects and their backgrounds. Furthermore, we introduce a polarization edge-guided frequency refinement strategy that enhances high-frequency components under polarization constraints, effectively breaking camouflage patterns. Finally, we develop an iterative feedback decoder to perform coarse-to-fine feature calibration and progressively refine camouflage prediction. Extensive experiments on polarization datasets across multiple tasks, along with evaluations on non-polarization datasets, demonstrate that CPGNet consistently outperforms state-of-the-art methods.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30008v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29990v1", "url": "http://arxiv.org/abs/2603.29990v1", "pdf_url": "https://arxiv.org/pdf/2603.29990v1", "title": "SurgNavAR: An Augmented Reality Surgical Navigation Framework for Optical See-Through Head Mounted Displays", "authors": ["Abdullah Thabit", "Mohamed Benmahdjoub", "Rafiuddin Jinabade", "Hizirwan S. Salim", "Marie-Lise C. van Veelen", "Mark G. van Vledder", "Eppo B. Wolvius", "Theo van Walsum"], "annotation": "Augmented reality (AR) devices with head mounted displays (HMDs) facilitate the direct superimposition of 3D preoperative imaging data onto the patient during surgery. To use an HMD-AR device as a stand-alone surgical navigation system, the device should be able to locate the patient and surgical instruments, align preoperative imaging data with the patient, and visualize navigation data in real time during surgery. Whereas some of the technologies required for this are known, integration in such devices is cumbersome and requires specific knowledge and expertise, hampering scientific progress in this field. This work therefore aims to present and evaluate an integrated HMD-based AR surgical navigation framework that is adaptable to diverse surgical applications. The framework tracks 2D patterns as reference markers attached to the patient and surgical instruments. It allows for the calibration of surgical tools using pivot and reference-based calibration techniques. It enables image-to-patient registration using point-based matching and manual positioning. The integrated functionalities of the framework are evaluated on two HMD devices, the HoloLens 2 and Magic Leap 2, with two surgical use cases being evaluated in a phantom setup: AR-guided needle insertion and rib fracture localization. The framework was able to achieve a mean tooltip calibration accuracy of 1 mm, a registration accuracy of 3 mm, and a targeting accuracy below 5 mm on the two surgical use cases. The framework presents an easy-to-use configurable tool for HMD-based AR surgical navigation, which can be extended and adapted to many surgical applications. The framework is publicly available at https://github.com/abdullahthabit/SurgNavAR.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29990v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29968v1", "url": "http://arxiv.org/abs/2603.29968v1", "pdf_url": "https://arxiv.org/pdf/2603.29968v1", "title": "Trimodal Deep Learning for Glioma Survival Prediction: A Feasibility Study Integrating Histopathology, Gene Expression, and MRI", "authors": ["Iain Swift", "JingHua Ye"], "annotation": "Multimodal deep learning has improved prognostic accuracy for brain tumours by integrating histopathology and genomic data, yet the contribution of volumetric MRI within unified survival frameworks remains unexplored. This pilot study extends a bimodal framework by incorporating Fluid Attenuated Inversion Recovery (FLAIR) MRI from BraTS2021 as a third modality. Using the TCGA-GBMLGG cohort (664 patients), we evaluate three unimodal models, nine bimodal configurations, and three trimodal configurations across early, late, and joint fusion strategies. In this small cohort setting, trimodal early fusion achieves an exploratory Composite Score (CS = 0.854), with a controlled $Δ$CS of +0.011 over the bimodal baseline on identical patients, though this difference is not statistically significant (p = 0.250, permutation test). MRI achieves reasonable unimodal discrimination (CS = 0.755) but does not substantially improve bimodal pairs, while providing measurable uplift in the three-way combination. All MRI containing experiments are constrained to 19 test patients, yielding wide bootstrap confidence intervals (e.g. [0.400,1.000]) that preclude definitive conclusions. These findings provide preliminary evidence that a third imaging modality may add prognostic value even with limited sample sizes, and that additional modalities require sufficient multimodal context to contribute effectively.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29968v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29967v1", "url": "http://arxiv.org/abs/2603.29967v1", "pdf_url": "https://arxiv.org/pdf/2603.29967v1", "title": "Learning Structural-Functional Brain Representations through Multi-Scale Adaptive Graph Attention for Cognitive Insight", "authors": ["Badhan Mazumder", "Sir-Lord Wiafe", "Aline Kotoski", "Vince D. Calhoun", "Dong Hye Ye"], "annotation": "Understanding how brain structure and function interact is key to explaining intelligence yet modeling them jointly is challenging as the structural and functional connectome capture complementary aspects of organization. We introduced Multi-scale Adaptive Graph Network (MAGNet), a Transformer-style graph neural network framework that adaptively learns structure-function interactions. MAGNet leverages source-based morphometry from structural MRI to extract inter-regional morphological features and fuses them with functional network connectivity from resting-state fMRI. A hybrid graph integrates direct and indirect pathways, while local-global attention refines connectivity importance and a joint loss simultaneously enforces cross-modal coherence and optimizes the prediction objective end-to-end. On the ABCD dataset, MAGNet outperformed relevant baselines, demonstrating effective multimodal integration for advancing our understanding of cognitive function.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29967v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29966v1", "url": "http://arxiv.org/abs/2603.29966v1", "pdf_url": "https://arxiv.org/pdf/2603.29966v1", "title": "Scaling Video Pretraining for Surgical Foundation Models", "authors": ["Sicheng Lu", "Zikai Xiao", "Jianhui Wei", "Danyu Sun", "Qi Lu", "Keli Hu", "Yang Feng", "Jian Wu", "Zongxin Yang", "Zuozhu Liu"], "annotation": "Surgical video understanding is essential for computer-assisted interventions, yet existing surgical foundation models remain constrained by limited data scale, procedural diversity, and inconsistent evaluation, often lacking a reproducible training pipeline. We propose SurgRec, a scalable and reproducible pretraining recipe for surgical video understanding, instantiated with two variants: SurgRec-MAE and SurgRec-JEPA. We curate a large multi-source corpus of 10,535 videos and 214.5M frames spanning endoscopy, laparoscopy, cataract, and robotic surgery. Building on this corpus, we develop a unified pretraining pipeline with balanced sampling and standardize a reproducible benchmark across 16 downstream datasets and four clinical domains with consistent data splits. Across extensive comparisons against SSL baselines and vision-language models, SurgRec consistently achieves superior performance across downstream datasets. In contrast, VLMs prove unreliable for fine-grained temporal recognition, exhibiting both performance gaps and sensitivity to prompt phrasing. Our work provides a reproducible, scalable foundation for the community to build more general surgical video models. All code, models, and data will be publicly released.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29966v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29962v1", "url": "http://arxiv.org/abs/2603.29962v1", "pdf_url": "https://arxiv.org/pdf/2603.29962v1", "title": "SurgTEMP: Temporal-Aware Surgical Video Question Answering with Text-guided Visual Memory for Laparoscopic Cholecystectomy", "authors": ["Shi Li", "Vinkle Srivastav", "Nicolas Chanel", "Saurav Sharma", "Nabani Banik", "Lorenzo Arboit", "Kun Yuan", "Pietro Mascagni", "Nicolas Padoy"], "annotation": "Surgical procedures are inherently complex and risky, requiring extensive expertise and constant focus to well navigate evolving intraoperative scenes. Computer-assisted systems such as surgical visual question answering (VQA) offer promises for education and intraoperative support. Current surgical VQA research largely focuses on static frame analysis, overlooking rich temporal semantics. Surgical video question answering is further challenged by low visual contrast, its highly knowledge-driven nature, diverse analytical needs spanning scattered temporal windows, and the hierarchy from basic perception to high-level intraoperative assessment. To address these challenges, we propose SurgTEMP, a multimodal LLM framework featuring (i) a query-guided token selection module that builds hierarchical visual memory (spatial and temporal memory banks) and (ii) a Surgical Competency Progression (SCP) training scheme. Together, these components enable effective modeling of variable-length surgical videos while preserving procedure-relevant cues and temporal coherence, and better support diverse downstream assessment tasks. To support model development, we introduce CholeVidQA-32K, a surgical video question answering dataset comprising 32K open-ended QA pairs and 3,855 video segments (approximately 128 h total) from laparoscopic cholecystectomy. The dataset is organized into a three-level hierarchy -- Perception, Assessment, and Reasoning -- spanning 11 tasks from instrument/action/anatomy perception to Critical View of Safety (CVS), intraoperative difficulty, skill proficiency, and adverse event assessment. In comprehensive evaluations against state-of-the-art open-source multimodal and video LLMs (fine-tuned and zero-shot), SurgTEMP achieves substantial performance improvements, advancing the state of video-based surgical VQA.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29962v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29960v1", "url": "http://arxiv.org/abs/2603.29960v1", "pdf_url": "https://arxiv.org/pdf/2603.29960v1", "title": "NeuroBRIDGE: Behavior-Conditioned Koopman Dynamics with Riemannian Alignment for Early Substance Use Initiation Prediction from Longitudinal Functional Connectome", "authors": ["Badhan Mazumder", "Sir-Lord Wiafe", "Vince D. Calhoun", "Dong Hye Ye"], "annotation": "Early identification of adolescents at risk for substance use initiation (SUI) is vital yet difficult, as most predictors treat connectivity as static or cross-sectional and miss how brain networks change over time and with behavior. We proposed NeuroBRIDGE (Behavior conditioned RIemannian Koopman Dynamics on lonGitudinal connEctomes), a novel graph neural network-based framework that aligns longitudinal functional connectome in a Riemannian tangent space and couples dual-time attention with behavioral-conditioned Koopman dynamics to capture temporal change. Evaluated on ABCD, NeuroBRIDGE improved future SUI prediction over relevant baselines while offering interpretable insights into neural pathways, refining our understanding of neurodevelopmental risk and informing targeted prevention.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29960v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29954v1", "url": "http://arxiv.org/abs/2603.29954v1", "pdf_url": "https://arxiv.org/pdf/2603.29954v1", "title": "Detecting Unknown Objects via Energy-based Separation for Open World Object Detection", "authors": ["Jun-Woo Heo", "Keonhee Park", "Gyeong-Moon Park"], "annotation": "In this work, we tackle the problem of Open World Object Detection (OWOD). This challenging scenario requires the detector to incrementally learn to classify known objects without forgetting while identifying unknown objects without supervision. Previous OWOD methods have enhanced the unknown discovery process and employed memory replay to mitigate catastrophic forgetting. However, since existing methods heavily rely on the detector's known class predictions for detecting unknown objects, they struggle to effectively learn and recognize unknown object representations. Moreover, while memory replay mitigates forgetting of old classes, it often sacrifices the knowledge of newly learned classes. To resolve these limitations, we propose DEUS (Detecting Unknowns via energy-based Separation), a novel framework that addresses the challenges of Open World Object Detection. DEUS consists of Equiangular Tight Frame (ETF)-Subspace Unknown Separation (EUS) and an Energy-based Known Distinction (EKD) loss. EUS leverages ETF-based geometric properties to create orthogonal subspaces, enabling cleaner separation between known and unknown object representations. Unlike prior energy-based approaches that consider only the known space, EUS utilizes energies from both spaces to better capture distinct patterns of unknown objects. Furthermore, EKD loss enforces the separation between previous and current classifiers, thus minimizing knowledge interference between previous and newly learned classes during memory replay. We thoroughly validate DEUS on OWOD benchmarks, demonstrating outstanding performance improvements in unknown detection while maintaining competitive known class performance.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29954v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29943v1", "url": "http://arxiv.org/abs/2603.29943v1", "pdf_url": "https://arxiv.org/pdf/2603.29943v1", "title": "EC-Bench: Enumeration and Counting Benchmark for Ultra-Long Videos", "authors": ["Fumihiko Tsuchiya", "Taiki Miyanishi", "Mahiro Ukai", "Nakamasa Inoue", "Shuhei Kurita", "Yusuke Iwasawa", "Yutaka Matsuo"], "annotation": "Counting in long videos remains a fundamental yet underexplored challenge in computer vision. Real-world recordings often span tens of minutes or longer and contain sparse, diverse events, making long-range temporal reasoning particularly difficult. However, most existing video counting benchmarks focus on short clips and evaluate only the final numerical answer, providing little insight into what should be counted or whether models consistently identify relevant instances across time. We introduce EC-Bench, a benchmark that jointly evaluates enumeration, counting, and temporal evidence grounding in long-form videos. EC-Bench contains 152 videos longer than 30 minutes and 1,699 queries paired with explicit evidence spans. Across 22 multimodal large language models (MLLMs), the best model achieves only 29.98% accuracy on Enumeration and 23.74% on Counting, while human performance reaches 78.57% and 82.97%, respectively. Our analysis reveals strong relationships between enumeration accuracy, temporal grounding, and counting performance. These results highlight fundamental limitations of current MLLMs and establish EC-Bench as a challenging benchmark for long-form quantitative video reasoning.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29943v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29941v1", "url": "http://arxiv.org/abs/2603.29941v1", "pdf_url": "https://arxiv.org/pdf/2603.29941v1", "title": "Better than Average: Spatially-Aware Aggregation of Segmentation Uncertainty Improves Downstream Performance", "authors": ["Vanessa Emanuela Guarino", "Claudia Winklmayr", "Jannik Franzen", "Josef Lorenz Rumberger", "Manuel Pfeuffer", "Sonja Greven", "Klaus Maier-Hein", "Carsten T. Lüth", "Christoph Karg", "Dagmar Kainmueller"], "annotation": "Uncertainty Quantification (UQ) is crucial for ensuring the reliability of automated image segmentations in safety-critical domains like biomedical image analysis or autonomous driving. In segmentation, UQ generates pixel-wise uncertainty scores that must be aggregated into image-level scores for downstream tasks like Out-of-Distribution (OoD) or failure detection. Despite routine use of aggregation strategies, their properties and impact on downstream task performance have not yet been comprehensively studied. Global Average is the default choice, yet it does not account for spatial and structural features of segmentation uncertainty. Alternatives like patch-, class- and threshold-based strategies exist, but lack systematic comparison, leading to inconsistent reporting and unclear best practices. We address this gap by (1) formally analyzing properties, limitations, and pitfalls of common strategies; (2) proposing novel strategies that incorporate spatial uncertainty structure and (3) benchmarking their performance on OoD and failure detection across ten datasets that vary in image geometry and structure. We find that aggregators leveraging spatial structure yield stronger performance in both downstream tasks studied. However, the performance of individual aggregators depends heavily on dataset characteristics, so we (4) propose a meta-aggregator that integrates multiple aggregators and performs robustly across datasets.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29941v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29931v1", "url": "http://arxiv.org/abs/2603.29931v1", "pdf_url": "https://arxiv.org/pdf/2603.29931v1", "title": "Gloria: Consistent Character Video Generation via Content Anchors", "authors": ["Yuhang Yang", "Fan Zhang", "Huaijin Pi", "Shuai Guo", "Guowei Xu", "Wei Zhai", "Yang Cao", "Zheng-Jun Zha"], "annotation": "Digital characters are central to modern media, yet generating character videos with long-duration, consistent multi-view appearance and expressive identity remains challenging. Existing approaches either provide insufficient context to preserve identity or leverage non-character-centric information as the memory, leading to suboptimal consistency. Recognizing that character video generation inherently resembles an outside-looking-in scenario. In this work, we propose representing the character visual attributes through a compact set of anchor frames. This design provides stable references for consistency, while reference-based video generation inherently faces challenges of copy-pasting and multi-reference conflicts. To address these, we introduce two mechanisms: Superset Content Anchoring, providing intra- and extra-training clip cues to prevent duplication, and RoPE as Weak Condition, encoding positional offsets to distinguish multiple anchors. Furthermore, we construct a scalable pipeline to extract these anchors from massive videos. Experiments show our method generates high-quality character videos exceeding 10 minutes, and achieves expressive identity and appearance consistency across views, surpassing existing methods.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29931v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29927v1", "url": "http://arxiv.org/abs/2603.29927v1", "pdf_url": "https://arxiv.org/pdf/2603.29927v1", "title": "End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines", "authors": ["Raül Pérez-Gonzalo", "Andreas Espersen", "Søren Forchhammer", "Antonio Agudo"], "annotation": "Transferring large volumes of high-resolution images during wind turbine inspections introduces a bottleneck in assessing and detecting severe defects. Efficient coding must preserve high fidelity in blade regions while aggressively compressing the background. In this work, we propose an end-to-end deep learning framework that jointly performs segmentation and dual-mode (lossy and lossless) compression. The segmentation module accurately identifies the blade region, after which our region-of-interest (ROI) compressor encodes it at superior quality compared to the rest of the image. Unlike conventional ROI schemes that merely allocate more bits to salient areas, our framework integrates: (i) a robust segmentation network (BU-Netv2+P) with a CRF-regularized loss for precise blade localization, (ii) a hyperprior-based autoencoder optimized for lossy compression, and (iii) an extended bits-back coder with hierarchical models for fully lossless blade reconstruction. Furthermore, our ROI framework removes the sequential dependency in bits-back coding by reusing background-coded bits, enabling parallelized and efficient dual-mode compression. To the best of our knowledge, this is the first fully integrated learning-based ROI codec combining segmentation, lossy, and lossless compression, ensuring that subsequent defect detection is not compromised. Experiments on a large-scale wind turbine dataset demonstrate superior compression performance and efficiency, offering a practical solution for automated inspections.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29927v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29924v1", "url": "http://arxiv.org/abs/2603.29924v1", "pdf_url": "https://arxiv.org/pdf/2603.29924v1", "title": "Abstraction in Style", "authors": ["Min Lu", "Yuanfeng He", "Anthony Chen", "Jianhuang He", "Pu Wang", "Daniel Cohen-Or", "Hui Huang"], "annotation": "Artistic styles often embed abstraction beyond surface appearance, involving deliberate reinterpretation of structure rather than mere changes in texture or color. Conventional style transfer methods typically preserve the input geometry and therefore struggle to capture this deeper abstraction behavior, especially for illustrative and nonphotorealistic styles. In this work, we introduce Abstraction in Style (AiS), a generative framework that separates structural abstraction from visual stylization. Given a target image and a small set of style exemplars, AiS first derives an intermediate abstraction proxy that reinterprets the target's structure in accordance with the abstraction logic exhibited by the style. The proxy captures semantic structure while relaxing geometric fidelity, enabling subsequent stylization to operate on an abstracted representation rather than the original image. In a second stage, the abstraction proxy is rendered to produce the final stylized output, preserving visual coherence with the reference style. Both stages are implemented using a shared image space analogy, enabling transformations to be learned from visual exemplars without explicit geometric supervision. By decoupling abstraction from appearance and treating abstraction as an explicit, transferable process, AiS supports a wider range of stylistic transformations, improves controllability, and enables more expressive stylization.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29924v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29922v1", "url": "http://arxiv.org/abs/2603.29922v1", "pdf_url": "https://arxiv.org/pdf/2603.29922v1", "title": "Training deep learning based dynamic MR image reconstruction using synthetic fractals", "authors": ["Anirudh Raman", "Olivier Jaubert", "Mark Wrobel", "Tina Yao", "Ruaraidh Campbell", "Rebecca Baker", "Ruta Virsinskaite", "Daniel Knight", "Michael Quail", "Jennifer Steeden", "Vivek Muthurangu"], "annotation": "Purpose: To investigate whether synthetically generated fractal data can be used to train deep learning (DL) models for dynamic MRI reconstruction, thereby avoiding the privacy, licensing, and availability limitations associated with cardiac MR training datasets. Methods: A training dataset was generated using quaternion Julia fractals to produce 2D+time images. Multi-coil MRI acquisition was simulated to generate paired fully sampled and radially undersampled k-space data. A 3D UNet deep artefact suppression model was trained using these fractal data (F-DL) and compared with an identical model trained on cardiac MRI data (CMR-DL). Both models were evaluated on prospectively acquired radial real-time cardiac MRI from 10 patients. Reconstructions were compared against compressed sensing(CS) and low-rank deep image prior (LR-DIP). All reconstrctuions were ranked for image quality, while ventricular volumes and ejection fraction were compared with reference breath-hold cine MRI. Results: There was no significant difference in qualitative ranking between F-DL and CMR-DL (p=0.9), while both outperformed CS and LR-DIP (p<0.001). Ventricular volumes and function derived from F-DL were similar to CMR-DL, showing no significant bias and accptable limits of agreement compared to reference cine imaging. However, LR-DIP had a signifcant bias (p=0.016) and wider lmits of agreement. Conclusion: DL models trained using synthetic fractal data can reconstruct real-time cardiac MRI with image quality and clinical measurements comparable to models trained on true cardiac MRI data. Fractal training data provide an open, scalable alternative to clinical datasets and may enable development of more generalisable DL reconstruction models for dynamic MRI.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29922v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29917v1", "url": "http://arxiv.org/abs/2603.29917v1", "pdf_url": "https://arxiv.org/pdf/2603.29917v1", "title": "Diffusion-Based Feature Denoising with NNMF for Robust handwritten digit multi-class classification", "authors": ["Hiba Adil Al-kharsan", "Róbert Rajkó"], "annotation": "This work presents a robust multi-class classification framework for handwritten digits that combines diffusion-driven feature denoising with a hybrid feature representation. Inspired by our previous work on brain tumor classification, the proposed approach operates in a feature space to improve the robustness to noise and adversarial attacks. First, the input images are converted into tight, interpretable exemplification using Nonnegative Matrix Factorization (NNMF). In parallel, special deep features are extracted using a computational neural network (CNN). These integral features are combined into a united hybrid representation. To improve robustness, a step diffusion operation is used in the feature space by gradually adding Gaussian noise. A feature denoiser network is trained to reverse this operation and rebuild clean representations from tilted inputs. The courteous features are then applied for multi-class classification. The suggested method is evaluated in both baseline and adversarial settings using AutoAttack. The experimental outcome present that the diffusion-based hybrid model is both effective and robust, the CNN baseline models outperforming while maintain powerful classification performance. These results explain the activity of feature-level diffusion defense for reliable multi-class handwritten digit classification.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29917v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29901v1", "url": "http://arxiv.org/abs/2603.29901v1", "pdf_url": "https://arxiv.org/pdf/2603.29901v1", "title": "Less Is More? Selective Visual Attention to High-Importance Regions for Multimodal Radiology Summarization", "authors": ["Mst. Fahmida Sultana Naznin", "Adnan Ibney Faruq", "Mushfiqur Rahman", "Niloy Kumar Mondal", "Md. Mehedi Hasan Shawon", "Md Rakibul Hasan"], "annotation": "Automated radiology report summarization aims to distill verbose findings into concise clinical impressions, but existing multimodal models often struggle with visual noise and fail to meaningfully improve over strong text-only baselines in the FINDINGS $\\to$ IMPRESSION transformation. We challenge two prevailing assumptions: (1) that more visual input is always better, and (2) that multimodal models add limited value when findings already contain rich image-derived detail. Through controlled ablations on MIMIC-CXR benchmark, we show that selectively focusing on pathology-relevant visual patches rather than full images yields substantially better performance. We introduce ViTAS, Visual-Text Attention Summarizer, a multi-stage pipeline that combines ensemble-guided MedSAM2 lung segmentation, bidirectional cross-attention for multi-view fusion, Shapley-guided adaptive patch clustering, and hierarchical visual tokenization feeding a ViT. ViTAS achieves SOTA results with 29.25% BLEU-4 and 69.83% ROUGE-L, improved factual alignment in qualitative analysis, and the highest expert-rated human evaluation scores. Our findings demonstrate that less but more relevant visual input is not only sufficient but superior for multimodal radiology summarization.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29901v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29844v1", "url": "http://arxiv.org/abs/2603.29844v1", "pdf_url": "https://arxiv.org/pdf/2603.29844v1", "title": "DIAL: Decoupling Intent and Action via Latent World Modeling for End-to-End VLA", "authors": ["Yi Chen", "Yuying Ge", "Hui Zhou", "Mingyu Ding", "Yixiao Ge", "Xihui Liu"], "annotation": "The development of Vision-Language-Action (VLA) models has been significantly accelerated by pre-trained Vision-Language Models (VLMs). However, most existing end-to-end VLAs treat the VLM primarily as a multimodal encoder, directly mapping vision-language features to low-level actions. This paradigm underutilizes the VLM's potential in high-level decision making and introduces training instability, frequently degrading its rich semantic representations. To address these limitations, we introduce DIAL, a framework bridging high-level decision making and low-level motor execution through a differentiable latent intent bottleneck. Specifically, a VLM-based System-2 performs latent world modeling by synthesizing latent visual foresight within the VLM's native feature space; this foresight explicitly encodes intent and serves as the structural bottleneck. A lightweight System-1 policy then decodes this predicted intent together with the current observation into precise robot actions via latent inverse dynamics. To ensure optimization stability, we employ a two-stage training paradigm: a decoupled warmup phase where System-2 learns to predict latent futures while System-1 learns motor control under ground-truth future guidance within a unified feature space, followed by seamless end-to-end joint optimization. This enables action-aware gradients to refine the VLM backbone in a controlled manner, preserving pre-trained knowledge. Extensive experiments on the RoboCasa GR1 Tabletop benchmark show that DIAL establishes a new state-of-the-art, achieving superior performance with 10x fewer demonstrations than prior methods. Furthermore, by leveraging heterogeneous human demonstrations, DIAL learns physically grounded manipulation priors and exhibits robust zero-shot generalization to unseen objects and novel configurations during real-world deployment on a humanoid robot.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29844v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29842v1", "url": "http://arxiv.org/abs/2603.29842v1", "pdf_url": "https://arxiv.org/pdf/2603.29842v1", "title": "Toward Generalizable Whole Brain Representations with High-Resolution Light-Sheet Data", "authors": ["Minyoung E. Kim", "Dae Hee Yun", "Aditi V. Patel", "Madeline Hon", "Webster Guan", "Taegeon Lee", "Brian Nguyen"], "annotation": "Unprecedented visual details of biological structures are being revealed by subcellular-resolution whole-brain 3D microscopy data, enabled by recent advances in intact tissue processing and light-sheet fluorescence microscopy (LSFM). These volumetric data offer rich morphological and spatial cellular information, however, the lack of scalable data processing and analysis methods tailored to these petabyte-scale data poses a substantial challenge for accurate interpretation. Further, existing models for visual tasks such as object detection and classification struggle to generalize to this type of data. To accelerate the development of suitable methods and foundational models, we present CANVAS, a comprehensive set of high-resolution whole mouse brain LSFM benchmark data, encompassing six neuronal and immune cell-type markers, along with cell annotations and a leaderboard. We also demonstrate challenges in generalization of baseline models built on existing architectures, especially due to the heterogeneity in cellular morphology across phenotypes and anatomical locations in the brain. To the best of our knowledge, CANVAS is the first and largest LSFM benchmark that captures intact mouse brain tissue at subcellular level, and includes extensive annotations of cells throughout the brain.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29842v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29832v1", "url": "http://arxiv.org/abs/2603.29832v1", "pdf_url": "https://arxiv.org/pdf/2603.29832v1", "title": "AutoFormBench: Benchmark Dataset for Automating Form Understanding", "authors": ["Gaurab Baral", "Junxiu Zhou"], "annotation": "Automated processing of structured documents such as government forms, healthcare records, and enterprise invoices remains a persistent challenge due to the high degree of layout variability encountered in real-world settings. This paper introduces AutoFormBench, a benchmark dataset of 407 annotated real-world forms spanning government, healthcare, and enterprise domains, designed to train and evaluate form element detection models. We present a systematic comparison of classical OpenCV approaches and four YOLO architectures (YOLOv8, YOLOv11, YOLOv26-s, and YOLOv26-l) for localizing and classifying fillable form elements. specifically checkboxes, input lines, and text boxes across diverse PDF document types. YOLOv11 demonstrates consistently superior performance in both F1 score and Jaccard accuracy across all element classes and tolerance levels.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29832v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29798v1", "url": "http://arxiv.org/abs/2603.29798v1", "pdf_url": "https://arxiv.org/pdf/2603.29798v1", "title": "SceneTeract: Agentic Functional Affordances and VLM Grounding in 3D Scenes", "authors": ["Léopold Maillard", "Francis Engelmann", "Tom Durand", "Boxiao Pan", "Yang You", "Or Litany", "Leonidas Guibas", "Maks Ovsjanikov"], "annotation": "Embodied AI depends on interactive 3D environments that support meaningful activities for diverse users, yet assessing their functional affordances remains a core challenge. We introduce SceneTeract, a framework that verifies 3D scene functionality under agent-specific constraints. Our core contribution is a grounded verification engine that couples high-level semantic reasoning with low-level geometric checks. SceneTeract decomposes complex activities into sequences of atomic actions and validates each step against accessibility requirements (e.g., reachability, clearance, and navigability) conditioned on an embodied agent profile, using explicit physical and geometric simulations. We deploy SceneTeract to perform an in-depth evaluation of (i) synthetic indoor environments, uncovering frequent functional failures that prevent basic interactions, and (ii) the ability of frontier Vision-Language Models (VLMs) to reason about and predict functional affordances, revealing systematic mismatches between semantic confidence and physical feasibility even for the strongest current models. Finally, we leverage SceneTeract as a reward engine for VLM post-training, enabling scalable distillation of geometric constraints into reasoning models. We release the SceneTeract verification suite and data to bridge perception and physical reality in embodied 3D scene understanding.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29798v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29788v1", "url": "http://arxiv.org/abs/2603.29788v1", "pdf_url": "https://arxiv.org/pdf/2603.29788v1", "title": "Multi-Feature Fusion Approach for Generative AI Images Detection", "authors": ["Abderrezzaq Sendjasni", "Mohamed-Chaker Larabi"], "annotation": "The rapid evolution of Generative AI (GenAI) models has led to synthetic images of unprecedented realism, challenging traditional methods for distinguishing them from natural photographs. While existing detectors often rely on single-feature spaces, such as statistical regularities, semantic embeddings, or texture patterns, these approaches tend to lack robustness when confronted with diverse and evolving generative models. In this work, we investigate and systematically evaluate a multi-feature fusion framework that combines complementary cues from three distinct spaces: (1) Mean Subtracted Contrast Normalized (MSCN) features capturing low-level statistical deviations; (2) CLIP embeddings encoding high-level semantic coherence; and (3) Multi-scale Local Binary Patterns (MLBP) characterizing mid-level texture anomalies. Through extensive experiments on four benchmark datasets covering a wide range of generative models, we show that individual feature spaces exhibit significant performance variability across different generators. Crucially, the fusion of all three representations yields superior and more consistent performance, particularly in a challenging mixed-model scenario. Compared to state-of-the-art methods, the proposed framework yields consistently improved performance across all evaluated datasets. Overall, this work highlights the importance of hybrid representations for robust GenAI image detection and provides a principled framework for integrating complementary visual cues.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29788v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29784v1", "url": "http://arxiv.org/abs/2603.29784v1", "pdf_url": "https://arxiv.org/pdf/2603.29784v1", "title": "MAPLE: Multi-Path Adaptive Propagation with Level-Aware Embeddings for Hierarchical Multi-Label Image Classification", "authors": ["Boshko Koloski", "Marjan Stoimchev", "Jurica Levatić", "Dragi Kocev", "Sašo Džeroski"], "annotation": "Hierarchical multi-label classification (HMLC) is essential for modeling structured label dependencies in remote sensing. Yet existing approaches struggle in multi-path settings, where images may activate multiple taxonomic branches, leading to underuse of hierarchical information. We propose MAPLE (Multi-Path Adaptive Propagation with Level-Aware Embeddings), a framework that integrates (i) hierarchical semantic initialization from graph-aware textual descriptions, (ii) graph-based structure encoding via graph convolutional networks (GCNs), and (iii) adaptive multi-modal fusion that dynamically balances semantic priors and visual evidence. An adaptive level-aware objective automatically selects appropriate losses per hierarchy level. Evaluations on CORINE-aligned remote sensing datasets (AID, DFC-15, and MLRSNet) show consistent improvements of up to +42% in few-shot regimes while adding only 2.6% parameter overhead, demonstrating that MAPLE effectively and efficiently models hierarchical semantics for Earth observation (EO).", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29784v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29777v1", "url": "http://arxiv.org/abs/2603.29777v1", "pdf_url": "https://arxiv.org/pdf/2603.29777v1", "title": "From Skeletons to Semantics: Design and Deployment of a Hybrid Edge-Based Action Detection System for Public Safety", "authors": ["Ganen Sethupathy", "Lalit Dumka", "Jan Schagen"], "annotation": "Public spaces such as transport hubs, city centres, and event venues require timely and reliable detection of potentially violent behaviour to support public safety. While automated video analysis has made significant progress, practical deployment remains constrained by latency, privacy, and resource limitations, particularly under edge-computing conditions. This paper presents the design and demonstrator-based deployment of a hybrid edge-based action detection system that combines skeleton-based motion analysis with vision-language models for semantic scene interpretation. Skeleton-based processing enables continuous, privacy-aware monitoring with low computational overhead, while vision-language models provide contextual understanding and zero-shot reasoning capabilities for complex and previously unseen situations. Rather than proposing new recognition models, the contribution focuses on a system-level comparison of both paradigms under realistic edge constraints. The system is implemented on a GPU-enabled edge device and evaluated with respect to latency, resource usage, and operational trade-offs using a demonstrator-based setup. The results highlight the complementary strengths and limitations of motioncentric and semantic approaches and motivate a hybrid architecture that selectively augments fast skeletonbased detection with higher-level semantic reasoning. The presented system provides a practical foundation for privacy-aware, real-time video analysis in public safety applications.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29777v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29773v1", "url": "http://arxiv.org/abs/2603.29773v1", "pdf_url": "https://arxiv.org/pdf/2603.29773v1", "title": "Beyond Ground-Truth: Leveraging Image Quality Priors for Real-World Image Restoration", "authors": ["Fengyang Xiao", "Peng Hu", "Lei Xu", "XingE Guo", "Guanyi Qin", "Yuqi Shen", "Chengyu Fang", "Rihan Zhang", "Chunming He", "Sina Farsiu"], "annotation": "Real-world image restoration aims to restore high-quality (HQ) images from degraded low-quality (LQ) inputs captured under uncontrolled conditions. Existing methods typically depend on ground-truth (GT) supervision, assuming that GT provides perfect reference quality. However, GT can still contain images with inconsistent perceptual fidelity, causing models to converge to the average quality level of the training data rather than achieving the highest perceptual quality attainable. To address these problems, we propose a novel framework, termed IQPIR, that introduces an Image Quality Prior (IQP)-extracted from pre-trained No-Reference Image Quality Assessment (NR-IQA) models-to guide the restoration process toward perceptually optimal outputs explicitly. Our approach synergistically integrates IQP with a learned codebook prior through three key mechanisms: (1) a quality-conditioned Transformer, where NR-IQA-derived scores serve as conditioning signals to steer the predicted representation toward maximal perceptual quality. This design provides a plug-and-play enhancement compatible with existing restoration architectures without structural modification; and (2) a dual-branch codebook structure, which disentangles common and HQ-specific features, ensuring a comprehensive representation of both generic structural information and quality-sensitive attributes; and (3) a discrete representation-based quality optimization strategy, which mitigates over-optimization effects commonly observed in continuous latent spaces. Extensive experiments on real-world image restoration demonstrate that our method not only surpasses cutting-edge methods but also serves as a generalizable quality-guided enhancement strategy for existing methods. The code is available.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29773v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29759v1", "url": "http://arxiv.org/abs/2603.29759v1", "pdf_url": "https://arxiv.org/pdf/2603.29759v1", "title": "TSHA: A Benchmark for Visual Language Models in Trustworthy Safety Hazard Assessment Scenarios", "authors": ["Qiucheng Yu", "Ruijie Xu", "Mingang Chen", "Xuequan Lu", "Jianfeng Dong", "Chaochao Lu", "Xin Tan"], "annotation": "Recent advances in vision-language models (VLMs) have accelerated their application to indoor safety hazards assessment. However, existing benchmarks suffer from three fundamental limitations: (1) heavy reliance on synthetic datasets constructed via simulation software, creating a significant domain gap with real-world environments; (2) oversimplified safety tasks with artificial constraints on hazard and scene types, thereby limiting model generalization; and (3) absence of rigorous evaluation protocols to thoroughly assess model capabilities in complex home safety scenarios. To address these challenges, we introduce TSHA (\\textbf{T}rustworthy \\textbf{S}afety \\textbf{H}azards \\textbf{A}ssessment), a comprehensive benchmark comprising 81,809 carefully curated training samples drawn from four complementary sources: existing indoor datasets, internet images, AIGC images, and newly captured images. This benchmark set also includes a highly challenging test set with 1707 samples, comprising not only a carefully selected subset from the training distribution but also newly added videos and panoramic images containing multiple safety hazards, used to evaluate the model's robustness in complex safety scenarios. Extensive experiments on 23 popular VLMs demonstrate that current VLMs lack robust capabilities for safety hazard assessment. Importantly, models trained on the TSHA training set not only achieve a significant performance improvement of up to +18.3 points on the TSHA test set but also exhibit enhanced generalizability across other benchmarks, underscoring the substantial contribution and importance of the TSHA benchmark.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29759v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29742v1", "url": "http://arxiv.org/abs/2603.29742v1", "pdf_url": "https://arxiv.org/pdf/2603.29742v1", "title": "SHIFT: Stochastic Hidden-Trajectory Deflection for Removing Diffusion-based Watermark", "authors": ["Rui Bao", "Zheng Gao", "Xiaoyu Li", "Xiaoyan Feng", "Yang Song", "Jiaojiao Jiang"], "annotation": "Diffusion-based watermarking methods embed verifiable marks by manipulating the initial noise or the reverse diffusion trajectory. However, these methods share a critical assumption: verification can succeed only if the diffusion trajectory can be faithfully reconstructed. This reliance on trajectory recovery constitutes a fundamental and exploitable vulnerability. We propose $\\underline{\\mathbf{S}}$tochastic $\\underline{\\mathbf{Hi}}$dden-Trajectory De$\\underline{\\mathbf{f}}$lec$\\underline{\\mathbf{t}}$ion ($\\mathbf{SHIFT}$), a training-free attack that exploits this common weakness across diverse watermarking paradigms. SHIFT leverages stochastic diffusion resampling to deflect the generative trajectory in latent space, making the reconstructed image statistically decoupled from the original watermark-embedded trajectory while preserving strong visual quality and semantic consistency. Extensive experiments on nine representative watermarking methods spanning noise-space, frequency-domain, and optimization-based paradigms show that SHIFT achieves 95%--100% attack success rates with nearly no loss in semantic quality, without requiring any watermark-specific knowledge or model retraining.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29742v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29734v1", "url": "http://arxiv.org/abs/2603.29734v1", "pdf_url": "https://arxiv.org/pdf/2603.29734v1", "title": "GRVS: a Generalizable and Recurrent Approach to Monocular Dynamic View Synthesis", "authors": ["Thomas Tanay", "Mohammed Brahimi", "Michal Nazarczuk", "Qingwen Zhang", "Sibi Catley-Chandar", "Arthur Moreau", "Zhensong Zhang", "Eduardo Pérez-Pellitero"], "annotation": "Synthesizing novel views from monocular videos of dynamic scenes remains a challenging problem. Scene-specific methods that optimize 4D representations with explicit motion priors often break down in highly dynamic regions where multi-view information is hard to exploit. Diffusion-based approaches that integrate camera control into large pre-trained models can produce visually plausible videos but frequently suffer from geometric inconsistencies across both static and dynamic areas. Both families of methods also require substantial computational resources. Building on the success of generalizable models for static novel view synthesis, we adapt the framework to dynamic inputs and propose a new model with two key components: (1) a recurrent loop that enables unbounded and asynchronous mapping between input and target videos and (2) an efficient use of plane sweeps over dynamic inputs to disentangle camera and scene motion, and achieve fine-grained, six-degrees-of-freedom camera controls. We train and evaluate our model on the UCSD dataset and on Kubric-4D-dyn, a new monocular dynamic dataset featuring longer, higher resolution sequences with more complex scene dynamics than existing alternatives. Our model outperforms four Gaussian Splatting-based scene-specific approaches, as well as two diffusion-based approaches in reconstructing fine-grained geometric details across both static and dynamic regions.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29734v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29733v1", "url": "http://arxiv.org/abs/2603.29733v1", "pdf_url": "https://arxiv.org/pdf/2603.29733v1", "title": "Leveraging Synthetic Data for Enhancing Egocentric Hand-Object Interaction Detection", "authors": ["Rosario Leonardi", "Antonino Furnari", "Francesco Ragusa", "Giovanni Maria Farinella"], "annotation": "In this work, we explore the role of synthetic data in improving the detection of Hand-Object Interactions from egocentric images. Through extensive experimentation and comparative analysis on VISOR, EgoHOS, and ENIGMA-51 datasets, our findings demonstrate the potential of synthetic data to significantly improve HOI detection, particularly when real labeled data are scarce or unavailable. By using synthetic data and only 10% of the real labeled data, we achieve improvements in Overall AP over models trained exclusively on real data, with gains of +5.67% on VISOR, +8.24% on EgoHOS, and +11.69% on ENIGMA-51. Furthermore, we systematically study how aligning synthetic data to specific real-world benchmarks with respect to objects, grasps, and environments, showing that the effectiveness of synthetic data consistently improves with better synthetic-real alignment. As a result of this work, we release a new data generation pipeline and the new HOI-Synth benchmark, which augments existing datasets with synthetic images of hand-object interaction. These data are automatically annotated with hand-object contact states, bounding boxes, and pixel-wise segmentation masks. All data, code, and tools for synthetic data generation are available at: https://fpv-iplab.github.io/HOI-Synth/.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29733v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29732v1", "url": "http://arxiv.org/abs/2603.29732v1", "pdf_url": "https://arxiv.org/pdf/2603.29732v1", "title": "Compressive sensing inspired self-supervised single-pixel imaging", "authors": ["Jijun Lu", "Yifan Chen", "Libang Chen", "Yiqiang Zhou", "Ye Zheng", "Mingliang Chen", "Zhe Sun", "Xuelong Li"], "annotation": "Single-pixel imaging (SPI) is a promising imaging modality with distinctive advantages in strongly perturbed environments. Existing SPI methods lack physical sparsity constraints and overlook the integration of local and global features, leading to severe noise vulnerability, structural distortions and blurred details. To address these limitations, we propose SISTA-Net, a compressive sensing-inspired self-supervised method for single-pixel imaging. SISTA-Net unfolds the Iterative Shrinkage-Thresholding Algorithm (ISTA) into an interpretable network consisting of a data fidelity module and a proximal mapping module. The fidelity module adopts a hybrid CNN-Visual State Space Model (VSSM) architecture to integrate local and global feature modeling, enhancing reconstruction integrity and fidelity. We leverage deep nonlinear networks as adaptive sparse transforms combined with a learnable soft-thresholding operator to impose explicit physical sparsity in the latent domain, enabling noise suppression and robustness to interference even at extremely low sampling rates. Extensive experiments on multiple simulation scenarios demonstrate that SISTA-Net outperforms state-of-the-art methods by 2.6 dB in PSNR. Real-world far-field underwater tests yield a 3.4 dB average PSNR improvement, validating its robust anti-interference capability.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29732v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29697v1", "url": "http://arxiv.org/abs/2603.29697v1", "pdf_url": "https://arxiv.org/pdf/2603.29697v1", "title": "FED-Bench: A Cross-Granular Benchmark for Disentangled Evaluation of Facial Expression Editing", "authors": ["Fengjian Xue", "Xuecheng Wu", "Heli Sun", "Yunyun Shi", "Shi Chen", "Liangyu Fu", "Jinheng Xie", "Dingkang Yang", "Hao Wang", "Junxiao Xue", "Liang He"], "annotation": "Facial expression image editing requires fine-grained control to strictly preserve human identity and background while precisely manipulating expression. However, existing editing benchmarks primarily focus on general scenarios, lacking high-quality facial images and corresponding editing instructions. Furthermore, current evaluation metrics exhibit systemic biases in this task, often favoring lazy editing or overfit editing. To bridge these gaps, we propose FED-Bench, a comprehensive benchmark featuring rigorous testing and an accurate evaluation suite. First, we carefully construct a benchmark of 747 triplets through a cascaded and scalable pipeline, each comprising an original image, an editing instruction, and a ground-truth image for precise evaluation. Second, we introduce FED-Score, a cross-granularity evaluation protocol that disentangles assessment into three dimensions: Alignment for verifying instruction following, Fidelity for testing image quality and identity preservation, and Relative Expression Gain for quantifying the magnitude of expression changes, effectively mitigating the aforementioned evaluation biases. Third, we benchmark 18 image editing models, revealing that current approaches struggle to simultaneously achieve high fidelity and accurate expression manipulation, with fine-grained instruction following identified as the primary bottleneck. Finally, leveraging the scalable characteristic of introduced benchmark engine, we provide a 20k+ in-the-wild facial training set and demonstrate its effectiveness by fine-tuning a baseline model that achieves significant performance gains. Our benchmark and related code will be made publicly open soon.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29697v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29694v1", "url": "http://arxiv.org/abs/2603.29694v1", "pdf_url": "https://arxiv.org/pdf/2603.29694v1", "title": "Exploring the Impact of Skin Color on Skin Lesion Segmentation", "authors": ["Kuniko Paxton", "Medina Kapo", "Amila Akagić", "Koorosh Aslansefat", "Dhavalkumar Thakker", "Yiannis Papadopoulos"], "annotation": "Skin cancer, particularly melanoma, remains a major cause of morbidity and mortality, making early detection critical. AI-driven dermatology systems often rely on skin lesion segmentation as a preprocessing step to delineate the lesion from surrounding skin and support downstream analysis. While fairness concerns regarding skin tone have been widely studied for lesion classification, the influence of skin tone on the segmentation stage remains under-quantified and is frequently assessed using coarse, discrete skin tone categories. In this work, we evaluate three strong segmentation architectures (UNet, DeepLabV3 with a ResNet50 backbone, and DINOv2) on two public dermoscopic datasets (HAM10000 and ISIC2017) and introduce a continuous pigment or contrast analysis that treats pixel-wise ITA values as distributions. Using Wasserstein distances between within-image distributions for skin-only, lesion-only, and whole-image regions, we quantify lesion skin contrast and relate it to segmentation performance across multiple metrics. Within the range represented in these datasets, global skin tone metrics (Fitzpatrick grouping or mean ITA) show weak association with segmentation quality. In contrast, low lesion-skin contrast is consistently associated with larger segmentation errors in models, indicating that boundary ambiguity and low contrast are key drivers of failure. These findings suggest that fairness improvements in dermoscopic segmentation should prioritize robust handling of low-contrast lesions, and the distribution-based pigment measures provide a more informative audit signal than discrete skin-tone categories.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29694v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29692v1", "url": "http://arxiv.org/abs/2603.29692v1", "pdf_url": "https://arxiv.org/pdf/2603.29692v1", "title": "SkeletonContext: Skeleton-side Context Prompt Learning for Zero-Shot Skeleton-based Action Recognition", "authors": ["Ning Wang", "Tieyue Wu", "Naeha Sharif", "Farid Boussaid", "Guangming Zhu", "Lin Mei", "Mohammed Bennamoun", "zhang liang"], "annotation": "Zero-shot skeleton-based action recognition aims to recognize unseen actions by transferring knowledge from seen categories through semantic descriptions. Most existing methods typically align skeleton features with textual embeddings within a shared latent space. However, the absence of contextual cues, such as objects involved in the action, introduces an inherent gap between skeleton and semantic representations, making it difficult to distinguish visually similar actions. To address this, we propose SkeletonContext, a prompt-based framework that enriches skeletal motion representations with language-driven contextual semantics. Specifically, we introduce a Cross-Modal Context Prompt Module, which leverages a pretrained language model to reconstruct masked contextual prompts under guidance derived from LLMs. This design effectively transfers linguistic context to the skeleton encoder for instance-level semantic grounding and improved cross-modal alignment. In addition, a Key-Part Decoupling Module is incorporated to decouple motion-relevant joint features, ensuring robust action understanding even in the absence of explicit object interactions. Extensive experiments on multiple benchmarks demonstrate that SkeletonContext achieves state-of-the-art performance under both conventional and generalized zero-shot settings, validating its effectiveness in reasoning about context and distinguishing fine-grained, visually similar actions.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29692v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29676v1", "url": "http://arxiv.org/abs/2603.29676v1", "pdf_url": "https://arxiv.org/pdf/2603.29676v1", "title": "A Comprehensive Information-Decomposition Analysis of Large Vision-Language Models", "authors": ["Lixin Xiu", "Xufang Luo", "Hideki Nakayama"], "annotation": "Large vision-language models (LVLMs) achieve impressive performance, yet their internal decision-making processes remain opaque, making it difficult to determine if the success stems from true multimodal fusion or from reliance on unimodal priors. To address this attribution gap, we introduce a novel framework using partial information decomposition (PID) to quantitatively measure the \"information spectrum\" of LVLMs -- decomposing a model's decision-relevant information into redundant, unique, and synergistic components. By adapting a scalable estimator to modern LVLM outputs, our model-agnostic pipeline profiles 26 LVLMs on four datasets across three dimensions -- breadth (cross-model & cross-task), depth (layer-wise information dynamics), and time (learning dynamics across training). Our analysis reveals two key results: (i) two task regimes (synergy-driven vs. knowledge-driven) and (ii) two stable, contrasting family-level strategies (fusion-centric vs. language-centric). We also uncover a consistent three-phase pattern in layer-wise processing and identify visual instruction tuning as the key stage where fusion is learned. Together, these contributions provide a quantitative lens beyond accuracy-only evaluation and offer insights for analyzing and designing the next generation of LVLMs. Code and data are available at https://github.com/RiiShin/pid-lvlm-analysis .", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29676v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29670v1", "url": "http://arxiv.org/abs/2603.29670v1", "pdf_url": "https://arxiv.org/pdf/2603.29670v1", "title": "Clinical DVH metrics as a loss function for 3D dose prediction in head and neck radiotherapy", "authors": ["Ruochen Gao", "Marius Staring", "Frank Dankers"], "annotation": "Purpose: Deep-learning-based three-dimensional (3D) dose prediction is widely used in automated radiotherapy workflows. However, most existing models are trained with voxel-wise regression losses, which are poorly aligned with clinical plan evaluation criteria based on dose-volume histogram (DVH) metrics. This study aims to develop a clinically guided loss formulation that directly optimizes clinically used DVH metrics while remaining computationally efficient for head and neck (H\\&N) dose prediction.   Methods: We propose a clinical DVH metric loss (CDM loss) that incorporates differentiable \\textit{D-metrics} and surrogate \\textit{V-metrics}, together with a lossless bit-mask region-of-interest (ROI) encoding to improve training efficiency. The method was evaluated on 174 H\\&N patients using a temporal split (137 training, 37 testing).   Results: Compared with MAE- and DVH-curve based losses, CDM loss substantially improved target coverage and satisfied all clinical constraints. Using a standard 3D U-Net, the PTV Score was reduced from 1.544 (MAE) to 0.491 (MAE + CDM), while OAR sparing remained comparable. Bit-mask encoding reduced training time by 83\\% and lowered GPU memory usage.   Conclusion: Directly optimizing clinically used DVH metrics enables 3D dose predictions that are better aligned with clinical treatment planning criteria than conventional voxel-wise or DVH-curve-based supervision. The proposed CDM loss, combined with efficient ROI bit-mask encoding, provides a practical and scalable framework for H\\&N dose prediction.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29670v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29666v1", "url": "http://arxiv.org/abs/2603.29666v1", "pdf_url": "https://arxiv.org/pdf/2603.29666v1", "title": "CoRe-DA: Contrastive Regression for Unsupervised Domain Adaptation in Surgical Skill Assessment", "authors": ["Dimitrios Anastasiou", "Razvan Caramalau", "Jialang Xu", "Runlong He", "Freweini Tesfai", "Matthew Boal", "Nader Francis", "Danail Stoyanov", "Evangelos B. Mazomenos"], "annotation": "Vision-based surgical skill assessment (SSA) enables objective and scalable evaluation of operative performance. Progress in this field is constrained by the high cost and time demands for manual annotation of quantitative skill scores, as well as the poor generalization of existing regression models to new surgical tasks and environments. Meanwhile, appreciable volumes of unlabeled video data are now available, motivating the development of unsupervised domain adaptation (UDA) methods for SSA. We introduce the first benchmark for UDA in SSA regression, spanning four datasets across dry-lab and clinical settings as well as open and robotic surgery. We evaluate eight representative models under challenging domain shifts and propose CoRe-DA, a novel contrastive regression-based adaptation framework. Our method learns domain-invariant representations through relative-score supervision and target-domain self-training. Comprehensive experiments across two UDA settings show that CoRe-DA is superior to state-of-the-art methods, achieving Spearman Correlation Coefficients of 0.46 and 0.41 on dry-lab and clinical target datasets, respectively, without using any labeled target data for training. Overall, CoRe-DA enables scalable SSA with reliable cross-domain generalization, where existing methods underperform. Our code and datasets will be released at https://github.com/anastadimi/CoRe-DA.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29666v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29664v1", "url": "http://arxiv.org/abs/2603.29664v1", "pdf_url": "https://arxiv.org/pdf/2603.29664v1", "title": "CutClaw: Agentic Hours-Long Video Editing via Music Synchronization", "authors": ["Shifang Zhao", "Yihan Hu", "Ying Shan", "Yunchao Wei", "Xiaodong Cun"], "annotation": "Editing the video content with audio alignment forms a digital human-made art in current social media. However, the time-consuming and repetitive nature of manual video editing has long been a challenge for filmmakers and professional content creators alike. In this paper, we introduce CutClaw, an autonomous multi-agent framework designed to edit hours-long raw footage into meaningful short videos that leverages the capabilities of multiple Multimodal Language Models~(MLLMs) as an agent system. It produces videos with synchronized music, followed by instructions, and a visually appealing appearance. In detail, our approach begins by employing a hierarchical multimodal decomposition that captures both fine-grained details and global structures across visual and audio footage. Then, to ensure narrative consistency, a Playwriter Agent orchestrates the whole storytelling flow and structures the long-term narrative, anchoring visual scenes to musical shifts. Finally, to construct a short edited video, Editor and Reviewer Agents collaboratively optimize the final cut via selecting fine-grained visual content based on rigorous aesthetic and semantic criteria. We conduct detailed experiments to demonstrate that CutClaw significantly outperforms state-of-the-art baselines in generating high-quality, rhythm-aligned videos. The code is available at: https://github.com/GVCLab/CutClaw.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29664v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29660v1", "url": "http://arxiv.org/abs/2603.29660v1", "pdf_url": "https://arxiv.org/pdf/2603.29660v1", "title": "STRADAViT: Towards a Foundational Model for Radio Astronomy through Self-Supervised Transfer", "authors": ["Andrea DeMarco", "Ian Fenech Conti", "Hayley Camilleri", "Ardiana Bushi", "Simone Riggi"], "annotation": "Next-generation radio astronomy surveys are producing millions of resolved sources, but robust morphology analysis remains difficult across heterogeneous telescopes and imaging pipelines. We present STRADAViT, a self-supervised Vision Transformer continued-pretraining framework for transferable radio astronomy image encoders. STRADAViT combines a mixed-survey pretraining dataset, radio astronomy-aware view generation, and controlled continued pretraining through reconstruction-only, contrastive-only, and two-stage branches. Pretraining uses 512x512 radio astronomy cutouts from MeerKAT, ASKAP, LOFAR/LoTSS, and SKA data. We evaluate transfer with linear probing and fine-tuning on three morphology benchmarks: MiraBest, LoTSS DR2, and Radio Galaxy Zoo. Relative to the initialization used for continued pretraining, the best two-stage STRADAViT models improve Macro-F1 in all reported linear-probe settings and in most fine-tuning settings, with the largest gain on RGZ DR1. Relative to strong DINOv2 baselines, gains are selective but remain positive on LoTSS DR2 and RGZ DR1 under linear probing, and on MiraBest and RGZ DR1 under fine-tuning. A targeted DINOv2-initialized HCL ablation further shows that the adaptation recipe is not specific to a single starting point. The released STRADAViT checkpoint remains the preferred model because it offers competitive transfer at lower token count and downstream cost than the DINOv2-based alternative. These results show that radio astronomy-aware view generation and staged continued pretraining provide a stronger starting point than out-of-the-box Vision Transformers for radio astronomy transfer.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29660v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29655v1", "url": "http://arxiv.org/abs/2603.29655v1", "pdf_url": "https://arxiv.org/pdf/2603.29655v1", "title": "Not All Frames Are Equal: Complexity-Aware Masked Motion Generation via Motion Spectral Descriptors", "authors": ["Pengfei Zhou", "Xiangyue Zhang", "Xukun Shen", "Yong Hu"], "annotation": "Masked generative models have become a strong paradigm for text-to-motion synthesis, but they still treat motion frames too uniformly during masking, attention, and decoding. This is a poor match for motion, where local dynamic complexity varies sharply over time. We show that current masked motion generators degrade disproportionately on dynamically complex motions, and that frame-wise generation error is strongly correlated with motion dynamics. Motivated by this mismatch, we introduce the Motion Spectral Descriptor (MSD), a simple and parameter-free measure of local dynamic complexity computed from the short-time spectrum of motion velocity. Unlike learned difficulty predictors, MSD is deterministic, interpretable, and derived directly from the motion signal itself. We use MSD to make masked motion generation complexity-aware. In particular, MSD guides content-focused masking during training, provides a spectral similarity prior for self-attention, and can additionally modulate token-level sampling during iterative decoding. Built on top of masked motion generators, our method, DynMask, improves motion generation most clearly on dynamically complex motions while also yielding stronger overall FID on HumanML3D and KIT-ML. These results suggest that respecting local motion complexity is a useful design principle for masked motion generation. Project page: https://xiangyue-zhang.github.io/DynMask", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29655v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29634v1", "url": "http://arxiv.org/abs/2603.29634v1", "pdf_url": "https://arxiv.org/pdf/2603.29634v1", "title": "MacTok: Robust Continuous Tokenization for Image Generation", "authors": ["Hengyu Zeng", "Xin Gao", "Guanghao Li", "Yuxiang Yan", "Jiaoyang Ruan", "Junpeng Ma", "Haoyu Albert Wang", "Jian Pu"], "annotation": "Continuous image tokenizers enable efficient visual generation, and those based on variational frameworks can learn smooth, structured latent representations through KL regularization. Yet this often leads to posterior collapse when using fewer tokens, where the encoder fails to encode informative features into the compressed latent space. To address this, we introduce \\textbf{MacTok}, a \\textbf{M}asked \\textbf{A}ugmenting 1D \\textbf{C}ontinuous \\textbf{Tok}enizer that leverages image masking and representation alignment to prevent collapse while learning compact and robust representations. MacTok applies both random masking to regularize latent learning and DINO-guided semantic masking to emphasize informative regions in images, forcing the model to encode robust semantics from incomplete visual evidence. Combined with global and local representation alignment, MacTok preserves rich discriminative information in a highly compressed 1D latent space, requiring only 64 or 128 tokens. On ImageNet, MacTok achieves a competitive gFID of 1.44 at 256$\\times$256 and a state-of-the-art 1.52 at 512$\\times$512 with SiT-XL, while reducing token usage by up to 64$\\times$. These results confirm that masking and semantic guidance together prevent posterior collapse and achieve efficient, high-fidelity tokenization.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29634v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29633v1", "url": "http://arxiv.org/abs/2603.29633v1", "pdf_url": "https://arxiv.org/pdf/2603.29633v1", "title": "Self-Supervised Federated Learning under Data Heterogeneity for Label-Scarce Diatom Classification", "authors": ["Mingkun Tan", "Xilu Wang", "Michael Kloster", "Tim W. Nattkemper"], "annotation": "Label-scarce visual classification under decentralized and heterogeneous data is a fundamental challenge in pattern recognition, especially when sites exhibit partially overlapping class sets. While self-supervised federated learning (SSFL) offers a promising solution, existing studies commonly assume the same data heterogeneity pattern throughout pre-training and fine-tuning. Moreover, current partitioning schemes often fail to generate pure partially class-disjoint data settings, limiting controllable simulation of real-world label-space heterogeneity. In this work, we introduce SSFL for diatom classification as a representative real-world instance and systematically investigate stage-specific data heterogeneity. We study cross-site variation in unlabeled data volume during pre-training and label-space misalignment during downstream fine-tuning. To study the latter in a controllable setting, we propose PreDi, a partitioning scheme that disentangles label-space heterogeneity into two orthogonal dimensions, namely class Prevalence and class-set size Disparity, enabling separate analysis of their effects. Guided by the resulting insights, we further propose PreP-WFL (Prevalence-based Personalized Weighted Federated Learning) to adaptively strengthen rare-class representations in low-prevalence scenarios. Extensive experiments show that SSFL consistently outperforms local-only training under both homogeneous and heterogeneous settings. The pronounced heterogeneity in unlabeled data volume is associated with improved representation pre-training, whereas under label-space heterogeneity, prevalence dominates performance and disparity has a smaller effect. PreP-WFL effectively mitigates this degradation, with gains increasing as prevalence decreases. These findings provide a mechanistic basis for characterizing label-space heterogeneity in decentralized recognition systems.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29633v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29631v1", "url": "http://arxiv.org/abs/2603.29631v1", "pdf_url": "https://arxiv.org/pdf/2603.29631v1", "title": "Storing Less, Finding More: How Novelty Filtering Improves Cross-Modal Retrieval on Edge Cameras", "authors": ["Sherif Abdelwahab"], "annotation": "Always-on edge cameras generate continuous video streams where redundant frames degrade cross-modal retrieval by crowding correct results out of top-k search. This paper presents a streaming retrieval architecture: an on-device epsilon-net filter retains only semantically novel frames, building a denoised embedding index; a cross-modal adapter and cloud re-ranker compensate for the compact encoder's weak alignment. A single-pass streaming filter outperforms offline alternatives (k-means, farthest-point, uniform, random) across eight vision-language models (8M-632M) on two egocentric datasets (AEA, EPIC-KITCHENS). Combined, the architecture reaches 45.6% Hit@5 on held-out data using an 8M on-device encoder at an estimated 2.7 mW.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29631v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29630v1", "url": "http://arxiv.org/abs/2603.29630v1", "pdf_url": "https://arxiv.org/pdf/2603.29630v1", "title": "BigEarthNet.txt: A Large-Scale Multi-Sensor Image-Text Dataset and Benchmark for Earth Observation", "authors": ["Johann-Ludwig Herzog", "Mathis Jürgen Adler", "Leonard Hackel", "Yan Shu", "Angelos Zavras", "Ioannis Papoutsis", "Paolo Rota", "Begüm Demir"], "annotation": "Vision-langugage models (VLMs) have shown strong performance in computer vision (CV), yet their performance on remote sensing (RS) data remains limited due to the lack of large-scale, multi-sensor RS image-text datasets with diverse textual annotations. Existing datasets predominantly include aerial Red-Green-Blue imagery, with short or weakly grounded captions, and provide limited diversity in annotation types. To address this limitation, we introduce BigEarthNet.txt, a large-scale, multi-sensor image-text dataset designed to advance instruction-driven image-text learning in Earth observation across multiple tasks. BigEarthNet.txt contains 464044 co-registered Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral images with 9.6M text annotations, including: i) geographically anchored captions describing land-use/land-cover (LULC) classes, their spatial relations, and environmental context; ii) visual question answering pairs relevant for different tasks; and iii) referring expression detection instructions for bounding box prediction. Through a comparative statistical analysis, we demonstrate that BigEarthNet.txt surpasses existing RS image-text datasets in textual richness and annotation type variety. We further establish a manually-verified benchmark split to evaluate VLMs in RS and CV. The results show the limitations of these models on tasks that involve complex LULC classes, whereas fine-tuning using BigEarthNet.txt results in consistent performance gains across all considered tasks.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29630v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29620v1", "url": "http://arxiv.org/abs/2603.29620v1", "pdf_url": "https://arxiv.org/pdf/2603.29620v1", "title": "Unify-Agent: A Unified Multimodal Agent for World-Grounded Image Synthesis", "authors": ["Shuang Chen", "Quanxin Shou", "Hangting Chen", "Yucheng Zhou", "Kaituo Feng", "Wenbo Hu", "Yi-Fan Zhang", "Yunlong Lin", "Wenxuan Huang", "Mingyang Song", "Dasen Dai", "Bolin Jiang", "Manyuan Zhang", "Shi-Xue Zhang", "Zhengkai Jiang", "Lucas Wang", "Zhao Zhong", "Yu Cheng", "Nanyun Peng"], "annotation": "Unified multimodal models provide a natural and promising architecture for understanding diverse and complex real-world knowledge while generating high-quality images. However, they still rely primarily on frozen parametric knowledge, which makes them struggle with real-world image generation involving long-tail and knowledge-intensive concepts. Inspired by the broad success of agents on real-world tasks, we explore agentic modeling to address this limitation. Specifically, we present Unify-Agent, a unified multimodal agent for world-grounded image synthesis, which reframes image generation as an agentic pipeline consisting of prompt understanding, multimodal evidence searching, grounded recaptioning, and final synthesis. To train our model, we construct a tailored multimodal data pipeline and curate 143K high-quality agent trajectories for world-grounded image synthesis, enabling effective supervision over the full agentic generation process. We further introduce FactIP, a benchmark covering 12 categories of culturally significant and long-tail factual concepts that explicitly requires external knowledge grounding. Extensive experiments show that our proposed Unify-Agent substantially improves over its base unified model across diverse benchmarks and real world generation tasks, while approaching the world knowledge capabilities of the strongest closed-source models. As an early exploration of agent-based modeling for world-grounded image synthesis, our work highlights the value of tightly coupling reasoning, searching, and generation for reliable open-world agentic image synthesis.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29620v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29616v1", "url": "http://arxiv.org/abs/2603.29616v1", "pdf_url": "https://arxiv.org/pdf/2603.29616v1", "title": "Video-Oasis: Rethinking Evaluation of Video Understanding", "authors": ["Geuntaek Lim", "Minho Shim", "Sungjune Park", "Jaeyun Lee", "Inwoong Lee", "Taeoh Kim", "Dongyoon Wee", "Yukyung Choi"], "annotation": "The inherent complexity of video understanding makes it difficult to attribute whether performance gains stem from visual perception, linguistic reasoning, or knowledge priors. While many benchmarks have emerged to assess high-level reasoning, the essential criteria that constitute video understanding remain largely overlooked. Instead of introducing yet another benchmark, we take a step back to re-examine the current landscape of video understanding. In this work, we provide Video-Oasis, a sustainable diagnostic suite designed to systematically evaluate existing evaluations and distill spatio-temporal challenges for video understanding. Our analysis reveals two critical findings: (1) 54% of existing benchmark samples are solvable without visual input or temporal context, and (2) on the remaining samples, state-of-the-art models exhibit performance barely exceeding random guessing. To bridge this gap, we investigate which algorithmic design choices contribute to robust video understanding, providing practical guidelines for future research. We hope our work serves as a standard guideline for benchmark construction and the rigorous evaluation of architecture development. Code is available at https://github.com/sejong-rcv/Video-Oasis.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29616v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29591v1", "url": "http://arxiv.org/abs/2603.29591v1", "pdf_url": "https://arxiv.org/pdf/2603.29591v1", "title": "FlowID : Enhancing Forensic Identification with Latent Flow-Matching Models", "authors": ["Jules Ripoll", "David Bertoin", "Alasdair Newson", "Charles Dossal", "Jose Pablo Baraybar"], "annotation": "Every day, many people die under violent circumstances, whether from crimes, war, migration, or climate disasters. Medico-legal and law enforcement institutions document many portraits of the deceased for evidence, but cannot immediately carry out identification on them. While traditional image editing tools can process these photos for public release, the workflow is lengthy and produces suboptimal results. In this work, we leverage advances in image generation models, which can now produce photorealistic human portraits, to introduce FlowID, an identity-preserving facial reconstruction method. Our approach combines single-image fine-tuning, which adapts the generative model to out-of-distribution injured faces, with attention-based masking that localizes edits to damaged regions while preserving identity-critical features. Together, these components enable the removal of artifacts from violent death while retaining sufficient identity information to support identification. To evaluate our method, we introduce InjuredFaces, a novel benchmark for identity-preserving facial reconstruction under severe facial damage. Beyond serving as an evaluation tool for this work, InjuredFaces provides a standardized resource for the community to study and compare methods addressing facial reconstruction in extreme conditions. Experimental results show that FlowID outperforms state-of-the-art open-source methods while maintaining low memory requirements, making it suitable for local deployment without compromising data privacy.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29591v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29578v1", "url": "http://arxiv.org/abs/2603.29578v1", "pdf_url": "https://arxiv.org/pdf/2603.29578v1", "title": "Emotion Diffusion Classifier with Adaptive Margin Discrepancy Training for Facial Expression Recognition", "authors": ["Rongkang Dong", "Cuixin Yang", "Cong Zhang", "Yushen Zuo", "Kin-Man Lam"], "annotation": "Facial Expression Recognition (FER) is essential for human-machine interaction, as it enables machines to interpret human emotions and internal states from facial affective behaviors. Although deep learning has significantly advanced FER performance, most existing deep-learning-based FER methods rely heavily on discriminative classifiers for fast predictions. These models tend to learn shortcuts and are vulnerable to even minor distribution shifts. To address this issue, we adopt a conditional generative diffusion model and introduce the Emotion Diffusion Classifier (EmoDC) for FER, which demonstrates enhanced adversarial robustness. However, retraining EmoDC using standard strategies fails to penalize incorrect categorical descriptions, leading to suboptimal recognition performance. To improve EmoDC, we propose margin-based discrepancy training, which encourages accurate predictions when conditioned on correct categorical descriptions and penalizes predictions conditioned on mismatched ones. This method enforces a minimum margin between noise-prediction errors for correct and incorrect categories, thereby enhancing the model's discriminative capability. Nevertheless, using a fixed margin fails to account for the varying difficulty of noise prediction across different images, limiting its effectiveness. To overcome this limitation, we propose Adaptive Margin Discrepancy Training (AMDiT), which dynamically adjusts the margin for each sample. Extensive experiments show that AMDiT significantly improves the accuracy of EmoDC over the Base model with standard denoising diffusion training on the RAF-DB basic subset, the RAF-DB compound subset, SFEW-2.0, and AffectNet, in 100-step evaluations. Additionally, EmoDC outperforms state-of-the-art discriminative classifiers in terms of robustness against noise and blur.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29578v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29570v1", "url": "http://arxiv.org/abs/2603.29570v1", "pdf_url": "https://arxiv.org/pdf/2603.29570v1", "title": "Generating Key Postures of Bharatanatyam Adavus with Pose Estimation", "authors": ["Jagadish Kashinath Kamble", "Jayanta Mukhopadhyay", "Debaditya Roy", "Partha Pratim Das"], "annotation": "Preserving intangible cultural dances rooted in centuries of tradition and governed by strict structural and symbolic rules presents unique challenges in the digital era. Among these, Bharatanatyam, a classical Indian dance form, stands out for its emphasis on codified adavus and precise key postures. Accurately generating these postures is crucial not only for maintaining anatomical and stylistic integrity, but also for enabling effective documentation, analysis, and transmission to broader global audiences through digital means. We propose a pose-aware generative framework integrated with a pose estimation module, guided by keypoint-based loss and pose consistency constraints. These supervisory signals ensure anatomical accuracy and stylistic integrity in the synthesized outputs. We evaluate four configurations: standard conditional generative adversarial network (cGAN), cGAN with pose supervision, conditional diffusion, and conditional diffusion with pose supervision. Each model is conditioned on key posture class labels and optimized to maintain geometric structure. In both cGAN and conditional diffusion settings, the integrated pose guidance aligns generated poses with ground-truth keypoint structures, promoting cultural fidelity. Our results demonstrate that incorporating pose supervision significantly enhances the quality, realism, and authenticity of generated Bharatanatyam postures. This framework provides a scalable approach for the digital preservation, education, and dissemination of traditional dance forms, enabling high-fidelity generation without compromising cultural precision. Code is available at https://github.com/jagidsh/Generating-Key-Postures-of-Bharatanatyam-Adavus-with-Pose-Estimation.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29570v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29535v1", "url": "http://arxiv.org/abs/2603.29535v1", "pdf_url": "https://arxiv.org/pdf/2603.29535v1", "title": "Quantization with Unified Adaptive Distillation to enable multi-LoRA based one-for-all Generative Vision Models on edge", "authors": ["Sowmya Vajrala", "Aakash Parmar", "Prasanna R", "Sravanth Kodavanti", "Manjunath Arveti", "Srinivas Soumitri Miriyala", "Ashok Senapati"], "annotation": "Generative Artificial Intelligence (GenAI) features such as image editing, object removal, and prompt-guided image transformation are increasingly integrated into mobile applications. However, deploying Large Vision Models (LVMs) for such tasks on resource-constrained devices remains challenging due to their high memory and compute requirements. While Low-Rank Adapters (LoRAs) enable parameter-efficient task adaptation, existing Mobile deployment pipelines typically compile separate model binaries for each LoRA + a copy of the foundation model, resulting in redundant storage and increased runtime overhead. In this work, we present a unified framework for enabling multi-task GenAI inference on edge devices using a single shared model. Our key idea is to treat LoRA weights as runtime inputs rather than embedding them into the compiled model graph, allowing dynamic task switching at runtime without recompilation. Then, to support efficient on-device execution, we introduce QUAD (Quantization with Unified Adaptive Distillation), a quantizationaware training strategy that aligns multiple LoRA adapters under a shared quantization profile. We implement the proposed system with a lightweight runtime stack compatible with mobile NPUs and evaluate it across multiple chipsets. Experimental results demonstrate up to 6x and 4x reduction in memory footprint and latency improvements, respectively, while maintaining high visual quality across multiple GenAI tasks.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29535v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29507v1", "url": "http://arxiv.org/abs/2603.29507v1", "pdf_url": "https://arxiv.org/pdf/2603.29507v1", "title": "Transmittance-Guided Structure-Texture Decomposition for Nighttime Image Dehazing", "authors": ["Francesco Moretti", "Giulia Bianchi", "Andrea Gallo"], "annotation": "Nighttime images captured under hazy conditions suffer from severe quality degradation, including low visibility, color distortion, and reduced contrast, caused by the combined effects of atmospheric scattering, absorption by suspended particles, and non-uniform illumination from artificial light sources. While existing nighttime dehazing methods have achieved partial success, they typically address only a subset of these issues, such as glow suppression or brightness enhancement, without jointly tackling the full spectrum of degradation factors. In this paper, we propose a two-stage nighttime image dehazing framework that integrates transmittance correction with structure-texture layered optimization. In the first stage, we introduce a novel transmittance correction method that establishes boundary-constrained initial transmittance maps and subsequently applies region-adaptive compensation and normalization based on whether image regions correspond to light source areas. A quadratic Gaussian filtering scheme operating in the YUV color space is employed to estimate the spatially varying atmospheric light map. The corrected transmittance map and atmospheric light map are then used in conjunction with an improved nighttime imaging model to produce the initial dehazed image. In the second stage, we propose a STAR-YUV decomposition model that separates the dehazed image into structure and texture layers within the YUV color space. Gamma correction and MSRCR-based color restoration are applied to the structure layer for illumination compensation and color bias correction, while Laplacian-of-Gaussian filtering is applied to the texture layer for detail enhancement. A novel two-phase fusion strategy, comprising nonlinear Retinex-based fusion of the enhanced layers followed by linear blending with the initial dehazing result, yields the final output.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29507v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29495v1", "url": "http://arxiv.org/abs/2603.29495v1", "pdf_url": "https://arxiv.org/pdf/2603.29495v1", "title": "All-in-One Augmented Reality Guided Head and Neck Tumor Resection", "authors": ["Yue Yang", "Matthieu Chabanas", "Carrie Reale", "Annie Benson", "Jason Slagle", "Matthew Weinger", "Michael Topf", "Jie Ying Wu"], "annotation": "Positive margins are common in head and neck squamous cell carcinoma, yet intraoperative re-resection is often imprecise because margin locations are typically communicated verbally from pathology. We present an all-in-one augmented reality (AR) system that relocalizes positive margins from a resected specimen to the resection bed and visualizes them in situ using HoloLens 2 depth sensing and fully automated markerless surface registration. In a silicone phantom study with six medical trainees, markerless registration achieved target registration errors comparable to a marker-based baseline (median 1.8 mm vs. 1.7 mm; maximum < 4 mm). In a margin relocalization task, AR guidance reduced error from verbal guidance (median 14.2 mm) to a few millimeters (median 3.2 mm), with all AR localizations within 5 mm error. These results support the feasibility of markerless AR margin guidance for more precise intraoperative re-excision.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29495v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29494v1", "url": "http://arxiv.org/abs/2603.29494v1", "pdf_url": "https://arxiv.org/pdf/2603.29494v1", "title": "VecAttention: Vector-wise Sparse Attention for Accelerating Long Context Inference", "authors": ["Anmin Liu", "Ruixuan Yang", "Huiqiang Jiang", "Bin Lin", "Minmin Sun", "Yong Li", "Chen Zhang", "Tao Xie"], "annotation": "Long-context video understanding and generation pose a significant computational challenge for Transformer-based video models due to the quadratic complexity of self-attention. While existing sparse attention methods employ coarse-grained patterns to improve efficiency, they typically incur redundant computation and suboptimal performance. To address this issue, in this paper, we propose \\textbf{VecAttention}, a novel framework of vector-wise sparse attention that achieves superior accuracy-efficiency trade-offs for video models. We observe that video attention maps exhibit a strong vertical-vector sparse pattern, and further demonstrate that this vertical-vector pattern offers consistently better accuracy-sparsity trade-offs compared with existing coarse-grained sparse patterns. Based on this observation, VecAttention dynamically selects and processes only informative vertical vectors through a lightweight important-vector selection that minimizes memory access overhead and an optimized kernel of vector sparse attention. Comprehensive evaluations on video understanding (VideoMME, LongVideoBench, and VCRBench) and generation (VBench) tasks show that VecAttention delivers a 2.65$\\times$ speedup over full attention and a 1.83$\\times$ speedup over state-of-the-art sparse attention methods, with comparable accuracy to full attention. Our code is available at https://github.com/anminliu/VecAttention.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29494v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29460v1", "url": "http://arxiv.org/abs/2603.29460v1", "pdf_url": "https://arxiv.org/pdf/2603.29460v1", "title": "Square Superpixel Generation and Representation Learning via Granular Ball Computing", "authors": ["Shuyin Xia", "Meng Yang", "Dawei Dai", "Fan Chen", "Shilin Zhao", "Junwei Han", "Xinbo Gao", "Guoyin Wang", "Wen Lu"], "annotation": "Superpixels provide a compact region-based representation that preserves object boundaries and local structures, and have therefore been widely used in a variety of vision tasks to reduce computational cost. However, most existing superpixel algorithms produce irregularly shaped regions, which are not well aligned with regular operators such as convolutions. Consequently, superpixels are often treated as an offline preprocessing step, limiting parallel implementation and hindering end-to-end optimization within deep learning pipelines. Motivated by the adaptive representation and coverage property of granular-ball computing, we develop a square superpixel generation approach. Specifically, we approximate superpixels using multi-scale square blocks to avoid the computational and implementation difficulties induced by irregular shapes, enabling efficient parallel processing and learnable feature extraction. For each block, a purity score is computed based on pixel-intensity similarity, and high-quality blocks are selected accordingly. The resulting square superpixels can be readily integrated as graph nodes in graph neural networks (GNNs) or as tokens in Vision Transformers (ViTs), facilitating multi-scale information aggregation and structured visual representation. Experimental results on downstream tasks demonstrate consistent performance improvements, validating the effectiveness of the proposed method.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29460v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29455v1", "url": "http://arxiv.org/abs/2603.29455v1", "pdf_url": "https://arxiv.org/pdf/2603.29455v1", "title": "FedDBP: Enhancing Federated Prototype Learning with Dual-Branch Features and Personalized Global Fusion", "authors": ["Ningzhi Gao", "Siquan Huang", "Leyu Shi", "Ying Gao"], "annotation": "Federated prototype learning (FPL), as a solution to heterogeneous federated learning (HFL), effectively alleviates the challenges of data and model heterogeneity.However, existing FPL methods fail to balance the fidelity and discriminability of the feature, and are limited by a single global prototype. In this paper, we propose FedDBP, a novel FPL method to address the above issues. On the client-side, we design a Dual-Branch feature projector that employs L2 alignment and contrastive learning simultaneously, thereby ensuring both the fidelity and discriminability of local features. On the server-side, we introduce a Personalized global prototype fusion approach that leverages Fisher information to identify the important channels of local prototypes. Extensive experiments demonstrate the superiority of FedDBP over ten existing advanced methods.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29455v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29450v1", "url": "http://arxiv.org/abs/2603.29450v1", "pdf_url": "https://arxiv.org/pdf/2603.29450v1", "title": "Few-shot Writer Adaptation via Multimodal In-Context Learning", "authors": ["Tom Simon", "Stephane Nicolas", "Pierrick Tranouez", "Clement Chatelain", "Thierry Paquet"], "annotation": "While state-of-the-art Handwritten Text Recognition (HTR) models perform well on standard benchmarks, they frequently struggle with writers exhibiting highly specific styles that are underrepresented in the training data. To handle unseen and atypical writers, writer adaptation techniques personalize HTR models to individual handwriting styles. Leading writer adaptation methods require either offline fine-tuning or parameter updates at inference time, both involving gradient computation and backpropagation, which increase computational costs and demand careful hyperparameter tuning. In this work, we propose a novel context-driven HTR framework3 inspired by multimodal in-context learning, enabling inference-time writer adaptation using only a few examples from the target writer without any parameter updates. We further demonstrate the impact of context length, design a compact 8M-parameter CNN-Transformer that enables few-shot in-context adaptation, and show that combining context-driven and standard OCR training strategies leads to complementary improvements. Experiments on IAM and RIMES validate our approach with Character Error Rates of 3.92% and 2.34%, respectively, surpassing all writer-independent HTR models without requiring any parameter updates at inference time.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29450v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29449v1", "url": "http://arxiv.org/abs/2603.29449v1", "pdf_url": "https://arxiv.org/pdf/2603.29449v1", "title": "NeoNet: An End-to-End 3D MRI-Based Deep Learning Framework for Non-Invasive Prediction of Perineural Invasion via Generation-Driven Classification", "authors": ["Youngung Han", "Minkyung Cha", "Kyeonghun Kim", "Induk Um", "Myeongbin Sho", "Joo Young Bae", "Jaewon Jung", "Jung Hyeok Park", "Seojun Lee", "Nam-Joon Kim", "Woo Kyoung Jeong", "Won Jae Lee", "Pa Hong", "Ken Ying-Kai Liao", "Hyuk-Jae Lee"], "annotation": "Minimizing invasive diagnostic procedures to reduce the risk of patient injury and infection is a central goal in medical imaging. And yet, noninvasive diagnosis of perineural invasion (PNI), a critical prognostic factor involving infiltration of tumor cells along the surrounding nerve, still remains challenging, due to the lack of clear and consistent imaging criteria criteria for identifying PNI. To address this challenge, we present NeoNet, an integrated end-to-end 3D deep learning framework for PNI prediction in cholangiocarcinoma that does not rely on predefined image features. NeoNet integrates three modules: (1) NeoSeg, utilizing a Tumor-Localized ROI Crop (TLCR) algorithm; (2) NeoGen, a 3D Latent Diffusion Model (LDM) with ControlNet, conditioned on anatomical masks to generate synthetic image patches, specifically balancing the dataset to a 1:1 ratio; and (3) NeoCls, the final prediction module. For NeoCls, we developed the PNI-Attention Network (PattenNet), which uses the frozen LDM encoder and specialized 3D Dual Attention Blocks (DAB) designed to detect subtle intensity variations and spatial patterns indicative of PNI. In 5-fold cross-validation, NeoNet outperformed baseline 3D models and achieved the highest performance with a maximum AUC of 0.7903.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29449v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29441v1", "url": "http://arxiv.org/abs/2603.29441v1", "pdf_url": "https://arxiv.org/pdf/2603.29441v1", "title": "EarthEmbeddingExplorer: A Web Application for Cross-Modal Retrieval of Global Satellite Images", "authors": ["Yijie Zheng", "Weijie Wu", "Bingyue Wu", "Long Zhao", "Guoqing Li", "Mikolaj Czerkawski", "Konstantin Klemmer"], "annotation": "While the Earth observation community has witnessed a surge in high-impact foundation models and global Earth embedding datasets, a significant barrier remains in translating these academic assets into freely accessible tools. This tutorial introduces EarthEmbeddingExplorer, an interactive web application designed to bridge this gap, transforming static research artifacts into dynamic, practical workflows for discovery. We will provide a comprehensive hands-on guide to the system, detailing its cloud-native software architecture, demonstrating cross-modal queries (natural language, visual, and geolocation), and showcasing how to derive scientific insights from retrieval results. By democratizing access to precomputed Earth embeddings, this tutorial empowers researchers to seamlessly transition from state-of-the-art models and data archives to real-world application and analysis. The web application is available at https://modelscope.ai/studios/Major-TOM/EarthEmbeddingExplorer.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29441v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29438v1", "url": "http://arxiv.org/abs/2603.29438v1", "pdf_url": "https://arxiv.org/pdf/2603.29438v1", "title": "Polyhedral Unmixing: Bridging Semantic Segmentation with Hyperspectral Unmixing via Polyhedral-Cone Partitioning", "authors": ["Antoine Bottenmuller", "Etienne Decencière", "Petr Dokládal"], "annotation": "Semantic segmentation and hyperspectral unmixing are two central problems in spectral image analysis. The former assigns each pixel a discrete label corresponding to its material class, whereas the latter estimates pure material spectra, called endmembers, and, for each pixel, a vector representing material abundances in the observed scene. Despite their complementarity, these two problems are usually addressed independently. This paper aims to bridge these two lines of work by formally showing that, under the linear mixing model, pixel classification by dominant materials induces polyhedral-cone regions in the spectral space. We leverage this fundamental property to propose a direct segmentation-to-unmixing pipeline that performs blind hyperspectral unmixing from any semantic segmentation by constructing a polyhedral-cone partition of the space that best fits the labeled pixels. Signed distances from pixels to the estimated regions are then computed, linearly transformed via a change of basis in the distance space, and projected onto the probability simplex, yielding an initial abundance estimate. This estimate is used to extract endmembers and recover final abundances via matrix pseudo-inversion. Because the segmentation method can be freely chosen, the user gains explicit control over the unmixing process, while the rest of the pipeline remains essentially deterministic and lightweight. Beyond improving interpretability, experiments on three real datasets demonstrate the effectiveness of the proposed approach when associated with appropriate clustering algorithms, and show consistent improvements over recent deep and non-deep state-of-the-art methods. The code is available at: https://github.com/antoine-bottenmuller/polyhedral-unmixing", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29438v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29437v1", "url": "http://arxiv.org/abs/2603.29437v1", "pdf_url": "https://arxiv.org/pdf/2603.29437v1", "title": "SeGPruner: Semantic-Geometric Visual Token Pruner for 3D Question Answering", "authors": ["Wenli Li", "Kai Zhao", "Haoran Jiang", "Enquan Yang", "Yi Su", "Dan Zeng"], "annotation": "Vision-language models (VLMs) have been widely adopted for 3D question answering (3D QA). In typical pipelines, visual tokens extracted from multiple viewpoints are concatenated with language tokens and jointly processed by a large language model (LLM) for inference. However, aggregating multi-view observations inevitably introduces severe token redundancy, leading to an overly large visual token set that significantly hinders inference efficiency under constrained token budgets. Visual token pruning has emerged as a prevalent strategy to address this issue. Nevertheless, most existing pruners are primarily tailored to 2D inputs or rely on indirect geometric cues, which limits their ability to explicitly retain semantically critical objects and maintain sufficient spatial coverage for robust 3D reasoning. In this paper, we propose SeGPruner, a semantic-aware and geometry-guided token reduction framework for efficient 3D QA with multi-view images. Specifically, SeGPruner first preserves semantically salient tokens through an attention-based importance module (Saliency-aware Token Selector), ensuring that object-critical evidence is retained. It then complements these tokens with spatially diverse ones via a geometry-guided selector (Geometry-aware Token Diversifier), which jointly considers semantic relevance and 3D geometric distance. This cooperation between saliency preservation and geometry-guided diversification balances object-level evidence and global scene coverage under aggressive token reduction. Extensive experiments on ScanQA and OpenEQA demonstrate that SeGPruner substantially improves inference efficiency, reducing the visual token budget by 91% and inference latency by 86%, while maintaining competitive performance in 3D reasoning tasks.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29437v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29428v1", "url": "http://arxiv.org/abs/2603.29428v1", "pdf_url": "https://arxiv.org/pdf/2603.29428v1", "title": "Seeing the Evidence, Missing the Answer: Tool-Guided Vision-Language Models on Visual Illusions", "authors": ["Xuesong Wang", "Harry Wang"], "annotation": "Vision-language models (VLMs) exhibit a systematic bias when confronted with classic optical illusions: they overwhelmingly predict the illusion as \"real\" regardless of whether the image has been counterfactually modified. We present a tool-guided inference framework for the DataCV 2026 Challenge (Tasks I and II) that addresses this failure mode without any model training. An off-the-shelf vision-language model is given access to a small set of generic image manipulation tools: line drawing, region cropping, side-by-side comparison, and channel isolation, together with an illusion-type-routing system prompt that prescribes which tools to invoke for each perceptual question category. Critically, every tool call produces a new, immutable image resource appended to a persistent registry, so the model can reference and compose any prior annotated view throughout its reasoning chain. Rather than hard-coding illusion-specific modules, this generic-tool-plus-routing design yields strong cross-structural generalization: performance remained consistent from the validation set to a test set containing structurally unfamiliar illusion variants (e.g., Mach Bands rotated from vertical to horizontal stacking). We further report three empirical observations that we believe warrant additional investigation: (i) a strong positive-detection bias likely rooted in imbalanced illusion training data, (ii) a striking dissociation between pixel-accurate spatial reasoning and logical inference over self-generated annotations, and (iii) pronounced sensitivity to image compression artifacts that compounds false positives.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29428v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29423v1", "url": "http://arxiv.org/abs/2603.29423v1", "pdf_url": "https://arxiv.org/pdf/2603.29423v1", "title": "A2BFR: Attribute-Aware Blind Face Restoration", "authors": ["Chenxin Zhu", "Yushun Fang", "Lu Liu", "Shibo Yin", "Xiaohong Liu", "Xiaoyun Zhang", "Qiang Hu", "Guangtao Zhai"], "annotation": "Blind face restoration (BFR) aims to recover high-quality facial images from degraded inputs, yet its inherently ill-posed nature leads to ambiguous and uncontrollable solutions. Recent diffusion-based BFR methods improve perceptual quality but remain uncontrollable, whereas text-guided face editing enables attribute manipulation without reliable restoration. To address these issues, we propose A$^2$BFR, an attribute-aware blind face restoration framework that unifies high-fidelity reconstruction with prompt-controllable generation. Built upon a Diffusion Transformer backbone with unified image-text cross-modal attention, A$^2$BFR jointly conditions the denoising trajectory on both degraded inputs and textual prompts. To inject semantic priors, we introduce attribute-aware learning, which supervises denoising latents using facial attribute embeddings extracted by an attribute-aware encoder. To further enhance prompt controllability, we introduce semantic dual-training, which leverages the pairwise attribute variations in our newly curated AttrFace-90K dataset to enforce attribute discrimination while preserving fidelity. Extensive experiments demonstrate that A$^2$BFR achieves state-of-the-art performance in both restoration fidelity and instruction adherence, outperforming diffusion-based BFR baselines by -0.0467 LPIPS and +52.58% attribute accuracy, while enabling fine-grained, prompt-controllable restoration even under severe degradations.", "category": "cs.CV", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29423v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30013v1", "url": "http://arxiv.org/abs/2603.30013v1", "pdf_url": "https://arxiv.org/pdf/2603.30013v1", "title": "Counting partial Hadamard matrices in the cubic regime", "authors": ["Damek Davis"], "annotation": "We give a precise asymptotic formula for the number of $n\\times 4t$ partial Hadamard matrices in the regimes $t/n^3\\to\\infty$ and $t/n^3\\toΘ$ for sufficiently large fixed $Θ$. This strengthens earlier results of de~Launey and Levin, who obtained the asymptotic for $t/n^{12}\\to\\infty$, and of Canfield, who extended this to $t/n^4\\to\\infty$.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30013v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30009v1", "url": "http://arxiv.org/abs/2603.30009v1", "pdf_url": "https://arxiv.org/pdf/2603.30009v1", "title": "Construction of additively graceful signed graphs-I", "authors": ["Mukti Acharya"], "annotation": "In this paper, we construct additively graceful signed graphs S from a given graph G that may be additively graceful or not be additively graceful. We also show the construction of additively graceful signed graphs from additively graceful signed graphs. We find the values of m, n in non-divisible sum graph, denoted as G(m, n), that admit additively graceful labeling.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30009v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29998v1", "url": "http://arxiv.org/abs/2603.29998v1", "pdf_url": "https://arxiv.org/pdf/2603.29998v1", "title": "Some geometric series for Euler's constant", "authors": ["Jean-François Burnol"], "annotation": "We provide representations of Euler's constant $γ=0.577...$ as series which converge geometrically fast. This is based upon our earlier work on the Euler alternating series.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29998v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29988v1", "url": "http://arxiv.org/abs/2603.29988v1", "pdf_url": "https://arxiv.org/pdf/2603.29988v1", "title": "Simplex Layers and Phase Boundaries in the Partition Graph", "authors": ["Fedor B. Lyudogovskiy"], "annotation": "For the partition graph $G_n$ on the set of partitions of $n$, we study the stratification induced by the local simplex dimension $\\dim_{\\mathrm{loc}}(λ)$, defined as the maximal dimension of a simplex of the clique complex $K_n=\\mathrm{Cl}(G_n)$ containing $λ$. Using the previously established description of maximal cliques through a vertex in terms of star and top capacities, we define the simplex layers $L_r(n):=\\{λ\\vdash n:\\dim_{\\mathrm{loc}}(λ)=r\\}$ and study their global structure. We formalize the resulting layer stratification, rewrite layer membership in terms of local capacities, and record its basic consequences, including conjugation invariance. We then investigate first occurrence of layers across $n$, introducing the indices $n_r^{\\mathrm{first}}$ and the corresponding first-occurrence sets $\\mathcal{F}_r$. For the initial layer values, we obtain explicit exact results; more generally, we record a finite first-occurrence table and several natural sequence questions. We also define the adjacent-layer edge boundary $\\partial^E_{r,r+1}(n)$, consisting of edges joining $L_r(n)$ to $L_{r+1}(n)$, together with the associated one-sided and vertex-boundary variants. This provides an exact interface language for the layer stratification, distinct from the broader shell-type geometric language used elsewhere in the project.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29988v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29980v1", "url": "http://arxiv.org/abs/2603.29980v1", "pdf_url": "https://arxiv.org/pdf/2603.29980v1", "title": "Voronoi-Based Vacuum Leakage Detection in Composite Manufacturing", "authors": ["Christoph Brauer", "Arne Hindersmann", "Timo de Wolff"], "annotation": "In this article, we investigate vacuum leakage detection problems in composite manufacturing. Our approach uses Voronoi diagrams, a well-known structure in discrete geometry. The Voronoi diagram of the vacuum connection positions partitions the component surface. We use this partition to narrow down potential leak locations to a small area, making an efficient manual search feasible. To further reduce the search area, we propose refined Voronoi diagrams. We evaluate both variants using a novel dataset consisting of several hundred one- and two-leak positions along with their corresponding flow values. Our experimental results demonstrate that Voronoi-based predictive models are highly accurate and have the potential to resolve the leakage detection bottleneck in composite manufacturing.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29980v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29973v1", "url": "http://arxiv.org/abs/2603.29973v1", "pdf_url": "https://arxiv.org/pdf/2603.29973v1", "title": "Various conjectural series identities", "authors": ["Zhi-Wei Sun"], "annotation": "In this paper we collect over 75 new series identities (involving binomial coefficients) conjectured by the author in 2026. For example, we conjecture that $$\\sum_{k=0}^\\infty\\frac{16k+3}{(-202^2)^k}\\binom{2k}kT_k(19,-20)T_{2k}(9,-5)=\\frac{43\\sqrt{101}}{75π},$$ where $T_n(b,c)$ denotes the coefficient of $x^n$ in the expansion of $(x^2+bx+c)^n$. The conjectures in this paper might interest some readers and stimulate further research.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29973v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29961v1", "url": "http://arxiv.org/abs/2603.29961v1", "pdf_url": "https://arxiv.org/pdf/2603.29961v1", "title": "Short proofs in combinatorics and number theory", "authors": ["Boris Alexeev", "Moe Putterman", "Mehtaab Sawhney", "Mark Sellke", "Gregory Valiant"], "annotation": "We give a triplet of short proofs, each of which answers a question raised by Erdős. The first concerns the small prime factors of $\\binom{n}{k}$, the second concerns whether an additive basis $A$ can always be split into pieces $A_1$ and $A_2$ such that each of $A_i + A_i$ has bounded gaps, and the final concerns whether $\\{αp\\}$ is \"well-distributed\" in the sense introduced by Hlawka and Petersen. In each case, the proof is due entirely to an internal model at OpenAI.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29961v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29938v1", "url": "http://arxiv.org/abs/2603.29938v1", "pdf_url": "https://arxiv.org/pdf/2603.29938v1", "title": "Sparse counting lemma for $K_4$", "authors": ["Warach Veeranonchai"], "annotation": "The sparse analogue of Szemerédi's regularity method has played a central role in the development of extremal results for random graphs. While the sparse embedding lemma (the KLR conjecture) has been resolved, the corresponding sparse counting lemma remains widely open. The conjecture, formulated by Gerke, Marciniszyn, and Steger, states that for every fixed graph $H$ and any $β>0$, there exists $\\varepsilon>0$ such that the following holds. Consider a balanced blow-up of $H$ with vertex classes of size $n$, where each pair corresponding to an edge of $H$ forms an $(\\varepsilon)$-regular bipartite graph with exactly $m$ edges. Assume that $m$ is above the natural threshold $m \\gg n^{2-1/m_2(H)}$, then all but a $β^m$ proportion of such graphs contain at least $(1-δ)$ times the expected number of copies of $H$.   At present, among the complete graphs, the conjecture is known only for $H=K_3$. In this paper, we establish the $H=K_4$ case of the conjecture.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29938v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29925v1", "url": "http://arxiv.org/abs/2603.29925v1", "pdf_url": "https://arxiv.org/pdf/2603.29925v1", "title": "On lower bounds for the number of ideal and finite vertices of right-angled hyperbolic polyhedra in dimensions from 5 to 12", "authors": ["Andrey Egorov"], "annotation": "We investigate lower bounds for the number of ideal and finite vertices of right-angled hyperbolic polyhedra of finite volume. We use a geometric method of orthogonal gluings to establish new bounds in low dimensions, specifically $v_\\infty(P^5) \\ge 3$ and $v_{fin}(P^7) \\ge 4$. By combining these initial bounds with double counting arguments and recurrence relations, we obtain improved lower bounds for both types of vertices in all higher dimensions up to $n=12$, the maximal dimension where polyhedra of this class exist.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29925v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29836v1", "url": "http://arxiv.org/abs/2603.29836v1", "pdf_url": "https://arxiv.org/pdf/2603.29836v1", "title": "Two Littlewood identities for fully inhomogeneous spin Hall-Littlewood symmetric rational functions", "authors": ["Ilse Fischer", "Moritz Gangl"], "annotation": "Fully inhomogeneous spin Hall-Littlewood symmetric rational functions $F_λ$ arise as partition functions of certain path configurations in the $\\mathfrak{sl}_2$ higher spin six vertex models. They are multiparameter generalizations of the classical Hall-Littlewood symmetric polynomials. We establish two new generalizations of the classical Littlewood identity, where we express a weighted sum of $F_λ$'s over all partitions $λ$ as a product of the Littlewood kernel and another simple product in one case, and a product of the Littlewood kernel and a Pfaffian in the other case. As a corollary we obtain a novel Littlewood identity for Hall-Littlewood symmetric polynomials.   We also elaborate on the newly established connection between the fully inhomogeneous spin Hall-Littlewood symmetric rational functions $F_λ$ and the modified Robbins polynomials, the latter being multivariate generating functions for alternating sign matrices. This connection allowed us to discover the two generalizations of the Littlewood identity and we provide a bijection between the underlying combinatorial models in the case where $λ$ is strictly decreasing.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29836v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29728v1", "url": "http://arxiv.org/abs/2603.29728v1", "pdf_url": "https://arxiv.org/pdf/2603.29728v1", "title": "Reciprocity of Skew Hall-Littlewood-Schubert Series", "authors": ["Ron M. Adin", "Tomer Bauer"], "annotation": "Carnevale, Schein and Voll proved self-reciprocity of the generalized Igusa functions, and Maglione and Voll did the same for the Hall-Littlewood-Schubert series. We introduce a simultaneous generalization and refinement of these two rational functions, and prove that it satisfies a self-reciprocity property. This answers a problem posed by Maglione and Voll. Our method of proof is elementary, avoiding the use of $p$-adic integration.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29728v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29629v1", "url": "http://arxiv.org/abs/2603.29629v1", "pdf_url": "https://arxiv.org/pdf/2603.29629v1", "title": "On Lexicographic Product and Multi-Word-Representability", "authors": ["Benny George Kenkireth", "Gopalan Sajith", "Sreyas Sasidharan"], "annotation": "We investigate the relationship between the lexicographic product of graphs and their multi-word-representation number. Although the lexicographic product of two word-representable graphs need not itself be word-representable, a precise characterization has not previously been established. We provide a complete characterization, showing that for word-representable graphs $G_1$ and $G_2$, the lexicographic product $G_1 \\circ G_2$ is word-representable if and only if $G_2$ is a comparability graph. For lexicographic powers, we prove that $G^{[k]}$ is word-representable if and only if $G$ is a comparability graph. The multi-word-representation number $μ$ for lexicographic powers and products satisfies the following bounds. If $G$ is a non-comparability graph, then $μ(G^{[k]}) \\le k$, whereas if $G$ is the union of two comparability graphs, then $μ(G^{[k]}) = 2$. More generally, for graphs $G_1$ and $G_2$ with $μ(G_1) = k_1$ and $μ(G_2) = k_2$, the lexicographic product $H = G_1 \\circ G_2$ satisfies the upper bound $μ(H) \\le k_1 + k_2$. This bound is tight, with equality $μ(H) = k_1$, when $k_1 \\ge k_2$ and $G_2$ is the union of $k_1$ comparability graphs. Moreover, if $G_1$ and $G_2$ are minimal non-word-representable graphs, then $μ(G_1 \\circ G_2) \\le 3$. Finally, we study the function $τ(n)$, which measures the size of the largest word-representable induced subgraph guaranteed in every $n$-vertex graph. By constructing extremal graphs via lexicographic powers, we establish a sublinear upper bound, showing that $τ(n) \\le n^{0.86}$ for sufficiently large $n$.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29629v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29626v1", "url": "http://arxiv.org/abs/2603.29626v1", "pdf_url": "https://arxiv.org/pdf/2603.29626v1", "title": "Seymour-tight orientations", "authors": ["Krystal Guo", "Ross J. Kang", "Gabriëlle Zwaneveld"], "annotation": "We investigate `almost counterexamples' to Seymour's second neighbourhood conjecture. In what we call Seymour-tight orientations, the size of the first neighbourhood of each vertex equals the size of its second neighbourhood. We give several examples and constructions. Specifically, we prove that the class of Seymour-tight orientations is closed under taking (generalized) lexicographic products. Moreover, the lexicographic product of a putative counterexample to Seymour's second neighbourhood conjecture and a Seymour-tight orientation is again a counterexample.   Using lexicographic products, we show that if the conjecture is false, then there exist counterexamples that are close to regular tournaments, and moreover that any digraph occurs as an induced subgraph of a counterexample. We then use this same machinery to construct special putative counterexamples to Sullivan's conjecture.   The inherent symmetry of these orientations give access to an algebraic perspective. Seymour-tight orientations that are also Cayley digraphs correspond to special pairs of critical sets in groups, which connects potentially to additive combinatorics. We use Kemperman's theorem to characterize those Seymour-tight orientations that are the Cayley digraph of an abelian group.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29626v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29622v1", "url": "http://arxiv.org/abs/2603.29622v1", "pdf_url": "https://arxiv.org/pdf/2603.29622v1", "title": "A Finite-State Proof of the Well-Definedness of a Perturbed Hofstadter Sequence", "authors": ["Marco Mantovanelli"], "annotation": "We prove that the perturbed Hofstadter-type sequence Q(1)=1, Q(2)=1, and Q(n)=Q(n-Q(n-1))+Q(n-Q(n-2))+(-1)^n is well-defined for all n>=1, in the sense that all recursive arguments remain positive. This contrasts with the classical Hofstadter Q-sequence, for which global well-definedness remains open.   The proof reduces the infinite recursion to a finite combinatorial constraint system. We introduce a symbolic encoding of local configurations, compute the finite set of admissible contexts, and construct a compatibility relation that captures all valid local transitions. We then show that valid assignments split into two global modes, which reduces all potential obstructions to a finite critical core. A complete finite verification excludes these obstructions and establishes global well-definedness.   More generally, the argument shows that certain meta-Fibonacci recursions admit a finite-state description whose global consistency can be decided by exhaustive combinatorial analysis.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29622v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29610v1", "url": "http://arxiv.org/abs/2603.29610v1", "pdf_url": "https://arxiv.org/pdf/2603.29610v1", "title": "Critical dimensions and small cycle dominance from all-orders asymptotics of $d$-matrix theory", "authors": ["Yang Lei", "Sanjaye Ramgoolam"], "annotation": "Supersymmetric sectors of $\\mathcal{N}=4$ super-Yang-Mills theory motivate the study of the partition function for the counting of gauge-invariant functions of $d=2,3$ matrices transforming under the adjoint action of $U(N)$. The partition function $ \\mathcal{Z}_d ( x) $ in the large $N$ limit has a known Hagedorn phase transition at $ x = d^{-1} $ which provides a simple model for the phase structure of the thermal partition function of SYM. We study the all-orders asymptotic expansion of $ \\mathcal{Z}_d(x)$ based on a geometric picture of concentric circles of poles in the complex plane accumulating in a natural boundary at $|x| =1$. We find that the order by order structure has a precise combinatorial interpretation organized in terms of increasing cycle size of permutations arising in the enumeration of the invariants. We refer to this organization as small-cycle dominance, and find that it extends to refined versions of the partition functions depending on several complex variables. An analysis of the coefficients in the asymptotic expansion of $ \\mathcal{Z}_d(x) $ using the modular property of the Dedekind eta function reveals that the asymptotic expansion is actually convergent for $d\\ge d_{ \\rm crit } = 13$. A fermionic version of $\\mathcal{Z}_d (x)$ has an analogous critical dimension of $ d_{ \\rm crit} = 7$. This distinction indicates that the partition functions of the matrix models can be completely reconstructed from their high-energy (UV) limit for $d\\ge d_{ \\rm crit}$ whereas additional input is required to reconstruct the exact coefficients of the low-energy (IR) expansion for $2\\le d \\le d_{ \\rm crit } -1 $.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29610v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29606v1", "url": "http://arxiv.org/abs/2603.29606v1", "pdf_url": "https://arxiv.org/pdf/2603.29606v1", "title": "Permutation modules for Ramsey structures", "authors": ["David M. Evans"], "annotation": "Suppose $R$ is a commutative ring and $G$ is a group acting on a set $W$. We consider the $RG$-module $RW$ in the case where $G$ is the automorphism group of an $ω$-categorical structure $M$ and $W$ is, for example, $M^n$ (for $n \\in \\mathbb{N}$). We develop methods which may provide information about two questions in the case where $R$ is a field $F$: whether $FW$ has a.c.c. on submodules; and in the case where $M$ is finitely homogeneous, whether $FW$ is of finite composition length. In the case where $M$ is a Ramsey structure and so $G$ is extremely amenable, we give a simple `decision procedure' for membership in a submodule of $RW$ specified by a given generating set. If $F$ is a field, we show that there is a duality between submodules of $FW$ and the topological $FG$-module of definable functions from $W$ to $F$.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29606v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29577v1", "url": "http://arxiv.org/abs/2603.29577v1", "pdf_url": "https://arxiv.org/pdf/2603.29577v1", "title": "Resolving problems on the polynomial identity characterization of daisy cubes", "authors": ["Xuan Zheng", "Yan-Ting Xie", "Shou-Jun Xu"], "annotation": "Let $X\\subseteq\\{0,1\\}^n$ be a set of binary strings of length $n$. The daisy cube $Q_n(X)$ is the subgraph of the hypercube $Q_n$ induced by the union of the intervals $I(x,0^n)$ for $x\\in X$. As a subclass of partial cubes, it generalizes Fibonacci cubes and Lucas cubes. For a graph $G$ and a vertex $u\\in V(G)$, we consider the cube polynomial $C_G(x)$, the distance cube polynomial $D_{G,u}(x,y)$, and the polynomial $W_{G,u}(x)$, which count $k$-cubes, $k$-cubes at distance from $u$, and vertices at distance $k$ from $u$, respectively.   In this paper, we prove that for a partial cube $G$ with a vertex $u\\in V(G)$, $G$ is a daisy cube and $u=0^n$ if and only if one of the following equivalent conditions holds: (1) $C_{G}(x)=W_{G,u}(x+1)$; (2) $D_{G,u}(x,y)=W_{G,u}(x+y)$; (3) $D_{G,u}(x,y)=C_{G}(x+y-1)$. In particular, conditions (1) and (3) give affirmative answers to two open problems posed by Klavžar and Mollard [European J. Combin., 80 (2019) 214--223]. Further, we obtain that for arbitrary partial cube $G$, $D_{G,u}(x,y)\\leq W_{G,u}(x+y)$ and $C_{G}(x)\\leq W_{G,u}(x+1)$. Besides, another bound for $C_G(x)$ due to Xie et al. [J. Graph Theory, 106 (2024) 907--922] is given by the clique polynomial $Cl_{G^\\#}(x+1)$ of the crossing graph of $G$. We also compare these two bounds and show that the simplex graphs form the unique class of graphs for which the two bounds coincide.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29577v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29571v1", "url": "http://arxiv.org/abs/2603.29571v1", "pdf_url": "https://arxiv.org/pdf/2603.29571v1", "title": "Randomstrasse101: Open Problems of 2025", "authors": ["Afonso S. Bandeira", "Daniil Dmitriev", "Kevin Lucca", "Petar Nizić-Nikolac", "Almut Rödder"], "annotation": "Randomstrasse101 is a blog dedicated to Open Problems in Mathematics, with a focus on Probability Theory, Computation, Combinatorics, Statistics, and related topics. This manuscript serves as a stable record of the Open Problems posted in 2025, with the goal of easing academic referencing. The blog can currently be accessed at randomstrasse101.math.ethz.ch", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29571v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29548v1", "url": "http://arxiv.org/abs/2603.29548v1", "pdf_url": "https://arxiv.org/pdf/2603.29548v1", "title": "A SAT-based Filtering Framework for Exact Coverings of K33 by Cliques of Order 3, 4 or 5", "authors": ["Petr Kovař", "Yifan Zhang"], "annotation": "We investigate the minimum number of cliques of orders $3$, $4$, and $5$ needed to cover the edges of $K_{33}$ with zero excess. General covering results yield the lower bound 57. The main result of the paper is that no decomposition of $K_{33}$ into $57$ blocks from $\\{K_3,K_4,K_5\\}$ exists.   Our approach is algorithmic and relies on a layered exact-search pipeline rather than a single monolithic solver. We combine symmetry reduction, enumeration of local signatures, arithmetic profile restrictions, geometric tests for partial configurations, SAT realisation on reduced instances, and final decoding checks. The benchmark comparison shows that this structured approach is substantially more effective than direct ILP, DLX, or SAT formulations on the full problem.   As a consequence, we obtain $C^ξ(33,\\{3,4,5\\},2)\\ge 58$. A short additional counting argument further strengthens this to $C^ξ(33,\\{3,4,5\\},2)\\ge 59$. We also give new compressed proofs for the known exceptional cases $K_{18}$ and $K_{19}$ in the setting of $\\{K_3,K_4\\}$-decompositions, illustrating the same combination of theoretical reduction and exact computation.   Finally, we explain the relevance of the $K_{33}$ result to the open packing problem of determining the packing number $D(33,5,2)$. A packing of $51$ copies of $K_5$ in $K_{33}$ would leave a $4$-regular graph on $9$ vertices, and our exclusion already rules out two natural candidate leave structures.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29548v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29435v1", "url": "http://arxiv.org/abs/2603.29435v1", "pdf_url": "https://arxiv.org/pdf/2603.29435v1", "title": "On the combinatorics of the refined 1-leg DT/PT correspondence", "authors": ["Davide Accadia", "Danilo Lewański", "Sergej Monavari"], "annotation": "We provide a new proof of a result of Bessenrodt on the relation among the generating series of reversed plane partitions and skew plane partitions, motivated by the geometric DT/PT wallcrossing formula for local curves recently proved by the third author. This also recovers a result of Sagan.   We moreover establish various new closed formulas for the weighted enumeration of reversed and skew plane partitions, proving a result dual to a theorem by Gansner, we find a new identity on the generating series counting internal and external hooks of a given Young diagram, and we combine the latter with Bessenrodt's theorem. Finally, we interpret our results as identities in the Fock space via the bosonic/fermionic formalism.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29435v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29431v1", "url": "http://arxiv.org/abs/2603.29431v1", "pdf_url": "https://arxiv.org/pdf/2603.29431v1", "title": "Binomial determinants: some closed formulae", "authors": ["Laura González", "Francesc Planas-Vilanova"], "annotation": "This paper is intended to give closed formulae for binomial determinants with consecutive or almost consecutive rows or columns, as well as calculating the generator of left nullspaces defined by some binomial matrices. In the meantime, we reprove, by different means, the positivity of binomial determinants shown by Gessel and Viennot.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29431v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29335v1", "url": "http://arxiv.org/abs/2603.29335v1", "pdf_url": "https://arxiv.org/pdf/2603.29335v1", "title": "A short proof of a perturbation inequality for the spectral radius", "authors": ["Lele Liu", "Bo Ning"], "annotation": "Let $G$ be a simple graph, and denote by $λ(G)$ its spectral radius. Sun and Das (2020) established that for any non-isolated vertex $v$ with degree $d(v)$, \\[ λ(G)\\leq \\sqrt{λ(G-v)^2 + 2d(v) - 1}, \\] which is a conjecture original posed by Guo, Wang, and Li (2019). Sun and Das's proof uses several tools from spectral graph theory. In this short note, we provide a concise and self-contained proof of this inequality using matrix analysis.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29335v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29280v1", "url": "http://arxiv.org/abs/2603.29280v1", "pdf_url": "https://arxiv.org/pdf/2603.29280v1", "title": "Graph Eigenvalues and Projection Constants", "authors": ["Tanay Wakhare"], "annotation": "Let $λ_1(G)\\ge λ_2(G)\\ge \\cdots \\ge λ_n(G)$ denote the adjacency eigenvalues of a graph $G$ of order $n$. We prove that for every $k\\geq 2$ and every graph $G$ on $n\\geq k$ vertices, $$ λ_k(G)\\le \\frac{λ_{\\mathbb{R}}(k-1)}{2(k-1)}\\,n-1, $$ where $$ λ_{\\mathbb{R}}(r)=\\sup_{N\\ge r}\\frac1N \\max_{Q\\in \\mathcal P_r(N)}\\sum_{i,j=1}^N |q_{ij}| $$ and $\\mathcal P_r(N)$ denotes the set of rank-$r$ orthogonal projections in $\\mathbb{R}^{N\\times N}$. In Banach space theory, $λ_{\\mathbb{R}}(r)$ is well known as the maximal absolute projection constant, which has been shown to equal the quasimaximal absolute projection constant $μ_{\\mathbb{R}}(r)$.   This yields a new conceptual connection: universal upper bounds on $λ_k(G)$ are controlled by the real maximal absolute projection constant $λ_{\\mathbb{R}}(k-1)$. In dimensions where $λ_{\\mathbb{R}}(k-1)$ is known explicitly, this gives explicit coefficients. In particular, for $k=3$ this recovers Tang's recent sharp bound $λ_3(G)\\le n/3-1$. For $k=4$, using $λ_{\\mathbb{R}}(3)=\\frac{1+\\sqrt5}{2}$ together with Linz's closed blowups of the icosahedral graph, we obtain the result $$ λ_4(G) \\leq \\frac{1+\\sqrt5}{12}n-1. $$ The method allows us to transfer known upper bounds on $λ_{\\mathbb{R}}(k-1)$ to match the best known upper bounds on $λ_k(G)$ for other values of $k$, such as $k=5$.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29280v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29277v1", "url": "http://arxiv.org/abs/2603.29277v1", "pdf_url": "https://arxiv.org/pdf/2603.29277v1", "title": "Multicolor $K_r$-Tilings with High Discrepancy", "authors": ["Henry Chan", "Daniel Cheng", "Lior Gishboliner", "Xiangyu Li"], "annotation": "We study the minimum degree threshold $δ_{r,q}$ guaranteeing the existence of $K_r$-tilings of high discrepancy in any $q$-edge-coloring. Balogh, Csaba, Pluhár and Treglown handled the 2-color case, proving that $δ_{r,2} = \\frac{r}{r+1}$ for all $r \\geq 3$. Here we determine $δ_{r,q}$ for all $q$ large enough, namely $q \\geq \\binom{r}{2}$. For example, we show that for $r \\geq 4$, $δ_{r,q} = \\frac{r}{r+1}$ for $\\binom{r}{2} \\leq q \\leq \\binom{r+1}{2}$ and $δ_{r,q} = \\frac{r-1}{r}$ for $q \\geq \\binom{r+1}{2}+2$. Thus, $δ_{r,q}$ has a phase transition at $q = \\binom{r+1}{2}$, where it drops from $\\frac{r}{r+1}$ and then stabilizes at the existence threshold $\\frac{r-1}{r}$. We also show that $δ_{r,q} \\leq \\frac{r}{r+1}$ for all $r,q$, supplementing and giving a new proof for the result of Balogh, Csaba, Pluhár and Treglown.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29277v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29260v1", "url": "http://arxiv.org/abs/2603.29260v1", "pdf_url": "https://arxiv.org/pdf/2603.29260v1", "title": "Unexpected toric Richardson varieties", "authors": ["Eugene Gorsky", "Soyeon Kim", "Melissa Sherman-Bennett"], "annotation": "We prove that an open Richardson variety in the complete flag variety for $\\mathrm{GL}_n$ is isomorphic to a torus if and only if the corresponding closed Richardson variety is toric. Such toric varieties can be classified in terms of the combinatorics of Bruhat intervals, and include many varieties of dimension larger than $n-1$. We give a combinatorial description of the corresponding polytopes, and compute several explicit examples.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29260v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29145v1", "url": "http://arxiv.org/abs/2603.29145v1", "pdf_url": "https://arxiv.org/pdf/2603.29145v1", "title": "Bourgain's projection theorem over normed division algebras", "authors": ["William O'Regan"], "annotation": "We give a simple and self-contained proof of an extension of a projection theorem of Bourgain over the reals to division algebras over local fields of zero characteristic.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29145v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29127v1", "url": "http://arxiv.org/abs/2603.29127v1", "pdf_url": "https://arxiv.org/pdf/2603.29127v1", "title": "New Lower Bounds for C4-Free Subgraphs of the Hypercubes Q7 and Q8", "authors": ["Minamo Minamoto"], "annotation": "We establish new lower bounds ex$(Q_7,C_4) \\ge 304$ and ex$(Q_8,C_4) \\ge 680$ for the maximum number of edges in a $C_4$-free subgraph of the 7- and 8-dimensional hypercubes. Both bounds are witnessed by explicit constructions whose $C_4$-freeness is certified by exhaustive enumeration of all four-cycles (672 for $Q_7$, 1792 for $Q_8$). The $Q_7$ bound improves the previously known lower bound of approximately 300.2 by 3.8 edges; the $Q_8$ bound improves the previously known lower bound of approximately 674.9 by 5.1 edges. For $Q_7$ we additionally found 19866 $C_4$-free subgraphs with pairwise distinct edge sets (not quotiented by automorphism), all achieving 304 edges and all locally maximal. The consistent failure of 305-edge searches (minimum $C_4$ violation never reaching 0 over 1076 independent trials) supports the conjecture ex$(Q_7,C_4)=304$. Edge lists and verification code are publicly available at https://github.com/minamominamoto/c4free-hypercube", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29127v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29073v1", "url": "http://arxiv.org/abs/2603.29073v1", "pdf_url": "https://arxiv.org/pdf/2603.29073v1", "title": "Beyond the Laurent phenomenon", "authors": ["Andrei Zabolotskii"], "annotation": "In a cluster algebra, a subset of initial cluster variables can be specialised in such a way that all elements of the resulting algebra become polynomial in the remaining variables.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29073v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29030v1", "url": "http://arxiv.org/abs/2603.29030v1", "pdf_url": "https://arxiv.org/pdf/2603.29030v1", "title": "Graphs of group actions and group actions on tree", "authors": ["Florian Lehner", "Christian Lindorfer", "Rögnvaldur G. Möller", "Wolfgang Woess"], "annotation": "Bass-Serre theory provides a powerful framework for studying group actions on trees. While extremely effective for structural questions in group theory, it is less suited to the systematic construction of group actions with prescribed local behaviour. Motivated by local-to-global constructions such as the Burger-Mozes universal groups and local action diagrams, we develop an analogue of Bass-Serre theory for group actions.   The central object of study in our are graphs of group actions, combinatorial structures similar to graphs of groups from Bass-Serre theory, encoding compatible local permutation actions on a base graph. From these we can construct groups which act on tree-like graphs called scaffoldings and hence also on trees.   We prove uniqueness and universality results for the resulting groups and show that our framework unifies and generalises (among other known constructions) both graphs of groups and local action diagrams. Remarkably, we are able to encapsulate the full generality of the former while still allowing for efficient construction of groups with certain local properties like in the latter.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29030v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28940v1", "url": "http://arxiv.org/abs/2603.28940v1", "pdf_url": "https://arxiv.org/pdf/2603.28940v1", "title": "Hypergeometric Bernoulli Polynomials Defined on Simplicial $d$-Polytopic Numbers", "authors": ["Ronald Orozco"], "annotation": "We introduce an ${\\rm S}_d$-analogue of the hypergeometric Bernoulli polynomials and study their properties. To achieve this goal, we introduce a calculus defined on the simplicial $d$-polytopic numbers. Two definitions of the ${\\rm S}_d$-derivatives are given. These two definitions allow us to derive an identity relating Kummer confluent hypergeometric function and Touchard polynomials. This calculus is closely related to the $d$-Hoggatt binomial coefficients. ${\\rm S}_d$-analogs of the exponential function and the hypergeometric functions are given.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28940v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28936v1", "url": "http://arxiv.org/abs/2603.28936v1", "pdf_url": "https://arxiv.org/pdf/2603.28936v1", "title": "Composition of random functions and word reconstruction", "authors": ["Guillaume Chapuy", "Guillem Perarnau"], "annotation": "Given two functions $\\mathbf{a}\\!:\\! [n] \\rightarrow [n]$ and $\\mathbf{b}\\!:\\! [n] \\rightarrow [n]$ chosen uniformly at random, any word $w=w_1w_2\\dots w_k\\in \\{a,b\\}^k$ induces a random function $\\mathbf{w}\\!:\\! [n] \\rightarrow [n]$ by composition, i.e. $\\mathbf{w}=φ_{w_k}\\circ \\dots \\circ φ_{w_1}$ with $φ_a=\\mathbf{a}$ and $φ_b=\\mathbf{b}$. We study the following question: assuming $w$ is fixed but unknown, and $n$ goes to infinity, does one sample of $\\mathbf{w}$ carry enough information to (partially) recover the word $w$ with good enough probability?   We show that the length of $w$, and its exponent (largest $d$ such that $w={u}^d$ for some word ${u}$) can be recovered with high probability. We also prove that the random functions stemming from two different words are separated in total variation distance, provided that certain ``auto-correlation'' word-depending constant $c(w)$ is different for each of them. We give an explicit expression for $c(w)$ and conjecture that non-isomorphic words have different constants. We prove that this is the case assuming a major conjecture in transcendental number theory, Schanuel's conjecture.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28936v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28905v1", "url": "http://arxiv.org/abs/2603.28905v1", "pdf_url": "https://arxiv.org/pdf/2603.28905v1", "title": "The Priority Lattice", "authors": ["Adrián Lillo", "Mercedes Rosas"], "annotation": "We introduce the priority lattice, a structure arising from the priority search algorithm on rooted trees and forests. We prove bijectively that its maximal chains are labeled by parking functions, and that the maximal chains of its principal ideals are labeled by partial parking functions. We establish that it is a graded lattice and compute its Möbius function and characteristic polynomials.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28905v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28748v1", "url": "http://arxiv.org/abs/2603.28748v1", "pdf_url": "https://arxiv.org/pdf/2603.28748v1", "title": "Odd Hadwiger number and graph products", "authors": ["Henry Echeverría", "Andrea Jiménez", "Suchismita Mishra", "Daniel A. Quiroz", "Mauricio Yépez"], "annotation": "The Odd Hadwiger number of a graph $G$ is the largest integer $r$ such that $G$ has a clique of size $r$ as an odd minor. In this paper, we investigate how large is the Odd Hadwiger number of the product of two graphs, when considering any of the four standard graph products: Cartesian, direct, lexicographic, strong. We provide an optimal lower bound in the cases of the strong and lexicographic products.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28748v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28738v1", "url": "http://arxiv.org/abs/2603.28738v1", "pdf_url": "https://arxiv.org/pdf/2603.28738v1", "title": "Upper bound on the $k$-th eigenvalue of a graph", "authors": ["Varun Sivashankar"], "annotation": "We prove a general upper bound on the $k$-th adjacency eigenvalue of a graph. For $k\\ge 2$, we show that \\[ λ_k(G)\\le \\frac{(k-2)\\sqrt{k+1}+2}{2k(k-1)}\\,n-1 \\] for every graph $G$ on $n$ vertices. We build on a recent approach that addresses the case $k=3$ and generalize the upper bound for all $k \\geq 3$ by using the positivity of Gegenbauer polynomials. The upper bound is tight for $k \\in \\{2,3,4,8,24\\}$. We also highlight the close relation of $λ_k(G)$ to questions about equiangular lines.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28738v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28636v1", "url": "http://arxiv.org/abs/2603.28636v1", "pdf_url": "https://arxiv.org/pdf/2603.28636v1", "title": "Optimal bounds for an Erdős problem on matching integers to distinct multiples", "authors": ["Wouter van Doorn", "Yanyang Li", "Quanyu Tang"], "annotation": "Let $f(m)$ be the largest integer such that for every set $A = \\{a_1 < \\cdots < a_m\\}$ of $m$ positive integers and every open interval $I$ of length $2a_m$, there exist at least $f(m)$ disjoint pairs $(a, b)$ with $a \\in A$ dividing $b \\in I$. Solving a problem of Erdős, we determine $f(m)$ exactly, and show $$ f(m)=\\min\\bigl(m,\\lceil 2\\sqrt{m}\\,\\rceil\\bigr) $$ for all $m$. The proof was obtained through an AI-assisted workflow: the proof strategy was first proposed by ChatGPT, and the detailed argument was subsequently made fully rigorous and formally verified in Lean by Aristotle. The exposition and final proofs presented here are entirely human-written. [This paper solves Problem #650 on Bloom's website \"Erdős problems\".]", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28636v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28614v1", "url": "http://arxiv.org/abs/2603.28614v1", "pdf_url": "https://arxiv.org/pdf/2603.28614v1", "title": "A Gray code for arborescences of tournaments", "authors": ["Marthe Bonamy", "Michael Hoffmann", "Clément Legrand-Duchesne", "Günter Rote"], "annotation": "We consider the following question of Knuth: given a directed graph $G$ and a root $r$, can the arborescences of $G$ rooted in $r$ be listed such that any two consecutive arborescences differ by only one arc? Such an ordering is called a pivot Gray code and can be formulated as a Hamiltonian path in the reconfiguration graph of the arborescences of $G$ under arc flips, also called flip graph of $G$. We give a positive answer for tournaments and explore several conditions showing that the flip graph of a directed graph may contain no Hamiltonian cycles.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28614v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28571v1", "url": "http://arxiv.org/abs/2603.28571v1", "pdf_url": "https://arxiv.org/pdf/2603.28571v1", "title": "Enumeration of general planar hypermaps with an alternating boundary", "authors": ["Valentin Baillard", "Ariane Carrance", "Bertrand Eynard"], "annotation": "In this paper, we extend the enumerative study of planar hypermaps with an alternating boundary introduced in an earlier work of Bouttier and the second author. In that article, an explicit rational parametrization was obtained for the associated generating function in the case of m-constellations, using a variant of the kernel method. We develop here a new strategy to obtain an algebraic equation in the general case, which includes maps decorated by the Ising model, through a classical many-to-one correspondence. One of the main steps of our strategy is the simultaneous elimination of two catalytic variables. We then apply this strategy to the case of Ising quadrangulations, where we obtain an explicit rational parametrization. As a consequence, we show that some notable properties of the constellations case are no longer satisfied in general.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28571v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28510v1", "url": "http://arxiv.org/abs/2603.28510v1", "pdf_url": "https://arxiv.org/pdf/2603.28510v1", "title": "Zeros in the character table of the symmetric group", "authors": ["Sarah Peluse", "Kannan Soundararajan"], "annotation": "Computations of Miller and Scheinerman suggest that the vast majority of the zeros appearing in the character table of the symmetric group are of a certain special type. While we cannot prove this, we resolve a conjecture arising in their paper concerning these zeros, and address a related question of Stanley.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28510v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28485v1", "url": "http://arxiv.org/abs/2603.28485v1", "pdf_url": "https://arxiv.org/pdf/2603.28485v1", "title": "On Generalizations of Maiorana-McFarland and $\\mathcal{PS}_{ap}$ Functions", "authors": ["Sezel Alkan", "Nurdagül Anbar", "Athina Avrantini", "Erroxe Etxabarri-Alberdi", "Tekgül Kalaycı", "Beatrice Toesca"], "annotation": "We study generalizations of two classical primary constructions of Boolean bent functions, namely the Maiorana-McFarland ($MM$) class and the (Desarguesian) partial spread ($\\mathcal{PS}_{ap}$) class. The construction of bent functions lying outside the completed $MM$ class has attracted considerable attention in recent years. In this direction, we construct families of generalized Maiorana--McFarland bent functions that are not equivalent to any function in the classical $MM$ or $\\mathcal{PS}_{ap}$ classes, and hence lie outside their completed classes. As a second contribution, we investigate the decomposition of generalized $\\mathcal{PS}_{ap}$ functions. We prove that when the degree is sufficiently small relative to the size of the underlying finite field, such functions do not, in general, admit a decomposition into bent or semibent functions. Consequently, they cannot be obtained from known secondary constructions based on concatenation. Finally, we present a secondary construction of Boolean bent functions arising from the concatenation of components of vectorial generalized $\\mathcal{PS}_{ap}$ functions. Our constructions and proofs rely on classical results concerning second-order derivatives of bent functions and their duals. In addition, we employ methods from the theory of algebraic curves and their function fields.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28485v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28437v1", "url": "http://arxiv.org/abs/2603.28437v1", "pdf_url": "https://arxiv.org/pdf/2603.28437v1", "title": "The free tracial post-Lie-Rinehart algebra of planar aromatic trees for the design of divergence-free Lie-group methods", "authors": ["Adrien Busnot Laurent", "Hans Munthe-Kaas", "Venkatesh G. S"], "annotation": "Aromatic Butcher series were successfully introduced for the study and design of numerical integrators that preserve volume while solving differential equations in Euclidean spaces. They are naturally associated to pre-Lie-Rinehart algebras and pre-Hopf algebroids structures, and aromatic trees were shown to form the free tracial pre-Lie-Rinehart algebra. In this paper, we present the generalisation of aromatic trees for the study of divergence-free integrators on manifolds. We introduce planar aromatic trees, show that they span the free tracial post-Lie-Rinehart algebra, and apply them for deriving new Lie-group methods that preserve geometric divergence-free features up to a high order of accuracy.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28437v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28266v1", "url": "http://arxiv.org/abs/2603.28266v1", "pdf_url": "https://arxiv.org/pdf/2603.28266v1", "title": "Nonvanishing $k$-flats of Boolean and vectorial functions", "authors": ["Christian Kaspers"], "annotation": "$k$th-order sum-free functions are a natural generalization of APN functions using the concept of (non)vanishing flats. In this paper, we introduce a new combinatorial technique to study the nonvanishing flats of Boolean functions. This approach allows us to determine the number of nonvanishing flats for an infinite family of Boolean functions. We moreover use it to show that any $k$th-order sum-free $(n,n)$-function of algebraic degree $k$ gives rise to an $(n-k)$th-order sum-free $(n,n)$-function of algebraic degree $n-k$. This implies the existence of millions of $(n-2)$th-order sum-free functions.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28266v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28247v1", "url": "http://arxiv.org/abs/2603.28247v1", "pdf_url": "https://arxiv.org/pdf/2603.28247v1", "title": "Private neighbors, perfect codes and their relation with the $\\vt$-number of closed neighborhood ideals", "authors": ["Delio Jaramillo-Velez", "Hiram H. López", "Rodrigo San-José"], "annotation": "In this work, we investigate the connections between dominating sets, private neighbors, and perfect codes in graphs, and their relationships with commutative algebra. In particular, we estimate the $\\vt$-number of closed neighborhood ideals in terms of minimal dominating sets and private neighbors. We show how the $\\vt$-number is related to other graph invariants, such as the cover number, domination number, and matching number. Moreover, we explore the relation with the Castelnuovo-Mumford regularity, proving that the $\\vt$-number is a lower bound for the regularity of bipartite and well-covered graphs. Finally, drawing from the relation between efficient dominating set and perfect codes, we use the redundancy of Hamming codes to present lower and upper bounds for the $\\vt$-number of some special family of graphs.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28247v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28242v1", "url": "http://arxiv.org/abs/2603.28242v1", "pdf_url": "https://arxiv.org/pdf/2603.28242v1", "title": "Cyclic sieving phenomena on parabolic classes of faces of the cluster complex", "authors": ["Lucas Pouillart"], "annotation": "The cyclic sieving phenomenon was introduced by Reiner, Stanton and White in 2004 as a generalization of Stembridge's $q=-1$ phenomenon. In a paper from 2008, Eu and Fu studied many occurrences of this phenomenon on the faces of the generalized cluster complex with the action of the Fomin-Reading rotation in the classical types $A_n$, $B_n$, $D_n$ and $I_2(k)$. There was yet no known uniform $q$-analogue of the $k$-face numbers of these complexes. In a more recent paper from 2023, Douvropoulos and Josuat-Vergès provided a refinement of the enumeration of the faces of the generalized cluster complex using a uniform formula. For a parabolic subgroup $W_X \\subset W$ of the associated Coxeter group $W$, their formula factorises nicely under the assumption that $N_W(W_X)/W_X$ acts as a reflection group on $X$, which is very often the case. Using this condition, we provide a uniform refinement of these cyclic sieving phenomena using a $q$-analogue of their main formula with a type by type proof based on the classification of finite irreducible Coxeter groups.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28242v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28216v1", "url": "http://arxiv.org/abs/2603.28216v1", "pdf_url": "https://arxiv.org/pdf/2603.28216v1", "title": "Off-diagonal Rado number for $x+y+c=z$ and $x+y+k=z$", "authors": ["Rajat Adak", "Yash Bakshi", "L. Sunil Chandran", "Saraswati Girish Nanoti"], "annotation": "The study of Ramsey-type problems for linear equations originated with Schur's theorem and was later placed in a systematic framework by Richard Rado. In the off-diagonal setting, one fixes a pair of distinct linear equations $(\\mathcal{E}_1, \\mathcal{E}_2)$ and asks for the least integer $N$ such that every red--blue coloring of $\\{1, 2, \\dots, N\\}$ must yield either a red solution to $\\mathcal{E}_1$ or a blue solution to $\\mathcal{E}_2$. This threshold integer is referred to as the off-diagonal Rado number of the system $(\\mathcal{E}_1, \\mathcal{E}_2)$. In this work, we study the discrete and continuous off-diagonal Rado number for non-homogeneous linear system of equations $x+y+c=z$ and $x+y+k=z$ where $c\\le k$. We determine the exact two-color discrete and continuous off-diagonal Rado number $R_2(c,k)$ associated with this system of equations.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28216v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28212v1", "url": "http://arxiv.org/abs/2603.28212v1", "pdf_url": "https://arxiv.org/pdf/2603.28212v1", "title": "Limit Laws for the Distance to Fréchet Means of Random Graphs", "authors": ["Qunqiang Feng", "Zixin Tang", "Zhishui Hu"], "annotation": "This paper investigates the Fréchet mean of the Erdős-Rényi random graph $G_{n,p}$ with respect to the Frobenius distance on graph Laplacians, a metric that captures global structural information beyond local edge flips. We first characterize the Fréchet mean set as consisting of quasi-regular graphs (i.e., graphs where all vertex degrees differ by at most one). We then analyze the asymptotic behavior of the Frobenius distance $F_n=d_{\\mathrm{F}}(G_{n,p},R)$ as $n\\to\\infty$, where $R$ is any Fréchet mean. Closed-form expressions for the mean and variance of $F_n^2$ are derived, which are invariant to the choice of $R$. Leveraging these results, we establish several weak convergence laws for the Frobenius distance over all regimes of $p \\in (0,1)$ as $n \\to \\infty$. Finally, under the scaling condition $n^2 p(1-p) \\to \\infty$ we prove the asymptotic normality of this distance, which exhibits a phase transition governed by the growth rate of $np(1-p)$. Our results reveal how metric selection fundamentally shapes Fréchet mean geometry in random graphs.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28212v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28202v1", "url": "http://arxiv.org/abs/2603.28202v1", "pdf_url": "https://arxiv.org/pdf/2603.28202v1", "title": "Towards Pósa's Conjecture for $3$-graphs", "authors": ["Debmalya Bandyopadhyay", "Allan Lo", "Richard Mycroft"], "annotation": "We prove that every $3$-graph $H$ on $n$ vertices with minimum codegree $δ_2(H) \\geq 7n/9 + o(n)$ contains the square of a tight Hamilton cycle. This strengthens a theorem of Bedenknecht and Reiher that $δ_2(H) \\geq 4n/5 + o(n)$ is sufficient. The central novelty of our arguments is an improved understanding of the connectivity structure of $3$-graphs with large minimum codegree.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28202v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28171v1", "url": "http://arxiv.org/abs/2603.28171v1", "pdf_url": "https://arxiv.org/pdf/2603.28171v1", "title": "Simplicial shells and thickness in the partition graph", "authors": ["Fedor B. Lyudogovskiy"], "annotation": "For each positive integer $n$, let $G_n$ be the graph whose vertices are the partitions of $n$, with edges given by elementary transfers of one unit between parts, followed by reordering. We study the local simplex dimension in the clique complex $K_n=\\Cl(G_n)$ as a geometric thickness invariant of $G_n$. For a partition $λ\\vdash n$, let $τ_n(λ):=\\dim_{\\mathrm{loc}}(λ)$ be its simplicial thickness. This gives threshold thick zones $T_{\\ge r}(n)=\\{λ: τ_n(λ)\\ge r\\}$ and, relative to the boundary framework of $G_n$, a shell/core decomposition into outer shells $Sh_r(n)$ and inner cores $Core_r(n)$. Using local-morphology results established earlier in the series, we work with simplicial thickness as a local invariant. We prove that it is preserved by conjugation, that the induced thick zones, shells, and cores are conjugation-invariant, and that the antennas remain strictly one-dimensional in the simplicial sense and are excluded from all nontrivial thick zones. The first shell order at which a nontrivial shell can occur is therefore $2$, and the corresponding shell $Sh_2(n)$ is the triangular skin, while higher simplicial regimes form nested higher-order shells inside the triangular regime. We also develop a complete finite computational atlas for $1\\le n\\le 30$, giving first-occurrence tables for the regimes $T_{\\ge r}(n)$ and supporting a finite-range rear-central thickening pattern.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28171v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28154v1", "url": "http://arxiv.org/abs/2603.28154v1", "pdf_url": "https://arxiv.org/pdf/2603.28154v1", "title": "Some new results on Andrews' and Warnaar's q-identities", "authors": ["Qi Chen"], "annotation": "In this paper, by the technique of inverse relations and comparing coefficients, we establish some generalized forms of Andrews' q-series identity and two new Bailey pairs and q-identities closely related to Andrews-Warnaar's sum identity for partial theta functions.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28154v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28009v1", "url": "http://arxiv.org/abs/2603.28009v1", "pdf_url": "https://arxiv.org/pdf/2603.28009v1", "title": "A note on irreducible representations of symmetric groups and Sergeev superalgebras", "authors": ["Minjia Chen", "Jinkui Wan", "Hongbo Zhao"], "annotation": "We provide an explicit construction and a closed dimension formula in terms of hook lengths for the irreducible representations for the symmetric groups $\\mathfrak{S}_p$ and the Sergeev superalgebras $\\mathcal{Y}_p$ over an algebraically closed field $\\mathbb{F}$ of characteristic $p>0$.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28009v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27975v1", "url": "http://arxiv.org/abs/2603.27975v1", "pdf_url": "https://arxiv.org/pdf/2603.27975v1", "title": "Cycles of length $2$ modulo odd $k$ and admissible cycles in graphs", "authors": ["Jifu Lin"], "annotation": "In 1976, Burr and Erdős conjectured that if $k\\mathbb{Z} + \\ell$ contains an even integer, there exists a constant $c$ such that every graph with at least $cn$ edges contains a cycle of length $\\ell \\pmod k$. This conjecture was settled for odd $k$ by Bollobás, while Thomassen completed the proof for all $k$ by resolving the case for even residues. Let $c_{\\ell,k}$ denote the smallest constant such that every $n$-vertex graph with at least $c_{\\ell,k}n$ edges contains a cycle of length $\\ell \\pmod{k}$. $k$ cycles are said to be admissible if they form an arithmetic progression of length $k$ with common difference one or two. The exact value of $c_{\\ell,k}$ remains unknown for most $(\\ell,k)$. Recently, Gao, Huo, Liu, and Ma showed that every graph with minimum degree at least $k+1$ contains $k$ admissible cycles. In this paper, we provide a sharp size version of their result. As a corollary, we show that $c_{2,k}=k$ for all odd $k$. In 2016, Verstraëte conjectured that every $n$-vertex graph $G$ containing no $k$ cycles of consecutive even lengths has at most $(2k+1)(n-1)/2$ edges, with equality only if every block of $G$ is a clique of order $2k+1$. We prove this conjecture for $2k+2\\leq n\\leq 4k+1$, and in fact obtain a stronger result in this range.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27975v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27973v1", "url": "http://arxiv.org/abs/2603.27973v1", "pdf_url": "https://arxiv.org/pdf/2603.27973v1", "title": "A characterization of graphs with no $K_{3,4}$ minor", "authors": ["On-Hei Solomon Lo"], "annotation": "A complete structural characterization of graphs with no $K_{3,4}$ minor is obtained, and the following consequences are established. Every $4$-connected non-planar graph with at least seven vertices and minimum degree at least five contains both $K_{3,4}$ and $K_6^-$ as minors, thereby proving a conjecture of Kawarabayashi and Maharry in a strengthened form. Moreover, every $4$-connected graph with no $K_{3,4}$ minor is hamiltonian-connected, extending a theorem of Thomassen, and admits an embedding on the torus.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27973v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27968v1", "url": "http://arxiv.org/abs/2603.27968v1", "pdf_url": "https://arxiv.org/pdf/2603.27968v1", "title": "Note on the thickness of the Cartesian product of a complete graph and a path", "authors": ["Kenta Noguchi"], "annotation": "We determine the thickness of the Cartesian product $K_{6p+4} \\square P_2$ for $p \\ge 0$ and of the Cartesian product $K_8 \\square P_m$ for $m \\ge 1$, where $K_n$ and $P_m$ denote the complete graph on $n$ vertices and the path on $m$ vertices, respectively.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27968v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27890v1", "url": "http://arxiv.org/abs/2603.27890v1", "pdf_url": "https://arxiv.org/pdf/2603.27890v1", "title": "Determining the normal subgroups of the automorphism groups of some ultrahomogeneous structures via stabilisers", "authors": ["Thomas Bernert", "Rob Sullivan", "Jeroen Winkel", "Shujie Yang"], "annotation": "We show the simplicity of the automorphism groups of the generic $n$-hypertournament and the semigeneric tournament, and determine the normal subgroups of the automorphism groups of several other ultrahomogeneous oriented graphs. We also give a new proof of the simplicity of the automorphism group of the dense $\\frac{2π}{n}$-local order $\\mathbb{S}(n)$ for $n \\geq 2$ (a result due to Droste, Giraudet and Macpherson). Previous techniques of Li, Macpherson, Tent and Ziegler involving stationary weak independence relations (SWIRs) cannot be applied directly to these structures; our approach involves applying these techniques to a certain expansion of each structure, where the expansion has a SWIR and its automorphism group is isomorphic to a stabiliser subgroup of the automorphism group of the original structure.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27890v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27888v1", "url": "http://arxiv.org/abs/2603.27888v1", "pdf_url": "https://arxiv.org/pdf/2603.27888v1", "title": "Log-concavity from enumerative geometry of planar curve singularities", "authors": ["Tao Su", "Baiting Xie", "Chenglong Yu"], "annotation": "We propose a log-concavity conjecture for BPS invariants arising in the enumerative geometry of planar curve singularities, identified with the local Euler obstructions of Severi strata in their versal deformations. We further extend this conjecture to ruling polynomials of Legendrian links and to E-polynomials of character varieties. We establish these conjectures for irreducible weighted-homogeneous singularities (torus knots) and for ADE singularities, and prove a multiplicative property for ruling polynomials compatible with log-concavity.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27888v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27835v1", "url": "http://arxiv.org/abs/2603.27835v1", "pdf_url": "https://arxiv.org/pdf/2603.27835v1", "title": "Geometry of ample/lopsided sets", "authors": ["Hans--Jürgen Bandelt", "Victor Chepoi", "Andreas Dress", "Jack Koolen"], "annotation": "Lopsided sets were introduced by Jim Lawrence in 1983 when he studied the subsets of $\\{-1,+1\\}^E$ that encode the intersection pattern of a convex set $K$ with the orthants of ${\\mathbb R}^E$. Lopsided sets have been independently rediscovered by several other authors, in particular by Andreas Dress in 1995, who called them \\emph{ample} sets. Dress defined ample sets as the set families satisfying equality in a combinatorial inequality, which holds for all set families.   In a previous article we characterized ample sets in various combinatorial and graph-theoretical ways. In this paper we study geometric realizations of ample sets as cubihedra (cube complexes), which yields several new characterizations. One such characterization establishes that the cubihedra of ample sets endowed with the intrinsic $\\ell_1$-metric are exactly the isometric subspaces of $\\ell_1$-spaces (which we call, weakly convex sets). We also view the barycenter maps of faces of cubihedra of ample sets as collections of $\\{ \\pm 1, 0\\}$-sign vectors and, in analogy with the characterization of oriented matroids by the covectors and the cocircuits. Moreover, we characterize the collections of $\\{ \\pm 1, 0\\}$-sign vectors corresponding to barycenter maps of all faces and all maximal faces of an ample set. Furthermore, we show that any ample set $\\covectors\\subseteq \\{ -1,+1\\}^E$ is realizable as the intersection pattern of a weakly convex set $K$ with the orthants of ${\\mathbb R}^E$. All this testifies that the concept of ample sets is quite natural in the context of cube complexes.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27835v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27827v1", "url": "http://arxiv.org/abs/2603.27827v1", "pdf_url": "https://arxiv.org/pdf/2603.27827v1", "title": "Unboundedness of the Heesch Number for Hyperbolic Convex Monotiles", "authors": ["Arun Maiti"], "annotation": "We provide a resolution of the Heesch problem for homogeneous (also known as semi-regular) tilings, and as a corollary, for tilings by convex monotiles in the hyperbolic plane. We also provide the first known example of weakly aperiodic convex monotiles arising from the dual of homogeneous tilings.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27827v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27807v1", "url": "http://arxiv.org/abs/2603.27807v1", "pdf_url": "https://arxiv.org/pdf/2603.27807v1", "title": "Buffon Discrepancy and the Steinhaus Longimeter", "authors": ["Stefan Steinerberger"], "annotation": "Let $Ω\\subset \\mathbb{R}^2$ be a convex set. We study the problem of distributing a one-dimensional set $S$ with total length $L$ so that for any line $\\ell$ in $\\mathbb{R}^2$ the number of intersections $\\#(\\ell \\cap S)$ is proportional to the length $\\mathcal{H}^1(\\ell \\cap Ω)$ as much as possible; we use the term Buffon discrepancy for the largest error. A construction of Steinhaus can be generalized to prove the existence of sets with Buffon discrepancy $\\lesssim L^{1/3}$. We also show that the unit disk $\\mathbb{D}$ admits a set with uniformly bounded Buffon discrepancy as $L \\rightarrow \\infty$.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27807v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27783v1", "url": "http://arxiv.org/abs/2603.27783v1", "pdf_url": "https://arxiv.org/pdf/2603.27783v1", "title": "Graphs with core(G) = nucleus(G)", "authors": ["Vadim E. Levit", "Eugen Mandrescu", "Kevin Pereyra"], "annotation": "Let $G$ be a finite simple graph. An independent set $I$ of $G$ is critical if $\\left|I\\right|-\\left|N(I)\\right|\\ge\\left|J\\right|-\\left|N(J)\\right|$ for every independent set $J$ of $G$. A critical independent set is maximum if it has maximum cardinality. The $core$ and the $nucleus$ of $G$ are defined as the intersection of all maximum independent sets and the intersection of all maximum critical independent sets, respectively. In 2019, Jarden, Levit, and Mandrescu posed the problem of characterizing the graphs satisfying $core(G)=nucleus(G)$. In this paper, we provide a complete solution to this problem. Using Larson's independence decomposition, which partitions any graph into a König--Egerváry component $L_G$ an a $2$-bicritical component $L_G^c$, we establish that $core(G)=nucleus(G)$ holds if and only if $core ({L_G^c})=\\emptyset$ and no vertex of $corona(G)$ lies in the boundary between $L_G$ and $L_G^c$. We also show that the same boundary condition is equivalent to the identity $diadem(G)=corona(G) \\cap L(G)$. Several consequences and related structural properties are also derived.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27783v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28828v1", "url": "http://arxiv.org/abs/2603.28828v1", "pdf_url": "https://arxiv.org/pdf/2603.28828v1", "title": "Truncated Plethystic Exponentials Preserve Power Sum Constraints", "authors": ["Yogesh Phalak"], "annotation": "Given an arbitrary sequence $(α_1, \\ldots, α_n) \\in \\mathbb{C}^n$, we show that the degree-$n$ truncation of the formal exponential $\\exp\\bigl(-\\sum_{k=1}^{\\infty} \\frac{α_k}{k} x^k\\bigr)$ produces a polynomial whose roots $ρ_1, \\ldots, ρ_n$ satisfy $\\sum_{i=1}^n ρ_i^{-k} = α_k$ exactly for $k = 1, \\ldots, n$. This truncation-exactness property is an algebraic identity in the ring of formal power series, proved by coefficient matching. It defines a natural embedding of sequences into multisets of complex numbers and yields an $O(n^2)$ algorithm for computing the polynomial from the prescribed power sums. We apply the result to the polylogarithm family $α_k = k^{1-s}$, where the associated exponential $\\exp(-\\mathrm{Li}_s(x))$ produces factorial-integer coefficient sequences for $s \\leq 0$ and encodes values of the Riemann zeta function through $\\lim_{n\\to\\infty} P_n^{(s)}(1) = \\exp(-ζ(s))$ for $\\mathrm{Re}(s) > 1$.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28828v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27748v1", "url": "http://arxiv.org/abs/2603.27748v1", "pdf_url": "https://arxiv.org/pdf/2603.27748v1", "title": "An infinite family of non-extendable MRD codes", "authors": ["Daniele Bartoli", "Alessandro Giannoni", "Giuseppe Marino", "Alessandro Neri"], "annotation": "In the realm of rank-metric codes, Maximum Rank Distance (MRD) codes are optimal algebraic structures attaining the Singleton-like bound. A major open problem in this field is determining whether an MRD code can be extended to a longer one while preserving its optimality. This work investigates $\\mathbb{F}_{q^m}$-linear MRD codes that are non-extendable but do not attain the maximum possible length. Geometrically, these correspond to scattered subspaces with respect to hyperplanes that are maximal with respect to inclusion but not of maximum dimension. By exploiting this geometric connection, we introduce the first infinite family of non-extendable $[4,2,3]_{q^5/q}$ MRD codes. Furthermore, we prove that these codes are self-dual up to equivalence.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27748v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27696v1", "url": "http://arxiv.org/abs/2603.27696v1", "pdf_url": "https://arxiv.org/pdf/2603.27696v1", "title": "On four network monitoring parameters in graphs and their gaps", "authors": ["Zin Mar Myint", "Avikal Srivastava"], "annotation": "Let \\( G \\) be a finite simple undirected graph. Four graph parameters related to network monitoring are the \\emph{geodetic set}, \\emph{edge geodetic set}, \\emph{strong edge geodetic set}, and \\emph{monitoring edge geodetic set}, with corresponding minimum sizes, denoted by \\( g(G), eg(G), seg(G) \\), and \\( meg(G) \\), respectively. These parameters quantify the minimum number of vertices required to monitor all vertices and edges of \\( G \\) under progressively stricter path-based conditions. As established by Florent \\textit{et al.}\\ (CALDAM 2023), these parameters satisfy the chain of inequalities: \\( g(G) \\leq eg(G) \\leq seg(G) \\leq meg(G). \\) In 2025, Florent \\textit{et al.}\\ posed the following question: given integers \\( a, b, c, d \\) satisfying \\( 2 \\leq a \\leq b \\leq c \\leq d \\), does there exist a graph \\( G \\) such that \\( g(G) = a, \\quad eg(G) = b, \\quad seg(G) = c, \\quad \\text{and} \\quad meg(G) = d? \\) They partially answered this affirmatively under three specific hypotheses and gave some constructions to support it. In this article, we first identify quadruples of values that cannot be realized by any connected graph. For all remaining admissible quadruples, we provide explicit constructions of connected graphs that realize the specified parameters. These constructions are modular and efficient, with the number of vertices and edges growing linearly with the largest parameter, providing a complete and constructive characterization of such realizable quadruples.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27696v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27689v1", "url": "http://arxiv.org/abs/2603.27689v1", "pdf_url": "https://arxiv.org/pdf/2603.27689v1", "title": "Sets of subspaces with restricted hyperplane intersection numbers", "authors": ["Tim Alderson", "Simeon Ball"], "annotation": "Let $\\mathcal{X}$ be a set of $(h-1)$-dimensional subspaces of $\\mathrm{PG}(kh-1,q)$ with the property that every hyperplane contains at most $t$ elements of $\\mathcal{X}$. We prove the upper bound $|\\mathcal{X}| \\leq (t-k+2)q^h + t$, and characterise the structure of $\\mathcal{X}$ in the case of equality. We call sets attaining this bound \\emph{length-maximal}. For $k=3$, such sets are known as maximal arcs and have been well-studied. They are known to exist for $t<q^h$ if and only if $q$ is even and $t$ divides $q^h$. For $k=4$ and $q>2$, we show that any length-maximal set must satisfy $t = q^h+1$ and that every hyperplane is either a $t$-secant or a $1$-secant. For $k \\geq 5$ and $q>2$, no length-maximal set exists. In the language of additive codes, these results assert that additive two-weight codes over $\\mathbb{F}_{q^h}$ attaining the natural Griesmer-type bound do not exist when the code dimension is $5$ or more and $q>2$.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27689v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27655v1", "url": "http://arxiv.org/abs/2603.27655v1", "pdf_url": "https://arxiv.org/pdf/2603.27655v1", "title": "Exact Algorithms for Edge Deletion to Cactus", "authors": ["Sheikh Shakil Akhtar", "Geevarghese Philip"], "annotation": "We study two related problems on simple, un-directed graphs: Edge Deletion to Cactus and Spanning Tree to Cactus. Edge Deletion to Cactus has been known to be NP-hard on general graphs at least since 1988. We show improved exact algorithms for the former and a polynomial time algorithm for the latter.", "category": "math.CO", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27655v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29916v1", "url": "http://arxiv.org/abs/2603.29916v1", "pdf_url": "https://arxiv.org/pdf/2603.29916v1", "title": "Growth-rate distributions at stationarity", "authors": ["Edgardo Brigatti"], "annotation": "We propose new analytical tools for describing growth-rate distributions generated by stationary time-series. Our analysis shows how deviations from normality are not pathological behaviour, as suggested by some traditional views, but instead can be accounted for by clean and general statistical considerations. In contrast, strict normality is the effect of specific modelling choices. Systems characterized by stationary Gamma or heavy-tailed abundance distributions produce log-growth-rate distributions well described by a generalized logistic distribution, which can describe tent-shaped or nearly normal datasets and serves as a useful null model for these observables. These results prove that, for large enough time lags, in practice, growth-rate distributions cease to be time-dependent and exhibit finite variance. Based on this analysis, we identify some key stylized macroecological patterns and specific stochastic differential equations capable of reproducing them. A pragmatic workflow for heuristic selection between these models is then introduced. This approach is particularly useful for systems with limited data-tracking quality, where applying sophisticated inference methods is challenging.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29916v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29684v1", "url": "http://arxiv.org/abs/2603.29684v1", "pdf_url": "https://arxiv.org/pdf/2603.29684v1", "title": "FcsIT: An Open-Source, Cross-Platform Tool for Correlation and Analysis of Fluorescence Correlation Spectroscopy Data", "authors": ["Tomasz Kalwarczyk"], "annotation": "FcsIT is a platform-independent, open-source tool for calculating the correlation and fitting fluorescence correlation spectroscopy data. The software is written in Python and uses a powerful Dear PyGUI engine for its interface. It provides reading and correlating the TTTR data, as well as TCSPC filtering of the photon time-trace data. The circular-block bootstrap method applied to the calculation of correlation data and its variance results in data quality comparable to that obtained with commercially available software. An intuitive fitting interface provides efficient analysis of large datasets and includes nine predefined mathematical models for fitting correlation curves. Moreover, it allows users to add their own models in a user-friendly manner. Validation of the FcsIT tool against simulated FCS data and real FCS experiments confirms its usability and potential appeal to a wide variety of FCS users.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29684v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29628v1", "url": "http://arxiv.org/abs/2603.29628v1", "pdf_url": "https://arxiv.org/pdf/2603.29628v1", "title": "A systematic approach to Covariance matrix formulation in charged particle activation experiments", "authors": ["Tanmoy Bar"], "annotation": "This work presents a detailed covariance and correlation matrix analysis for experimentally measured cross sections obtained using the activation technique. Both statistical and systematic contributions to the covariance matrix were explicitly calculated using sensitivity coefficients. The detector efficiency was determined by refitting standard source data with an exponential function, and the associated covariance matrix of the fitted parameters was propagated to estimate the uncertainty in efficiency at the relevant $γ$-ray energy. The cross sections and the corresponding experimental parameters, such as beam flux, target thickness, $γ$-ray intensity, and decay corrections, were taken from previously published measurements and are used here for the purpose of illustrating the covariance formalism. The resulting covariance and correlation matrices provide a comprehensive representation of uncertainties and their interdependencies. This formalism demonstrates the importance of including correlated uncertainties for reliable interpretation and comparison of experimental cross section data.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29628v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29039v1", "url": "http://arxiv.org/abs/2603.29039v1", "pdf_url": "https://arxiv.org/pdf/2603.29039v1", "title": "AI Cosplaying as Astrophysicists: A Controlled Synthetic-Agent Study of AI-Assisted Astrophysical Research Workflows", "authors": ["Chun Huang"], "annotation": "Large Language Models (LLMs) are now widely used in astrophysics, but do they actually make our lives easier, or do they merely invent new physics with enough confidence to hide a minus sign? In a specialized field where checking fluent hallucinations is itself labor-intensive, AI assistance can demand as much work as the task it claims to simplify. To evaluate where AI genuinely improves scientific workflows, we bypassed human trials and instead forced AI agents to cosplay as astrophysicists. We simulated 144 synthetic researchers, varying in career stage, AI awareness, and willingness to verify outputs, across 2,592 daily astrophysics research assignments. Comparing solo work against four styles of AI assistance produced 12,960 scored episodes. No assisted policy universally outperformed unassisted work in the primary Qwen production run. Instead, performance depends strongly on the task, the style of AI use, and the identity of the actor. While cautious assistance helps on creative, extractive, and critique-oriented tasks, it can fail catastrophically on derivation-heavy physics. A full actor-swap DeepSeek rerun changes that picture materially: verification-heavy use becomes the strongest assisted policy, two assisted modes enter the higher-utility/lower-risk quadrant, and the derivation-heavy fragility that dominates the Qwen production run largely disappears. In its current form, AI is useful, but only conditionally, its value is uneven, task-specific, and shaped jointly by workflow, usage policy, and which LLM you are using.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29039v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28884v1", "url": "http://arxiv.org/abs/2603.28884v1", "pdf_url": "https://arxiv.org/pdf/2603.28884v1", "title": "The Closure Challenge: a benchmark task for machine learning in turbulence modelling", "authors": ["Ryley McConkey", "Tyler Buchanan", "Tess Smidt", "Abigail Bodner", "Richard Dwight", "Paola Cinnella"], "annotation": "We introduce a field-wide benchmark challenge for machine learning in Reynolds-averaged Navier-Stokes (RANS) turbulence modelling. Though open-source datasets exist for training data-driven turbulence closure models, the field has been notably lacking a standard benchmark metric and test dataset. The Closure Challenge is a curated collection of open-source datasets and evaluation code that remedies this problem. We provide a variety of high-fidelity training data in a standardized format, including mean velocity gradients. The test cases (periodic hills, square duct, and NASA wall-mounted hump) evaluate Reynolds number and geometry generalization, two key issues in the field. We present results from three early submissions to the challenge. This is an ongoing challenge, intended to continuously spur innovation in machine learning for turbulence modelling. Our goal is for this benchmark to become the standard evaluation for new machine learning frameworks in RANS. The Closure Challenge is available at https://github.com/rmcconke/closure-challenge-benchmark.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28884v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28847v1", "url": "http://arxiv.org/abs/2603.28847v1", "pdf_url": "https://arxiv.org/pdf/2603.28847v1", "title": "Declarative bespoke modelling: A new approach", "authors": [" DBM Collaboration", "David Komanek", "Vaclav Pavlík", "Santiago Jimenez", "Rhys Taylor"], "annotation": "Modern numerical models are increasingly complex, opaque, and computationally expensive, yet frequently fail to predict even qualitative features of observed phenomena. We propose a new paradigm, Declarative Bespoke Modelling, in which the modeller explicitly declares the relationship between model inputs and outputs. We demonstrate that this approach achieves perfect predictive accuracy, unconditional numerical stability, and complete interpretability. It represents a natural endpoint of contemporary modelling practice and near-zero CO2 emission.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28847v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28534v1", "url": "http://arxiv.org/abs/2603.28534v1", "pdf_url": "https://arxiv.org/pdf/2603.28534v1", "title": "Compressing Transformer Language Models via Matrix Product Operator Decomposition: A Case Study on PicoGPT", "authors": ["Younes Javanmard", "Tanmoy Pandit", "Masoud Mardani"], "annotation": "Transformer-based language models achieve strong performance across NLP tasks, but their quadratic parameter scaling with hidden dimension makes deployment on resource-constrained hardware expensive. We study Matrix Product Operator (MPO) decomposition as a principled compression method for transformers. MPO factorises weight matrices into chains of low-rank cores, with approximation quality controlled by the bond dimension chi. We replace every nn.Linear layer in PicoGPT, a GPT-2-style character-level language model with about 1M parameters, with an MPOLinear module parameterised as an MPO chain. Cores are initialised either by TT-SVD from pretrained dense weights or from random initialisation, and trained using standard PyTorch autograd without a custom backward pass. We derive balanced factorisation schemes for the five distinct weight shapes in PicoGPT and evaluate bond dimensions chi in {4, 8, 16, 32} on Tiny Shakespeare. MPO compression achieves up to 13x compression per transformer block at chi = 4. At chi = 16, the model uses 191,872 parameters instead of 1,020,224 while retaining 97.7% of baseline token accuracy (51.6% vs 52.8%). Reconstruction error follows the expected trend and is lower for three-site than two-site factorisations at the same bond dimension. The chi = 8 model gives the best accuracy per parameter, exceeding the dense baseline by 2.7x on this metric. These results show that MPO parameterisation is a practical and theoretically grounded alternative to low-rank methods and unstructured pruning for transformer compression.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28534v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27684v1", "url": "http://arxiv.org/abs/2603.27684v1", "pdf_url": "https://arxiv.org/pdf/2603.27684v1", "title": "Solving the inverse problem of X-ray absorption spectroscopy via physics-informed deep learning", "authors": ["Suyang Zhong", "Boying Huang", "Pengwei Xu", "Fanjie Xu", "Yuhao Zhao", "Jun Cheng", "Fujie Tang", "Weinan E", "Zhong-Qun Tian"], "annotation": "Resolving transient atomic configurations in non-crystalline or dynamic environments remains a fundamental bottleneck in the physical sciences. While X-ray absorption spectroscopy (XAS) is a premier probe of local structure, inverting spectra into structural descriptors is a notoriously ill-posed problem due to inherent many-to-one mapping. Here, we present the Spectral Pattern Translator (SPT), a physics-informed deep learning framework that establishes a robust bridge between large-scale theoretical datasets and experimental reality. Our strategy exploits the Fourier duality between spectral energy oscillations and spatial scattering paths to overcome the \"simulation-to-experiment\" gap. By decomposing spectra into frequency domains, SPT effectively isolates robust structural coordination signals from the destabilizing noise inherent in experimental data. Trained on a massive library of diverse atomic environments, this approach achieves state-of-the-art accuracy in resolving continuous phase transitions in battery cathodes and deciphering local order in amorphous materials. With millisecond-scale latency, SPT removes the primary computational barrier to autonomous materials discovery, establishing a robust, noise-resilient engine for closed-loop robotic chemistry.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27684v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27322v1", "url": "http://arxiv.org/abs/2603.27322v1", "pdf_url": "https://arxiv.org/pdf/2603.27322v1", "title": "Deep brain microelectrode signal: $q$-statistical approach", "authors": ["Ana Luiza Souza Tavares", "Henrique Santos Lima", "Artur Pedro Martins Neto", "Bruno Duarte Gomes", "Constantino Tsallis"], "annotation": "We characterize the amplitude statistics of intraoperative microelectrode recordings (MERs) obtained during deep brain stimulation (DBS) surgery in 46 patients with Parkinson's disease, using 184 recordings equally balanced between inside and outside the subthalamic nucleus (STN). The probability density of every recording is quantitatively well described by a $q$-Gaussian (grounded on a nonadditive entropic functional), $ρ(x) \\propto [1 + β(q-1) x^2]^{-1/(q-1)}$, with $q > 1$ in all cases, reflecting persistent long-range temporal correlations inconsistent with Gaussian dynamics. Within the superstatistics framework, the slowly fluctuating local variance visible in the raw MER signals is a physical mechanism that directly generates the $q > 1$ form. Beyond individual fits, $q$ and $β$ collapse across all 184 recordings onto the single functional constraint $q = 3 - 1.85\\,β^{-0.33}$ ($R \\approx -0.91$), a reduction to one effective degree of freedom that is the quantitative hallmark of near-critical dynamics, previously identified in scale-free network growth and in acoustic precursors of material fracture. The index $q$ is statistically indistinguishable across the STN boundary ($\\langle\\bar{q}_\\text{out}/\\bar{q}_\\text{in} \\rangle = 1.03$), while the inverse-widthparameter shows a modest systematic difference ($\\langle\\barβ_\\text{out}/\\barβ_\\text{in} \\rangle = 1.18$). Since $q > 1$ is expected for any brain structure exhibiting long-range correlations, healthy or pathological, it is the tight $q(β)$ coupling, not $q > 1$ per se, that constitutes the candidate near-criticality signature of the parkinsonian cortico-basal-ganglia-thalamocortical loop.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27322v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26334v1", "url": "http://arxiv.org/abs/2603.26334v1", "pdf_url": "https://arxiv.org/pdf/2603.26334v1", "title": "Bayesian estimation of optical constants using mixtures of Gaussian process experts", "authors": ["Teemu Härkönen", "Hui Chen", "Erik Vartiainen"], "annotation": "We propose modeling absorption spectrum measurements as mixtures of Gaussian process experts. This enables us to construct a flexible statistical model for interpolating and extrapolating measurements, facilitating statistical integration of Kramers-Kronig relations to estimate the whole complex refractive index. Additionally, we statistically model the anchoring points used in subtractive Kramers-Kronig relations to account for possible measurement errors of the anchor point. In addition to flexible statistical modeling, the mixtures of Gaussian process formulation enables automatic selection of measurement points to use for extrapolation. We apply the method to experimental absorption spectrum measurements of gallium arsenide, potassium chloride, and transparent wood.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26334v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.25793v1", "url": "http://arxiv.org/abs/2603.25793v1", "pdf_url": "https://arxiv.org/pdf/2603.25793v1", "title": "Vision Transformers and Graph Neural Networks for Charged Particle Tracking in the ATLAS Muon Spectrometer", "authors": ["Jonathan Renusch"], "annotation": "The identification and reconstruction of charged particles, such as muons, is a main challenge for the physics program of the ATLAS experiment at the Large Hadron Collider. This task will become increasingly difficult with the start of the High-Luminosity LHC era after 2030, when the number of proton-proton collisions per bunch crossing will increase from 60 to up to 200. This elevated interaction density will also increase the occupancy within the ATLAS Muon Spectrometer, requiring more efficient and robust real-time data processing strategies within the experiment's trigger system, particularly the Event Filter. To address these algorithmic challenges, we present two machine-learning-based approaches. First, we target the problem of background-hit rejection in the Muon Spectrometer using Graph Neural Networks integrated into the non-ML baseline reconstruction chain, demonstrating a 15 % improvement in reconstruction speed (from 255 ms to 217 ms). Second, we present a proof-of-concept for end-to-end muon tracking using state-of-the-art Vision Transformer architectures, achieving ultra-fast approximate muon reconstruction in 2.3 ms on consumer-grade GPUs at 98 % tracking efficiency.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.25793v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.24867v1", "url": "http://arxiv.org/abs/2603.24867v1", "pdf_url": "https://arxiv.org/pdf/2603.24867v1", "title": "Increasing trends in the severity of Australian fire weather conditions over the past century", "authors": ["Soubhik Biswas", "Andrew Dowdy", "Savin Chand"], "annotation": "Understanding how weather and climate influence fire risk is important for many purposes, including climate adaptation planning and decision-making in sectors such as emergency management, finance, health and infrastructure (e.g., for energy and water availability). In this study, bias-corrected 20CRv2c reanalysis data are used to investigate the climatology and long-term trends of weather conditions associated with landscape fires in Australia. The McArthur Forest Fire Danger Index (FFDI) is used here as a broad-scale representation of weather conditions known to influence fire behaviour based on wind speed, humidity, temperature and rainfall measures. In particular, using this reanalysis dataset allows analysis over a longer time period than previous studies, from 1876 to 2011. Another novel aspect is that trends are examined using several different approaches, including a method to help account for the influence of interannual drivers of climate variability not previously used for fire weather analysis. Results show increases in mean and extreme seasonal FFDI values throughout Australia in general, with all statistically significant trends being positive in sign for individual climate zones. Humidity and temperature trends, attributable to human-caused climate change, are shown to be the main cause of the increase in dangerous weather conditions for fires. These findings build on previous studies, with the novel data and methods used adding confidence to the overall understanding of fire risk factors in a changing climate.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.24867v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26793v1", "url": "http://arxiv.org/abs/2603.26793v1", "pdf_url": "https://arxiv.org/pdf/2603.26793v1", "title": "Chiral moments make chiral measures", "authors": ["Emilio Pisanty", "Nicola Mayer", "Andrés Ordóñez", "Alexander Löhr", "Margarita Khokhlova"], "annotation": "We develop a family of chiral measures to quantify the chirality of a distribution and assign it a handedness. Our measures are built using the tensorial moments of the distribution, which naturally encode its spatial character, not only via its angular shape consistently with existing multipolar-moment approaches, but also its radial dependence. We combine these tensorial moments into a rotationally-invariant pseudoscalar using a newly-defined cross product and triple product for arbitrary symmetric tensors. We analyze these measures for a variety of toy-model distributions, providing intuition for the geometry and guiding the choice of chiral measure optimal for a given distribution. We also apply our measures to a physically-motivated example coming from photoionization in polychromatic chiral light. Our work provides a robust, flexible, intuitive, highly geometrical, and physically-driven framework for understanding and quantifying the chirality of a wide variety of distributions, together with an open-source software package that makes this toolbox readily applicable for the analysis of numerical or experimental data.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26793v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.24466v1", "url": "http://arxiv.org/abs/2603.24466v1", "pdf_url": "https://arxiv.org/pdf/2603.24466v1", "title": "Short-Term Turbulence Prediction for Seeing Using Machine Learning", "authors": ["Mary Joe Medlej", "Rahul Srinivasan", "Simon Prunet", "Aziz Ziad", "Christophe Giordano"], "annotation": "Optical turbulence, driven by fluctuations of the atmospheric refractive index, poses a significant challenge to ground-based optical systems, as it distorts the propagation of light. This degradation affects both astronomical observations and free-space optical communications. While adaptive optics systems correct turbulence effects in real-time, their reactive nature limits their effectiveness under rapidly changing conditions, underscoring the need for predictive solutions. In this study, we address the problem of short-term turbulence forecasting by leveraging machine learning models to predict the atmospheric seeing parameter up to two hours in advance. We compare statistical and deep learning approaches, with a particular focus on probabilistic models that not only produce accurate forecasts but also quantify predictive uncertainty, crucial for robust decision-making in dynamic environments. Our evaluation includes Gaussian processes (GP) for statistical modeling, recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) as deterministic baselines, and our novel implementation of a normalizing flow for time series (FloTS) as a flexible probabilistic deep learning method. All models are trained exclusively on historical seeing data, allowing for a fair performance comparison. We show that FloTS achieves the best overall balance between predictive accuracy and well-calibrated uncertainty.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.24466v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.24431v1", "url": "http://arxiv.org/abs/2603.24431v1", "pdf_url": "https://arxiv.org/pdf/2603.24431v1", "title": "Learning Response-Statistic Shifts and Parametric Roll Episodes from Wave--Vessel Time Series via LSTM Functional Models", "authors": ["Jose del Aguila Ferrandis"], "annotation": "Parametric roll is a rare but high-consequence instability that can trigger abrupt regime changes in ship response, including pronounced shifts in roll statistics and tail risk. This paper develops a data-driven surrogate that learns the nonlinear, causal functional mapping from incident wave--motion time series to vessel motions, and demonstrates that the surrogate reproduces both (i) parametric roll episodes and (ii) the associated statistical shifts in the response. Crucially, the learning framework is data-source agnostic: the paired wave--motion time series can be obtained from controlled experiments (e.g., towing-tank or basin tests with wave probes and motion tracking) when a hull exists, or from high-fidelity simulations during design when experiments are not yet available. To provide a controlled severe-sea demonstration, we generate training data with a URANS numerical wave tank, using long-crested irregular seas synthesized from a modified Pierson--Moskowitz spectrum. The demonstration dataset comprises 49 random-phase realizations for each of three sea states, simulated at a fixed forward speed selected to yield encounter conditions under which parametric-roll episodes can occur. A stacked LSTM surrogate is trained on wave-elevation time series and evaluated on held-out realizations using time-domain accuracy and distributional fidelity metrics. In the most severe case, the model tracks the onset and growth of large-amplitude roll consistent with parametric excitation, and captures the corresponding changes in roll probability density functions (PDFs). We further compare loss-function choices (MSE, relative-entropy-based objectives, and amplitude-weighted variants) and show how they trade average error for improved tail fidelity relevant to operability and risk assessment.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.24431v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.24075v1", "url": "http://arxiv.org/abs/2603.24075v1", "pdf_url": "https://arxiv.org/pdf/2603.24075v1", "title": "Ising noise filter: physics-informed filtering for particle detectors", "authors": ["I. Kharuk"], "annotation": "We present the Ising noise filter, a highly portable, graph-based pre-filtering algorithm for early-stage background suppression in particle accelerators and astrophysical detectors. Standard noise rejection methods relying on track fitting suffer from severe combinatorial explosion. Our method bypasses this by mapping individual detector hits to a network of binary spins and minimizing an energy functional. The interaction kernels are physics-informed, tailored to the underlying physics and geometry of the experiment. We demonstrate the efficacy of this approach in two distinct experimental regimes. Applied to the Baikal-GVD neutrino telescope the filter yields fast, standard-quality noise rejection with 96.8\\% recall for astrophysical neutrinos. For the SPD detector at the NICA collider the filter attains recall of 97\\% on a toy Monte Carlo sample. Furthermore, when combined with a Peterson--Hopfield network for track finding, our physics-informed coupling improves the TrackML score from 0.5 to 0.95.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.24075v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.23974v1", "url": "http://arxiv.org/abs/2603.23974v1", "pdf_url": "https://arxiv.org/pdf/2603.23974v1", "title": "Machine vision with small numbers of detected photons per inference", "authors": ["Shi-Yuan Ma", "Jérémie Laydevant", "Mandar M. Sohoni", "Logan G. Wright", "Tianyu Wang", "Peter L. McMahon"], "annotation": "Machine vision, including object recognition and image reconstruction, is a central technology in many consumer devices and scientific instruments. The design of machine-vision systems has been revolutionized by the adoption of end-to-end optimization, in which the optical front end and the post-processing back end are jointly optimized. However, while machine vision currently works extremely well in moderate-light or bright-light situations -- where a camera may detect thousands of photons per pixel and billions of photons per frame -- it is far more challenging in very low-light situations. We introduce photon-aware neuromorphic sensing (PANS), an approach for end-to-end optimization in highly photon-starved scenarios. The training incorporates knowledge of the low photon budget and the stochastic nature of light detection when the average number of photons per pixel is near or less than 1. We report a proof-of-principle experimental demonstration in which we performed low-light image classification using PANS, achieving 73% (82%) accuracy on FashionMNIST with an average of only 4.9 (17) detected photons in total per inference, and 86% (97%) on MNIST with 8.6 (29) detected photons -- orders of magnitude more photon-efficient than conventional approaches. We also report simulation studies showing how PANS could be applied to other classification, event-detection, and image-reconstruction tasks. By taking into account the statistics of measurement results for non-classical states or alternative sensing hardware, PANS could in principle be adapted to enable high-accuracy results in quantum and other photon-starved setups.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.23974v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.23593v1", "url": "http://arxiv.org/abs/2603.23593v1", "pdf_url": "https://arxiv.org/pdf/2603.23593v1", "title": "Searching for Anomalies with Foundation Models", "authors": ["Vinicius Mikuni", "Benjamin Nachman"], "annotation": "Foundation models have the potential to extend the discovery reach for anomaly detection searches. When studying the large OmniLearned foundation model on data from the CMS experiment, unexpected behavior was observed in a mass sideband. The purpose of this paper is to perform a full analysis, including a complete background estimate, on the phase space picked out by the large model. We find that the background estimation describes the data well in validation regions, but is unable to accurately model the signal region. We invite further scrutiny of these events and our methods.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.23593v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.23567v1", "url": "http://arxiv.org/abs/2603.23567v1", "pdf_url": "https://arxiv.org/pdf/2603.23567v1", "title": "Beyond the Central Limit: Universality of the Gamma Distribution from Padé-Enhanced Large Deviations", "authors": ["Mario Castro", "José A. Cuesta"], "annotation": "The central limit theorem provides the theoretical foundation for the universality of the normal distribution: under broad conditions, the asymptotic distribution of a sum of independent random variables approaches a Gaussian. Yet, physical systems described by positive random variable -- from earthquakes to microbial growth to epidemic spreading -- consistently exhibit gamma rather than Gaussian statistics -- what leads to field-specific mechanistic explanations that are non robust to small changes in the model details. We show that gamma distributions emerge naturally from large deviation theory when Padé approximants replace polynomial expansions of the derivative of the scaled cumulant generating function, respecting positivity constraints that the central limit theorem violates. Gamma universality thus emerges as the constrained analog of Gaussian universality, providing a mechanism-free explanation for its pervasive appearance across different disciplines.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.23567v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.21247v1", "url": "http://arxiv.org/abs/2603.21247v1", "pdf_url": "https://arxiv.org/pdf/2603.21247v1", "title": "Accelerate Vector Diffusion Maps by Landmarks", "authors": ["Sing-Yuan Yeh", "Yi-An Wu", "Hau-Tieng Wu", "Mao-Pei Tsui"], "annotation": "We propose a landmark-constrained algorithm, LA-VDM (Landmark Accelerated Vector Diffusion Maps), to accelerate the Vector Diffusion Maps (VDM) framework built upon the Graph Connection Laplacian (GCL), which captures pairwise connection relationships within complex datasets. LA-VDM introduces a novel two-stage normalization that effectively address nonuniform sampling densities in both the data and the landmark sets. Under a manifold model with the frame bundle structure, we show that we can accurately recover the parallel transport with landmark-constrained diffusion from a point cloud, and hence asymptotically LA-VDM converges to the connection Laplacian. The performance and accuracy of LA-VDM are demonstrated through experiments on simulated datasets and an application to nonlocal image denoising.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.21247v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.21126v1", "url": "http://arxiv.org/abs/2603.21126v1", "pdf_url": "https://arxiv.org/pdf/2603.21126v1", "title": "Construction of the Global $χ^2$ Function for the Simultaneous Fitting of Correlated Energy-Dependent Cross Sections", "authors": ["Linquan Shao", "Haoyu Yan", "Yingjun Chen", "Jiaxin Pi", "Xingyu Zhou"], "annotation": "In this paper, the global $χ^2$ function for the simultaneous fitting of correlated energy-dependent cross sections is constructed, where the correlations between the measured cross sections of different processes and/or at different center-of-mass energy points, as well as the contributions from the integrated luminosity measurement and the center-of-mass energy measurement, are taken into account.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.21126v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.20904v3", "url": "http://arxiv.org/abs/2603.20904v3", "pdf_url": "https://arxiv.org/pdf/2603.20904v3", "title": "Sparse Weak-Form Discovery of Stochastic Generators", "authors": ["Eshwar R A", "Gajanan V. Honnavar"], "annotation": "The proposed algorithm seeks to provide a novel data-driven framework for the discovery of stochastic differential equations (SDEs) by application of the Weak-formulation to stochastic SINDy. This Weak formulation of the algorithm provides a noise-robust methodology that avoids traditional noisy derivative computation using finite differences. An additional novelty is the adoption of spatial Gaussian test functions in place of temporal test functions, wherein the use of the kernel weight $K_j(X_{t_n})$ guarantees unbiasedness in expectation and prevents the structural regression bias that is otherwise pertinent with temporal test functions. The proposed framework converts the SDE identification problem into two SINDy based linear sparse identification problems. We validate the algorithm on three SDEs, for which we recover all active non-linear terms with coefficient errors below 4%, stationary-density total-variation distances below 0.01, and autocorrelation functions that reproduce true relaxation timescales across all three benchmarks faithfully.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.20904v3.pdf", "pdf_downloaded": true}
+{"slug": "2603.23549v1", "url": "http://arxiv.org/abs/2603.23549v1", "pdf_url": "https://arxiv.org/pdf/2603.23549v1", "title": "Enhancing Neutrinoless Double-Beta Decay Sensitivity of Liquid-Xenon Time Projection Chamber with Augmented Convolutional Neural Network", "authors": ["E. Aprile", "J. Aalbers", "K. Abe", "M. Adrover", "S. Ahmed Maouloud", "L. Althueser", "B. Andrieu", "E. Angelino", "D. Antón Martin", "S. R. Armbruster", "F. Arneodo", "L. Baudis", "M. Bazyk", "L. Bellagamba", "R. Biondi", "A. Bismark", "K. Boese", "R. M. Braun", "G. Bruni", "G. Bruno", "R. Budnik", "C. Cai", "C. Capelli", "J. M. R. Cardoso", "A. P. Cimental Chávez", "A. P. Colijn", "J. Conrad", "J. J. Cuenca-García", "V. D'Andrea", "L. C. Daniel Garcia", "M. P. Decowski", "A. Deisting", "C. Di Donato", "P. Di Gangi", "S. Diglio", "K. Eitel", "S. el Morabit", "R. Elleboro", "A. Elykov", "A. D. Ferella", "C. Ferrari", "H. Fischer", "T. Flehmke", "M. Flierman", "R. Frankel", "D. Fuchs", "W. Fulgione", "C. Fuselli", "R. Gaior", "F. Gao", "R. Giacomobono", "F. Girard", "R. Glade-Beucke", "L. Grandi", "J. Grigat", "H. Guan", "M. Guida", "P. Gyorgy", "R. Hammann", "A. Higuera", "C. Hils", "L. Hoetzsch", "N. F. Hood", "M. Iacovacci", "Y. Itow", "J. Jakob", "F. Joerg", "Y. Kaminaga", "M. Kara", "S. Kazama", "P. Kharbanda", "M. Kobayashi", "D. Koke", "K. Kooshkjalali", "A. Kopec", "H. Landsman", "R. F. Lang", "L. Levinson", "A. Li", "I. Li", "S. Li", "S. Liang", "Z. Liang", "Y. -T. Lin", "S. Lindemann", "M. Lindner", "K. Liu", "M. Liu", "F. Lombardi", "J. A. M. Lopes", "G. M. Lucchetti", "T. Luce", "Y. Ma", "C. Macolino", "J. Mahlstedt", "F. Marignetti", "T. Marrodán Undagoitia", "K. Martens", "J. Masbou", "S. Mastroianni", "V. Mazza", "A. Melchiorre", "J. Merz", "M. Messina", "A. Michel", "K. Miuchi", "A. Molinario", "S. Moriyama", "K. Morå", "M. Murra", "J. Müller", "K. Ni", "C. T. Oba Ishikawa", "U. Oberlack", "S. Ouahada", "B. Paetsch", "Y. Pan", "Q. Pellegrini", "R. Peres", "J. Pienaar", "M. Pierre", "G. Plante", "T. R. Pollmann", "A. Prajapati", "L. Principe", "J. Qin", "D. Ramírez García", "A. Ravindran", "A. Razeto", "R. Singh", "L. Sanchez", "J. M. F. dos Santos", "I. Sarnoff", "G. Sartorelli", "J. Schreiner", "P. Schulte", "H. Schulze Eißing", "M. Schumann", "L. Scotto Lavina", "M. Selvi", "F. Semeria", "F. N. Semler", "P. Shagin", "S. Shi", "H. Simgen", "Z. Song", "A. Stevens", "C. Szyszka", "A. Takeda", "Y. Takeuchi", "P. -L. Tan", "D. Thers", "G. Trinchero", "C. D. Tunnell", "K. Valerius", "S. Vecchi", "S. Vetter", "G. Volta", "C. Weinheimer", "M. Weiss", "D. Wenz", "C. Wittweg", "V. H. S. Wu", "Y. Xing", "D. Xu", "Z. Xu", "M. Yamashita", "J. Yang", "L. Yang", "J. Ye", "M. Yoshida", "L. Yuan", "G. Zavattini", "Y. Zhao", "M. Zhong", "T. Zhu"], "annotation": "Dual-phase time projection chamber (TPC) that employs a multi-ton-scale liquid xenon (LXe) target mass is a pioneering detector technology to search for dark matter. Beyond its advantage in dark matter direct detection efforts, the natural xenon target allows it to search for the neutrinoless double-beta decay ($0νββ$) process, which would violate lepton number conservation and indicate that neutrinos are Majorana particles. However, such $0νββ$ searches have been limited by gamma-ray backgrounds originating from the detector materials. In this work, we designed an augmented convolutional neural network (A-CNN) model to extract additional event-topology information from detector data. Using simulation and calibration data from XENONnT, a leading LXe TPC experiment, our model achieved over 60% background rejection while maintaining 90% signal acceptance. This rejection power improves XENONnT's projected sensitivity of the $^{136}$Xe $0νββ$ search by about 40%. The implementation of A-CNN in the data analysis of future liquid xenon observatories, such as XLZD, will further enhance their sensitivities for $0νββ$ with $^{136}$Xe.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.23549v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.20474v1", "url": "http://arxiv.org/abs/2603.20474v1", "pdf_url": "https://arxiv.org/pdf/2603.20474v1", "title": "From Data to Laws: Neural Discovery of Conservation Laws Without False Positives", "authors": ["Rahul D Ray"], "annotation": "Conservation laws are fundamental to understanding dynamical systems, but discovering them from data remains challenging due to parameter variation, non-polynomial invariants, local minima, and false positives on chaotic systems. We introduce NGCG, a neural-symbolic pipeline that decouples dynamics learning from invariant discovery and systematically addresses these challenges. A multi-restart variance minimiser learns a near-constant latent representation; system-specific symbolic extraction (polynomial Lasso, log-basis Lasso, explicit PDE candidates, and PySR) yields closed-form expressions; a strict constancy gate and diversity filter eliminate spurious laws. On a benchmark of nine diverse systems including Hamiltonian and dissipative ODEs, chaos, and PDEs, NGCG achieves consistent discovery (DR=1.0, FDR=0.0, F1=1.0) on all four systems with true conservation laws, with constancy two to three orders of magnitude lower than the best baseline. It is the only method that succeeds on the Lotka--Volterra system, and it correctly outputs no law on all five systems without invariants. Extensive experiments demonstrate robustness to noise ($σ= 0.1$), sample efficiency (50--100 trajectories), insensitivity to hyperparameters, and runtime under one minute per system. A Pareto analysis shows that the method provides a range of candidate expressions, allowing users to trade complexity for constancy. NGCG achieves strong performance relative to prior methods for data-driven conservation-law discovery, combining high accuracy with interpretability.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.20474v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.20423v2", "url": "http://arxiv.org/abs/2603.20423v2", "pdf_url": "https://arxiv.org/pdf/2603.20423v2", "title": "From the Stochastic Embedding Sufficiency Theorem to a Superspace Diffusion Framework", "authors": ["Carolina Garcia", "Lucía Perea Durán", "Agnese Venezia", "Alex Conradie"], "annotation": "A generalisation of Takens' delay-coordinate embedding theorem to stochastic systems, the Stochastic Embedding Sufficiency Theorem, is an inverse methodology enabling non-parametric recovery of both drift and diffusion fields from scalar time series without prior assumptions about the governing physics.   A blind protocol, receiving only raw time series and sampling interval, is applied identically to nine domains: classical mechanics, statistical mechanics, nuclear physics, quantum mechanics, chemical kinetics, electromagnetism, relativistic quantum mechanics, quantum harmonic oscillator dynamics, and quantum electrodynamics. Fundamental constants (the Boltzmann constant, the Planck constant, the speed of light, the Fano factor, and the Van Kampen scaling exponent) emerge in both drift and diffusion channels without prior specification. The recovered diffusion coefficients, viewed across domains, constitute an empirical pattern, the $σ$-continuum, in which $k_B$, $\\hbar$, and $c$ play structurally distinct roles. The Gravitational Diffusion Theorem, derived from the fluctuation-dissipation theorem, massless mode structure of linearised gravity, and gravitational self-coupling via the equivalence principle, determines the gravitational diffusion coefficient as one Planck length per square root of Planck time.   Four canonical axioms formalise the framework, within which the noise character, drift, covariance operator, and fluctuation amplitude are uniquely determined by theorem, yielding the superspace diffusion hypothesis:   $\\mathrm{d}g_{ij} = \\mathcal{D}_{ij}[g]\\,\\mathrm{d}τ+ \\ell_P\\,\\mathrm{d}W_{ij}$   where all coefficients are non-parametric, first-principles consequences of the axioms. Coarse-graining of the superspace Fokker-Planck equation via Mori-Zwanzig projection yields predictions for galactic-scale gravitational acceleration testable against kinematic data.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.20423v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.20157v2", "url": "http://arxiv.org/abs/2603.20157v2", "pdf_url": "https://arxiv.org/pdf/2603.20157v2", "title": "Detecting the 3D Ising model phase transition with a ground-state-trained autoencoder", "authors": ["Ahmed Abuali", "David A. Clarke", "Morten Hjorth-Jensen", "Ioannis Konstantinidis", "Claudia Ratti", "Jianyi Yang"], "annotation": "We develop a one-class, deep-learning framework to detect the phase transition and recover critical behavior of the 3D Ising model. A 3D convolutional neural network autoencoder (CAE) is trained on ground-state configurations only, without prior knowledge of the critical temperature, the Hamiltonian, or the order parameter. After training, the model is applied to Monte Carlo configurations across a wide temperature range and different lattice sizes. The mean-square reconstruction error is shown to be sensitive to the transition. Finite-size scaling of the peak location for the reconstruction error susceptibility yields the critical temperature $T_c=4.5128(58)$ and the correlation-length critical exponent $ν=0.63(27)$, consistent with results from the literature. Our results show that a one-class CAE, trained on zero-temperature configurations only, can recover nontrivial critical behavior of the 3D Ising model.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.20157v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.20066v1", "url": "http://arxiv.org/abs/2603.20066v1", "pdf_url": "https://arxiv.org/pdf/2603.20066v1", "title": "VecAmpFit: vectorized amplitude-analysis fitting library", "authors": ["K. Chilikin"], "annotation": "A new library VecAmpFit for multidimensional amplitude analyses in high-energy physics has been developed for an ongoing amplitude analysis at Belle II experiment. It includes a fitter performing likelihood calculation and explicitly-vectorized subprograms for amplitude implementation. The fitter supports explicit gradient calculation and simultaneous fitting of multiple data sets.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.20066v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.18814v1", "url": "http://arxiv.org/abs/2603.18814v1", "pdf_url": "https://arxiv.org/pdf/2603.18814v1", "title": "Jet flavor tagging with Particle Transformer for Higgs factories", "authors": ["Taikan Suehara", "Takahiro Kawahara", "Tomohiko Tanabe", "Risako Tagami"], "annotation": "We study the performance of the Particle Transformer (ParT) for jet flavor tagging using ILD full simulation events (1M jets) as well as fast simulation samples (10M and 1M jets). We perform 3-category ($b/c/d$), 6-category ($b/c/d/u/s/g$), and 11-category trainings (including quark--antiquark separation), incorporating multivariate hadron particle identification information from $dE/dx$ and time-of-flight. For $b$/$c$ tagging, we observe a factor of 5--10 improvement over previous BDT-based taggers, and we obtain reasonable performance for strange tagging and quark/antiquark separation. The 10M-jet fast simulation study indicates that further gains are possible with higher training statistics.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.18814v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.18259v1", "url": "http://arxiv.org/abs/2603.18259v1", "pdf_url": "https://arxiv.org/pdf/2603.18259v1", "title": "ALABI: Active Learning for Accelerated Bayesian Inference", "authors": ["Jessica Birky", "Rory K. Barnes"], "annotation": "We present Active Learning for Accelerated Bayesian Inference (\\texttt{alabi}): an open-source Python package for performing Bayesian inference with computationally expensive models. Given a forward model and observational data to construct a likelihood and priors, \\texttt{alabi}\\ uses a Gaussian Process (GP) surrogate model trained to predict posterior probability as a function of input parameters, and employs active learning to iteratively improve GP predictive performance in high-likelihood regions where the GP is most uncertain. \\texttt{alabi}\\ provides a uniform interface for using Markov chain Monte Carlo (MCMC) with different packages, including the affine-invariant sampler \\texttt{emcee}, and nested samplers \\texttt{dynesty}, \\texttt{multinest}, and \\texttt{ultranest}. This approach facilitates accurate estimation of the desired posterior distribution, while reducing the number of computationally expensive model evaluations required by factors of thousands. We demonstrate the performance of \\texttt{alabi}\\ on a variety of test cases, including where inference is challenging due to complex posterior structure or high dimensionality. We show that \\texttt{alabi}\\ offers a substantial improvement for likelihood functions with evaluation times $\\gtrsim 1$\\,s, speeding up MCMC computations by a factor of $10-1000\\times$ when tested on problems with up to 64 dimensions.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.18259v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.17130v2", "url": "http://arxiv.org/abs/2603.17130v2", "pdf_url": "https://arxiv.org/pdf/2603.17130v2", "title": "Long-term outburst activity of comet 17P/Holmes and constraints on ejecta size distributions", "authors": ["Maria Gritsevich", "Marcin Wesołowski", "Josep M. Trigo-Rodríguez", "Alberto J. Castro-Tirado", "Jorma Ryske", "Markku Nissinen", "Peter Carson"], "annotation": "A quantitative understanding of cometary outbursts requires robust constraints on the size distribution of ejected particles, which governs outburst dynamics and underpins estimates of released gas and dust. In the absence of direct measurements of particle sizes, assumptions about the size distribution play a central role in modelling dust-trail formation, their dynamical evolution and observability, and the potential production of meteor showers following encounters with Earth. We analyse brightness amplitude variations associated with outbursts of comet 17P/Holmes from 1892 to 2021, with particular emphasis on the exceptional 2007 mega-outburst. During this event the comet underwent a rapid and substantial brightening: at its peak, the expanding coma reached a diameter exceeding that of the Sun and briefly became the largest object in the Solar System visible to the naked eye. We constrain the size distribution and total mass of porous agglomerates composed of ice, organics, and dust ejected during the outburst. The inferred particle size distribution is consistent with a power law of index q, yielding effective particle sizes between 10^-6 m for q = 4 and 5 x 10^-3 m for q = 2. Accounting for effective particle size, sublimation flux, and bulk density, we find that the total number of ejected particles increases with both q and sublimation flux. These results place constraints on the physical properties of outburst ejecta and provide physically motivated initial conditions for long-term dust-trail evolution modelling. They further indicate that cometary outburst brightness is determined primarily by the number of particles and their size distribution, rather than by the total ejected mass alone, with direct implications for the origin and evolution of meteoroid streams and the interplanetary dust population.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.17130v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.16524v1", "url": "http://arxiv.org/abs/2603.16524v1", "pdf_url": "https://arxiv.org/pdf/2603.16524v1", "title": "An approximate graph elicits detonation lattice", "authors": ["Vansh Sharma", "Venkat Raman"], "annotation": "This study presents a novel algorithm based on graph theory for the precise segmentation and measurement of detonation cells from 3D pressure traces, termed detonation lattices, addressing the limitations of manual and primitive 2D edge detection methods prevalent in the field. Using a segmentation model, the proposed training-free algorithm is designed to accurately extract cellular patterns, a longstanding challenge in detonations research. First, the efficacy of segmentation on generated data is shown with a prediction error 2%. Next, 3D simulation data is used to establish performance of the graph-based workflow. The results of statistics and joint probability densities show oblong cells aligned with the wave propagation axis with 17% deviation, whereas larger dispersion in volume reflects cubic amplification of linear variability. Although the framework is robust, it remains challenging to reliably segment and quantify highly complex cellular patterns. However, the graph-based formulation generalizes across diverse cellular geometries, positioning it as a practical tool for detonation analysis and a strong foundation for future extensions in triple-point collision studies.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.16524v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.15290v1", "url": "http://arxiv.org/abs/2603.15290v1", "pdf_url": "https://arxiv.org/pdf/2603.15290v1", "title": "Extreme-Value Criticality and Gain Decomposition at the Integer Quantum Hall Transition", "authors": ["Wei-Han Li", "Abbas Ali Saberi"], "annotation": "Extreme-value fluctuations at quantum critical points remain poorly understood in the presence of strong correlations and openness. At the integer quantum Hall transition in the open Chalker--Coddington network, we show that the maximal wave-function amplitude separates into a global gain and an intrinsic extreme component, $|ψ|_{\\max}=A\\,|\\tildeψ|_{\\max}$. We introduce extreme-moment scaling for $|ψ|_{\\max}$ and observe an approximately parabolic exponent function $τ_{\\max}(q)$ over moderate $q$, while $\\ln|ψ|_{\\max}$ displays an almost Gaussian bulk over the studied sizes. The gain factor is close to log-normal and largely controls the raw extremes. Gain normalization reorganizes the statistics: $\\tildeτ_{\\max}(q)$ changes qualitatively and $|\\tildeψ|_{\\max}$ does not support a single-parameter generalized extreme-value collapse under standard centering/scaling in the accessible size window. Extreme observables thus provide a robust probe of correlated criticality in open quantum systems.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.15290v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.16946v1", "url": "http://arxiv.org/abs/2603.16946v1", "pdf_url": "https://arxiv.org/pdf/2603.16946v1", "title": "Automatic Termination Strategy of Inelastic Neutron-scattering Measurement Using Bayesian Optimization for Bin-width Selection", "authors": ["Kensuke Muto", "Hirotaka Sakamoto", "Kenji Nagata", "Taka-hisa Arima", "Masato Okada"], "annotation": "Currently, an excessive amount of event data is being obtained in four-dimensional inelastic neutron-scattering experiments. A method for automatic bin-width optimization of multidimensional histograms has been developed and recently validated on real inelastic neutron-scattering data. However, measuring beyond the equipment resolution leads to inefficient use of valuable beam time. To improve experimental efficiency, an automatic termination strategy is essential. We propose a Bayesian-optimization-based method to compute stopping criteria and determine whether to continue or terminate the experiment in real time. In the proposed method, the bin-width optimization is performed using Bayesian optimization to efficiently compute the optimal bin widths. The experiment is terminated when the optimal bin widths become smaller than the target resolutions. In numerical experiments using real inelastic neutron-scattering data, the optimal bin widths decrease as the number of events increases. Even the optimal bin widths for data downsampled to 1/5 are comparable with the resolutions limited by the sample size, choppers, and so on. This implies excessive measurement of the inelastic neutron experiments for the moment. Moreover, we found that Bayesian optimization can reduce the search cost to approximately 10% of an exhaustive search in our numerical experiments.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.16946v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.18044v1", "url": "http://arxiv.org/abs/2603.18044v1", "pdf_url": "https://arxiv.org/pdf/2603.18044v1", "title": "A complex network approach to characterize clustering of events in irregular time series", "authors": ["Ambedkar Sanket Sukdeo", "K. Shri Vignesh", "Sachin S. Gunthe", "T Narayan Rao", "Amit Kumar Patra", "R. I. Sujith"], "annotation": "In complex systems, events occur at irregular intervals that inherently encode the underlying dynamics of the system. Analyzing the temporal clustering of these events reveals critical insights into the non-random patterns and the temporal evolution. Existing techniques can effectively quantify the overall clustering tendency of events using global statistical measures. However, these macroscopic approaches leave a critical gap, as they do not attempt to investigate the dynamics of individual clusters. Analyzing individual clusters is essential, as it helps comprehend the local interactions that actively drive the system dynamics, which may be obscured by global averaging, while simultaneously revealing the time scales involved. To address these limitations, we propose a complex network-based framework for analyzing clustering of events occurring at irregular intervals. The framework establishes connections using arrival times, transforming the time series into a network. Network properties are then used to quantify the clustering. Further, a community detection algorithm is used to identify individual clusters in time series. We illustrate the method by applying it to standard arrival processes, such as the Poisson process and the Markov-modulated Poisson process. To further demonstrate its scope, we apply the method to two diverse systems: the time series of droplet arrivals in turbulent flows and the R-R intervals in electrocardiogram (ECG) signals.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.18044v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.15703v2", "url": "http://arxiv.org/abs/2603.15703v2", "pdf_url": "https://arxiv.org/pdf/2603.15703v2", "title": "py5vec: a modular Python package for the 5-vector method to search for continuous gravitational waves", "authors": ["Luca D'Onofrio", "Federico Muciaccia", "Lorenzo Mirasola", "Matthew Pitkin", "Cristiano Palomba", "Paola Leaci", "Francesco Safai Tehrani", "Francesco Amicucci", "Lorenzo Silvestri", "Lorenzo Pierini"], "annotation": "We present \\texttt{py5vec}, a Python package for implementing and extending the 5-vector method, used to search for continuous gravitational wave (CW) signals. We also provide a comprehensive theoretical review of the 5-vector method and extend the relative likelihood formalism by marginalizing over the noise variance, resulting in a more robust Student's t-likelihood, and over the initial phase to account for pulsar glitches. \\texttt{py5vec} provides a modular architecture that separates data representation, signal demodulation, and statistical inference into independent abstract stages. It supports multiple input data formats and interoperates with existing Python software, providing a bridge between different approaches. For example, using a \\texttt{bilby}-based interface, \\texttt{py5vec} implements Bayesian parameter estimation within the 5-vector formalism for the first time. The modular design also allows for making exact multi-level and direct comparisons between other software, such as \\texttt{cwinpy} and \\texttt{SNAG} in MATLAB. In \\texttt{py5vec}, we implement a multidetector targeted search for known pulsars, validated using LIGO data from the O4a run and hardware injections, demonstrating consistent reconstruction of signal parameters. This package therefore provides a flexible platform for current targeted searches and for future extensions to other CW search strategies.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.15703v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.14728v1", "url": "http://arxiv.org/abs/2603.14728v1", "pdf_url": "https://arxiv.org/pdf/2603.14728v1", "title": "A Deep-Learning-Boosted Framework for Quantum Sensing with Nitrogen-Vacancy Centers in Diamond", "authors": ["Changyu Yao", "Haochen Shen", "Zhongyuan Liu", "Ruotian Gong", "Md Shakil Bin Kashem", "Stella Varnum", "Liangyu Li", "Hangyue Li", "Yue Yu", "Yizhou Wang", "Xiaoshui Lin", "Jonathan Brestoff", "Chenyang Lu", "Shankar Mukherji", "Chuanwei Zhang", "Chong Zu"], "annotation": "Nitrogen-vacancy (NV) centers in diamond are a versatile quantum sensing platform for high sensitivity measurements of magnetic fields, temperature and strain with nanoscale spatial resolution. A common bottleneck is the analysis of optically detected magnetic resonance (ODMR) spectra, where target quantities are encoded in resonance features. Conventional nonlinear fitting is often computationally expensive, sensitive to initialization, and prone to failure at low signal-to-noise ratio (SNR). Here we introduce a robust, efficient machine learning (ML) framework for real-time ODMR analysis based on a one-dimensional convolutional neural network (1D-CNN). The model performs direct parameter inference without initial guesses or iterative optimization, and is naturally parallelizable on graphics processing units (GPU) for high-throughput processing. We validate the approach on both synthetic and experimental datasets, showing improved throughput, accuracy and robustness than standard nonlinear fitting, with the largest gains in the low-SNR regime. We further validate our methods in two representative sensing applications: diagnosing intracellular temperature changes using nanodiamond probes and widefield magnetic imaging of superconducting vortices in a high-temperature superconductor. This deep-learning inference framework enables fast and reliable extraction of physical parameters from complex ODMR data and provides a scalable route to real-time quantum sensing and imaging.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.14728v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.14477v1", "url": "http://arxiv.org/abs/2603.14477v1", "pdf_url": "https://arxiv.org/pdf/2603.14477v1", "title": "Directed Polymer Transfer Matrices as a Unified Generator of Distinct One-Point Fluctuation Laws", "authors": ["Sen Mu", "Abbas Ali Saberi", "Roderich Moessner", "Mehran Kardar"], "annotation": "We revisit the transfer-matrix approach to directed polymers in random media and show that a single ensemble of random transfer-matrix products provides a unified realization of the canonical one-point fluctuation laws in $(1+1)$ dimensions. For a fixed disorder realization, the polymer partition function is obtained as a contraction of the same product matrix $W(t)$, and different contractions reproduce the standard KPZ subclasses: Tracy-Widom GUE (point-to-point), GOE (point-to-line), GSE (half-space point-to-point), and Baik-Rains (stationary line-to-point). In each case, we observe $t^{1/3}$ free-energy fluctuation growth and convergence of standardized distributions with low-order cumulants close to the corresponding universal benchmarks. Viewing geometry-dependent subclasses as projections of a single matrix-product ensemble naturally suggests additional observables intrinsic to $W(t)$. As an example, we examine the leading eigenvalue $λ_1(t)$ whose logarithm exhibits $t^{1/3}$ scaling, while its standardized statistics remain distinct from the canonical Tracy-Widom laws within the accessible range. This transfer-matrix perspective thus organizes known KPZ one-point subclasses within a finite-dimensional matrix framework and highlights matrix-level fluctuation observables beyond geometry-selected universality classes.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.14477v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.15682v1", "url": "http://arxiv.org/abs/2603.15682v1", "pdf_url": "https://arxiv.org/pdf/2603.15682v1", "title": "Survival probability of random networks", "authors": ["Kevin Peralta-Martinez", "J. A. Méndez-Bermúdez"], "annotation": "In this work we study in detail all phases of the time evolution of a delta-like excitation in Erdös-Renyi (ER) random networks by means of the survival probability (SP): The initial decay of the SP (both, the fast decay followed by the power-law decay), the correlation hole regime (the regime between the minimum value of the SP and its saturation value), and the saturation of the SP. Specifically, we found that (i) the power-law decay of the SP and the time-averaged SP are proportional to $t^{-D_{2}}$ and $t^{-\\widetilde{D}_{2}}$, respectively (where $D_2$ and $\\widetilde{D}_2$ are the correlation dimension of the eigenstates of the randomly weighted adjacency matrices of the ER random networks and the correlation dimension associated with the initial state, respectively) and (ii) the relative depth of the correlation hole of the SP scales with the average degree $\\langle k\\rangle\\approx np$ (here, $n$ and $p$ are the size and the connection probability of the ER random networks). In addition, we show that the eigenstates of the randomly weighted adjacency matrices of ER networks display clear multifractal properties.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.15682v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.14205v1", "url": "http://arxiv.org/abs/2603.14205v1", "pdf_url": "https://arxiv.org/pdf/2603.14205v1", "title": "Data-driven Experimental Modal Analysis by Dynamic Mode Decomposition", "authors": ["Akira Saito", "Tomohiro Kuno"], "annotation": "This paper discusses the application of Dynamic Mode Decomposition (DMD) to the extraction of modal properties of linear mechanical systems, i.e., experimental modal analysis (EMA). First, theoretical background of the DMD is briefly reviewed and its relevance to the Ibrahim time-domain method is discussed. Second, DMD is applied to a single DOF system and multi-DOF discrete system to discuss the applicability and interpretation of the DMD as a method of EMA. Furthermore, the effects of measurement errors on the results of DMD are discussed. It is shown that with relatively small measurement errors, DMD can capture modal parameters accurately. However, with relatively large measurement errors, DMD fails to capture modal parameters. Finally, DMD is applied to experimentally-obtained displacement field of a cantilevered beam, and its modal parameters are extracted. It is shown that the modal parameters extracted by DMD are as accurate as the ones obtained by the existing modal parameter extraction method.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.14205v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.13778v2", "url": "http://arxiv.org/abs/2603.13778v2", "pdf_url": "https://arxiv.org/pdf/2603.13778v2", "title": "Optimality and annealing path planning of dynamical analog solvers", "authors": ["Shu Zhou", "K. Y. Michael Wong", "Juntao Wang", "David Shui Wing Hui", "Daniel Ebler", "Jie Sun"], "annotation": "Recently proposed analog solvers based on dynamical systems, such as Ising machines, are promising platforms for large-scale combinatorial optimization. Yet, given the heuristic nature of the field, there is very limited insight on optimality guarantees of the solvers, as well as how parameter schedules shape dynamics and outcomes. Here, we develop a dynamical mean-field framework to analyze Ising-machine dynamics for finding the ground state energy of the Sherrington-Kirkpatrick(SK) model of spin glasses and identify mechanisms that enable rapid convergence to provenly near-optimal energies. For a fixed target energy density Ec, we show that solutions are typically reached within O(1) matrix vector multiplications, indicating constant time complexity. We further delineate theoretical limitations arising from different parameter-scheduling trajectories and demonstrate a pronounced benefit of temperature-only annealing for the Coherent Ising Machine. Building on these insights, we propose a general framework for designing optimized parameter schedules, thereby improving the practical effectiveness of Ising machines for complex optimization tasks. The superior performance of the dynamical solvers is illustrated by the attainment of the ground state energy of the SK model.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.13778v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.13727v1", "url": "http://arxiv.org/abs/2603.13727v1", "pdf_url": "https://arxiv.org/pdf/2603.13727v1", "title": "Data-driven Progressive Discovery of Physical Laws", "authors": ["Mingkun Xia", "Weiwei Zhang"], "annotation": "Symbolic regression is a powerful tool for knowledge discovery, enabling the extraction of interpretable mathematical expressions directly from data. However, conventional symbolic discovery typically follows an end-to-end, \"one-step\" process, which often generates lengthy and physically meaningless expressions when dealing with real physical systems, leading to poor model generalization. This limitation fundamentally stems from its deviation from the basic path of scientific discovery: physical laws do not exist in a single form but follow a hierarchical and progressive pattern from simplicity to complexity. Motivated by this principle, we propose Chain of Symbolic Regression (CoSR), a novel framework that models the discovery of physical laws as a chain of symbolic knowledge. This knowledge chain is formed by progressively combining multiple knowledge units with clear physical meanings along a specific logic, ultimately enabling the precise discovery of the underlying physical laws from data. CoSR fully recapitulates the progressive discovery path from Kepler's third law to the law of universal gravitation in classical mechanics, and is applied to three types of problems: turbulent Rayleigh-Benard convection, viscous flows in a circular pipe, and laser-metal interaction, demonstrating its ability to improve classical scaling theories. Finally, CoSR showcases its capability to discover new knowledge in the complex engineering problem of aerodynamic coefficients scaling for different aircraft.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.13727v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.13000v1", "url": "http://arxiv.org/abs/2603.13000v1", "pdf_url": "https://arxiv.org/pdf/2603.13000v1", "title": "Recent advances and trends in pattern recognition and data analysis for RICH detectors", "authors": ["Luka Santelj"], "annotation": "Ring Imaging Cherenkov (RICH) detectors are a key component of particle identification systems in many particle, nuclear and astroparticle physics experiments. Their ultimate performance depends not only on detector design and hardware implementation, but also crucially on the quality of pattern recognition and data analysis algorithms used to reconstruct Cherenkov ring images and to perform particle identification. In recent years, significant advances have been made both in traditional reconstruction approaches, such as likelihood-based methods and Hough-transform techniques, and in the application of modern machine learning tools. This contribution reviews the current state of RICH reconstruction algorithms, highlights representative use cases from operating experiments, and discusses emerging trends including global particle identification strategies and generative machine learning approaches for fast simulation and reconstruction.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.13000v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.12775v1", "url": "http://arxiv.org/abs/2603.12775v1", "pdf_url": "https://arxiv.org/pdf/2603.12775v1", "title": "A unifying approach to diffusive transport in heterogeneous media", "authors": ["Yann Lanoiselée", "Denis S. Grebenkov", "Gianni Pagnini"], "annotation": "We introduce the concept of Randomly Modulated Gaussian Processes as a unifying framework for modeling, analyzing and classifying anomalous diffusion models in heterogeneous media. This formulation incorporates correlations in the displacements together with correlated fluctuations of their amplitudes. Most known models of anomalous diffusion (including Continuous-Time Random Walk and fractional Brownian motion) and random diffusivity can be described and generalized within this framework. Moreover, the unified view identifies the main statistical properties to be probed experimentally for a reliable classification of diffusive dynamics. The proposed matrix formulation facilitates the computation of the first four moments and allows for a systematic statistical characterization of the considered processes. The necessary and sufficient conditions are provided for the emergence of anomalous diffusion. General expressions for the non-Gaussian parameter, ergodicity breaking parameter and covariance of squared increments are derived. Biological applications of this framework for systematic analysis and biophysical interpretations of experimental single-particle trajectories are discussed.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.12775v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.12306v1", "url": "http://arxiv.org/abs/2603.12306v1", "pdf_url": "https://arxiv.org/pdf/2603.12306v1", "title": "Classifying hadronic objects in ATLAS with ML/AI algorithms", "authors": ["Leonardo Toffolin"], "annotation": "The identification of hadronic final states plays a crucial role in the physics programme of the ATLAS Experiment at the CERN LHC. Sophisticated artificial intelligence (AI) algorithms are employed to classify jets according to their origin, distinguishing between quark- and gluon-initiated jets, and identifying hadronically decaying heavy objects such as W bosons and top quarks. This contribution summarises recent developments in constituent-based tagging architectures, including graph neural networks (GNNs) and transformer-based approaches, their performance in simulated and real data, and future perspectives towards data-driven optimisation and model-independent tagging strategies.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.12306v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.10381v1", "url": "http://arxiv.org/abs/2603.10381v1", "pdf_url": "https://arxiv.org/pdf/2603.10381v1", "title": "A mapping-based projection of detailed kinetics uncertainty onto reduced manifolds", "authors": ["Vansh Sharma", "Shuzhi Zhang", "Rahul Jain", "Venkat Raman"], "annotation": "Propagating uncertainties introduced by chemical reaction rate parameters to high-fidelity numerical simulations of complex combustion devices is necessary to ascertain impact on computational predictions. However, the high cost of detailed computations combined with the need to conduct multiple simulations to propagate uncertainty makes such an estimation computationally challenging. In order to reduce the computational cost, a two-step framework for quantifying uncertainty introduced by detailed chemical kinetics model parameters using reduced chemistry models is developed here. First, reduced-manifold states are uniquely reconstructed in full-composition space by following trajectories at an unburnt mixing state and integrating forward to a prescribed progress variable constraint. Second, parametric uncertainty is propagated by sampling perturbed rate coefficients from mechanism covariance matrices and integrating each realization to the target state, yielding uncertainty maps for reduced-space quantities. The method is applied in two configurations: a subsonic multi-tube combustor with interacting jet flames and recirculation, and a three-dimensional reacting high-speed flowpath. Uncertainty-instrumented estimated are reported for a trajectory time (time for the reconstructed unreacted mixture to reach the local target state) and for the time to equilibrium, revealing order-of-magnitude spatial variations driven by mixing, stratification, and residence-time effects. The largest relative variability occurs in low-to-intermediate temperature regimes associated with induction and the onset of heat release, where branching-related chemistry amplifies sensitivity, particularly away from stoichiometric conditions. The method provides a scalable route to spatially resolved, physically interpretable chemistry-UQ for practical reacting-flow simulations.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.10381v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.10252v1", "url": "http://arxiv.org/abs/2603.10252v1", "pdf_url": "https://arxiv.org/pdf/2603.10252v1", "title": "Bayesian Hierarchical Models and the Maximum Entropy Principle", "authors": ["Brendon J. Brewer"], "annotation": "Bayesian hierarchical models are frequently used in practical data analysis contexts. One interpretation of these models is that they provide an indirect way of assigning a prior for unknown parameters, through the introduction of hyperparameters. The resulting marginal prior for the parameters (integrating over the hyperparameters) is usually dependent, so that learning one parameter provides some information about the others. In this contribution, I will demonstrate that, when the prior given the hyperparameters is a canonical distribution (a maximum entropy distribution with moment constraints), the dependent marginal prior also has a maximum entropy property, with a different constraint. This constraint is on the marginal distribution of some function of the unknown quantities. The results shed light on what information is actually being assumed when we assign a hierarchical model.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.10252v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.20250v1", "url": "http://arxiv.org/abs/2603.20250v1", "pdf_url": "https://arxiv.org/pdf/2603.20250v1", "title": "Developing Machine Learning-Based Watch-to-Warning Severe Weather Guidance from the Warn-on-Forecast System", "authors": ["Montgomery Flora", "Samuel Varga", "Corey Potvin", "Noah Lang"], "annotation": "While machine learning (ML) post-processing of convection-allowing model (CAM) output for severe weather hazards (large hail, damaging winds, and/or tornadoes) has shown promise for very short lead times (0-3 hours), its application to slightly longer forecast windows remains relatively underexplored. In this study, we develop and evaluate a grid-based ML framework to predict the probability of severe weather hazards over the next 2-6 hours using forecast output from the Warn-on-Forecast System (WoFS). Our dataset includes WoFS ensemble forecasts valid every 5 minutes out to 6 hours from 108 days during the 2019--2023 NOAA Hazardous Weather Testbed Spring Forecasting Experiments. We train ML models to generate probabilistic forecasts of severe weather akin to Storm Prediction Center outlooks (i.e., likelihood of a tornado, severe wind, or severe hail event within 36 km of each point). We compare a histogram gradient-boosted tree (HGBT) model and a deep learning U-Net approach against a carefully calibrated baseline generated from 2-5 km updraft helicity. Results indicate that the HGBT and U-Net outperform the baseline, particularly at higher probability thresholds. The HGBT achieves the best performance metrics, but predicted probabilities cap at 60% while the U-net forecasts extend to 100%. Similar to previous studies, the U-Net produces spatially smoother guidance than the tree-based method. These findings add to the growing evidence of the effectiveness of ML-based CAM post-processing for providing short-term severe weather guidance.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.20250v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.08457v1", "url": "http://arxiv.org/abs/2603.08457v1", "pdf_url": "https://arxiv.org/pdf/2603.08457v1", "title": "Adaptive Entropy-Driven Sensor Selection in a Camera-LiDAR Particle Filter for Single-Vessel Tracking", "authors": ["Andrei Starodubov", "Yaqub Aris Prabowo", "Andreas Hadjipieris", "Ioannis Kyriakides", "Roberto Galeazzi"], "annotation": "Robust single-vessel tracking from fixed coastal platforms is hindered by modality-specific degradations: cameras suffer from illumination and visual clutter, while LiDAR performance drops with range and intermittent returns. We present a heterogeneous multi-sensor fusion particle-filter tracker that incorporates an information-gain (entropy-reduction) adaptive sensing policy to select the most informative configuration at each fusion time bin. The approach is validated in a real maritime deployment at the CMMI Smart Marina Testbed (Ayia Napa Marina, Cyprus), using a shore-mounted 3D LiDAR and an elevated fixed camera to track a rigid inflatable boat with onboard GNSS ground truth. We compare LiDAR-only, camera-only, all-sensors, and adaptive configurations. Results show LiDAR dominates near-field accuracy, the camera sustains longer-range coverage when LiDAR becomes unavailable, and the adaptive policy achieves a favorable accuracy-continuity trade-off by switching modalities based on information gain. By avoiding continuous multi-stream processing, the adaptive configuration provides a practical baseline for resilient and resource-aware maritime surveillance.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.08457v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.15660v1", "url": "http://arxiv.org/abs/2603.15660v1", "pdf_url": "https://arxiv.org/pdf/2603.15660v1", "title": "Machine Learning Based Identification of Solvents from Post-Desiccation Patterns", "authors": ["Jesús Israel Morán-Cortés", "Felipe Pacheco-Vázquez"], "annotation": "We introduce an optimized protocol of fracture pattern classification using an artificial neural network to identify the solvent involved in the desiccation cracking process of starch-liquid slurries, even after it has been completely evaporated. For this purpose, image analysis techniques were used to characterize patterns obtained from drying suspensions using single solvents (water, ethanol, acetone) and two-component solvents (water-ethanol mixtures at different concentrations). Frequency histograms were generated based on nine morphological features, taking into account their size, shape, geometry and orientational ordering. Subsequently, we used these histograms as input data into artificial neural network variants to determine the set of features that lead to the higher accuracy in solvent identification. We obtained an average accuracy of $96(\\pm 1)\\%$ considering all solvents in the analysis. The highest accuracy was obtained with sets of features that include the crack area distribution. The proposed protocol can help to determine the combination of features that optimize pattern recognition in other fields of science and engineering.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.15660v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.07634v1", "url": "http://arxiv.org/abs/2603.07634v1", "pdf_url": "https://arxiv.org/pdf/2603.07634v1", "title": "Dissecting Spectral Granger Causality through Partial Information Decomposition", "authors": ["Luca Faes", "Gorana Mijatovic", "Riccardo Pernice", "Daniele Marinazzo", "Sebastiano Stramaglia", "Yuri Antonacci"], "annotation": "Granger causality (GC), a popular statistical method for the inference of directional influences between time series measured from a complex network, is sensitive to high-order (non-pairwise) interactions which fundamentally shape the collective network dynamics. This work introduces Partial Decomposition of Granger Causality (PDGC), a tool eliciting redundant and synergistic causal interactions in the pattern of information flow between the subsystems of physiological networks. The tool exploits the framework of partial information decomposition to dissect the multivariate GC from a set of driver random processes to a target process into unique effects carried exclusively by each driver, redundant effects carried identically by more drivers, and synergistic effects carried jointly by some drivers but not by any of them individually. Computation is based on multivariate state-space models expanded in the frequency domain to assess PDGC both in specific bands of physiological interest and in the time domain after whole-band integration. The spectral PDGC was tested in physiological networks probed by measuring the variability series of arterial pressure, heart period, respiration and cerebral blood velocity in patients prone to neurally-mediated syncope compared to healthy controls. This application revealed unprecedented modes of physiological interaction, related to the sympathetic control of low-frequency cardiovascular and cerebrovascular oscillations, characterizing distinctive patterns of autonomic dysfunction. The extraction of high-order causality patterns from the spectral GC favors dissecting the mechanisms of causal influence underlying multivariate interactions among oscillatory processes in many data-driven applications of network science.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.07634v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.07261v1", "url": "http://arxiv.org/abs/2603.07261v1", "pdf_url": "https://arxiv.org/pdf/2603.07261v1", "title": "Turning Time Series into Algebraic Equations: Symbolic Machine Learning for Interpretable Modeling of Chaotic Time Series", "authors": ["Madhurima Panja", "Grace Younes", "Tanujit Chakraborty"], "annotation": "Chaotic time series are notoriously difficult to forecast. Small uncertainties in initial conditions amplify rapidly, while strong nonlinearities and regime dependent variability constrain predictability. Although modern deep learning often delivers strong short horizon accuracy, its black box nature limits scientific insight and practical trust in settings where understanding the underlying dynamics matters. To address this gap, we propose two complementary symbolic forecasters that learn explicit, interpretable algebraic equations from chaotic time series data. Symbolic Neural Forecaster (SyNF) adapts a neural network based equation learning architecture to the forecasting setting, enabling fully differentiable discovery of compact and interpretable algebraic relations. The Symbolic Tree Forecaster (SyTF) builds on evolutionary symbolic regression to search directly over equation structures under a principled accuracy complexity trade off. We evaluate both approaches in a rolling window nowcasting setting with one step ahead forecasting using several accuracy metrics and compare against a broad suite of baselines spanning classical statistical models, tree ensembles, and modern deep learning architectures. Numerical experiments cover a benchmark of 132 low dimensional chaotic attractors and two real world chaotic time series, namely weekly dengue incidence in San Juan and the Nino 3.4 sea surface temperature index. Across datasets, symbolic forecasters achieve competitive one step ahead accuracy while providing transparent equations that reveal salient aspects of the underlying dynamics.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.07261v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.07206v1", "url": "http://arxiv.org/abs/2603.07206v1", "pdf_url": "https://arxiv.org/pdf/2603.07206v1", "title": "Pseudo-Coherence and Stochastic Synchronization: A Non-Normal Route to Collective Dynamics without Oscillators", "authors": ["V. Troude", "D. Sornette"], "annotation": "Collective temporal organization in complex systems is commonly attributed to synchronization, resonance, or proximity to dynamical instabilities. Here we identify a distinct mechanism by which coherent, synchronization-like behavior can emerge in stochastic systems that are linearly stable and contain no intrinsic oscillators. The mechanism arises from non-normal pseudospectral amplification and leads to what we term pseudo-coherence: an intermittent form of collective organization characterized by transient phase alignment, broken time-reversal symmetry, positive entropy production, and drifting spectral peaks. Using a minimal overdamped stochastic model, we show that increasing non-normality drives a sharp pseudo-critical transition. Beyond a well-defined threshold, fluctuations concentrate along a dominant reaction mode, generating intermittent growth of Kuramoto-like order parameters and irreversible probability currents without eigenvalue crossings or Hopf bifurcations. Analytically, we demonstrate that pseudo-critical non-normal dynamics reshapes the imaginary pseudospectrum, amplifying slow fluctuations and producing coherent frequency bands under finite-time observation. These results identify pseudo-coherence as a new route to collective temporal organization in non-equilibrium systems, suggesting that apparent rhythms and synchronization in natural systems may arise from non-normal stochastic amplification rather than intrinsic oscillators.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.07206v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.06953v1", "url": "http://arxiv.org/abs/2603.06953v1", "pdf_url": "https://arxiv.org/pdf/2603.06953v1", "title": "Universal electronic manifolds for extrapolative alloy discovery", "authors": ["Pranoy Ray", "Sayan Bhowmik", "Phanish Suryanarayana", "Surya R. Kalidindi", "Andrew J. Medford"], "annotation": "This study presents a computationally efficient framework for accelerated alloy discovery that uses the non-interacting electron density to capture intrinsic structure-property relationships in refractory high-entropy alloys (HEAs). Unlike state-of-the-art approaches relying on expensive, self-consistent density functional theory calculations, our method employs the non-interacting electron density as the primary structural descriptor. By extracting physical features through directionally resolved two-point spatial correlations and compressing them via Principal Component Analysis, we efficiently map the design space. Coupling these descriptors with Bayesian active learning, we achieve a normalized mean absolute error (NMAE) of <2% for the bulk modulus of Al-Nb-Ti-Zr alloys using only 10 training samples. Furthermore, we demonstrate that the model learns an electronic packing manifold that is transferable across distinct chemical species within refractory HEAs. Validated on a distinct 7-component refractory system (Mo-Nb-Ta-Ti-V-W-Zr) containing four elements entirely absent from the training data, the framework enables rigorous zero-shot extrapolation. Moreover, by augmenting the base model with just 20 samples from the target domain, we achieve high-fidelity predictions (NMAE < 3%) for 7-component alloys, reducing data acquisition costs by orders of magnitude compared to standard workflows. These results establish the non-interacting electron density as a rigorous, extrapolative descriptor for vast compositional landscapes.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.06953v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.06891v1", "url": "http://arxiv.org/abs/2603.06891v1", "pdf_url": "https://arxiv.org/pdf/2603.06891v1", "title": "Experimentally Resolving Gravity-Capillary Wave Evolution in Vessels of Unknown Boundary Conditions", "authors": ["Sean M. D. Gregory", "Vitor S. Barroso", "Silvia Schiattarella", "Anastasios Avgoustidis", "Silke Weinfurtner"], "annotation": "The geometries of surface wave modes are determined by the highly nontrivial interplay of capillarity and wetting effects at the boundaries of their domain. Aside from idealised scenarios, this commonly leads to unknown boundary conditions, thereby hindering theoretical formulation and experimental analysis. To address this problem, we introduce Extracted Mode Tracking (EMT), a data-analysis framework to obtain instantaneous amplitude and phase content of axisymmetric surface-wave modes from spatio-temporal measurements. This approach uses unsupervised machine learning techniques to extract a basis of wave modes directly from collected data; the spatial profiles require no prior theoretical modelling, and so the issue of unknown boundary conditions is circumvented. Time-resolved mode amplitudes are reconstructed by geometric fitting at each recorded time-step, and the success is evaluated by a spectral signal-to-noise quantifier. Capabilities and limitations of EMT are systematically benchmarked on synthetic datasets, finding strong resilience against noise, improved accuracy over alternative methodologies, and the ability to operate with restricted domains which poses significant merit for use in experimental systems with limited measurement field-of-view. Finally, we conduct a Faraday-wave experiment in a regime highly sensitive to boundary effects in order to further validate the method, and demonstrate the observational access to nonlinear wave-dynamics enabled by EMT. These results establish EMT as a general tool for analysing wave mode dynamics of axially-symmetric fluid interface systems, and open pathways for quantitative studies of nonlinear mode-interactions, stability, and turbulence.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.06891v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.06754v2", "url": "http://arxiv.org/abs/2603.06754v2", "pdf_url": "https://arxiv.org/pdf/2603.06754v2", "title": "Learning the Standard Model Manifold: Bayesian Latent Diffusion for Collider Anomaly Detection", "authors": ["Jigar Patel", "Tommaso Dorigo"], "annotation": "We propose a physics-informed anomaly detection framework for collider data based on a Bayesian latent diffusion model. Our method combines a probabilistic encoder with diffusion dynamics in the latent space, allowing for stable and flexible density estimation while explicitly enforcing physics constraints, such as mass decorrelation and regularization of latent correlations. We train and test the model on simulated LHC jet data and evaluate its performance using seed-averaged ROC curves together with discovery-oriented metrics. Through a series of ablation studies, we show that the diffusion process, Bayesian regularization, and physics-motivated loss terms each contribute in a complementary way: they help stabilize training and improve generalization, even when the gains in peak performance are moderate. Overall, our results emphasize the importance of incorporating both uncertainty estimates and physics consistency when building reliable anomaly detection methods for new Physics searches in high-energy physics.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.06754v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.05961v2", "url": "http://arxiv.org/abs/2603.05961v2", "pdf_url": "https://arxiv.org/pdf/2603.05961v2", "title": "A Tutorial on Bayesian Analysis of Linear Shock Compression Data", "authors": ["Jason Bernstein", "Philip C. Myint", "Beth A. Lindquist", "Justin Lee Brown"], "annotation": "Gas gun and other shock compression experiments often produce shock wave velocity measurements that are linearly associated with particle velocity. Traditionally, this empirical relationship is quantified with a single Hugoniot curve that is estimated using least squares regression. However, for downstream modeling and simulation tasks, it is often more useful to have multiple Hugoniot curves in the pressure-volume plane that are consistent with the data. We employ Bayesian uncertainty quantification methods as a framework for propagating measurement uncertainty through to model parameters and predictions. Specifically, this tutorial shows how to sample multiple Hugoniot curves in the pressure-volume plane that are consistent with the shock wave-particle velocity measurements in a two-step Bayesian approach. First, we obtain an analytical expression for the posterior distribution of the linear model parameters using Bayesian linear regression. Second, we propagate samples from the posterior distribution through the Rankine-Hugoniot equations to yield Hugoniot curves in the pressure-volume plane. The procedure is demonstrated with publicly available data on argon, copper, and nickel, and compared against bootstrapping and linear regression. The Bayesian procedure is shown to be interpretable, computationally inexpensive, and less sensitive than an alternative bootstrapping approach to the removal of the point in the copper dataset that has the largest particle velocity. As a tutorial on Bayesian methodology for the shock compression community, we provide several derivations and explanations that make this paper self-contained, and made all code and data available at https://github.com/llnl/BALSCD.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.05961v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.05712v1", "url": "http://arxiv.org/abs/2603.05712v1", "pdf_url": "https://arxiv.org/pdf/2603.05712v1", "title": "Non-intrusive Monitoring of Sealed Microreactor Cores Using Physics-Informed Muon Scattering Tomography With Momentum Measurements", "authors": ["Reshma Ughade", "Stylianos Chatzidakis"], "annotation": "Next-generation microreactors enable remote deployment and semi-autonomous operation, but compact, sealed, heterogeneous cores limit conventional safeguard approaches that rely on access and bulk accountancy. Limited inspection access and complex internal geometry reduce sensitivity to localized anomalies such as missing fuel. Here we demonstrate missing-fuel detection in microreactor scale geometries using muon scattering tomography under realistic cosmic-ray conditions. We introduce $μ$TRec, a physics-informed framework that reconstructs event-level curved muon trajectories by combining a Gaussian multiple Coulomb scattering model with Bayesian updating, then maps scattering density through voxel wise M-values for core integrity verification. We evaluate a representative hexagonal core containing 61 fuel flakes with embedded control drums and shutdown rods, using both idealized 5 GeV muons and zenith-angle-dependent 0-60 GeV cosmic-ray spectra. A single missing fuel flake is detected with $3\\times 10^{6}$ muons at 50 mm voxel resolution. Incorporating per-muon momentum further increases detectability by up to 149.85% for laser-driven sources and 105.11% for cosmic-ray sources relative to momentum-agnostic reconstruction. The approach remains robust under practical detector limits, with only an 8.88% reduction in detectability for 10 mm spatial resolution and 10% energy resolution. Compared with PoCA, $μ$TRec delivers 326.13% to 392.14% higher detectability at equal muon counts, enabling faster defect identification.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.05712v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.05260v1", "url": "http://arxiv.org/abs/2603.05260v1", "pdf_url": "https://arxiv.org/pdf/2603.05260v1", "title": "Extreme Value Analysis for Finite, Multivariate and Correlated Systems with Finance as an Example", "authors": ["Benjamin Köhler", "Anton J. Heckens", "Thomas Guhr"], "annotation": "Extreme values and the tail behavior of probability distributions are essential for quantifying and mitigating risk in complex systems of all kinds. In multivariate settings, accounting for correlations is crucial. Although extreme value analysis for infinite correlated systems remains an open challenge, we propose a practical framework for handling a large but finite number of correlated time series. We develop our approach for finance as a concrete example but emphasize its generality. We study the extremal behavior of high-frequency stock returns after rotating them into the eigenbasis of the correlation matrix. This separates and extracts various collective effects, including information on the correlated market as a whole and on correlated sectoral behavior from idiosyncratic features, while allowing us to use univariate tools of extreme value analysis. This holds even for high-frequency data where discretization effects normally complicate analysis. We employ a peaks-over-threshold approach and thereby fully avoid the analysis of block maxima. We estimate the tail shape of the rotated returns while explicitly accounting for nonstationarity, a key feature in finance and many other complex systems. Our framework facilitates tail risk estimation relative to larger trends and intraday seasonalities at both market and sectoral levels.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.05260v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.04093v1", "url": "http://arxiv.org/abs/2603.04093v1", "pdf_url": "https://arxiv.org/pdf/2603.04093v1", "title": "Reducing hyperparameter sensitivity in measurement-feedback based Ising machines", "authors": ["Toon Sevenants", "Guy Van der Sande", "Guy Verschaffelt"], "annotation": "Analog Ising machines have been proposed as heuristic hardware solvers for combinatorial optimization problems, with the potential to outperform conventional approaches, provided that their hyperparameters are carefully tuned. Their temporal evolution is often described using time-continuous dynamics. However, most experimental implementations rely on measurement-feedback architectures that operate in a time-discrete manner. We observe that in such setups, the range of effective hyperparameters is substantially smaller than in the envisioned time-continuous analog Ising machine. In this paper, we analyze this discrepancy and discuss its impact on the practical operation of Ising machines. Next, we propose and experimentally verify a method to reduce the sensitivity to hyperparameter selection of these measurement-feedback architectures.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.04093v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.03926v1", "url": "http://arxiv.org/abs/2603.03926v1", "pdf_url": "https://arxiv.org/pdf/2603.03926v1", "title": "A Structurally Localized Ensemble Kalman Filtering Approach", "authors": ["Boujemaa Ait-El-Fquih", "Ibrahim Hoteit"], "annotation": "State-of-the-art ensemble Kalman filtering (EnKF) algorithms require incorporating localization techniques to cope with the rank deficiency and the inherited spurious correlations in their error covariance matrices. Localization techniques are mostly ad-hoc, based on some distances between the state and observation variables, requiring demanding manual tuning. This work introduces a new ensemble filtering approach, which is inherently localized, avoiding the need for any auxiliary localization technique. Instead of explicitly applying localization on ensembles, the idea is to first localize the continuous analysis probability density function (pdf) before ensemble sampling. The localization of the analysis pdf is performed through an approximation by a product of independent marginal pdfs corresponding to small partitions of the state vector, using the variational Bayesian optimization. These marginals are then sampled following stochastic EnKF and deterministic ensemble transform Kalman filtering (ETKF) procedures, using ensembles larger than the partitions' size. The resulting filters involve the same forecast steps as their standard EnKF and ETKF counterparts but different analysis steps, iteratively adjusting the EnKF and ETKF updates of each partition based on the ensemble means of the other partitions. Numerical experiments are conducted with the Lorenz-96 model under different scenarios to demonstrate the potential of the proposed filters. The new filters' performances are comparable to those of the EnKF and ETKF with already tuned localization, both in terms of computational burden and estimation accuracy.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.03926v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.03826v1", "url": "http://arxiv.org/abs/2603.03826v1", "pdf_url": "https://arxiv.org/pdf/2603.03826v1", "title": "O-Sensing: Operator Sensing for Interaction Geometry and Symmetries", "authors": ["Meng Ye-Ming", "Shi Zhe-Yu"], "annotation": "We ask whether the Hamiltonian, interaction geometry, and symmetries of a quantum many-body system can be inferred from a few low-lying eigenstates without knowing which sites interact with each other. Directly solving the eigenvalue equations imposes constraints that yield a highly degenerate subspace of candidate operators, where the local Hamiltonian is hidden among an extensive family of conserved quantities, obscuring the interaction geometry. Here we introduce O-Sensing, a protocol designed to extract the Hamiltonian and symmetries directly from these states. Specifically, O-Sensing employs parsimony-driven optimization to extract a maximally sparse operator basis from the degenerate subspace. The Hamiltonian is then selected from this basis by maximizing spectral entropy (effectively minimizing degeneracy) within the sampled subspace. We validate O-Sensing on Heisenberg models on connected Erdős--Rényi graphs, where it reconstructs the interaction geometry and uncovers additional long-range conserved operators. We establish a learnability phase diagram across graph densities, featuring a pronounced ``confusion'' regime where parsimony favors a dual description on the complement graph. These results show that sparsity optimization can reconstruct interaction geometry as an emergent output, enabling simultaneous recovery of the Hamiltonian and its symmetries from low-energy eigenstates.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.03826v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.03513v1", "url": "http://arxiv.org/abs/2603.03513v1", "pdf_url": "https://arxiv.org/pdf/2603.03513v1", "title": "q-Gaussian Crossover in Overlap Spectra towards 3D Edwards-Anderson Criticality", "authors": ["Yaprak Onder", "Abbas Ali Saberi", "Roderich Moessner"], "annotation": "We introduce a spectral approach to characterizing the three-dimensional Edwards-Anderson spin glass. By analyzing the eigenvalue statistics of overlap matrices constructed from two-dimensional cross-sections, we identify a crossover from the Wigner semicircle law at high temperatures towards a Gaussian distribution, which is consistently attained near the spin-glass critical point. Visible for different distributions of the random coupling, the Gaussian distribution can potentially serve as a robust spectral indicator of criticality. Remarkably, the spectral density is well-described by Tsallis statistics, with the entropic index $q$ evolving from $q = -1$ (semicircle, $T=\\infty$) to $q = 1$ (Gaussian) at $T_c$, revealing a statistical structure inside the paramagnetic phase. We find $q\\le 1$ within numerical precision. While the local level statistics remain consistent with GOE statistics, reflecting standard level repulsion, the temperature dependence appears mainly in the global spectral density. Our results present spectral statistics as a computationally efficient complement to multi-replica correlator methods and provide a new perspective on cooperative and critical phenomena in disordered systems.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.03513v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.03168v1", "url": "http://arxiv.org/abs/2603.03168v1", "pdf_url": "https://arxiv.org/pdf/2603.03168v1", "title": "Data Unfolding: From Problem Formulation to Result Assessment", "authors": ["Nikolay D. Gagunashvili"], "annotation": "Experimental data in particle and nuclear physics, particle astrophysics, and radiation protection dosimetry are collected using experimental facilities that consist of a complex system of sensors, electronics, and software. Measured spectra or cross sections are considered as Probability Density Functions (PDFs) that deviate from true PDFs due to resolution, bias, and efficiency effects. Unfolding is viewed as a procedure for estimating an unknown true PDF. Reliable estimates of the true PDF are necessary for testing theoretical models, comparing results from different experiments, and combining results from various research endeavors. Both external and internal quality assessment methods can be applied for this purpose. In some cases, external criteria exist to evaluate deconvolution quality. A typical example is the deconvolution of a blurred image, where the sharpness of the restored image serves as an indicator of quality. However, defining such external criteria can be challenging, particularly when a measurement has not been performed previously. This paper discusses various internal criteria for assessing the quality of the results independently of external information, as well as factors that influence the quality of the unfolded distribution.", "category": "physics.data-an", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.03168v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.30017v1", "url": "http://arxiv.org/abs/2603.30017v1", "pdf_url": "https://arxiv.org/pdf/2603.30017v1", "title": "Refined Detection for Gumbel Watermarking", "authors": ["Tor Lattimore"], "annotation": "We propose a simple detection mechanism for the Gumbel watermarking scheme proposed by Aaronson (2022). The new mechanism is proven to be near-optimal in a problem-dependent sense among all model-agnostic watermarking schemes under the assumption that the next-token distribution is sampled i.i.d.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.30017v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29981v1", "url": "http://arxiv.org/abs/2603.29981v1", "pdf_url": "https://arxiv.org/pdf/2603.29981v1", "title": "Aligning Validation with Deployment: Target-Weighted Cross-Validation for Spatial Prediction", "authors": ["Alexander Brenning", "Thomas Suesse"], "annotation": "Cross-validation (CV) is commonly used to estimate predictive risk when independent test data are unavailable. Its validity depends on the assumption that validation tasks are sampled from the same distribution as prediction tasks encountered during deployment. In spatial prediction and other settings with structured data, this assumption is frequently violated, leading to biased estimates of deployment risk. We propose Target-Weighted CV (TWCV), an estimator of deployment risk that accounts for discrepancies between validation and deployment task distributions, thus accounting for (1) covariate shift and (2) task-difficulty shift. We characterize prediction tasks by descriptors such as covariates and spatial configuration. TWCV assigns weights to validation losses such that the weighted empirical distribution of validation tasks matches the corresponding distribution over a target domain. The weights are obtained via calibration weighting, yielding an importance-weighted estimator that targets deployment risk. Since TWCV requires adequate coverage of the deployment distribution's support, we combine it with spatially buffered resampling that diversifies the task difficulty distribution. In a simulation study, conventional as well as spatial estimators exhibit substantial bias depending on sampling, whereas buffered TWCV remains approximately unbiased across scenarios. A case study in environmental pollution mapping further confirms that discrepancies between validation and deployment task distributions can affect performance assessment, and that buffered TWCV better reflects the prediction task over the target domain. These results establish task distribution mismatch as a primary source of CV bias in spatial prediction and show that calibration weighting combined with a suitable validation task generator provides a viable approach to estimating predictive risk under dataset shift.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29981v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29972v1", "url": "http://arxiv.org/abs/2603.29972v1", "pdf_url": "https://arxiv.org/pdf/2603.29972v1", "title": "Do covariates explain why these groups differ? The choice of reference group can reverse conclusions in the Oaxaca-Blinder decomposition", "authors": ["Manuel Quintero", "Advik Shreekumar", "William T. Stephenson", "Tamara Broderick"], "annotation": "Scientists often want to explain why an outcome is different in two groups. For instance, differences in patient mortality rates across two hospitals could be due to differences in the patients themselves (covariates) or differences in medical care (outcomes given covariates). The Oaxaca--Blinder decomposition (OBD) is a standard tool to tease apart these factors. It is well known that the OBD requires choosing one of the groups as a reference, and the numerical answer can vary with the reference. To the best of our knowledge, there has not been a systematic investigation into whether the choice of OBD reference can yield different substantive conclusions and how common this issue is. In the present paper, we give existence proofs in real and simulated data that the OBD references can yield substantively different conclusions and that these differences are not entirely driven by model misspecification or small data. We prove that substantively different conclusions occur in up to half of the parameter space, but find these discrepancies rare in the real-data analyses we study. We explain this empirical rarity by examining how realistic data-generating processes can be biased towards parameters that do not change conclusions under the OBD.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29972v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29889v1", "url": "http://arxiv.org/abs/2603.29889v1", "pdf_url": "https://arxiv.org/pdf/2603.29889v1", "title": "Penalized GMM Framework for Inference on Functionals of Nonparametric Instrumental Variable Estimators", "authors": ["Edvard Bakhitov"], "annotation": "This paper develops a penalized GMM (PGMM) framework for automatic debiased inference on functionals of nonparametric instrumental variable estimators. We derive convergence rates for the PGMM estimator and provide conditions for root-n consistency and asymptotic normality of debiased functional estimates, covering both linear and nonlinear functionals. Monte Carlo experiments on average derivative show that the PGMM-based debiased estimator performs on par with the analytical debiased estimator that uses the known closed-form Riesz representer, achieving 90-96% coverage while the plug-in estimator falls below 5%. We apply our procedure to estimate mean own-price elasticities in a semiparametric demand model for differentiated products. Simulations confirm near-nominal coverage while the plug-in severely undercovers. Applied to IRI scanner data on carbonated beverages, debiased semiparametric estimates are approximately 20% more elastic compared to the logit benchmark, and debiasing corrections are heterogeneous across products, ranging from negligible to several times the standard error.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29889v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29730v1", "url": "http://arxiv.org/abs/2603.29730v1", "pdf_url": "https://arxiv.org/pdf/2603.29730v1", "title": "mlr3mbo: Bayesian Optimization in R", "authors": ["Marc Becker", "Lennart Schneider", "Martin Binder", "Lars Kotthoff", "Bernd Bischl"], "annotation": "We present mlr3mbo, a comprehensive and modular toolbox for Bayesian optimization in R. mlr3mbo supports single- and multi-objective optimization, multi-point proposals, batch and asynchronous parallelization, input and output transformations, and robust error handling. While it can be used for many standard Bayesian optimization variants in applied settings, researchers can also construct custom BO algorithms from its flexible building blocks. In addition to an introduction to the software, its design principles, and its building blocks, the paper presents two extensive empirical evaluations of the software on the surrogate-based benchmark suite YAHPO Gym. To identify robust default configurations for both numeric and mixed-hierarchical optimization regimes, and to gain further insights into the respective impacts of individual settings, we run a coordinate descent search over the mlr3mbo configuration space and analyze its results. Furthermore, we demonstrate that mlr3mbo achieves state-of-the-art performance by benchmarking it against a wide range of optimizers, including HEBO, SMAC3, Ax, and Optuna.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29730v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29725v1", "url": "http://arxiv.org/abs/2603.29725v1", "pdf_url": "https://arxiv.org/pdf/2603.29725v1", "title": "Unbounded Density Ratio Estimation and Its Application to Covariate Shift Adaptation", "authors": ["Ren-Rui Liu", "Jun Fan", "Lei Shi", "Zheng-Chu Guo"], "annotation": "This paper focuses on the problem of unbounded density ratio estimation -- an understudied yet critical challenge in statistical learning -- and its application to covariate shift adaptation. Much of the existing literature assumes that the density ratio is either uniformly bounded or unbounded but known exactly. These conditions are often violated in practice, creating a gap between theoretical guarantees and real-world applicability. In contrast, this work directly addresses unbounded density ratios and integrates them into importance weighting for effective covariate shift adaptation. We propose a three-step estimation method that leverages unlabeled data from both the source and target distributions: (1) estimating a relative density ratio; (2) applying a truncation operation to control its unboundedness; and (3) transforming the truncated estimate back into the standard density ratio. The estimated density ratio is then employed as importance weights for regression under covariate shift. We establish rigorous, non-asymptotic convergence guarantees for both the proposed density ratio estimator and the resulting regression function estimator, demonstrating optimal or near-optimal convergence rates. Our findings offer new theoretical insights into density ratio estimation and learning under covariate shift, extending classical learning theory to more practical and challenging scenarios.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29725v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29715v1", "url": "http://arxiv.org/abs/2603.29715v1", "pdf_url": "https://arxiv.org/pdf/2603.29715v1", "title": "Nonnegative Matrix Factorization in the Component-Wise L1 Norm for Sparse Data", "authors": ["Giovanni Seraghiti", "Kévin Dubrulle", "Arnaud Vandaele", "Nicolas Gillis"], "annotation": "Nonnegative matrix factorization (NMF) approximates a nonnegative matrix, $X$, by the product of two nonnegative factors, $WH$, where $W$ has $r$ columns and $H$ has $r$ rows. In this paper, we consider NMF using the component-wise L1 norm as the error measure (L1-NMF), which is suited for data corrupted by heavy-tailed noise, such as Laplace noise or salt and pepper noise, or in the presence of outliers. Our first contribution is an NP-hardness proof for L1-NMF, even when $r=1$, in contrast to the standard NMF that uses least squares. Our second contribution is to show that L1-NMF strongly enforces sparsity in the factors for sparse input matrices, thereby favoring interpretability. However, if the data is affected by false zeros, too sparse solutions might degrade the model. Our third contribution is a new, more general, L1-NMF model for sparse data, dubbed weighted L1-NMF (wL1-NMF), where the sparsity of the factorization is controlled by adding a penalization parameter to the entries of $WH$ associated with zeros in the data. The fourth contribution is a new coordinate descent (CD) approach for wL1-NMF, denoted as sparse CD (sCD), where each subproblem is solved by a weighted median algorithm. To the best of our knowledge, sCD is the first algorithm for L1-NMF whose complexity scales with the number of nonzero entries in the data, making it efficient in handling large-scale, sparse data. We perform extensive numerical experiments on synthetic and real-world data to show the effectiveness of our new proposed model (wL1-NMF) and algorithm (sCD).", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29715v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29654v1", "url": "http://arxiv.org/abs/2603.29654v1", "pdf_url": "https://arxiv.org/pdf/2603.29654v1", "title": "Concept frustration: Aligning human concepts and machine representations", "authors": ["Enrico Parisini", "Christopher J. Soelistyo", "Ahab Isaac", "Alessandro Barp", "Christopher R. S. Banerji"], "annotation": "Aligning human-interpretable concepts with the internal representations learned by modern machine learning systems remains a central challenge for interpretable AI. We introduce a geometric framework for comparing supervised human concepts with unsupervised intermediate representations extracted from foundation model embeddings. Motivated by the role of conceptual leaps in scientific discovery, we formalise the notion of concept frustration: a contradiction that arises when an unobserved concept induces relationships between known concepts that cannot be made consistent within an existing ontology. We develop task-aligned similarity measures that detect concept frustration between supervised concept-based models and unsupervised representations derived from foundation models, and show that the phenomenon is detectable in task-aligned geometry while conventional Euclidean comparisons fail. Under a linear-Gaussian generative model we derive a closed-form expression for Bayes-optimal concept-based classifier accuracy, decomposing predictive signal into known-known, known-unknown and unknown-unknown contributions and identifying analytically where frustration affects performance. Experiments on synthetic data and real language and vision tasks demonstrate that frustration can be detected in foundation model representations and that incorporating a frustrating concept into an interpretable model reorganises the geometry of learned concept representations, to better align human and machine reasoning. These results suggest a principled framework for diagnosing incomplete concept ontologies and aligning human and machine conceptual reasoning, with implications for the development and validation of safe interpretable AI for high-risk applications.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29654v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28999v1", "url": "http://arxiv.org/abs/2603.28999v1", "pdf_url": "https://arxiv.org/pdf/2603.28999v1", "title": "Transfer Learning in Bayesian Optimization for Aircraft Design", "authors": ["Ali Tfaily", "Youssef Diouane", "Nathalie Bartoli", "Michael Kokkolaras"], "annotation": "The use of transfer learning within Bayesian optimization addresses the disadvantages of the so-called \\textit{cold start} problem by using source data to aid in the optimization of a target problem. We present a method that leverages an ensemble of surrogate models using transfer learning and integrates it in a constrained Bayesian optimization framework. We identify challenges particular to aircraft design optimization related to heterogeneous design variables and constraints. We propose the use of a partial-least-squares dimension reduction algorithm to address design space heterogeneity, and a \\textit{meta} data surrogate selection method to address constraint heterogeneity. Numerical benchmark problems and an aircraft conceptual design optimization problem are used to demonstrate the proposed methods. Results show significant improvement in convergence in early optimization iterations compared to standard Bayesian optimization, with improved prediction accuracy for both objective and constraint surrogate models.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28999v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28987v1", "url": "http://arxiv.org/abs/2603.28987v1", "pdf_url": "https://arxiv.org/pdf/2603.28987v1", "title": "Multi-fidelity approaches for general constrained Bayesian optimization with application to aircraft design", "authors": ["Oihan Cordelier", "Youssef Diouane", "Nathalie Bartoli", "Eric Laurendeau"], "annotation": "Aircraft design relies heavily on solving challenging and computationally expensive Multidisciplinary Design Optimization problems. In this context, there has been growing interest in multi-fidelity models for Bayesian optimization to improve the MDO process by balancing computational cost and accuracy through the combination of high- and low-fidelity simulation models, enabling efficient exploration of the design process at a minimal computational effort. In the existing literature, fidelity selection focuses only on the objective function to decide how to integrate multiple fidelity levels, balancing precision and computational cost using variance reduction criteria. In this work, we propose novel multi-fidelity selection strategies. Specifically, we demonstrate how incorporating information from both the objective and the constraints can further reduce computational costs without compromising the optimality of the solution. We validate the proposed multi-fidelity optimization strategy by applying it to four analytical test cases, showcasing its effectiveness. The proposed method is used to efficiently solve a challenging aircraft wing aero-structural design problem. The proposed setting uses a linear vortex lattice method and a finite element method for the aerodynamic and structural analysis respectively. We show that employing our proposed multi-fidelity approach leads to $86\\%$ to $200\\%$ more constraint compliant solutions given a limited budget compared to the state-of-the-art approach.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28987v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28917v1", "url": "http://arxiv.org/abs/2603.28917v1", "pdf_url": "https://arxiv.org/pdf/2603.28917v1", "title": "Symmetrizing Bregman Divergence on the Cone of Positive Definite Matrices: Which Mean to Use and Why", "authors": ["Tushar Sial", "Abhishek Halder"], "annotation": "This work uncovers variational principles behind symmetrizing the Bregman divergences induced by generic mirror maps over the cone of positive definite matrices. We show that computing the canonical means for this symmetrization can be posed as minimizing the desired symmetrized divergences over a set of mean functionals defined axiomatically to satisfy certain properties. For the forward symmetrization, we prove that the arithmetic mean over the primal space is canonical for any mirror map over the positive definite cone. For the reverse symmetrization, we show that the canonical mean is the arithmetic mean over the dual space, pulled back to the primal space. Applying this result to three common mirror maps used in practice, we show that the canonical means for reverse symmetrization, in those cases, turn out to be the arithmetic, log-Euclidean and harmonic means. Our results improve understanding of existing symmetrization practices in the literature, and can be seen as a navigational chart to help decide which mean to use when.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28917v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28739v1", "url": "http://arxiv.org/abs/2603.28739v1", "pdf_url": "https://arxiv.org/pdf/2603.28739v1", "title": "Expectation Error Bounds for Transfer Learning in Linear Regression and Linear Neural Networks", "authors": ["Meitong Liu", "Christopher Jung", "Rui Li", "Xue Feng", "Han Zhao"], "annotation": "In transfer learning, the learner leverages auxiliary data to improve generalization on a main task. However, the precise theoretical understanding of when and how auxiliary data help remains incomplete. We provide new insights on this issue in two canonical linear settings: ordinary least squares regression and under-parameterized linear neural networks. For linear regression, we derive exact closed-form expressions for the expected generalization error with bias-variance decomposition, yielding necessary and sufficient conditions for auxiliary tasks to improve generalization on the main task. We also derive globally optimal task weights as outputs of solvable optimization programs, with consistency guarantees for empirical estimates. For linear neural networks with shared representations of width $q \\leq K$, where $K$ is the number of auxiliary tasks, we derive a non-asymptotic expectation bound on the generalization error, yielding the first non-vacuous sufficient condition for beneficial auxiliary learning in this setting, as well as principled directions for task weight curation. We achieve this by proving a new column-wise low-rank perturbation bound for random matrices, which improves upon existing bounds by preserving fine-grained column structures. Our results are verified on synthetic data simulated with controlled parameters.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28739v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28681v1", "url": "http://arxiv.org/abs/2603.28681v1", "pdf_url": "https://arxiv.org/pdf/2603.28681v1", "title": "Functional Natural Policy Gradients", "authors": ["Aurelien Bibaut", "Houssam Zenati", "Thibaud Rahier", "Nathan Kallus"], "annotation": "We propose a cross-fitted debiasing device for policy learning from offline data. A key consequence of the resulting learning principle is $\\sqrt N$ regret even for policy classes with complexity greater than Donsker, provided a product-of-errors nuisance remainder is $O(N^{-1/2})$. The regret bound factors into a plug-in policy error factor governed by policy-class complexity and an environment nuisance factor governed by the complexity of the environment dynamics, making explicit how one may be traded against the other.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28681v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28650v1", "url": "http://arxiv.org/abs/2603.28650v1", "pdf_url": "https://arxiv.org/pdf/2603.28650v1", "title": "Information-Theoretic Limits of Safety Verification for Self-Improving Systems", "authors": ["Arsenios Scrivens"], "annotation": "Can a safety gate permit unbounded beneficial self-modification while maintaining bounded cumulative risk? We formalize this question through dual conditions -- requiring sum delta_n < infinity (bounded risk) and sum TPR_n = infinity (unbounded utility) -- and establish a theory of their (in)compatibility.   Classification impossibility (Theorem 1): For power-law risk schedules delta_n = O(n^{-p}) with p > 1, any classifier-based gate under overlapping safe/unsafe distributions satisfies TPR_n <= C_alpha * delta_n^beta via Holder's inequality, forcing sum TPR_n < infinity. This impossibility is exponent-optimal (Theorem 3). A second independent proof via the NP counting method (Theorem 4) yields a 13% tighter bound without Holder's inequality.   Universal finite-horizon ceiling (Theorem 5): For any summable risk schedule, the exact maximum achievable classifier utility is U*(N, B) = N * TPR_NP(B/N), growing as exp(O(sqrt(log N))) -- subpolynomial. At N = 10^6 with budget B = 1.0, a classifier extracts at most U* ~ 87 versus a verifier's ~500,000.   Verification escape (Theorem 2): A Lipschitz ball verifier achieves delta = 0 with TPR > 0, escaping the impossibility. Formal Lipschitz bounds for pre-LayerNorm transformers under LoRA enable LLM-scale verification. The separation is strict. We validate on GPT-2 (d_LoRA = 147,456): conditional delta = 0 with TPR = 0.352. Comprehensive empirical validation is in the companion paper [D2].", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28650v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28595v1", "url": "http://arxiv.org/abs/2603.28595v1", "pdf_url": "https://arxiv.org/pdf/2603.28595v1", "title": "Optimistic Actor-Critic with Parametric Policies for Linear Markov Decision Processes", "authors": ["Max Qiushi Lin", "Reza Asad", "Kevin Tan", "Haque Ishfaq", "Csaba Szepesvari", "Sharan Vaswani"], "annotation": "Although actor-critic methods have been successful in practice, their theoretical analyses have several limitations. Specifically, existing theoretical work either sidesteps the exploration problem by making strong assumptions or analyzes impractical methods with complicated algorithmic modifications. Moreover, the actor-critic methods analyzed for linear MDPs often employ natural policy gradient (NPG) and construct \"implicit\" policies without explicit parameterization. Such policies are computationally expensive to sample from, making the environment interactions inefficient. To that end, we focus on the finite-horizon linear MDPs and propose an optimistic actor-critic framework that uses parametric log-linear policies. In particular, we introduce a tractable \\textit{logit-matching} regression objective for the actor. For the critic, we use approximate Thompson sampling via Langevin Monte Carlo to obtain optimistic value estimates. We prove that the resulting algorithm achieves $\\widetilde{\\mathcal{O}}(ε^{-4})$ and $\\widetilde{\\mathcal{O}}(ε^{-2})$ sample complexity in the on-policy and off-policy setting, respectively. Our results match prior theoretical works in achieving the state-of-the-art sample complexity, while our algorithm is more aligned with practice.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28595v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28466v1", "url": "http://arxiv.org/abs/2603.28466v1", "pdf_url": "https://arxiv.org/pdf/2603.28466v1", "title": "Post-hoc Self-explanation of CNNs", "authors": ["Ahcène Boubekki", "Line H. Clemmensen"], "annotation": "Although standard Convolutional Neural Networks (CNNs) can be mathematically reinterpreted as Self-Explainable Models (SEMs), their built-in prototypes do not on their own accurately represent the data. Replacing the final linear layer with a $k$-means-based classifier addresses this limitation without compromising performance. This work introduces a common formalization of $k$-means-based post-hoc explanations for the classifier, the encoder's final output (B4), and combinations of intermediate feature activations. The latter approach leverages the spatial consistency of convolutional receptive fields to generate concept-based explanation maps, which are supported by gradient-free feature attribution maps. Empirical evaluation with a ResNet34 shows that using shallower, less compressed feature activations, such as those from the last three blocks (B234), results in a trade-off between semantic fidelity and a slight reduction in predictive performance.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28466v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28455v1", "url": "http://arxiv.org/abs/2603.28455v1", "pdf_url": "https://arxiv.org/pdf/2603.28455v1", "title": "FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocation", "authors": ["Tiantian Wang", "Xiang Xiang", "Simon S. Du"], "annotation": "In federated healthcare systems, Federated Class-Incremental Learning (FCIL) has emerged as a key paradigm, enabling continuous adaptive model learning among distributed clients while safeguarding data privacy. However, in practical applications, data across agent nodes within the distributed framework often exhibits non-independent and identically distributed (non-IID) characteristics, rendering traditional continual learning methods inapplicable. To address these challenges, this paper covers more comprehensive incremental task scenarios and proposes a dynamic memory allocation strategy for exemplar storage based on the data replay mechanism. This strategy fully taps into the inherent potential of data heterogeneity, while taking into account the performance fairness of all participating clients, thereby establishing a balanced and adaptive solution to mitigate catastrophic forgetting. Unlike the fixed allocation of client exemplar memory, the proposed scheme emphasizes the rational allocation of limited storage resources among clients to improve model performance. Furthermore, extensive experiments are conducted on three medical image datasets, and the results demonstrate significant performance improvements compared to existing baseline models.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28455v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28423v1", "url": "http://arxiv.org/abs/2603.28423v1", "pdf_url": "https://arxiv.org/pdf/2603.28423v1", "title": "Profile Graphical Models", "authors": ["Alejandra Avalos-Pacheco", "Monia Lupparelli", "Francesco C. Stingo"], "annotation": "We introduce a novel class of graphical models, termed profile graphical models, that represent, within a single graph, how an external factor influences the dependence structure of a multivariate set of variables. This class is quite general and includes multiple graphs and chain graphs as special cases. Profile graphical models capture the conditional distributions of a multivariate random vector given different levels of a risk factor, and learn how the conditional independence structure among variables may vary across these risk profiles; we formally define this family of models and establish their corresponding Markov properties. We derive key structural and probabilistic properties that underpin a more powerful inferential framework than existing approaches, underscoring that our contribution extends beyond a novel graphical representation.Furthermore, we show that the resulting profile undirected graphical models are independence-compatible with two-block LWF chain graph models.We then develop a Bayesian approach for Gaussian undirected profile graphical models based on continuous spike-and-slab priors to learn shared sparsity structures across different levels of the risk factor. We also design a fast EM algorithm for efficient inference. Inferential properties are explored through simulation studies, including the comparison with competing methods. The practical utility of this class of models is demonstrated through the analysis of protein network data from various subtypes of acute myeloid leukemia. Our results show a more parsimonious network and greater patient heterogeneity than its competitors, highlighting its enhanced ability to capture subject-specific differences.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28423v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28410v1", "url": "http://arxiv.org/abs/2603.28410v1", "pdf_url": "https://arxiv.org/pdf/2603.28410v1", "title": "Mixture-Model Preference Learning for Many-Objective Bayesian Optimization", "authors": ["Manisha Dubey", "Sebastiaan De Peuter", "Wanrong Wang", "Samuel Kaski"], "annotation": "Preference-based many-objective optimization faces two obstacles: an expanding space of trade-offs and heterogeneous, context-dependent human value structures. Towards this, we propose a Bayesian framework that learns a small set of latent preference archetypes rather than assuming a single fixed utility function, modelling them as components of a Dirichlet-process mixture with uncertainty over both archetypes and their weights. To query efficiently, we designing hybrid queries that target information about (i) mode identity and (ii) within-mode trade-offs. Under mild assumptions, we provide a simple regret guarantee for the resulting mixture-aware Bayesian optimization procedure. Empirically, our method outperforms standard baselines on synthetic and real-world many-objective benchmarks, and mixture-aware diagnostics reveal structure that regret alone fails to capture.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28410v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28359v1", "url": "http://arxiv.org/abs/2603.28359v1", "pdf_url": "https://arxiv.org/pdf/2603.28359v1", "title": "The Conjugate Domain Dichotomy: Exact Risk of M-Estimators under Infinite-Variance Noise in High Dimensions", "authors": ["Charalampos Agiropoulos"], "annotation": "This paper studies high-dimensional M-estimation in the proportional asymptotic regime (p/n -> gamma > 0) when the noise distribution has infinite variance. For noise with regularly-varying tails of index alpha in (1,2), we establish that the asymptotic behavior of a regularized M-estimator is governed by a single geometric property of the loss function: the boundedness of the domain of its Fenchel conjugate.   When this conjugate domain is bounded -- as is the case for the Huber, absolute-value, and quantile loss functions -- the dual variable in the min-max formulation of the estimator is confined, the effective noise reduces to the finite first absolute moment of the noise distribution, and the estimator achieves bounded risk without recourse to external information. When the conjugate domain is unbounded -- as for the squared loss -- the dual variable scales with the noise, the effective noise involves the diverging second moment, and bounded risk can be achieved only through transfer regularization toward an external prior.   For the squared-loss class specifically, we derive the exact asymptotic risk via the Convex Gaussian Minimax Theorem under a noise-adapted regularization scaling. The resulting risk converges to a universal floor that is independent of the regularizer, yielding a loss-risk trichotomy: squared-loss estimators without transfer diverge; Huber-loss estimators achieve bounded but non-vanishing risk; transfer-regularized estimators attain the floor.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28359v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28346v1", "url": "http://arxiv.org/abs/2603.28346v1", "pdf_url": "https://arxiv.org/pdf/2603.28346v1", "title": "Machine Learning-Assisted High-Dimensional Matrix Estimation", "authors": ["Wan Tian", "Hui Yang", "Zhouhui Lian", "Lingyue Zhang", "Yijie Peng"], "annotation": "Efficient estimation of high-dimensional matrices-including covariance and precision matrices-is a cornerstone of modern multivariate statistics. Most existing studies have focused primarily on the theoretical properties of the estimators (e.g., consistency and sparsity), while largely overlooking the computational challenges inherent in high-dimensional settings. Motivated by recent advances in learning-based optimization method-which integrate data-driven structures with classical optimization algorithms-we explore high-dimensional matrix estimation assisted by machine learning. Specifically, for the optimization problem of high-dimensional matrix estimation, we first present a solution procedure based on the Linearized Alternating Direction Method of Multipliers (LADMM). We then introduce learnable parameters and model the proximal operators in the iterative scheme with neural networks, thereby improving estimation accuracy and accelerating convergence. Theoretically, we first prove the convergence of LADMM, and then establish the convergence, convergence rate, and monotonicity of its reparameterized counterpart; importantly, we show that the reparameterized LADMM enjoys a faster convergence rate. Notably, the proposed reparameterization theory and methodology are applicable to the estimation of both high-dimensional covariance and precision matrices. We validate the effectiveness of our method by comparing it with several classical optimization algorithms across different structures and dimensions of high-dimensional matrices.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28346v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28324v1", "url": "http://arxiv.org/abs/2603.28324v1", "pdf_url": "https://arxiv.org/pdf/2603.28324v1", "title": "LDDMM stochastic interpolants: an application to domain uncertainty quantification in hemodynamics", "authors": ["Sarah Katz", "Francesco Romor", "Jia-Jie Zhu", "Alfonso Caiazzo"], "annotation": "We introduce a novel conditional stochastic interpolant framework for generative modeling of three-dimensional shapes. The method builds on a recent LDDMM-based registration approach to learn the conditional drift between geometries. By leveraging the resulting pull-back and push-forward operators, we extend this formulation beyond standard Cartesian grids to complex shapes and random variables defined on distinct domains. We present an application in the context of cardiovascular simulations, where aortic shapes are generated from an initial cohort of patients. The conditioning variable is a latent geometric representation defined by a set of centerline points and the radii of the corresponding inscribed spheres. This methodology facilitates both data augmentation for three-dimensional biomedical shapes, and the generation of random perturbations of controlled magnitude for a given shape. These capabilities are essential for quantifying the impact of domain uncertainties arising from medical image segmentation on the estimation of relevant biomarkers.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28324v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28254v1", "url": "http://arxiv.org/abs/2603.28254v1", "pdf_url": "https://arxiv.org/pdf/2603.28254v1", "title": "MuonEq: Balancing Before Orthogonalization with Lightweight Equilibration", "authors": ["Da Chang", "Qiankun Shi", "Lvgang Zhang", "Yu Li", "Ruijie Zhang", "Yao Lu", "Yongxiang Liu", "Ganzhao Yuan"], "annotation": "Orthogonalized-update optimizers such as Muon improve training of matrix-valued parameters, but existing extensions mostly act either after orthogonalization by rescaling updates or before it with heavier whitening-based preconditioners. We introduce {\\method}, a lightweight family of pre-orthogonalization equilibration schemes for Muon in three forms: two-sided row/column normalization (RC), row normalization (R), and column normalization (C). These variants rebalance the momentum matrix before finite-step Newton--Schulz using row/column squared-norm statistics and only $\\mathcal{O}(m+n)$ auxiliary state. We show that finite-step orthogonalization is governed by input spectral properties, especially stable rank and condition number, and that row/column normalization is a zeroth-order whitening surrogate that removes marginal scale mismatch. For the hidden matrix weights targeted by {\\method}, the row-normalized variant R is the natural default and preserves the $\\widetilde{\\mathcal{O}}(T^{-1/4})$ stationarity guarantee of Muon-type methods. In LLaMA2 pretraining on C4, the default R variant consistently outperforms Muon on 130M and 350M models, yielding faster convergence and lower validation perplexity.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28254v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28201v1", "url": "http://arxiv.org/abs/2603.28201v1", "pdf_url": "https://arxiv.org/pdf/2603.28201v1", "title": "A Perturbation Approach to Unconstrained Linear Bandits", "authors": ["Andrew Jacobsen", "Dorian Baudry", "Shinji Ito", "Nicolò Cesa-Bianchi"], "annotation": "We revisit the standard perturbation-based approach of Abernethy et al. (2008) in the context of unconstrained Bandit Linear Optimization (uBLO). We show the surprising result that in the unconstrained setting, this approach effectively reduces Bandit Linear Optimization (BLO) to a standard Online Linear Optimization (OLO) problem. Our framework improves on prior work in several ways. First, we derive expected-regret guarantees when our perturbation scheme is combined with comparator-adaptive OLO algorithms, leading to new insights about the impact of different adversarial models on the resulting comparator-adaptive rates. We also extend our analysis to dynamic regret, obtaining the optimal $\\sqrt{P_T}$ path-length dependencies without prior knowledge of $P_T$. We then develop the first high-probability guarantees for both static and dynamic regret in uBLO. Finally, we discuss lower bounds on the static regret, and prove the folklore $Ω(\\sqrt{dT})$ rate for adversarial linear bandits on the unit Euclidean ball, which is of independent interest.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28201v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27903v1", "url": "http://arxiv.org/abs/2603.27903v1", "pdf_url": "https://arxiv.org/pdf/2603.27903v1", "title": "Persistence diagrams of random matrices via Morse theory: universality and a new spectral diagnostic", "authors": ["Matthew Loftus"], "annotation": "We prove that the persistence diagram of the sublevel set filtration of the quadratic form f(x) = x^T M x restricted to the unit sphere S^{n-1} is analytically determined by the eigenvalues of the symmetric matrix M. By Morse theory, the diagram has exactly n-1 finite bars, with the k-th bar living in homological dimension k-1 and having length equal to the k-th eigenvalue spacing s_k = λ_{k+1} - λ_k. This identification transfers random matrix theory (RMT) universality to persistence diagram universality: for matrices drawn from the Gaussian Orthogonal Ensemble (GOE), we derive the closed-form persistence entropy PE = log(8n/π) - 1, and verify numerically that the coefficient of variation of persistence statistics decays as n^{-0.6}. Different random matrix ensembles (GOE, GUE, Wishart) produce distinct universal persistence diagrams, providing topological fingerprints of RMT universality classes. As a practical consequence, we show that persistence entropy outperforms the standard level spacing ratio \\langle r \\rangle for discriminating GOE from GUE matrices (AUC 0.978 vs. 0.952 at n = 100, non-overlapping bootstrap 95% CIs), and detects global spectral perturbations in the Rosenzweig-Porter model to which \\langle r \\rangle is blind. These results establish persistence entropy as a new spectral diagnostic that captures complementary information to existing RMT tools.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27903v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27871v1", "url": "http://arxiv.org/abs/2603.27871v1", "pdf_url": "https://arxiv.org/pdf/2603.27871v1", "title": "Statistical Guarantees for Distributionally Robust Optimization with Optimal Transport and OT-Regularized Divergences", "authors": ["Jeremiah Birrell", "Xiaoxi Shen"], "annotation": "We study finite-sample statistical performance guarantees for distributionally robust optimization (DRO) with optimal transport (OT) and OT-regularized divergence model neighborhoods. Specifically, we derive concentration inequalities for supervised learning via DRO-based adversarial training, as commonly employed to enhance the adversarial robustness of machine learning models. Our results apply to a wide range of OT cost functions, beyond the $p$-Wasserstein case studied by previous authors. In particular, our results are the first to: 1) cover soft-constraint norm-ball OT cost functions; soft-constraint costs have been shown empirically to enhance robustness when used in adversarial training, 2) apply to the combination of adversarial sample generation and adversarial reweighting that is induced by using OT-regularized $f$-divergence model neighborhoods; the added reweighting mechanism has also been shown empirically to further improve performance. In addition, even in the $p$-Wasserstein case, our bounds exhibit better behavior as a function of the DRO neighborhood size than previous results when applied to the adversarial setting.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27871v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27864v1", "url": "http://arxiv.org/abs/2603.27864v1", "pdf_url": "https://arxiv.org/pdf/2603.27864v1", "title": "Vertical Consensus Inference for High-Dimensional Random Partition", "authors": ["Khai Nguyen", "Yang Ni", "Peter Mueller"], "annotation": "We review recently proposed Bayesian approaches for clustering high-dimensional data. After identifying the main limitations of available approaches, we introduce an alternative framework based on vertical consensus inference (VCI) to mitigate the curse of dimensionality in high-dimensional Bayesian clustering. VCI builds on the idea of consensus Monte Carlo by dividing the data into multiple shards (smaller subsets of variables), performing posterior inference on each shard, and then combining the shard-level posteriors to obtain a consensus posterior. The key distinction is that VCI splits the data vertically, producing vertical shards that retain the same number of observations but have lower dimensionality. We use an entropic regularized Wasserstein barycenter to define a consensus posterior. The shard-specific barycenter weights are constructed to favor shards that provide meaningful partitions, distinct from a trivial single cluster or all singleton clusters, favoring balanced cluster sizes and precise shard-specific posterior random partitions. We show that VCI can be interpreted as a variational approximation to the posterior under a hierarchical model with a generalized Bayes prior. For relatively low-dimensional problems, experiments suggest that VCI closely approximates inference based on clustering the entire multivariate data. For high-dimensional data and in the presence of many noninformative dimensions, VCI introduces a new framework for model-based and principled inference on random partitions. Although our focus here is on random partitions, VCI can be applied to any dimension-independent parameters and serves as a bridge to emerging areas in statistics such as consensus Monte Carlo, optimal transport, variational inference, and generalized Bayes.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27864v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27814v1", "url": "http://arxiv.org/abs/2603.27814v1", "pdf_url": "https://arxiv.org/pdf/2603.27814v1", "title": "RG-TTA: Regime-Guided Meta-Control for Test-Time Adaptation in Streaming Time Series", "authors": ["Indar Kumar", "Akanksha Tiwari", "Sai Krishna Jasti", "Ankit Hemant Lade"], "annotation": "Test-time adaptation (TTA) enables neural forecasters to adapt to distribution shifts in streaming time series, but existing methods apply the same adaptation intensity regardless of the nature of the shift. We propose Regime-Guided Test-Time Adaptation (RG-TTA), a meta-controller that continuously modulates adaptation intensity based on distributional similarity to previously-seen regimes. Using an ensemble of Kolmogorov-Smirnov, Wasserstein-1, feature-distance, and variance-ratio metrics, RG-TTA computes a similarity score for each incoming batch and uses it to (i) smoothly scale the learning rate -- more aggressive for novel distributions, conservative for familiar ones -- and (ii) control gradient effort via loss-driven early stopping rather than fixed budgets, allowing the system to allocate exactly the effort each batch requires. As a supplementary mechanism, RG-TTA gates checkpoint reuse from a regime memory, loading stored specialist models only when they demonstrably outperform the current model (loss improvement >= 30%). RG-TTA is model-agnostic and strategy-composable: it wraps any forecaster exposing train/predict/save/load interfaces and enhances any gradient-based TTA method. We demonstrate three compositions -- RG-TTA, RG-EWC, and RG-DynaTTA -- and evaluate 6 update policies (3 baselines + 3 regime-guided variants) across 4 compact architectures (GRU, iTransformer, PatchTST, DLinear), 14 datasets (6 real-world multivariate benchmarks + 8 synthetic regime scenarios), and 4 forecast horizons (96, 192, 336, 720) under a streaming evaluation protocol with 3 random seeds (672 experiments total). Regime-guided policies achieve the lowest MSE in 156 of 224 seed-averaged experiments (69.6%), with RG-EWC winning 30.4% and RG-TTA winning 29.0%. Overall, RG-TTA reduces MSE by 5.7% vs TTA while running 5.5% faster; RG-EWC reduces MSE by 14.1% vs standalone EWC.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27814v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27792v1", "url": "http://arxiv.org/abs/2603.27792v1", "pdf_url": "https://arxiv.org/pdf/2603.27792v1", "title": "What-If Explanations Over Time: Counterfactuals for Time Series Classification", "authors": ["Udo Schlegel", "Thomas Seidl"], "annotation": "Counterfactual explanations emerge as a powerful approach in explainable AI, providing what-if scenarios that reveal how minimal changes to an input time series can alter the model's prediction. This work presents a survey of recent algorithms for counterfactual explanations for time series classification. We review state-of-the-art methods, spanning instance-based nearest-neighbor techniques, pattern-driven algorithms, gradient-based optimization, and generative models. For each, we discuss the underlying methodology, the models and classifiers they target, and the datasets on which they are evaluated. We highlight unique challenges in generating counterfactuals for temporal data, such as maintaining temporal coherence, plausibility, and actionable interpretability, which distinguish the temporal from tabular or image domains. We analyze the strengths and limitations of existing approaches and compare their effectiveness along key dimensions (validity, proximity, sparsity, plausibility, etc.). In addition, we implemented an open-source implementation library, Counterfactual Explanations for Time Series (CFTS), as a reference framework that includes many algorithms and evaluation metrics. We discuss this library's contributions in standardizing evaluation and enabling practical adoption of explainable time series techniques. Finally, based on the literature and identified gaps, we propose future research directions, including improved user-centered design, integration of domain knowledge, and counterfactuals for time series forecasting.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27792v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27766v1", "url": "http://arxiv.org/abs/2603.27766v1", "pdf_url": "https://arxiv.org/pdf/2603.27766v1", "title": "AutoStan: Autonomous Bayesian Model Improvement via Predictive Feedback", "authors": ["Oliver Dürr"], "annotation": "We present AutoStan, a framework in which a command-line interface (CLI) coding agent autonomously builds and iteratively improves Bayesian models written in Stan. The agent operates in a loop, writing a Stan model file, executing MCMC sampling, then deciding whether to keep or revert each change based on two complementary feedback signals: the negative log predictive density (NLPD) on held-out data and the sampler's own diagnostics (divergences, R-hat, effective sample size). We evaluate AutoStan on five datasets with diverse modeling structures. On a synthetic regression dataset with outliers, the agent progresses from naive linear regression to a model with Student-t robustness, nonlinear heteroscedastic structure, and an explicit contamination mixture, matching or outperforming TabPFN, a state-of-the-art black-box method, while remaining fully interpretable. Across four additional experiments, the same mechanism discovers hierarchical partial pooling, varying-slope models with correlated random effects, and a Poisson attack/defense model for soccer. No search algorithm, critic module, or domain-specific instructions are needed. This is, to our knowledge, the first demonstration that a CLI coding agent can autonomously write and iteratively improve Stan code for diverse Bayesian modeling problems.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27766v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27672v1", "url": "http://arxiv.org/abs/2603.27672v1", "pdf_url": "https://arxiv.org/pdf/2603.27672v1", "title": "Energy Score-Guided Neural Gaussian Mixture Model for Predictive Uncertainty Quantification", "authors": ["Yang Yang", "Chunlin Ji", "Haoyang Li", "Ke Deng"], "annotation": "Quantifying predictive uncertainty is essential for real world machine learning applications, especially in scenarios requiring reliable and interpretable predictions. Many common parametric approaches rely on neural networks to estimate distribution parameters by optimizing the negative log likelihood. However, these methods often encounter challenges like training instability and mode collapse, leading to poor estimates of the mean and variance of the target output distribution. In this work, we propose the Neural Energy Gaussian Mixture Model (NE-GMM), a novel framework that integrates Gaussian Mixture Model (GMM) with Energy Score (ES) to enhance predictive uncertainty quantification. NE-GMM leverages the flexibility of GMM to capture complex multimodal distributions and leverages the robustness of ES to ensure well calibrated predictions in diverse scenarios. We theoretically prove that the hybrid loss function satisfies the properties of a strictly proper scoring rule, ensuring alignment with the true data distribution, and establish generalization error bounds, demonstrating that the model's empirical performance closely aligns with its expected performance on unseen data. Extensive experiments on both synthetic and real world datasets demonstrate the superiority of NE-GMM in terms of both predictive accuracy and uncertainty quantification.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27672v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27631v1", "url": "http://arxiv.org/abs/2603.27631v1", "pdf_url": "https://arxiv.org/pdf/2603.27631v1", "title": "On the Asymptotics of Self-Supervised Pre-training: Two-Stage M-Estimation and Representation Symmetry", "authors": ["Mohammad Tinati", "Stephen Tu"], "annotation": "Self-supervised pre-training, where large corpora of unlabeled data are used to learn representations for downstream fine-tuning, has become a cornerstone of modern machine learning. While a growing body of theoretical work has begun to analyze this paradigm, existing bounds leave open the question of how sharp the current rates are, and whether they accurately capture the complex interaction between pre-training and fine-tuning. In this paper, we address this gap by developing an asymptotic theory of pre-training via two-stage M-estimation. A key challenge is that the pre-training estimator is often identifiable only up to a group symmetry, a feature common in representation learning that requires careful treatment. We address this issue using tools from Riemannian geometry to study the intrinsic parameters of the pre-training representation, which we link with the downstream predictor through a notion of orbit-invariance, precisely characterizing the limiting distribution of the downstream test risk. We apply our main result to several case studies, including spectral pre-training, factor models, and Gaussian mixture models, and obtain substantial improvements in problem-specific factors over prior art when applicable.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27631v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27457v1", "url": "http://arxiv.org/abs/2603.27457v1", "pdf_url": "https://arxiv.org/pdf/2603.27457v1", "title": "Optimal Demixing of Nonparametric Densities", "authors": ["Jianqing Fan", "Zheng Tracy Ke", "Zhaoyang Shi"], "annotation": "Motivated by applications in statistics and machine learning, we consider a problem of unmixing convex combinations of nonparametric densities. Suppose we observe $n$ groups of samples, where the $i$th group consists of $N_i$ independent samples from a $d$-variate density $f_i(x)=\\sum_{k=1}^K π_i(k)g_k(x)$. Here, each $g_k(x)$ is a nonparametric density, and each $π_i$ is a $K$-dimensional mixed membership vector. We aim to estimate $g_1(x), \\ldots,g_K(x)$. This problem generalizes topic modeling from discrete to continuous variables and finds its applications in LLMs with word embeddings.   In this paper, we propose an estimator for the above problem, which modifies the classical kernel density estimator by assigning group-specific weights that are computed by topic modeling on histogram vectors and de-biased by U-statistics. For any $β>0$, assuming that each $g_k(x)$ is in the Nikol'ski class with a smooth parameter $β$, we show that the sum of integrated squared errors of the constructed estimators has a convergence rate that depends on $n$, $K$, $d$, and the per-group sample size $N$. We also provide a matching lower bound, which suggests that our estimator is rate-optimal.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27457v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27395v1", "url": "http://arxiv.org/abs/2603.27395v1", "pdf_url": "https://arxiv.org/pdf/2603.27395v1", "title": "Topological Detection of Hopf Bifurcations via Persistent Homology: A Functional Criterion from Time Series", "authors": ["Jhonathan Barrios", "Yásser Echávez", "Carlos F. Álvarez"], "annotation": "We propose a topological framework for the detection of Hopf bifurcations directly from time series, based on persistent homology applied to phase space reconstructions via Takens embedding within the framework of Topological Data Analysis. The central idea is that changes in the dynamical regime are reflected in the emergence or disappearance of a dominant one-dimensional homological features in the reconstructed attractor. To quantify this behavior, we introduce a simple and interpretable scalar topological functional defined as the maximum persistence of homology classes in dimension one. This functional is used to construct a computable criterion for identifying critical parameters in families of dynamical systems without requiring knowledge of the underlying equations. The proposed approach is validated on representative systems of increasing complexity, showing consistent detection of the bifurcation point. The results support the interpretation of dynamical transitions as topological phase transitions and demonstrate the potential of topological data analysis as a model-free tool for the quantitative analysis of nonlinear time series.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27395v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27389v1", "url": "http://arxiv.org/abs/2603.27389v1", "pdf_url": "https://arxiv.org/pdf/2603.27389v1", "title": "Diagnosing Non-Markovian Observations in Reinforcement Learning via Prediction-Based Violation Scoring", "authors": ["Naveen Mysore"], "annotation": "Reinforcement learning algorithms assume that observations satisfy the Markov property, yet real-world sensors frequently violate this assumption through correlated noise, latency, or partial observability. Standard performance metrics conflate Markov breakdowns with other sources of suboptimality, leaving practitioners without diagnostic tools for such violations. This paper introduces a prediction-based scoring method that quantifies non-Markovian structure in observation trajectories. A random forest first removes nonlinear Markov-compliant dynamics; ridge regression then tests whether historical observations reduce prediction error on the residuals beyond what the current observation provides. The resulting score is bounded in [0, 1] and requires no causal graph construction. Evaluation spans six environments (CartPole, Pendulum, Acrobot, HalfCheetah, Hopper, Walker2d), three algorithms (PPO, A2C, SAC), controlled AR(1) noise at six intensity levels, and 10 seeds per condition. In post-hoc detection, 7 of 16 environment-algorithm pairs, primarily high-dimensional locomotion tasks, show significant positive monotonicity between noise intensity and the violation score (Spearman rho up to 0.78, confirmed under repeated-measures analysis); under training-time noise, 13 of 16 pairs exhibit statistically significant reward degradation. An inversion phenomenon is documented in low-dimensional environments where the random forest absorbs the noise signal, causing the score to decrease as true violations grow, a failure mode analyzed in detail. A practical utility experiment demonstrates that the proposed score correctly identifies partial observability and guides architecture selection, fully recovering performance lost to non-Markovian observations. Source code to reproduce all results is provided at https://github.com/NAVEENMN/Markovianes.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27389v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27370v1", "url": "http://arxiv.org/abs/2603.27370v1", "pdf_url": "https://arxiv.org/pdf/2603.27370v1", "title": "The Risk Quadrangle in Optimization: An Overview with Recent Results and Extensions", "authors": ["Bogdan Grechuk", "Anton Malandii", "Terry Rockafellar", "Stan Uryasev"], "annotation": "This paper revisits and extends the 2013 development by Rockafellar and Uryasev of the Risk Quadrangle (RQ) as a unified scheme for integrating risk management, optimization, and statistical estimation. The RQ features four stochastics-oriented functionals -- risk, deviation, regret, and error, along with an associated statistic, and articulates their revealing and in some ways surprising interrelationships and dualizations. Additions to the RQ framework that have come to light since 2013 are reviewed in a synthesis focused on both theoretical advancements and practical applications. New quadrangles -- superquantile, superquantile norm, expectile, biased mean, quantile symmetric average union, and $\\varphi$-divergence-based quadrangles -- offer novel approaches to risk-sensitive decision-making across various fields such as machine learning, statistics, finance, and PDE-constrained optimization. The theoretical contribution comes in axioms for ``subregularity'' relaxing ``regularity'' of the quadrangle functionals, which is too restrictive for some applications. The main RQ theorems and connections are revisited and rigorously extended to this more ample framework. Examples are provided in portfolio optimization, regression, and classification, demonstrating the advantages and the role played by duality, especially in ties to robust optimization and generalized stochastic divergences.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27370v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27320v1", "url": "http://arxiv.org/abs/2603.27320v1", "pdf_url": "https://arxiv.org/pdf/2603.27320v1", "title": "Retrospective Counterfactual Prediction by Conditioning on the Factual Outcome: A Cross-World Approach", "authors": ["Juraj Bodik"], "annotation": "Retrospective causal questions ask what would have happened to an observed individual had they received a different treatment. We study the problem of estimating $μ(x,y)=\\mathbb{E}[Y(1)\\mid X=x,Y(0)=y]$, the expected counterfactual outcome for an individual with covariates $x$ and observed outcome $y$, and constructing valid prediction intervals under the Neyman-Rubin superpopulation model. This quantity is generally not identified without additional assumptions. To link the observed and unobserved potential outcomes, we work with a cross-world correlation $ρ(x)=cor(Y(1),Y(0)\\mid X=x)$; plausible bounds on $ρ(x)$ enable a principled approach to this otherwise unidentified problem. We introduce retrospective counterfactual estimators $\\hatμ_ρ(x,y)$ and prediction intervals $C_ρ(x,y)$ that asymptotically satisfy $P[Y(1)\\in C_ρ(x,y)\\mid X=x, Y(0)=y]\\ge1-α$ under standard causal assumptions. Many common baselines implicitly correspond to endpoint choices $ρ=0$ or $ρ=1$ (ignoring the factual outcome or treating the counterfactual as a shifted factual outcome). Interpolating between these cases through cross-world dependence yields substantial gains in both theory and practice.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27320v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27270v1", "url": "http://arxiv.org/abs/2603.27270v1", "pdf_url": "https://arxiv.org/pdf/2603.27270v1", "title": "Quantification of Credal Uncertainty: A Distance-Based Approach", "authors": ["Xabier Gonzalez-Garcia", "Siu Lun Chau", "Julian Rodemann", "Michele Caprio", "Krikamol Muandet", "Humberto Bustince", "Sébastien Destercke", "Eyke Hüllermeier", "Yusuf Sale"], "annotation": "Credal sets, i.e., closed convex sets of probability measures, provide a natural framework to represent aleatoric and epistemic uncertainty in machine learning. Yet how to quantify these two types of uncertainty for a given credal set, particularly in multiclass classification, remains underexplored. In this paper, we propose a distance-based approach to quantify total, aleatoric, and epistemic uncertainty for credal sets. Concretely, we introduce a family of such measures within the framework of Integral Probability Metrics (IPMs). The resulting quantities admit clear semantic interpretations, satisfy natural theoretical desiderata, and remain computationally tractable for common choices of IPMs. We instantiate the framework with the total variation distance and obtain simple, efficient uncertainty measures for multiclass classification. In the binary case, this choice recovers established uncertainty measures, for which a principled multiclass generalization has so far been missing. Empirical results confirm practical usefulness, with favorable performance at low computational cost.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27270v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27189v1", "url": "http://arxiv.org/abs/2603.27189v1", "pdf_url": "https://arxiv.org/pdf/2603.27189v1", "title": "Conformal Prediction Assessment: A Framework for Conditional Coverage Evaluation and Selection", "authors": ["Zheng Zhou", "Xiangfei Zhang", "Chongguang Tao", "Yuhong Yang"], "annotation": "Conformal prediction provides rigorous distribution-free finite-sample guarantees for marginal coverage under the assumption of exchangeability, but may exhibit systematic undercoverage or overcoverage for specific subpopulations. Assessing conditional validity is challenging, as standard stratification methods suffer from the curse of dimensionality. We propose Conformal Prediction Assessment (CPA), a framework that reframes the evaluation of conditional coverage as a supervised learning task by training a reliability estimator that predicts instance-level coverage probabilities. Building on this estimator, we introduce the Conditional Validity Index (CVI), which decomposes reliability into safety (undercoverage risk) and efficiency (overcoverage cost). We establish convergence rates for the reliability estimator and prove the consistency of CVI-based model selection. Extensive experiments on synthetic and real-world datasets demonstrate that CPA effectively diagnoses local failure modes and that CC-Select, our CVI-based model selection algorithm, consistently identifies predictors with superior conditional coverage performance.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27189v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27142v1", "url": "http://arxiv.org/abs/2603.27142v1", "pdf_url": "https://arxiv.org/pdf/2603.27142v1", "title": "Bayes-MICE: A Bayesian Approach to Multiple Imputation for Time Series Data", "authors": ["Amuche Ibenegbu", "Pierre Lafaye de Micheaux", "Rohitash Chandra"], "annotation": "Time-series analysis is often affected by missing data, a common problem across several fields, including healthcare and environmental monitoring.   Multiple Imputation by Chained Equations (MICE) has been prominent for imputing missing values through \"fully conditional specification\". We extend MICE using the Bayesian framework (Bayes-MICE), utilising Bayesian inference to impute missing values via Markov Chain Monte Carlo (MCMC) sampling to account for uncertainty in MICE model parameters and imputed values. We also include temporally informed initialisation and time-lagged features in the model to respect the sequential nature of time-series data. We evaluate the Bayes-MICE method using two real-world datasets (AirQuality and PhysioNet), and using both the Random Walk Metropolis (RWM) and the Metropolis-Adjusted Langevin Algorithm (MALA) samplers. Our results demonstrate that Bayes-MICE reduces imputation errors relative to the baseline methods over all variables and accounts for uncertainty in the imputation process, thereby providing a more accurate measure of imputation error. We also found that MALA converges faster than RWM, achieving comparable accuracy while providing more consistent posterior exploration. Overall, these findings suggest that the Bayes-MICE framework represents a practical and efficient approach to time-series imputation, balancing increased accuracy with meaningful quantification of uncertainty in various environmental and clinical settings.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27142v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27137v1", "url": "http://arxiv.org/abs/2603.27137v1", "pdf_url": "https://arxiv.org/pdf/2603.27137v1", "title": "A Mean Field Games Perspective on Evolutionary Clustering", "authors": ["Alessio Basti", "Fabio Camilli", "Adriano Festa"], "annotation": "We propose a control-theoretic framework for evolutionary clustering based on Mean Field Games (MFG). Moving beyond static or heuristic approaches, we formulate the problem as a population dynamics game governed by a coupled Hamilton-Jacobi-Bellman and Fokker-Planck system. Driven by a variational cost functional rather than predefined statistical shapes, this continuous-time formulation provides a flexible basis for non-parametric cluster evolution. To validate the framework, we analyze the setting of time-dependent Gaussian mixtures, showing that the MFG dynamics recover the trajectories of the classical Expectation-Maximization (EM) algorithm while ensuring mass conservation. Furthermore, we introduce time-averaged log-likelihood functionals to regularize temporal fluctuations. Numerical experiments illustrate the stability of our approach and suggest a path toward more general non-parametric clustering applications where traditional EM methods may face limitations.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27137v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27135v1", "url": "http://arxiv.org/abs/2603.27135v1", "pdf_url": "https://arxiv.org/pdf/2603.27135v1", "title": "Spectral-Aware Text-to-Time Series Generation with Billion-Scale Multimodal Meteorological Data", "authors": ["Shijie Zhang"], "annotation": "Text-to-time-series generation is particularly important in meteorology, where natural language offers intuitive control over complex, multi-scale atmospheric dynamics. Existing approaches are constrained by the lack of large-scale, physically grounded multimodal datasets and by architectures that overlook the spectral-temporal structure of weather signals. We address these challenges with a unified framework for text-guided meteorological time-series generation. First, we introduce MeteoCap-3B, a billion-scale weather dataset paired with expert-level captions constructed via a Multi-agent Collaborative Captioning (MACC) pipeline, yielding information-dense and physically consistent annotations. Building on this dataset, we propose MTransformer, a diffusion-based model that enables precise semantic control by mapping textual descriptions into multi-band spectral priors through a Spectral Prompt Generator, which guides generation via frequency-aware attention. Extensive experiments on real-world benchmarks demonstrate state-of-the-art generation quality, accurate cross-modal alignment, strong semantic controllability, and substantial gains in downstream forecasting under data-sparse and zero-shot settings. Additional results on general time-series benchmarks indicate that the proposed framework generalizes beyond meteorology.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27135v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27113v1", "url": "http://arxiv.org/abs/2603.27113v1", "pdf_url": "https://arxiv.org/pdf/2603.27113v1", "title": "Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation", "authors": ["Urvi Awasthi", "Alexander Arjun Lobo", "Leonid Zhukov"], "annotation": "Generating chemically valid 3D molecules is hindered by discrete bond topology: small local bond errors can cause global failures (valence violations, disconnections, implausible rings), especially for drug-like molecules with long-range constraints. Many unconditional 3D generators emphasize coordinates and then infer bonds or rely on post-processing, leaving topology feasibility weakly controlled. We propose Hierarchy-Guided Latent Topology Flow (HLTF), a planner-executor model that generates bond graphs with 3D coordinates, using a latent multi-scale plan for global context and a constraint-aware sampler to suppress topology-driven failures. On QM9, HLTF achieves 98.8% atom stability and 92.9% valid-and-unique, improving PoseBusters validity to 94.0% (+0.9 over the strongest reported baseline). On GEOM-DRUGS, HLTF attains 85.5%/85.0% validity/valid-unique-novel without post-processing and 92.2%/91.2% after standardized relaxation, within 0.9 points of the best post-processed baseline. Explicit topology generation also reduces \"false-valid\" samples that pass RDKit sanitization but fail stricter checks.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27113v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27074v1", "url": "http://arxiv.org/abs/2603.27074v1", "pdf_url": "https://arxiv.org/pdf/2603.27074v1", "title": "Forecastability as an Information-Theoretic Limit on Prediction", "authors": ["Peter Maurice Catt"], "annotation": "Forecasting is usually framed as a problem of model choice. This paper starts earlier, asking how much predictive information is available at each horizon. Under logarithmic loss, the answer is exact: the mutual information between the future observation and the declared information set equals the maximum achievable reduction in expected loss. This paper develops the consequences of that identity. Forecastability, defined as this mutual information evaluated across horizons, forms a profile whose shape reflects the dependence structure of the process and need not be monotone. Three structural properties are derived: compression of the information set can only reduce forecastability; the gap between the profile under a finite lag window and the full history gives an exact truncation error budget; and for processes with periodic dependence, the profile inherits the periodicity. Predictive loss decomposes into an irreducible component fixed by the information structure and an approximation component attributable to the method; their ratio defines the exploitation ratio, a normalised diagnostic for method adequacy. The exact equality is specific to log loss, but when forecastability is near zero, classical inequalities imply that no method under any loss can materially improve on the unconditional baseline. The framework provides a theoretical foundation for assessing, prior to any modelling, whether the declared information set contains sufficient predictive information at the horizon of interest.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27074v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27072v1", "url": "http://arxiv.org/abs/2603.27072v1", "pdf_url": "https://arxiv.org/pdf/2603.27072v1", "title": "On the Loss Landscape Geometry of Regularized Deep Matrix Factorization: Uniqueness and Sharpness", "authors": ["Anil Kamber", "Rahul Parhi"], "annotation": "Weight decay is ubiquitous in training deep neural network architectures. Its empirical success is often attributed to capacity control; nonetheless, our theoretical understanding of its effect on the loss landscape and the set of minimizers remains limited. In this paper, we show that $\\ell^2$-regularized deep matrix factorization/deep linear network training problems with squared-error loss admit a unique end-to-end minimizer for all target matrices subject to factorization, except for a set of Lebesgue measure zero formed by the depth and the regularization parameter. This observation reveals fundamental properties of the loss landscape of regularized deep matrix factorization problems: the Hessian spectrum is constant across all minimizers of the regularized deep scalar factorization problem with squared-error loss. Moreover, we show that, in regularized deep matrix factorization problems with squared-error loss, if the target matrix does not belong to the Lebesgue measure-zero set, then the Frobenius norm of each layer is constant across all minimizers. This, in turn, yields a global lower bound on the trace of the Hessian evaluated at any minimizer of the regularized deep matrix factorization problem. Furthermore, we establish a critical threshold for the regularization parameter above which the unique end-to-end minimizer collapses to zero.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27072v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27062v1", "url": "http://arxiv.org/abs/2603.27062v1", "pdf_url": "https://arxiv.org/pdf/2603.27062v1", "title": "Conformalized Signal Temporal Logic Inference under Covariate Shift", "authors": ["Yixuan Wang", "Danyang Li", "Matthew Cleaveland", "Roberto Tron", "Mingyu Cai"], "annotation": "Signal Temporal Logic (STL) inference learns interpretable logical rules for temporal behaviors in dynamical systems. To ensure the correctness of learned STL formulas, recent approaches have incorporated conformal prediction as a statistical tool for uncertainty quantification. However, most existing methods rely on the assumption that calibration and testing data are identically distributed and exchangeable, an assumption that is frequently violated in real-world settings. This paper proposes a conformalized STL inference framework that explicitly addresses covariate shift between training and deployment trajectories dataset. From a technical standpoint, the approach first employs a template-free, differentiable STL inference method to learn an initial model, and subsequently refines it using a limited deployment side dataset to promote distribution alignment. To provide validity guarantees under distribution shift, the framework estimates the likelihood ratio between training and deployment distributions and integrates it into an STL-robustness-based weighted conformal prediction scheme. Experimental results on trajectory datasets demonstrate that the proposed framework preserves the interpretability of STL formulas while significantly improving symbolic learning reliability at deployment time.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27062v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27049v1", "url": "http://arxiv.org/abs/2603.27049v1", "pdf_url": "https://arxiv.org/pdf/2603.27049v1", "title": "Overcoming the Incentive Collapse Paradox", "authors": ["Qichuan Yin", "Ziwei Su", "Shuangning Li"], "annotation": "AI-assisted task delegation is increasingly common, yet human effort in such systems is costly and typically unobserved. Recent work by Bastani and Cachon (2025); Sambasivan et al. (2021) shows that accuracy-based payment schemes suffer from incentive collapse: as AI accuracy improves, sustaining positive human effort requires unbounded payments. We study this problem in a budget-constrained principal-agent framework with strategic human agents whose output accuracy depends on unobserved effort. We propose a sentinel-auditing payment mechanism that enforces a strictly positive and controllable level of human effort at finite cost, independent of AI accuracy. Building on this incentive-robust foundation, we develop an incentive-aware active statistical inference framework that jointly optimizes (i) the auditing rate and (ii) active sampling and budget allocation across tasks of varying difficulty to minimize the final statistical loss under a single budget. Experiments demonstrate improved cost-error tradeoffs relative to standard active learning and auditing-only baselines.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27049v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27019v1", "url": "http://arxiv.org/abs/2603.27019v1", "pdf_url": "https://arxiv.org/pdf/2603.27019v1", "title": "Parameter Estimation in Stochastic Differential Equations via Wiener Chaos Expansion and Stochastic Gradient Descent", "authors": ["Francisco Delgado-Vences", "José Julián Pavón-Español", "Arelly Ornelas"], "annotation": "This study addresses the inverse problem of parameter estimation for Stochastic Differential Equations (SDEs) by minimizing a regularized discrepancy functional via Stochastic Gradient Descent (SGD). To achieve computational efficiency, we leverage the Wiener Chaos Expansion (WCE), a spectral decomposition technique that projects the stochastic solution onto an orthogonal basis of Hermite polynomials. This transformation effectively maps the stochastic dynamics into a hierarchical system of deterministic functions, termed the \\textit{propagator}. By reducing the stochastic inference task to a deterministic optimization problem, our framework circumvents the heavy computational burden and sampling requirements of traditional simulation-based methods like MCMC or MLE. The robustness and scalability of the proposed approach are demonstrated through numerical experiments on various non-linear SDEs, including models for individual biological growth. Results show that the WCE-SGD framework provides accurate parameter recovery even from discrete, noisy observations, offering a significant paradigm shift in the efficient modeling of complex stochastic systems.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27019v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26993v1", "url": "http://arxiv.org/abs/2603.26993v1", "pdf_url": "https://arxiv.org/pdf/2603.26993v1", "title": "On the Reliability Limits of LLM-Based Multi-Agent Planning", "authors": ["Ruicheng Ao", "Siyang Gao", "David Simchi-Levi"], "annotation": "This technical note studies the reliability limits of LLM-based multi-agent planning as a delegated decision problem. We model the LLM-based multi-agent architecture as a finite acyclic decision network in which multiple stages process shared model-context information, communicate through language interfaces with limited capacity, and may invoke human review. We show that, without new exogenous signals, any delegated network is decision-theoretically dominated by a centralized Bayes decision maker with access to the same information. In the common-evidence regime, this implies that optimizing over multi-agent directed acyclic graphs under a finite communication budget can be recast as choosing a budget-constrained stochastic experiment on the shared signal. We also characterize the loss induced by communication and information compression. Under proper scoring rules, the gap between the centralized Bayes value and the value after communication admits an expected posterior divergence representation, which reduces to conditional mutual information under logarithmic loss and to expected squared posterior error under the Brier score. These results characterize the fundamental reliability limits of delegated LLM planning. Experiments with LLMs on a controlled problem set further demonstrate these characterizations.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26993v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26982v1", "url": "http://arxiv.org/abs/2603.26982v1", "pdf_url": "https://arxiv.org/pdf/2603.26982v1", "title": "Online Statistical Inference of Constant Sample-averaged Q-Learning", "authors": ["Saunak Kumar Panda", "Tong Li", "Ruiqi Liu", "Yisha Xiang"], "annotation": "Reinforcement learning algorithms have been widely used for decision-making tasks in various domains. However, the performance of these algorithms can be impacted by high variance and instability, particularly in environments with noise or sparse rewards. In this paper, we propose a framework to perform statistical online inference for a sample-averaged Q-learning approach. We adapt the functional central limit theorem (FCLT) for the modified algorithm under some general conditions and then construct confidence intervals for the Q-values via random scaling. We conduct experiments to perform inference on both the modified approach and its traditional counterpart, Q-learning using random scaling and report their coverage rates and confidence interval widths on two problems: a grid world problem as a simple toy example and a dynamic resource-matching problem as a real-world example for comparison between the two solution approaches.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26982v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26963v1", "url": "http://arxiv.org/abs/2603.26963v1", "pdf_url": "https://arxiv.org/pdf/2603.26963v1", "title": "On the Optimal Number of Grids for Differentially Private Non-Interactive $K$-Means Clustering", "authors": ["Gokularam Muthukrishnan", "Anshoo Tandon"], "annotation": "Differentially private $K$-means clustering enables releasing cluster centers derived from a dataset while protecting the privacy of the individuals. Non-interactive clustering techniques based on privatized histograms are attractive because the released data synopsis can be reused for other downstream tasks without additional privacy loss. The choice of the number of grids for discretizing the data points is crucial, as it directly controls the quantization bias and the amount of noise injected to preserve privacy. The widely adopted strategy selects a grid size that is independent of the number of clusters and also relies on empirical tuning. In this work, we revisit this choice and propose a refined grid-size selection rule derived by minimizing an upper bound on the expected deviation in the K-means objective function, leading to a more principled discretization strategy for non-interactive private clustering. Compared to prior work, our grid resolution differs both in its dependence on the number of clusters and in the scaling with dataset size and privacy budget. Extensive numerical results elucidate that the proposed strategy results in accurate clustering compared to the state-of-the-art techniques, even under tight privacy budgets.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26963v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26940v1", "url": "http://arxiv.org/abs/2603.26940v1", "pdf_url": "https://arxiv.org/pdf/2603.26940v1", "title": "Static and Dynamic Approaches to Computing Barycenters of Probability Measures on Graphs", "authors": ["David Gentile", "James M. Murphy"], "annotation": "The optimal transportation problem defines a geometry of probability measures which leads to a definition for weighted averages (barycenters) of measures, finding application in the machine learning and computer vision communities as a signal processing tool. Here, we implement a barycentric coding model for measures which are supported on a graph, a context in which the classical optimal transport geometry becomes degenerate, by leveraging a Riemannian structure on the simplex induced by a dynamic formulation of the optimal transport problem. We approximate the exponential mapping associated to the Riemannian structure, as well as its inverse, by utilizing past approaches which compute action minimizing curves in order to numerically approximate transport distances for measures supported on discrete spaces. Intrinsic gradient descent is then used to synthesize barycenters, wherein gradients of a variance functional are computed by approximating geodesic curves between the current iterate and the reference measures; iterates are then pushed forward via a discretization of the continuity equation. Analysis of measures with respect to given dictionary of references is performed by solving a quadratic program formed by computing geodesics between target and reference measures. We compare our novel approach to one based on entropic regularization of the static formulation of the optimal transport problem where the graph structure is encoded via graph distance functions, we present numerical experiments validating our approach, and we conclude that intrinsic gradient descent on the probability simplex provides a coherent framework for the synthesis and analysis of measures supported on graphs.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26940v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26923v1", "url": "http://arxiv.org/abs/2603.26923v1", "pdf_url": "https://arxiv.org/pdf/2603.26923v1", "title": "Koopman Operator Identification of Model Parameter Trajectories for Temporal Domain Generalization (KOMET)", "authors": ["Randy C. Hoover", "Jacob James", "Paul May", "Kyle Caudle"], "annotation": "Parametric models deployed in non-stationary environments degrade as the underlying data distribution evolves over time (a phenomenon known as temporal domain drift). In the current work, we present KOMET (Koopman Operator identification of Model parameter Evolution under Temporal drift), a model-agnostic, data-driven framework that treats the sequence of trained parameter vectors as the trajectory of a nonlinear dynamical system and identifies its governing linear operator via Extended Dynamic Mode Decomposition (EDMD). A warm-start sequential training protocol enforces parameter-trajectory smoothness, and a Fourier-augmented observable dictionary exploits the periodic structure inherent in many real-world distribution drifts. Once identified, KOMET's Koopman operator predicts future parameter trajectories autonomously, without access to future labeled data, enabling zero-retraining adaptation at deployment. Evaluated on six datasets spanning rotating, oscillating, and expanding distribution geometries, KOMET achieves mean autonomous-rollout accuracies between 0.981 and 1.000 over 100 held-out time steps. Spectral and coupling analyses further reveal interpretable dynamical structure consistent with the geometry of the drifting decision boundary.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26923v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26611v1", "url": "http://arxiv.org/abs/2603.26611v1", "pdf_url": "https://arxiv.org/pdf/2603.26611v1", "title": "Benchmarking Tabular Foundation Models for Conditional Density Estimation in Regression", "authors": ["Rafael Izbicki", "Pedro L. C. Rodrigues"], "annotation": "Conditional density estimation (CDE) - recovering the full conditional distribution of a response given tabular covariates - is essential in settings with heteroscedasticity, multimodality, or asymmetric uncertainty. Recent tabular foundation models, such as TabPFN and TabICL, naturally produce predictive distributions, but their effectiveness as general-purpose CDE methods has not been systematically evaluated, unlike their performance for point prediction, which is well studied. We benchmark three tabular foundation model variants against a diverse set of parametric, tree-based, and neural CDE baselines on 39 real-world datasets, across training sizes from 50 to 20,000, using six metrics covering density accuracy, calibration, and computation time. Across all sample sizes, foundation models achieve the best CDE loss, log-likelihood, and CRPS on the large majority of datasets tested. Calibration is competitive at small sample sizes but, for some metrics and datasets, lags behind task-specific neural baselines at larger sample sizes, suggesting that post-hoc recalibration may be a valuable complement. In a photometric redshift case study using SDSS DR18, TabPFN exposed to 50,000 training galaxies outperforms all baselines trained on the full 500,000-galaxy dataset. Taken together, these results establish tabular foundation models as strong off-the-shelf conditional density estimators.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26611v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26554v1", "url": "http://arxiv.org/abs/2603.26554v1", "pdf_url": "https://arxiv.org/pdf/2603.26554v1", "title": "Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory", "authors": ["Juno Kim", "Eshaan Nichani", "Denny Wu", "Alberto Bietti", "Jason D. Lee"], "annotation": "Spectral optimizers such as Muon have recently shown strong empirical performance in large-scale language model training, but the source and extent of their advantage remain poorly understood. We study this question through the linear associative memory problem, a tractable model for factual recall in transformer-based models. In particular, we go beyond orthogonal embeddings and consider Gaussian inputs and outputs, which allows the number of stored associations to greatly exceed the embedding dimension. Our main result sharply characterizes the recovery rates of one step of Muon and SGD on the logistic regression loss under a power law frequency distribution. We show that the storage capacity of Muon significantly exceeds that of SGD, and moreover Muon saturates at a larger critical batch size. We further analyze the multi-step dynamics under a thresholded gradient approximation and show that Muon achieves a substantially faster initial recovery rate than SGD, while both methods eventually converge to the information-theoretic limit at comparable speeds. Experiments on synthetic tasks validate the predicted scaling laws. Our analysis provides a quantitative understanding of the signal amplification of Muon and lays the groundwork for establishing scaling laws across more practical language modeling tasks and optimizers.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26554v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26502v1", "url": "http://arxiv.org/abs/2603.26502v1", "pdf_url": "https://arxiv.org/pdf/2603.26502v1", "title": "Targeted learning of heterogeneous treatment effect curves for right censored or left truncated time-to-event data", "authors": ["Matthew Pryce", "Karla Diaz-Ordaz", "Ruth H. Keogh", "Stijn Vansteelandt"], "annotation": "In recent years, there has been growing interest in causal machine learning estimators for quantifying subject-specific effects of a binary treatment on time-to-event outcomes. Estimation approaches have been proposed which attenuate the inherent regularisation bias in machine learning predictions, with each of these estimators addressing measured confounding, right censoring, and in some cases, left truncation. However, the existing approaches are found to exhibit suboptimal finite-sample performance, with none of the existing estimators fully leveraging the temporal structure of the data, yielding non-smooth treatment effects over time. We address these limitations by introducing surv-iTMLE, a targeted learning procedure for estimating the difference in the conditional survival probabilities under two treatments. Unlike existing estimators, surv-iTMLE accommodates both left truncation and right censoring while enforcing smoothness and boundedness of the estimated treatment effect curve over time. Through extensive simulation studies under both right censoring and left truncation scenarios, we demonstrate that surv-iTMLE outperforms existing methods in terms of bias and smoothness of time-varying effect estimates in finite samples. We then illustrate surv-iTMLE's practical utility by exploring heterogeneity in the effects of immunotherapy on survival among non-small cell lung cancer (NSCLC) patients, revealing clinically meaningful temporal patterns that existing estimators may obscure.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26502v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26478v1", "url": "http://arxiv.org/abs/2603.26478v1", "pdf_url": "https://arxiv.org/pdf/2603.26478v1", "title": "Probabilistic Multilabel Graphical Modelling of Motif Transformations in Symbolic Music", "authors": ["Ron Taieb", "Yoel Greenberg", "Barak Sober"], "annotation": "Motifs often recur in musical works in altered forms, preserving aspects of their identity while undergoing local variation. This paper investigates how such motivic transformations occur within their musical context in symbolic music. To support this analysis, we develop a probabilistic framework for modeling motivic transformations and apply it to Beethoven's piano sonatas by integrating multiple datasets that provide melodic, rhythmic, harmonic, and motivic information within a unified analytical representation.   Motif transformations are represented as multilabel variables by comparing each motif instance to a designated reference occurrence within its local context, ensuring consistent labeling across transformation families. We introduce a multilabel Conditional Random Field to model how motif-level musical features influence the occurrence of transformations and how different transformation families tend to co-occur.   Our goal is to provide an interpretable, distributional analysis of motivic transformation patterns, enabling the study of their structural relationships and stylistic variation. By linking computational modeling with music-theoretical interpretation, the proposed framework supports quantitative investigation of musical structure and complexity in symbolic corpora and may facilitate the analysis of broader compositional patterns and writing practices.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26478v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26858v1", "url": "http://arxiv.org/abs/2603.26858v1", "pdf_url": "https://arxiv.org/pdf/2603.26858v1", "title": "A Hierarchical Sheaf Spectral Embedding Framework for Single-Cell RNA-seq Analysis", "authors": ["Xiang Xiang Wang", "Guo-Wei We"], "annotation": "Single-cell RNA-seq data analysis typically requires representations that capture heterogeneous local structure across multiple scales while remaining stable and interpretable. In this work, we propose a hierarchical sheaf spectral embedding (HSSE) framework that constructs informative cell-level features based on persistent sheaf Laplacian analysis. Starting from scale-dependent low-dimensional embeddings, we define cell-centered local neighborhoods at multiple resolutions. For each local neighborhood, we construct a data-driven cellular sheaf that encodes local relationships among cells. We then compute persistent sheaf Laplacians over sampled filtration intervals and extract spectral statistics that summarize the evolution of local relational structure across scales. These spectral descriptors are aggregated into a unified feature vector for each cell and can be directly used in downstream learning tasks without additional model training. We evaluate HSSE on twelve benchmark single-cell RNA-seq datasets covering diverse biological systems and data scales. Under a consistent classification protocol, HSSE achieves competitive or improved performance compared with existing multiscale and classical embedding-based methods across multiple evaluation metrics. The results demonstrate that sheaf spectral representations provide a robust and interpretable approach for single-cell RNA-seq data representation learning.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26858v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26418v1", "url": "http://arxiv.org/abs/2603.26418v1", "pdf_url": "https://arxiv.org/pdf/2603.26418v1", "title": "Kantorovich--Kernel Neural Operators: Approximation Theory, Asymptotics, and Neural Network Interpretation", "authors": ["Tian-Xiao He"], "annotation": "This paper studies a class of multivariate Kantorovich-kernel neural network operators, including the deep Kantorovich-type neural network operators studied by Sharma and Singh. We prove density results, establish quantitative convergence estimates, derive Voronovskaya-type theorems, analyze the limits of partial differential equations for deep composite operators, prove Korovkin-type theorems, and propose inversion theorems. This paper studies a class of multivariate Kantorovich-kernel neural network operators, including the deep Kantorovich-type neural network operators studied by Sharma and Singh. We prove density results, establish quantitative convergence estimates, derive Voronovskaya-type theorems, analyze the limits of partial differential equations for deep composite operators, prove Korovkin-type theorems, and propose inversion theorems. Furthermore, this paper discusses the connection between neural network architectures and the classical positive operators proposed by Chui, Hsu, He, Lorentz, and Korovkin.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26418v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26349v1", "url": "http://arxiv.org/abs/2603.26349v1", "pdf_url": "https://arxiv.org/pdf/2603.26349v1", "title": "Generative Score Inference for Multimodal Data", "authors": ["Xinyu Tian", "Xiaotong Shen"], "annotation": "Accurate uncertainty quantification is crucial for making reliable decisions in various supervised learning scenarios, particularly when dealing with complex, multimodal data such as images and text. Current approaches often face notable limitations, including rigid assumptions and limited generalizability, constraining their effectiveness across diverse supervised learning tasks. To overcome these limitations, we introduce Generative Score Inference (GSI), a flexible inference framework capable of constructing statistically valid and informative prediction and confidence sets across a wide range of multimodal learning problems. GSI utilizes synthetic samples generated by deep generative models to approximate conditional score distributions, facilitating precise uncertainty quantification without imposing restrictive assumptions about the data or tasks. We empirically validate GSI's capabilities through two representative scenarios: hallucination detection in large language models and uncertainty estimation in image captioning. Our method achieves state-of-the-art performance in hallucination detection and robust predictive uncertainty in image captioning, and its performance is positively influenced by the quality of the underlying generative model. These findings underscore the potential of GSI as a versatile inference framework, significantly enhancing uncertainty quantification and trustworthiness in multimodal learning.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26349v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26344v1", "url": "http://arxiv.org/abs/2603.26344v1", "pdf_url": "https://arxiv.org/pdf/2603.26344v1", "title": "A Power-Weighted Noncentral Complex Gaussian Distribution", "authors": ["Toru Nakashika"], "annotation": "The complex Gaussian distribution has been widely used as a fundamental spectral and noise model in signal processing and communication. However, its Gaussian structure often limits its ability to represent the diverse amplitude characteristics observed in individual source signals. On the other hand, many existing non-Gaussian amplitude distributions derived from hyperspherical models achieve good empirical fit due to their power-law structures, while they do not explicitly account for the complex-plane geometry inherent in complex-valued observations. In this paper, we propose a new probabilistic model for complex-valued random variables, which can be interpreted as a power-weighted noncentral complex Gaussian distribution. Unlike conventional hyperspherical amplitude models, the proposed model is formulated directly on the complex plane and preserves the geometric structure of complex-valued observations while retaining a higher-dimensional interpretation. The model introduces a nonlinear phase diffusion through a single shape parameter, enabling continuous control of the distributional geometry from arc-shaped diffusion along the phase direction to concentration of probability mass toward the origin. We formulate the proposed distribution and analyze the statistical properties of the induced amplitude distribution. The derived amplitude and power distributions provide a unified framework encompassing several widely used distributions in signal modeling, including the Rice, Nakagami, and gamma distributions. Experimental results on speech power spectra demonstrate that the proposed model consistently outperforms conventional distributions in terms of log-likelihood.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26344v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26301v1", "url": "http://arxiv.org/abs/2603.26301v1", "pdf_url": "https://arxiv.org/pdf/2603.26301v1", "title": "Complete Causal Identification from Ancestral Graphs under Selection Bias", "authors": ["Leihao Chen", "Joris M. Mooij"], "annotation": "Many causal discovery algorithms, including the celebrated FCI algorithm, output a Partial Ancestral Graph (PAG). PAGs serve as an abstract graphical representation of the underlying causal structure, modeled by directed acyclic graphs with latent and selection variables. This paper develops a characterization of the set of extended-type conditional independence relations that are invariant across all causal models represented by a PAG. This theory allows us to formulate a general measure-theoretic version of Pearl's causal calculus and a sound and complete identification algorithm for PAGs under selection bias. Our results also apply when PAGs are learned by certain algorithms that integrate observational data with experimental data and incorporate background knowledge.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26301v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26261v1", "url": "http://arxiv.org/abs/2603.26261v1", "pdf_url": "https://arxiv.org/pdf/2603.26261v1", "title": "Contrastive Conformal Sets", "authors": ["Yahya Alkhatib", "Wee Peng Tay"], "annotation": "Contrastive learning produces coherent semantic feature embeddings by encouraging positive samples to cluster closely while separating negative samples. However, existing contrastive learning methods lack principled guarantees on coverage within the semantic feature space. We extend conformal prediction to this setting by introducing minimum-volume covering sets equipped with learnable generalized multi-norm constraints. We propose a method that constructs conformal sets guaranteeing user-specified coverage of positive samples while maximizing negative sample exclusion. We establish theoretically that volume minimization serves as a proxy for negative exclusion, enabling our approach to operate effectively even when negative pairs are unavailable. The positive inclusion guarantee inherits the distribution-free coverage property of conformal prediction, while negative exclusion is maximized through learned set geometry optimized on a held-out training split. Experiments on simulated and real-world image datasets demonstrate improved inclusion-exclusion trade-offs compared to standard distance-based conformal baselines.", "category": "stat.ML", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26261v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29972v1", "url": "http://arxiv.org/abs/2603.29972v1", "pdf_url": "https://arxiv.org/pdf/2603.29972v1", "title": "Do covariates explain why these groups differ? The choice of reference group can reverse conclusions in the Oaxaca-Blinder decomposition", "authors": ["Manuel Quintero", "Advik Shreekumar", "William T. Stephenson", "Tamara Broderick"], "annotation": "Scientists often want to explain why an outcome is different in two groups. For instance, differences in patient mortality rates across two hospitals could be due to differences in the patients themselves (covariates) or differences in medical care (outcomes given covariates). The Oaxaca--Blinder decomposition (OBD) is a standard tool to tease apart these factors. It is well known that the OBD requires choosing one of the groups as a reference, and the numerical answer can vary with the reference. To the best of our knowledge, there has not been a systematic investigation into whether the choice of OBD reference can yield different substantive conclusions and how common this issue is. In the present paper, we give existence proofs in real and simulated data that the OBD references can yield substantively different conclusions and that these differences are not entirely driven by model misspecification or small data. We prove that substantively different conclusions occur in up to half of the parameter space, but find these discrepancies rare in the real-data analyses we study. We explain this empirical rarity by examining how realistic data-generating processes can be biased towards parameters that do not change conclusions under the OBD.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29972v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29889v1", "url": "http://arxiv.org/abs/2603.29889v1", "pdf_url": "https://arxiv.org/pdf/2603.29889v1", "title": "Penalized GMM Framework for Inference on Functionals of Nonparametric Instrumental Variable Estimators", "authors": ["Edvard Bakhitov"], "annotation": "This paper develops a penalized GMM (PGMM) framework for automatic debiased inference on functionals of nonparametric instrumental variable estimators. We derive convergence rates for the PGMM estimator and provide conditions for root-n consistency and asymptotic normality of debiased functional estimates, covering both linear and nonlinear functionals. Monte Carlo experiments on average derivative show that the PGMM-based debiased estimator performs on par with the analytical debiased estimator that uses the known closed-form Riesz representer, achieving 90-96% coverage while the plug-in estimator falls below 5%. We apply our procedure to estimate mean own-price elasticities in a semiparametric demand model for differentiated products. Simulations confirm near-nominal coverage while the plug-in severely undercovers. Applied to IRI scanner data on carbonated beverages, debiased semiparametric estimates are approximately 20% more elastic compared to the logit benchmark, and debiasing corrections are heterogeneous across products, ranging from negligible to several times the standard error.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29889v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28930v1", "url": "http://arxiv.org/abs/2603.28930v1", "pdf_url": "https://arxiv.org/pdf/2603.28930v1", "title": "Retrospective Economic Evaluation of Group Testing in the COVID-19 Pandemic", "authors": ["Michael Balzer", "Kainat Khowaja", "Christiane Fuchs"], "annotation": "Surveillance of diseases in a pandemic is an important part of public health policy. Diagnostic testing at the individual level is often infeasible due to resource constraints. To circumvent these constraints, group testing can be applied. The economic cost evaluation from the payer's perspective typically focuses only on deterministic costs which overlooks the substantial economic impact of productivity losses resulting from quarantine and workplace disruptions. The objective of this article is to develop a mathematical model for a retrospective economic evaluation of group testing that incorporates both deterministic costs and income-based economic loss. Group testing algorithms are revisited and simulated at optimized pool sizes to determine the required number of tests. Income data from the German Socio-Economic Panel are integrated into a mathematical model to capture the economic loss. Afterward, hybrid Monte Carlo experiments are conducted by evaluating the economic cost in the Coronavirus disease 2019 pandemic in Germany. Monte Carlo experiments show that the optimal choice of group testing algorithms changes substantially when income-based economic losses are included. Evaluations considering only deterministic costs systematically underestimate the total economic cost. Algorithms with a longer quarantine duration are less attractive than shorter quarantine duration if income-based economic loss is accounted for. The findings show that current evaluations underestimate the true economic cost. Group testing algorithms with shorter duration and fewer stages are preferred, even when they require a larger number of tests. These results underscore the importance of incorporating income-based economic loss into a mathematical model.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28930v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28470v1", "url": "http://arxiv.org/abs/2603.28470v1", "pdf_url": "https://arxiv.org/pdf/2603.28470v1", "title": "Counterfactual Density Effects and the German East--West Income Gap", "authors": ["Georg Keilbar", "Sonja Greven"], "annotation": "We propose a novel framework for conducting causal inference based on counterfactual densities. While the current paradigm of causal inference is mostly focused on estimating average treatment effects (ATEs), which restricts the analysis to the first moment of the outcome variable, our density-based approach is able to detect causal effects based on general distributional characteristics. Following the Oaxaca-Blinder decomposition approach, we consider two types of counterfactual density effects that together explain observed discrepancies between the densities of the treated and control group. First, the distribution effect is the counterfactual effect of changing the conditional density of the control group to that of the treatment group, while keeping the covariates fixed at the treatment group distribution. Second, the covariate effect represents the effect of a hypothetical change in the covariate distribution. Both effects have a causal interpretation under the classical unconfoundedness and overlap assumptions. Methodologically, our approach is based on analyzing the conditional densities as elements of a Bayes Hilbert space, which preserves the non-negativity and integration-to-one constraints. We specify a flexible functional additive regression model estimating the conditional densities. We apply our method to analyze the German East--West income gap, i.e., the observed differences in wages between East Germans and West Germans. While most of the existing studies focus on the average differences and neglect other distributional characteristics, our density-based approach is suited to detect all nuances of the counterfactual distributions, including differences in probability masses at zero.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28470v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27881v1", "url": "http://arxiv.org/abs/2603.27881v1", "pdf_url": "https://arxiv.org/pdf/2603.27881v1", "title": "A Simple and Powerful Diagnostic Test for Binary Choice Models", "authors": ["Ting Ji", "Laura Liu", "Yulong Wang", "Jiahe Xing"], "annotation": "This paper proposes a specification test for the conventional distributional assumptions of error terms in binary choice models, focusing on its tail properties. Based on extreme value theory, we first establish that the tail index of the unobserved error can be recovered by that of the observed covariates. The null hypothesis of the index being zero essentially covers the widely used probit and logit models. We then construct a simple and powerful statistical test for both cross-sectional and panel data, requiring no model estimation and no parametric assumptions. Monte Carlo simulations demonstrate that our test performs well in size and power, and applications to three empirical examples on firm export and innovation decisions and female labor force participation illustrate its general applicability.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27881v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27762v1", "url": "http://arxiv.org/abs/2603.27762v1", "pdf_url": "https://arxiv.org/pdf/2603.27762v1", "title": "When \"Normalization Without Loss of Generality\" Loses Generality", "authors": ["Wayne Gao"], "annotation": "Normalization is ubiquitous in economics, and a growing literature shows that ``normalizations'' can matter for interpretation, counterfactual analysis, misspecification, and inference. This paper provides a general framework for these issues, based on the formalized notion of modeling equivalence that partitions the space of unknowns into equivalence classes, and defines normalization as a WLOG selection of one representative from each class. A counterfactual parameter is normalization-free if and only if it is constant on equivalence classes; otherwise any point identification is created by the normalization rather than by the model. Applications to discrete choice, demand estimation, and network formation illustrate the insights made explicit through this criterion. We then study two further sources of fragility: an extension trilemma establishes that fidelity, invariance, and regularity cannot simultaneously hold at a boundary singularity, while a normalization can itself introduce a coordinate singularity that distorts the topological and metric structures of the parameter space, with consequences for estimation and inference.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27762v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27088v1", "url": "http://arxiv.org/abs/2603.27088v1", "pdf_url": "https://arxiv.org/pdf/2603.27088v1", "title": "Fast Posterior Sampling in Tightly Identified SVARs Using 'Soft' Sign Restrictions", "authors": ["Matthew Read", "Dan Zhu"], "annotation": "We propose algorithms for conducting Bayesian inference in structural vector autoregressions identified using sign restrictions. The key feature of our approach is a sampling step based on 'soft' sign restrictions. This step draws from a target density that smoothly penalises parameter values that violate the restrictions, facilitating the use of computationally efficient Markov chain Monte Carlo sampling algorithms. An importance-sampling step yields draws conditional on the 'hard' sign restrictions. Relative to standard accept-reject sampling, the method substantially speeds up sampling when identification is tight. It also facilitates implementing prior-robust Bayesian methods. We illustrate the broad applicability of the approach in an oil-market model identified using a rich set of sign, elasticity and narrative restrictions.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27088v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.25641v1", "url": "http://arxiv.org/abs/2603.25641v1", "pdf_url": "https://arxiv.org/pdf/2603.25641v1", "title": "The Econometrics of Utility Transferability in Dyadic Network Formation Models", "authors": ["Joseph Marshall"], "annotation": "This paper studies how to estimate an individual's taste for forming a connection with another individual in a network. It compares the difficulty of estimation with and without the assumption that utility is transferable between individuals, and with and without the assumption that regressors are symmetric across individuals in the pair. I show that when pair-specific regressors are symmetric, the sufficient conditions for consistency and asymptotic normality of the maximum likelihood estimator that assumes transferable utility (TU-MLE) are also sufficient for the maximum likelihood estimator that does not assume transferable utility (NTU-MLE). When regressors are asymmetric, I provide sufficient conditions for the consistency and asymptotic normality of the NTU-MLE. I also provide a specification test to assess the validity of the transferable utility assumption. Two applications from different fields of economics demonstrate the value of my results. I find evidence of researchers using the TU-MLE when the transferable utility assumption is violated, and evidence of researchers using NTU-model-based estimators when the validity of the transferable utility assumption cannot be rejected.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.25641v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.25529v2", "url": "http://arxiv.org/abs/2603.25529v2", "pdf_url": "https://arxiv.org/pdf/2603.25529v2", "title": "Sensitivity Analysis for Instrumental Variables Under Joint Relaxations of Monotonicity and Independence", "authors": ["Pedro Picchetti"], "annotation": "In this paper I develop a breakdown frontier approach to assess the sensitivity of Local Average Treatment Effects (LATE) estimates to violations of monotonicity and independence of the instrument. I parametrize violations of independence using the concept of $c$-dependence from Masten & Poirier (2018) and allow for the share of defiers to be greater than zero but smaller than the share of compliers. I derive identified sets for the LATE and the Average Treatment Effect (ATE) in which the bounds are functions of these two sensitivity parameters. Using these bounds, I derive the breakdown frontier for the LATE, which is the weakest set of assumptions such that a conclusion regarding the LATE holds. I derive consistent sample analogue estimators for the breakdown frontiers and provide a valid bootstrap procedure for inference. Monte Carlo simulations show the desirable finite-sample properties of the estimators and an empirical application shows that the conclusions regarding the effect of family size on unemployment from Angrist & Evans (1998) are highly sensitive to violations of independence and monotonicity.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.25529v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.25509v1", "url": "http://arxiv.org/abs/2603.25509v1", "pdf_url": "https://arxiv.org/pdf/2603.25509v1", "title": "Conformal Prediction for Nonparametric Instrumental Regression", "authors": ["Masahiro Kato"], "annotation": "We propose a method for constructing distribution-free prediction intervals in nonparametric instrumental variable regression (NPIV), with finite-sample coverage guarantees. Building on the conditional guarantee framework in conformal inference, we reformulate conditional coverage as marginal coverage over a class of IV shifts $\\mathcal{F}$. Our method can be combined with any NPIV estimator, including sieve 2SLS and other machine-learning-based NPIV methods such as neural networks minimax approaches. Our theoretical analysis establishes distribution-free, finite-sample coverage over a practitioner-chosen class of IV shifts.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.25509v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.24970v2", "url": "http://arxiv.org/abs/2603.24970v2", "pdf_url": "https://arxiv.org/pdf/2603.24970v2", "title": "Randomization Inference For the Always-Reporter Average Treatment Effect", "authors": ["Haoge Chang", "Zeyang Yu"], "annotation": "This article studies randomization inference for treatment effects in randomized controlled trials with attrition, where outcomes are observed for only a subset of units. We assume monotonicity in reporting behavior as in \\cite{lee2009training} and focus on the average treatment effect for always-reporters (AR-ATE), defined as units whose outcomes are observed under both treatment and control. Because always-reporter status is only partially revealed by observed assignment and response patterns, we propose a worst-case randomization test that maximizes the randomization p-value over all always-reporter configurations consistent with the data, with an optional pretest to prune implausible configurations. Using studentized Hajek- and chi-square-type statistics, we show the resulting procedure is finite-sample valid for the sharp null and asymptotically valid for the weak null. We also discuss computational implementations for discrete outcomes and integer-programming-based bounds for continuous outcomes.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.24970v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.24899v1", "url": "http://arxiv.org/abs/2603.24899v1", "pdf_url": "https://arxiv.org/pdf/2603.24899v1", "title": "Calibrating Resident Surveys with Operational Data in Community Planning", "authors": ["Irene S. Gabashvili"], "annotation": "Community associations rely heavily on resident surveys to guide decisions about amenities, infrastructure, and services. However, survey responses reflect perceptions that may not directly correspond to underlying operational conditions. This study bridges that gap by calibrating survey-based satisfaction measures against objective utilization data.   Using parking and facility data from Tellico Village, we map perceived problem rates to utilization exceedance probabilities to estimate behavioral congestion thresholds. Results show that dissatisfaction emerges near effective capacity - once spatial, temporal, and informational constraints are considered - rather than at nominal capacity limits. Perceived difficulty is concentrated among active users and is shaped by operational frictions and incomplete system knowledge.   These findings demonstrate that perceived congestion reflects constraints on access and reliability, not simply physical shortages. By distinguishing between effective and nominal capacity, the proposed framework enables more accurate diagnosis of system conditions. We propose incorporating behavioral metrics into community performance frameworks to support better decision-making, reduce unnecessary capital expansion, and target operational improvements more effectively.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.24899v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.24833v1", "url": "http://arxiv.org/abs/2603.24833v1", "pdf_url": "https://arxiv.org/pdf/2603.24833v1", "title": "Robust Matrix Estimation with Side Information", "authors": ["Anish Agarwal", "Jungjun Choi", "Ming Yuan"], "annotation": "We introduce a flexible framework for high-dimensional matrix estimation to incorporate side information for both rows and columns. Existing approaches, such as inductive matrix completion, often impose restrictive structure-for example, an exact low-rank covariate interaction term, linear covariate effects, and limited ability to exploit components explained only by one side (row or column) or by neither-and frequently omit an explicit noise component. To address these limitations, we propose to decompose the underlying matrix as the sum of four complementary components: (possibly nonlinear) interaction between row and column characteristics; row characteristic-driven component, column characteristic-driven component, and residual low-rank structure unexplained by observed characteristics. By combining sieve-based projection with nuclear-norm penalization, each component can be estimated separately and these estimated components can then be aggregated to yield a final estimate. We derive convergence rates that highlight robustness across a range of model configurations depending on the informativeness of the side information. We further extend the method to partially observed matrices under both missing-at-random and missing-not-at-random mechanisms, including block-missing patterns motivated by causal panel data. Simulations and a real-data application to tobacco sales show that leveraging side information improves imputation accuracy and can enhance treatment-effect estimation relative to standard low-rank and spectral-based alternatives.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.24833v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.24786v1", "url": "http://arxiv.org/abs/2603.24786v1", "pdf_url": "https://arxiv.org/pdf/2603.24786v1", "title": "Refined Cluster Robust Inference", "authors": ["Bulat Gafarov", "Takuya Ura"], "annotation": "It has become standard for empirical studies to conduct inference robust to cluster dependence and heterogeneity. With a small number of clusters, the normal approximation for the $t$-statistics of regression coefficients may be poor. This paper tackles this problem using a critical value based on the conditional Cramér-Edgeworth expansion for the $t$-statistics. Our approach guarantees third-order refinement, regardless of whether a regressor is discrete or not, and, unlike the cluster pairs bootstrap, avoids resampling data. Simulations show that our proposal can make a difference in size control with as few as 10 clusters.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.24786v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.24705v1", "url": "http://arxiv.org/abs/2603.24705v1", "pdf_url": "https://arxiv.org/pdf/2603.24705v1", "title": "Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks", "authors": ["Easton Huch", "Michael Keane"], "annotation": "Discrete choice models are fundamental tools in management science, economics, and marketing for understanding and predicting decision-making. Logit-based models are dominant in applied work, largely due to their convenient closed-form expressions for choice probabilities. However, these models entail restrictive assumptions on the stochastic utility component, constraining our ability to capture realistic and theoretically grounded choice behavior$-$most notably, substitution patterns. In this work, we propose an amortized inference approach using a neural network emulator to approximate choice probabilities for general error distributions, including those with correlated errors. Our proposal includes a specialized neural network architecture and accompanying training procedures designed to respect the invariance properties of discrete choice models. We provide group-theoretic foundations for the architecture, including a proof of universal approximation given a minimal set of invariant features. Once trained, the emulator enables rapid likelihood evaluation and gradient computation. We use Sobolev training, augmenting the likelihood loss with a gradient-matching penalty so that the emulator learns both choice probabilities and their derivatives. We show that emulator-based maximum likelihood estimators are consistent and asymptotically normal under mild approximation conditions, and we provide sandwich standard errors that remain valid even with imperfect likelihood approximation. Simulations show significant gains over the GHK simulator in accuracy and speed.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.24705v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.23993v1", "url": "http://arxiv.org/abs/2603.23993v1", "pdf_url": "https://arxiv.org/pdf/2603.23993v1", "title": "GARP-EFM: Improving Foundation Models with Revealed Preference Structure", "authors": ["Victor H. Aguiar", "Nail Kashaev"], "annotation": "Modern pretrained time-series foundation models can forecast without task-specific training, but they do not fully incorporate economic behavior. We show that teaching them basic economic logic improves how they predict demand using an experimental panel. We fine-tune Amazon Chronos-2, a transformer-based probabilistic time-series model, on synthetic data generated from utility-maximizing agents. We exploit Afriat's theorem, which guarantees that demand satisfies the Generalized Axiom of Revealed Preference (GARP) if and only if it can be generated by maximizing some utility function subject to a budget constraint. GARP is a simple condition to check that allows us to generate time series from a large class of utilities efficiently. The fine-tuned model serves as a rationality-constrained forecasting prior: it learns price-quantity relations from GARP-consistent synthetic histories and then uses those relations to predict the choices of real consumers. We find that fine-tuning on GARP-consistent synthetic data substantially improves prediction relative to zero-shot Chronos-2 at all forecast horizons we study. Our results show that economic theory can be used to generate structured synthetic data that improves foundation-model predictions when the theory implies observable patterns in the data.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.23993v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.23294v1", "url": "http://arxiv.org/abs/2603.23294v1", "pdf_url": "https://arxiv.org/pdf/2603.23294v1", "title": "Granger Causality in Expectiles: an M-vine copula test", "authors": ["Roberto Fuentes-Martínez", "Irene Crimaldi"], "annotation": "A model-free measure of Granger causality in expectiles is proposed, generalizing the traditional mean-based measure to arbitrary positions of the conditional distribution. Expectiles are the only law-invariant risk measures that are both coherent and elicitable, making them particularly well-suited for studying distributional Granger causality where risk quantification and forecast evaluation are both relevant. Based on this measure, a test is developed using M-vine copula models that accounts for multivariate Granger causality with $d+1$ series under non-linear and non-Gaussian dependence, without imposing parametric assumptions on the joint distribution. Strong consistency of the test statistic is established under some regularity conditions. In finite samples, simulations show accurate size control and power increasing with sample size. A key advantage is the joint testing capability: causal relationships invisible to pairwise tests can be detected, as demonstrated both theoretically and empirically. Two applications to international stock market indices at the global and Asian regional level illustrate the practical relevance of the proposed framework.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.23294v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.22914v1", "url": "http://arxiv.org/abs/2603.22914v1", "pdf_url": "https://arxiv.org/pdf/2603.22914v1", "title": "Nonparametric regression with dependent censoring or competing risks", "authors": ["Jia-Han Shih", "Simon M. S. Lo", "Ralf A. Wilke"], "annotation": "Single-index models or time-to-event models are frequently applied in empirical research. These models are non-identifiable in presence of unknown (dependent) censoring or competing risks and do not give informative results in empirical analysis unless rather strong, non-testable restrictions hold. Little is known, whether the known robustness properties of the single-index model carry over to models with dependent censoring or competing risks. This paper shows that the ratio of partial covariate effects on the margins is identifiable in nonparametric models with unknown dependent censoring or nonparametric competing risks models with nonparametric dependence structure, provided an exclusion restriction holds. Commonly used (semi)parametric models for the margin and independent censoring, such as Cox proportional hazards, accelerated failure time or proportional odds models, can be used to obtain relative covariate effects despite their misspecified censoring mechanism. Several nonparametric estimators for the general model are introduced and their numerical properties are studied.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.22914v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.22835v1", "url": "http://arxiv.org/abs/2603.22835v1", "pdf_url": "https://arxiv.org/pdf/2603.22835v1", "title": "Breaking news", "authors": ["Lars Winkelmann", "Wenying Yao"], "annotation": "This paper examines how regulatory interventions in high-frequency financial markets affect price discovery. We focus on Breaking news, where dynamic circuit breakers trigger trading halts immediately after the release of macroeconomic fundamentals. Within a high-frequency signal-in-noise model, we show that triggering rules complicate statistical inference for the price impact of news, rendering conventional non-parametric jump estimators inconsistent. Building on this insight, we develop a regression-based test for fundamental pricing that accounts for non-vanishing transition times. The test compares transition price changes to efficient jumps implied by observable factors. Our empirical analysis of CME E-mini S\\&P 500 futures shows that Breaking news are associated with systematic deviations from fundamental pricing, predominantly in the form of overshooting. Our findings highlight a regulatory trade-off: the appeal of simple and transparent circuit breaker rules must be weighed against their cost of preventing fundamentals from being priced contemporaneously, thereby creating adverse incentives and introducing distortions.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.22835v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.22599v1", "url": "http://arxiv.org/abs/2603.22599v1", "pdf_url": "https://arxiv.org/pdf/2603.22599v1", "title": "Cressie Read Power Divergence for Moment-Based Estimation: Hyperparameter and Finite Sample Behavior", "authors": ["Jieun Lee", "Anil K. Bera"], "annotation": "We study Cressie Read power divergence (CRPD) estimation for moment based models, focusing on finite sample behavior. While generalized empirical likelihood estimators, dual to CRPD, are known to outperform generalized method of moments estimators in small to moderate samples, the power parameter is typically chosen arbitrarily by the researcher, serving mainly as an index. We interpret it as a hyperparameter that determines the loss function and governs the learning procedure, shaping the curvature of the objective and influencing finite sample performance. Using second order asymptotics, we show that it affects both the structural estimator and the associated Lagrange multipliers, governing robustness, bias, and sensitivity to sampling variation. Monte Carlo simulations illustrate how estimator performance varies with the choice of the power parameter and underlying distributional features, with implications for second order bias and coverage distortion. An empirical illustration based on Owen (2001)s classical example highlights the practical relevance of tuning the power parameter.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.22599v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.21917v2", "url": "http://arxiv.org/abs/2603.21917v2", "pdf_url": "https://arxiv.org/pdf/2603.21917v2", "title": "The Cascade Identity: 2SLS as a Policy Parameter in Capacity-Constrained Settings", "authors": ["Niklas Bengtsson", "Per Engström"], "annotation": "A growing literature shows that two-stage least squares (2SLS) with multiple treatments yields coefficients that are difficult to interpret under heterogeneous treatment effects and cross-effects in the first stage. We show that in capacity-constrained allocation systems, these cross-effects are not a nuisance but the source of a clean policy interpretation. When treatments are rationed and the instrument operates on the same margin as the policy of interest, the 2SLS coefficient $β_k$ equals the total societal effect of expanding treatment $k$ by one slot, including all cascading reallocations through the system. The mechanism is general: it applies whenever fixed supply constrains allocation, whether through ranked queues, waitlists, or market-clearing prices. This cascade identity $\\mathbf{T} = \\mathbfβ$ holds for any first-stage matrix, under arbitrary treatment effect heterogeneity, and requires only instrument relevance and that the instrument operates on the same margin as the policy. The result applies to university admissions, school choice, medical residency matching, public housing, and other rationed allocation settings. We provide an empirical application using lottery-based admission to Swedish university programs and charitable giving as the outcome.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.21917v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.21699v1", "url": "http://arxiv.org/abs/2603.21699v1", "pdf_url": "https://arxiv.org/pdf/2603.21699v1", "title": "A Job I Like or a Job I Can Get: Designing Job Recommender Systems Using Field Experiments", "authors": ["Guillaume Bied", "Philippe Caillou", "Bruno Crépon", "Christophe Gaillac", "Elia Pérennes", "Michèle Sebag"], "annotation": "Recommendation systems (RSs) are increasingly used to guide job seekers on online platforms, yet the algorithms currently deployed are typically optimized for predictive objectives such as clicks, applications, or hires, rather than job seekers' welfare. We develop a job-search model with an application stage in which the value of a vacancy depends on two dimensions: the utility it delivers to the worker and the probability that an application succeeds. The model implies that welfare-optimal RSs rank vacancies by an expected-surplus index combining both, and shows why rankings based solely on utility, hiring probabilities, or observed application behavior are generically suboptimal, an instance of the inversion problem between behavior and welfare. We test these predictions and quantify their practical importance through two randomized field experiments conducted with the French public employment service. The first experiment, comparing existing algorithms and their combinations, provides behavioral evidence that both dimensions shape application decisions. Guided by the model and these results, the second experiment extends the comparison to an RS designed to approximate the welfare-optimal ranking. The experiments generate exogenous variation in the vacancies shown to job seekers, allowing us to estimate the model, validate its behavioral predictions, and construct a welfare metric. Algorithms informed by the model-implied optimal ranking substantially outperform existing approaches and perform close to the welfare-optimal benchmark. Our results show that embedding predictive tools within a simple job-search framework and combining it with experimental evidence yields recommendation rules with substantial welfare gains in practice.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.21699v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.22356v1", "url": "http://arxiv.org/abs/2603.22356v1", "pdf_url": "https://arxiv.org/pdf/2603.22356v1", "title": "Animal Welfare and Policy Risk Index (AWPRI): Constructing and Validating a Cross-National Governance Risk Measure, 25 Countries, 2004-2022", "authors": ["Jason Hung"], "annotation": "This paper introduces the Animal Welfare and Policy Risk Index (AWPRI), a composite risk index covering 25 countries over the period 2004-2022 (N = 475 country-year observations). The AWPRI is constructed from 15 variables organised across three equal-weighted conceptual layers: Current Welfare State (L1), Policy Trajectory (L2), and Artificial Intelligence (AI) Amplification Risk (L3). Variables are normalised to [0, 1] using min-max scaling, with higher values denoting greater policy risk. The index is validated through k-means cluster analysis (k = 4; silhouette coefficient = 0.447), principal component analysis (PCA) of the 15-variable cross-section, and sensitivity analysis under \\pm10 percentage-point layer weight perturbation (mean Spearman \\r{ho} = 0.993, minimum 0.979; mean Adjusted Rand Index (ARI) = 0.684, range 0.477-1.000). Our Hausman specification test favours random-effects (RE) panel estimation (H = 2.55, p = 0.467). We use a difference-in-differences (DiD) design to exploit the 2019 AI governance risk classification divergence and find that countries identified as high-AI-governance-risk carry AWPRI scores 0.080 points higher than their low-risk counterparts, after controlling for country and year fixed effects (\\b{eta} = 0.080, SE = 0.005, p < 0.001). The L3 layer records the highest mean score in the 2022 cross-section (0.552, SD = 0.175), significantly exceeding both L1 (Wilcoxon W= 102,651, p < 0.001) and L2 (W= 99,295, p < 0.001). China (0.802), Vietnam (0.612), and Thailand (0.586) record the highest composite risk scores in 2022; the United Kingdom (0.308) the lowest. AutoRegressive Integrated Moving Average (ARIMA)-based projections indicate that Thailand, Brazil, and Argentina face AWPRI risk deterioration by 2030. The AWPRI and its interactive visualisation are publicly accessible at https://awpri-dashboard.streamlit.app.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.22356v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.21004v1", "url": "http://arxiv.org/abs/2603.21004v1", "pdf_url": "https://arxiv.org/pdf/2603.21004v1", "title": "Power Bounds and Efficiency Loss for Asymptotically Optimal Tests in IV Regression", "authors": ["Marcelo J. Moreira", "Geert Ridder", "Mahrad Sharifvaghefi"], "annotation": "We characterize the maximal attainable power-size gap in overidentified instrumental variables models with heteroskedastic or autocorrelated (HAC) errors. Using total variation distance and Kraft's theorem, we define the decision theoretic frontier of the testing problem. We show that Lagrange multiplier and conditional quasi likelihood ratio tests can have power arbitrarily close to size even when the null and alternative are well separated, because they do not fully exploit the reduced-form likelihood. In contrast, the conditional likelihood ratio (CLR) test uses the full reduced-form likelihood. We prove that the power-size gap of CLR converges to one if and only if the testing problem becomes trivial in total variation distance, so that CLR attains the decision theoretic frontier whenever any test can. An empirical illustration based on Yogo (2004) shows that these failures arise in empirically relevant configurations.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.21004v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.20936v1", "url": "http://arxiv.org/abs/2603.20936v1", "pdf_url": "https://arxiv.org/pdf/2603.20936v1", "title": "Two Approaches to Direct Estimation of Riesz Representers", "authors": ["David Bruns-Smith"], "annotation": "The Riesz representer is a central object in semiparametric statistics and debiased/doubly-robust estimation. Two literatures in econometrics have highlighted the role for directly estimating Riesz representers: the automatic debiased machine learning literature (as in Chernozhukov et al., 2022b), and an independent literature on sieve methods for conditional moment models (as in Chen et al., 2014). These two literatures solve distinct optimization problems that in the population both have the Riesz representer as their solution. We show that with unregularized or ridge-regularized linear, sieve, or RKHS models, the two resulting estimators are numerically equivalent. However, for other regularization schemes such as the Lasso, or more general machine learning function classes including neural networks, the estimators are not necessarily equivalent. In the latter case, the Chen et al. (2014) formulation yields a novel constrained optimization problem for directly estimating Riesz representers with machine learning. Drawing on results from Birrell et al. (2022), we conjecture that this approach may offer statistical advantages at the cost of greater computational complexity.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.20936v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.20809v1", "url": "http://arxiv.org/abs/2603.20809v1", "pdf_url": "https://arxiv.org/pdf/2603.20809v1", "title": "The Structural Bite: A Methodological Framework for Minimum Wage Studies using Spanish Administrative Data", "authors": ["Marcos Lacasa-Cazcarra"], "annotation": "We study the employment effects of the 22% increase in the Spanish minimum wage in 2019, focusing on young workers. Using census-grade administrative tax data covering the universe of formal wage bills and employment (Models 190/390 linked to personal income tax records), we construct several measures of treatment intensity, including two structurally grounded bite indicators based on the incidence of young minimum-wage workers and the implied increase in the wage bill obtained via Exponential Tilting. Difference-in-differences estimates with two-way fixed effects, dynamic event-study specifications, and robust confidence intervals from the HonestDiD framework all point to the same conclusion: the reform did not generate net disemployment effects for young workers. Point estimates of the elasticity are small and often positive, and confidence internals comfortably include zero even with sizable deviations from parallel trends. A triple-difference design exploiting pre-existing tourism dependence further shows that the sharp employment collapse of 2020 is primarily explained by the COVID-19 shock operating through tourism-intensive sectors, rather than by the minimum-wage hike itself. Our results suggest that, in the macroeconomic and institutional environment prevailing in Spain in 2019, with the minimum wage rising to around 60% of the average wage in a recovering economy, the labour market absorbed a large discrete increase in the wage floor without destroying aggregate youth employment. More broadly, the paper highlights how the choice of treatment definition, the use of census-grade data, robust DiD inference, and explicit modelling of concurrent shocks can shape conclusions about the effects of minimum-wage policies.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.20809v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.20464v1", "url": "http://arxiv.org/abs/2603.20464v1", "pdf_url": "https://arxiv.org/pdf/2603.20464v1", "title": "Double Machine Learning for Static Panel Data with Instrumental Variables: New Method and Applications", "authors": ["Anna Baiardi", "Paul S. Clarke", "Andrea A. Naghi", "Annalivia Polselli"], "annotation": "Panel data methods are widely used in empirical analysis to address unobserved heterogeneity, but causal inference remains challenging when treatments are endogenous and confounding variables high-dimensional and potentially nonlinear. Standard instrumental variables (IV) estimators, such as two-stage least squares (2SLS), become unreliable when instrument validity requires flexibly conditioning on many covariates with potentially non-linear effects. This paper develops a Double Machine Learning estimator for static panel models with endogenous treatments (panel IV DML), and introduces weak-identification diagnostics for it. We revisit three influential migration studies that use shift-share instruments. In these settings, instrument validity depends on a rich covariate adjustment. In one application, panel IV DML strengthens the predictive power of the instrument and broadly confirms 2SLS results. In the other cases, flexible adjustment makes the instruments weak, leading to substantially more cautious causal inference than conventional 2SLS. Monte Carlo evidence supports these findings, showing that panel IV DML improves estimation accuracy under strong instruments and delivers more reliable inference under weak identification.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.20464v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.20394v1", "url": "http://arxiv.org/abs/2603.20394v1", "pdf_url": "https://arxiv.org/pdf/2603.20394v1", "title": "When are time series predictions causal? The potential system and dynamic causal effects", "authors": ["Jacob Carlson", "Neil Shephard"], "annotation": "The potential system is a nonparametric time series model for assessing the causal impact of moving an assignment at time $t$ on an outcome at future time $t+h$, accounting for the presence of features. The potential system provides nonparametric content for, e.g., time series experiments, time series regression, local projection, impulse response functions and SVARs. It closes a gap between time series causality and nonparametric cross-sectional causal methods, and provides a foundation for many new methods which have causal content.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.20394v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.20388v1", "url": "http://arxiv.org/abs/2603.20388v1", "pdf_url": "https://arxiv.org/pdf/2603.20388v1", "title": "From Cross-Validation to SURE: Asymptotic Risk of Tuned Regularized Estimators", "authors": ["Karun Adusumilli", "Maximilian Kasy", "Ashia Wilson"], "annotation": "We derive the asymptotic risk function of regularized empirical risk minimization (ERM) estimators tuned by $n$-fold cross-validation (CV). The out-of-sample prediction loss of such estimators converges in distribution to the squared-error loss (risk function) of shrinkage estimators in the normal means model, tuned by Stein's unbiased risk estimate (SURE). This risk function provides a more fine-grained picture of predictive performance than uniform bounds on worst-case regret, which are common in learning theory: it quantifies how risk varies with the true parameter. As key intermediate steps, we show that (i) $n$-fold CV converges uniformly to SURE, and (ii) while SURE typically has multiple local minima, its global minimum is generically well separated. Well-separation ensures that uniform convergence of CV to SURE translates into convergence of the tuning parameter chosen by CV to that chosen by SURE.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.20388v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.20134v1", "url": "http://arxiv.org/abs/2603.20134v1", "pdf_url": "https://arxiv.org/pdf/2603.20134v1", "title": "Triple/Double-Debiased Lasso", "authors": ["Denis Chetverikov", "Jesper R. -V. Sørensen", "Aleh Tsyvinski"], "annotation": "In this paper, we propose a triple (or double-debiased) Lasso estimator for inference on a low-dimensional parameter in high-dimensional linear regression models. The estimator is based on a moment function that satisfies not only first- but also second-order Neyman orthogonality conditions, thereby eliminating both the leading bias and the second-order bias induced by regularization. We derive an asymptotic linear representation for the proposed estimator and show that its remainder terms are never larger and are often smaller in order than those in the corresponding asymptotic linear representation for the standard double Lasso estimator. Because of this improvement, the triple Lasso estimator often yields more accurate finite-sample inference and confidence intervals with better coverage. Monte Carlo simulations confirm these gains. In addition, we provide a general recursive formula for constructing higher-order Neyman orthogonal moment functions in Z-estimation problems, which underlies the proposed estimator as a special case.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.20134v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.19211v1", "url": "http://arxiv.org/abs/2603.19211v1", "pdf_url": "https://arxiv.org/pdf/2603.19211v1", "title": "Synthetic Control Misconceptions: Recommendations for Practice", "authors": ["Robert Pickett", "Jennifer Hill", "Sarah Cowan"], "annotation": "To estimate the causal effect of an intervention, researchers need to identify a control group that represents what might have happened to the treatment group in the absence of that intervention. This is challenging without a randomized experiment and further complicated when few units (possibly only one) are treated. Nevertheless, when data are available on units over time, synthetic control (SC) methods provide an opportunity to construct a valid comparison by differentially weighting control units that did not receive the treatment so that their resulting pre-treatment trajectory is similar to that of the treated unit. The hope is that this weighted ``pseudo-counterfactual\" can serve as a valid counterfactual in the post-treatment time period. Since its origin twenty years ago, SC has been used over 5,000 times in the literature (Web of Science, December 2025), leading to a proliferation of descriptions of the method and guidance on proper usage that is not always accurate and does not always align with what the original developers appear to have intended. As such, a number of accepted pieces of wisdom have arisen: (1) SC is robust to various implementations; (2) covariates are unnecessary, and (3) pre-treatment prediction error should guide model selection. We describe each in detail and conduct simulations that suggest, both for standard and alternative implementations of SC, that these purported truths are not supported by empirical evidence and thus actually represent misconceptions about best practice. Instead of relying on these misconceptions, we offer practical advice for more cautious implementation and interpretation of results.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.19211v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.18870v1", "url": "http://arxiv.org/abs/2603.18870v1", "pdf_url": "https://arxiv.org/pdf/2603.18870v1", "title": "Inference in Regression Discontinuity Designs with Clustered Data", "authors": ["Claudia Noack", "Tomasz Olma", "Christoph Rothe"], "annotation": "Clustered sampling is prevalent in empirical regression discontinuity (RD) designs, but it has not received much attention in the theoretical literature. In this paper, we introduce a general model-based framework for such settings and derive high-level conditions under which the standard local linear RD estimator is asymptotically normal. We verify that our high-level assumptions hold across a wide range of empirical designs, including settings of growing cluster sizes. We further show that clustered standard errors that are currently used in practice can be either inconsistent or overly conservative in finite samples. To address these issues, we propose a novel nearest-neighbor-type variance estimator and illustrate its properties in a diverse set of empirical applications.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.18870v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.17881v1", "url": "http://arxiv.org/abs/2603.17881v1", "pdf_url": "https://arxiv.org/pdf/2603.17881v1", "title": "Towards Measuring Disruptive Innovation Across Countries", "authors": ["Christian Rutzer", "Dragan Filimonovic", "Jeffrey T. Macher", "Rolf Weder"], "annotation": "The CD index is a widely used measure of disruptive inventions. Most studies compute it using USPTO data. This creates a puzzle because the US appears less disruptive than European and Asian countries. We show that this largely stems from missing international citations. Using a global citation network, we quantify and correct this bias. The disruptiveness advantage of non-US inventors drops by 64% to 148% of the US baseline mean. The US emerges as a disruption leader over Europe, with Asia's advantage substantially reduced. Globally integrated citation data are essential for credible measurement of disruptive innovation in international contexts.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.17881v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.17463v1", "url": "http://arxiv.org/abs/2603.17463v1", "pdf_url": "https://arxiv.org/pdf/2603.17463v1", "title": "Multivariate GARCH and portfolio variance prediction: A forecast reconciliation perspective", "authors": ["Massimiliano Caporin", "Daniele Girolimetto", "Emanuele Lopetuso"], "annotation": "We assess the advantage of combining univariate and multivariate portfolio risk forecasts with the aid of forecast reconciliation techniques. In our analyzes, we assume knowledge of portfolio weights, a standard for portfolio risk management applications. With an extensive simulation experiment, we show that, if the true covariance is known, forecast reconciliation improves over a standard multivariate approach, in particular when the adopted multivariate model is misspecified. However, if noisy proxies are used, correctly specified models and the misspecified ones (for instance, neglecting spillovers) turn out to be, in several cases, indistinguishable, with forecast reconciliation still providing improvements. The noise in the covariance proxy plays a crucial role in determining the improvement of both the forecast reconciliation and the correct model specification. An empirical analysis shows how forecast reconciliation can be adopted with real data to improve traditional GARCH-based portfolio variance forecasts.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.17463v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.17381v3", "url": "http://arxiv.org/abs/2603.17381v3", "pdf_url": "https://arxiv.org/pdf/2603.17381v3", "title": "An Auditable AI Agent Loop for Empirical Economics: A Case Study in Forecast Combination", "authors": ["Minchul Shin"], "annotation": "AI coding agents make empirical specification search fast and cheap, but they also widen hidden researcher degrees of freedom. Building on an open-source agent-loop architecture, this paper adapts that framework to an empirical economics workflow and adds a post-search holdout evaluation. In a forecast-combination illustration, multiple independent agent runs outperform standard benchmarks in the original rolling evaluation, but not all continue to do so on a post-search holdout. Logged search and holdout evaluation together make adaptive specification search more transparent and help distinguish robust improvements from sample-specific discoveries.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.17381v3.pdf", "pdf_downloaded": true}
+{"slug": "2603.16729v1", "url": "http://arxiv.org/abs/2603.16729v1", "pdf_url": "https://arxiv.org/pdf/2603.16729v1", "title": "GeMA: Learning Latent Manifold Frontiers for Benchmarking Complex Systems", "authors": ["Jia Ming Li", " Anupriya", "Daniel J. Graham"], "annotation": "Benchmarking the performance of complex systems such as rail networks, renewable generation assets and national economies is central to transport planning, regulation and macroeconomic analysis. Classical frontier methods, notably Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), estimate an efficient frontier in the observed input-output space and define efficiency as distance to this frontier, but rely on restrictive assumptions on the production set and only indirectly address heterogeneity and scale effects.   We propose Geometric Manifold Analysis (GeMA), a latent manifold frontier framework implemented via a productivity-manifold variational autoencoder (ProMan-VAE). Instead of specifying a frontier function in the observed space, GeMA represents the production set as the boundary of a low-dimensional manifold embedded in the joint input-output space. A split-head encoder learns latent variables that capture technological structure and operational inefficiency. Efficiency is evaluated with respect to the learned manifold, endogenous peer groups arise as clusters in latent technology space, a quotient construction supports scale-invariant benchmarking, and a local certification radius, derived from the decoder Jacobian and a Lipschitz bound, quantifies the geometric robustness of efficiency scores.   We validate GeMA on synthetic data with non-convex frontiers, heterogeneous technologies and scale bias, and on four real-world case studies: global urban rail systems (COMET), British rail operators (ORR), national economies (Penn World Table) and a high-frequency wind-farm dataset. Across these domains GeMA behaves comparably to established methods when classical assumptions hold, and provides additional insight in settings with pronounced heterogeneity, non-convexity or size-related bias.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.16729v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.16035v1", "url": "http://arxiv.org/abs/2603.16035v1", "pdf_url": "https://arxiv.org/pdf/2603.16035v1", "title": "Identification Verification for Structural Vector Autoregressions with Sparse Heterogeneous Markov Switching Heteroskedasticity", "authors": ["Fei Shang", "Tomasz Woźniak"], "annotation": "We propose a structural vector autoregressive model with a new and flexible specification of the volatility process which we call Sparse Heterogeneous Markov-Switching Heteroskedasticity. In this model, the conditional variance of each structural shock changes in time according to its own Markov process. Additionally, it features a sparse representation of Markov processes, in which the number of regimes is set to exceed that of the data-generating process, with some regimes allowed to have zero occurrences throughout the sample. We complement these developments with a definition of a new distribution for normalised conditional variances that facilitates Gibbs sampling and identification verification. In effect, our model: (i) normalises the system and estimates the structural parameters more precisely than popular alternatives; (ii) can be used to verify homoskedasticity reliably and, thus, inform identification through heteroskedasticity; and (iii) features excellent forecasting performance comparable with Stochastic Volatility. Finally, revisiting a prominent macro-financial structural system, we provide evidence for the identification of the US monetary policy shock via heteroskedasticity, with estimates consistent with those reported in the literature.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.16035v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.13823v1", "url": "http://arxiv.org/abs/2603.13823v1", "pdf_url": "https://arxiv.org/pdf/2603.13823v1", "title": "Enhancing the Accuracy of Regional Input-Output Table Estimation: A Deep Learning Approach", "authors": ["Shogo Fukui"], "annotation": "Non-survey methods have been developed and applied for estimating regional input-output tables. However, there is an ongoing debate about the assumptions necessary for these methods and their accuracy.   To address these issues, this study presents a deep learning method for estimating regional input-output tables. First, the quantitative economic data for regions is augmented by linear combinations. Then, deep learning is performed on each item in the input-output table, treating these items as target variables. Finally, regional input-output tables are estimated through matrix balancing to the predicted values from the trained model.   The estimation accuracy of this method is verified using the 2015 input-output table for Japan as a benchmark. Compared to matrix balancing under the ideal assumption of known row and column sums, our method generally demonstrates higher estimation accuracy. Thus, this method is anticipated to provide a foundation for deriving more precise estimates of regional input-output tables.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.13823v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.13766v1", "url": "http://arxiv.org/abs/2603.13766v1", "pdf_url": "https://arxiv.org/pdf/2603.13766v1", "title": "Estimating Earth's Temperature Response with Transformed and Augmented OLS", "authors": ["Justin Sun"], "annotation": "The long-term relationship between radiative forcing and surface temperature is imperative for predicting the impacts of climate change. This study employs multicointegration to characterize this relationship and uses Transformed and Augmented Ordinary Least Squares (TAOLS) to estimate the model. The main goal is to estimate the Equilibrium Climate Sensitivity (ECS), defined as the global mean surface air temperature increase following a doubling of atmospheric carbon dioxide. Our results show that the ECS lies between $2.12^{\\circ}$C and $2.49^{\\circ}$C, which is lower than the existing maximum likelihood estimate of $2.8^{\\circ}$C. TAOLS offers a more robust and accessible tool for climate research, providing novel insights for ongoing debates about Earth's warming trajectory.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.13766v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.13505v1", "url": "http://arxiv.org/abs/2603.13505v1", "pdf_url": "https://arxiv.org/pdf/2603.13505v1", "title": "Testing the Exclusion Restriction in IV Models Using Non-Gaussianity: A LiNGAM-Based Approach", "authors": ["Fernando Delbianco"], "annotation": "Instrumental variable (IV) methods rely critically on the exclusion restriction, which is untestable in exactly-identified models under standard assumptions. We propose a framework combining IV analysis with the LiNGAM method to test this restriction by exploiting non-Gaussianity in the data. Under non-Gaussian structural errors, the exclusion violation parameter is point-identified without additional instruments. Five complementary tests (bootstrap percentile, asymptotic normal, permutation, likelihood ratio, and independence-based) are introduced to assess the restriction under varying data conditions. Monte Carlo simulations and an empirical application to the Card (1995) dataset demonstrate controlled Type I error rates and reasonable power against economically relevant violations.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.13505v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.12630v1", "url": "http://arxiv.org/abs/2603.12630v1", "pdf_url": "https://arxiv.org/pdf/2603.12630v1", "title": "The Economics of AI Supply Chain Regulation", "authors": ["Sihan Qian", "Amit Mehra", "Dengpan Liu"], "annotation": "The rise of foundation models has driven the emergence of AI supply chains, where upstream foundation model providers offer fine-tuning and inference services to downstream firms developing domain-specific applications. Downstream firms pay providers to use their computing infrastructure to fine-tune models with proprietary data, creating a co-creation dynamic that enhances model quality. Amid concerns that foundation model providers and downstream firms may capture excessive consumer surplus, along with increasing regulatory measures, this study employs a game-theoretic model involving a provider and two competing downstream firms to analyze how policy interventions affect consumer surplus in the AI supply chain. Our analysis shows that policies promoting price competition in downstream markets (i.e., pro-price-competitive policies) boost consumer surplus only when compute or data preprocessing costs are high, while compute subsidies are effective only when these costs are low, suggesting these policies complement each other. In contrast, policies promoting quality competition in downstream markets (i.e., pro-quality-competitive policies) always improve consumer surplus. We also find that under pro-price-competitive policies or compute subsidies, both the provider and downstream firms can achieve higher profits along with greater consumer surplus, creating a win-win-win outcome. However, pro-quality-competitive policies increase the provider's profits while reducing those of downstream firms. Finally, as compute costs decline, pro-price-competitive policies may lose their effectiveness, whereas compute subsidies may shift from ineffective to effective. These findings offer insights for policymakers seeking to foster AI supply chains that are economically efficient and socially beneficial.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.12630v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.12536v1", "url": "http://arxiv.org/abs/2603.12536v1", "pdf_url": "https://arxiv.org/pdf/2603.12536v1", "title": "Heterogeneous Elasticities, Aggregation, and Retransformation Bias", "authors": ["Ellen Munroe", "Alexander Newton", "Meet Shah"], "annotation": "Economists often interpret estimates from linear regressions with log dependent variables as elasticities. However, the coefficients from log-log regressions estimate the elasticity of the geometric mean of $y_i|x_i$, not the arithmetic mean. The unbounded difference between the two is known as retransformation bias and can take either sign. We develop a specification-robust debiased estimator of the average arithmetic elasticity and re-estimate 50 results from top 5 papers published in 2020. We find that 19 are significantly different, with the median absolute difference being 65% of the OLS elasticity estimate. Furthermore, we show standard instrumental variables assumptions with log dependent variables do not identify the elasticity. We specify a control function approach and re-estimate papers that use 2SLS with log dependent variables. We find that 13 of 19 results from top 5 papers are significantly different between the two approaches. Retransformation bias arises as a result of heterogeneous responses. The geometric mean elasticity corresponds to the average response. Arithmetic and geometric means are elements of the power mean family. We show power mean elasticities are sufficient statistics for a common class of decision problems.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.12536v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.12374v1", "url": "http://arxiv.org/abs/2603.12374v1", "pdf_url": "https://arxiv.org/pdf/2603.12374v1", "title": "The Privacy-Utility Trade-Off of Location Tracking in Ad Personalization", "authors": ["Mohammad Mosaffa", "Omid Rafieian"], "annotation": "Firms collect vast amounts of behavioral and geographical data on individuals. While behavioral data captures an individual's digital footprint, geographical data reflects their physical footprint. Given the significant privacy risks associated with combining these data sources, it is crucial to understand their respective value and whether they act as complements or substitutes in achieving firms' business objectives. In this paper, we combine economic theory, machine learning, and causal inference to quantify the value of geographical data, the extent to which behavioral data can substitute for it, and the mechanisms through which it benefits firms. Using data from a leading in-app advertising platform in a large Asian country, we document that geographical data is most valuable in the early cold-start stage, when behavioral histories are limited. In this stage, geographical data complements behavioral data, improving targeting performance by almost 20%. As users accumulate richer behavioral histories, however, the role of geographical data shifts: it becomes largely substitutable, as behavioral data alone captures the relevant heterogeneity. These results highlight a central privacy-utility trade-off in ad personalization and inform managerial decisions about when location tracking creates value.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.12374v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.11497v1", "url": "http://arxiv.org/abs/2603.11497v1", "pdf_url": "https://arxiv.org/pdf/2603.11497v1", "title": "Variance Estimation with Dependence and Heterogeneous Means", "authors": ["Luther Yap"], "annotation": "This paper considers the problem of estimating the variance of a sum of a triangular array of random vectors with heterogeneous means. When random vectors exhibit two-way cluster dependence or weak dependence, standard variance estimators designed under homogeneous means can underestimate the true variance, which results in subsequent tests being oversized. To restore validity, this paper proposes a simple conservative variance estimator robust to heterogeneous means and shows its asymptotic validity.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.11497v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.11457v1", "url": "http://arxiv.org/abs/2603.11457v1", "pdf_url": "https://arxiv.org/pdf/2603.11457v1", "title": "Bayesian Modular Inference for Copula Models with Potentially Misspecified Marginals", "authors": ["Lucas Kock", "David T. Frazier", "Michael Stanley Smith", "David J. Nott"], "annotation": "Copula models of multivariate data are popular because they allow separate specification of marginal distributions and the copula function. These components can be treated as inter-related modules in a modified Bayesian inference approach called ''cutting feedback'' that is robust to their misspecification. Recent work uses a two module approach, where all $d$ marginals form a single module, to robustify inference for the marginals against copula function misspecification, or vice versa. However, marginals can exhibit differing levels of misspecification, making it attractive to assign each its own module with an individual influence parameter controlling its contribution to a joint semi-modular inference (SMI) posterior. This generalizes existing two module SMI methods, which interpolate between cut and conventional posteriors using a single influence parameter. We develop a novel copula SMI method and select the influence parameters using Bayesian optimization. It provides an efficient continuous relaxation of the discrete optimization problem over $2^d$ cut/uncut configurations. We establish theoretical properties of the resulting semi-modular posterior and demonstrate the approach on simulated and real data. The real data application uses a skew-normal copula model of asymmetric dependence between equity volatility and bond yields, where robustifying copula estimation against marginal misspecification is strongly motivated.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.11457v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.11381v1", "url": "http://arxiv.org/abs/2603.11381v1", "pdf_url": "https://arxiv.org/pdf/2603.11381v1", "title": "On the Use of Design-Based Simulations", "authors": ["Bruno Ferman"], "annotation": "Design-based simulations - procedures that hold realized outcomes fixed and generate variation by resampling treatment assignment or shocks - are widely used in both methodological and applied work to assess inference procedures. This paper studies the extent to which such simulations are informative about inference validity. Focusing on shift-share designs, we show that standard simulations that fix outcomes and resample shocks may rely on a data-generating process that is not aligned with the true one. In particular, these simulations confound true treatment effects with error dependence, potentially overstating inference distortions due to spatial correlation. We propose alternative simulation designs that circumvent this problem and illustrate their use in prominent empirical applications. Our results highlight that the usefulness of design-based simulations depends critically on how closely the simulated data-generating process aligns with the true one.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.11381v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.11368v1", "url": "http://arxiv.org/abs/2603.11368v1", "pdf_url": "https://arxiv.org/pdf/2603.11368v1", "title": "Spatially Robust Inference with Predicted and Missing at Random Labels", "authors": ["Stephen Salerno", "Zhenke Wu", "Tyler McCormick"], "annotation": "When outcome data are expensive or onerous to collect, scientists increasingly substitute predictions from machine learning and AI models for unlabeled cases, a process which has consequences for downstream statistical inference. While recent methods provide valid uncertainty quantification under independent sampling, real-world applications involve missing at random (MAR) labeling and spatial dependence. For inference in this setting, we propose a doubly robust estimator with cross-fit nuisances. We show that cross-fitting induces fold-level correlation that distorts spatial variance estimators, producing unstable or overly conservative confidence intervals. To address this, we propose a jackknife spatial heteroscedasticity and autocorrelation consistent (HAC) variance correction that separates spatial dependence from fold-induced noise. Under standard identification and dependence conditions, the resulting intervals are asymptotically valid. Simulations and benchmark datasets show substantial improvement in finite-sample calibration, particularly under MAR labeling and clustered sampling.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.11368v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.10999v1", "url": "http://arxiv.org/abs/2603.10999v1", "pdf_url": "https://arxiv.org/pdf/2603.10999v1", "title": "Double Machine Learning for Time Series", "authors": ["Milos Ciganovic", "Federico D'Amario", "Massimiliano Tancioni"], "annotation": "We modify the Double Machine Learning estimator to broaden its applicability to macroeconomic time-series settings. A deterministic cross-fitting step, termed Reverse Cross-Fitting, leverages the time-reversibility of stationary series to improve sample utilization and efficiency. We detail and prove the conditions under which the estimator is asymptotically valid. We then demonstrate, through simulations, that its performance remains valid in realistic finite samples and is robust to model misspecification and violations of assumptions, such as heteroskedasticity. In high dimensions, predictive metrics for tuning nuisance learners do not generally minimize bias in the causal score. We propose a calibration rule targeting a \"Goldilocks zone\", a region of tuning parameters that delivers stable, partialled-out signals and reduced small-sample bias. Finally, we apply our procedure to residualized Local Projections to estimate the dynamic effects of a rise in Tier 1 regulatory capital. The results underscore the usefulness of the methodology for inference in macroeconomic applications.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.10999v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.10382v2", "url": "http://arxiv.org/abs/2603.10382v2", "pdf_url": "https://arxiv.org/pdf/2603.10382v2", "title": "Gimbal Regression: Orientation-Adaptive Local Linear Regression under Spatial Heterogeneity", "authors": ["Yuichiro Otani"], "annotation": "Local regression is widely used to explore spatial heterogeneity, but anisotropic or effectively low-dimensional neighborhoods can produce ill-conditioned local solves, causing coefficient variation driven by numerical artifacts rather than substantive structure. Such instability is often hidden when estimation relies on implicit tuning or optimization without exposing local diagnostics.   This paper proposes Gimbal Regression (GR), a deterministic, geometry-aware local regression framework for stable and auditable estimation. GR constructs directional weights from neighborhood geometry using explicit orientation objects and deterministic safeguards, and computes local coefficients by a closed-form solve. Theoretical results are stated conditional on the realized neighborhood configuration, under which the estimator is a deterministic linear operator with finite-perturbation stability bounds. Simulations and empirical examples demonstrate predictable computation, transparent diagnostics, and improved numerical stability relative to common local regression baselines.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.10382v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.10272v2", "url": "http://arxiv.org/abs/2603.10272v2", "pdf_url": "https://arxiv.org/pdf/2603.10272v2", "title": "An operator-level ARCH Model", "authors": ["Alexander Aue", "Sebastian Kühnert", "Gregory Rice", "Jeremy VanderDoes"], "annotation": "AutoRegressive Conditional Heteroscedasticity (ARCH) models are standard for modeling time series exhibiting volatility, with a rich literature in univariate and multivariate settings. In recent years, these models have been extended to function spaces. However, functional ARCH and generalized ARCH (GARCH) processes established in the literature have thus far been restricted to model ``pointwise'' variances. In this paper, we propose a new ARCH framework for data residing in general separable Hilbert spaces that accounts for the full evolution of the conditional covariance operator. We define a general operator-level ARCH model. For a simplified Constant Conditional Correlation version of the model, we establish conditions under which such models admit strictly and weakly stationary solutions, finite moments, and weak serial dependence. Additionally, we derive consistent Yule--Walker-type estimators of the infinite-dimensional model parameters. The practical relevance of the model is illustrated through simulations and a data application to high-frequency cumulative intraday returns.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.10272v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.10152v1", "url": "http://arxiv.org/abs/2603.10152v1", "pdf_url": "https://arxiv.org/pdf/2603.10152v1", "title": "Shrinkage Regularization for (Non)Linear Serial Dependence Test", "authors": ["Francesco Giancaterini", "Alain Hecq", "Joann Jasiak", "Aryan Manafi Neyazi"], "annotation": "This paper introduces a regularized test of the null hypothesis of the absence of linear and nonlinear serial dependence for high-dimensional non-Gaussian time series. Our approach extends the portmanteau test introduced in Jasiak and Neyazi (2023) to the high-dimensional setting.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.10152v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.08634v2", "url": "http://arxiv.org/abs/2603.08634v2", "pdf_url": "https://arxiv.org/pdf/2603.08634v2", "title": "Tractable Identification of Strategic Network Formation Models with Unobserved Heterogeneity", "authors": ["Wayne Yuan Gao", "Ming Li", "Zhengyan Xu"], "annotation": "We develop a tractable identification approach for strategic network formation models with both strategic link interdependence and individual unobserved heterogeneity (fixed effects). The key challenge is that endogenous network statistics (e.g. number of common friends) enter the link formation equation, while the mapping from model primitives to equilibrium network structure is generally intractable. Our approach sidesteps this difficulty using a ``bounding-by-$c$'' technique that treats endogenous covariates as random variables and exploits monotonicity restrictions to obtain identifying information. A central contribution is to develop a spectrum of fixed-effects handling strategies based on subnetwork configurations: tetrad-based restrictions that difference out all individual fixed effects, triad-based and weighted restrictions that combine ``difference-out'' and ``integrate-out'' steps by differencing out some fixed effects and profiling over the remainder conditional on observed characteristics, and general weighted cycle-based restrictions that unify these cases. We also provide point identification results. Preliminary simulations show that the approach can deliver informative bounds on the structural parameters.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.08634v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.08614v1", "url": "http://arxiv.org/abs/2603.08614v1", "pdf_url": "https://arxiv.org/pdf/2603.08614v1", "title": "Online Learning in Semiparametric Econometric Models", "authors": ["Xiaohong Chen", "Elie Tamer", "Qingsong Yao"], "annotation": "Data in modern economic and financial applications often arrive as a stream, requiring models and inference to be updated in real time -- yet most semiparametric methods remain batch-based and computationally impractical in large-scale streaming settings. We develop an online learning framework for semiparametric monotone index models with an unknown monotone link function. Our approach uses a two-phase learning paradigm. In a warm-start phase, we introduce a new online algorithm for the finite-dimensional parameter that is globally stable, yielding consistent estimation from arbitrary initialization. In a subsequent rate-optimal phase, we update the finite-dimensional parameter using an orthogonalized score while learning the unknown link via an online sieve method; this phase achieves optimal convergence rates for both components. The procedure processes only the most recent data batch, making it suitable when data cannot be stored (e.g., memory, privacy, or security constraints), and its resulting parameter trajectories enable online inference such as confidence regions--on parameters including policy-effect analysis with negligible additional computation. Monte Carlo experiments on both simulated and real data show adequate performance especially relative to full sample methods.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.08614v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.07914v1", "url": "http://arxiv.org/abs/2603.07914v1", "pdf_url": "https://arxiv.org/pdf/2603.07914v1", "title": "Event-Study Designs for Discrete Outcomes under Transition Independence", "authors": ["Young Ahn", "Hiroyuki Kasahara"], "annotation": "We develop a new identification strategy for average treatment effects on the treated (ATT) in panel data with discrete outcomes. Standard difference-in-differences (DiD) relies on parallel trends, which is frequently violated in categorical settings due to mean reversion, out-of-bounds counterfactuals, and ill-defined trends for multi-category outcomes. We propose an alternative identification strategy with transition independence: absent treatment, transition dynamics conditional on pre-treatment outcomes are identical between control and treated groups. To capture unobserved heterogeneity, we introduce a latent-type Markov structure delivering type-specific and aggregate treatment effects from short panels. Three empirical applications yield ATT estimates substantially different from conventional DiD.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.07914v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.07813v1", "url": "http://arxiv.org/abs/2603.07813v1", "pdf_url": "https://arxiv.org/pdf/2603.07813v1", "title": "At-Risk Transformation for U.S. Recession Prediction", "authors": ["Rahul Billakanti", "Minchul Shin"], "annotation": "We propose a simple binarization of predictors, an \"at-risk\" transformation, as an alternative to the standard practice of using continuous, standardized variables in recession forecasting models. By converting predictors into indicators of unusually weak states based on a thresholding rule estimated from training data, we demonstrate their ability to capture the discrete nature of rare events such as U.S. recessions. Using a large panel of monthly U.S. macroeconomic and financial data, we show that binarized predictors consistently improve out-of-sample forecasting performance, often making linear models competitive with flexible machine learning methods, and that the gains are particularly pronounced around the onset of recessions.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.07813v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.07780v1", "url": "http://arxiv.org/abs/2603.07780v1", "pdf_url": "https://arxiv.org/pdf/2603.07780v1", "title": "Testing for Endogeneity: A Moment-Based Bayesian Approach", "authors": ["Siddhartha Chib", "Minchul Shin", "Anna Simoni"], "annotation": "A standard assumption in the Bayesian estimation of linear regression models is that the regressors are exogenous in the sense that they are uncorrelated with the model error term. In practice, however, this assumption can be invalid. In this paper, using the exponentially tilted empirical likelihood framework, we develop a Bayes factor test for endogeneity that compares a base model that is correctly specified under exogeneity but misspecified under endogeneity against an extended model that is correctly specified in either case. We provide a comprehensive study of the log-marginal exponentially tilted empirical likelihood. We demonstrate that our testing procedure is consistent from a frequentist point of view: as the sample grows, it almost surely selects the base model if and only if the regressors are exogenous, and the extended model if and only if the regressors are endogenous. The methods are illustrated with simulated data, and problems concerning the causal effect of automobile prices on automobile demand and the causal effect of potentially endogenous airplane ticket prices on passenger volume.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.07780v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.07722v1", "url": "http://arxiv.org/abs/2603.07722v1", "pdf_url": "https://arxiv.org/pdf/2603.07722v1", "title": "Identification and Counterfactual Analysis in Incomplete Models with Support and Moment Restrictions", "authors": ["Lixiong Li"], "annotation": "This paper develops a unified identification framework for counterfactual analysis in incomplete models characterized by support and moment restrictions. I demonstrate that identifying structural parameters and conducting counterfactual analyses are isomorphic tasks. By embedding counterfactual restrictions within an augmented structural model specification, this approach bypasses the conventional \"estimate-then-simulate\" workflow and the need to simulate outcomes from models with set predictions. To make this approach operational, I extend sharp identification results for the support-function approach beyond the integrable boundedness condition that is imposed in sharp random-set characterizations but may be violated in economically relevant counterfactual analyses. Under minimal regularity conditions, I prove that the support-function approach remains sharp for the $moment$ $closure$ of the identified set. Furthermore, I introduce an irreducibility condition requiring all support implications to be made explicit. I show that for irreducible models, the identified set and its moment closure are statistically indistinguishable in finite samples. Together, these results justify using support-function methods in counterfactual settings where traditional sharpness fails and clarify the distinct roles of support and moment restrictions in empirical practice.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.07722v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.07458v1", "url": "http://arxiv.org/abs/2603.07458v1", "pdf_url": "https://arxiv.org/pdf/2603.07458v1", "title": "ForeComp: An R Package for Comparing Predictive Accuracy Using Fixed-Smoothing Asymptotics", "authors": ["Minchul Shin", "Nathan Schor"], "annotation": "We introduce ForeComp, an R package for comparing predictive accuracy using Diebold-Mariano type tests of equal predictive ability with standard and fixed smoothing inference. The package provides a common interface for loss differential based testing and includes Plot Tradeoff, a visual diagnostic for bandwidth sensitivity and the size-power tradeoff. We illustrate the toolkit with Survey of Professional Forecasters applications and Monte Carlo evidence on finite-sample performance.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.07458v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.07255v1", "url": "http://arxiv.org/abs/2603.07255v1", "pdf_url": "https://arxiv.org/pdf/2603.07255v1", "title": "On the Rates of Convergence of Induced Ordered Statistics and their Applications", "authors": ["Federico A. Bugni", "Ivan A. Canay", "Deborah Kim"], "annotation": "Induced order statistics (IOS) arise when sample units are reordered according to the value of an auxiliary variable, and the associated responses are analyzed in that induced order. IOS play a central role in applications where the goal is to approximate the conditional distribution of an outcome at a fixed covariate value using observations whose covariates lie closest to that point, including regression discontinuity designs, k-nearest-neighbor methods, and distributionally robust optimization. Existing asymptotic results allow the dimension of the IOS vector to grow with the sample size only under smoothness conditions that are often too restrictive for practical data-generating processes. In particular, these conditions rule out boundary points, which are central to regression discontinuity designs. This paper develops general convergence rates for IOS under primitive and comparatively weak assumptions. We derive sharp marginal rates for the approximation of the target conditional distribution in Hellinger and total variation distances under quadratic mean differentiability and show how these marginal rates translate into joint convergence rates for the IOS vector. Our results are widely applicable: they rely on a standard smoothness condition and accommodate both interior and boundary conditioning points, as required in regression discontinuity and related settings. In the supplementary appendix, we provide complementary results under a Taylor/Holder remainder condition. Our results reveal a clear trade-off between smoothness and speed of convergence, identify regimes in which Hellinger and total variation distances behave differently, and provide explicit growth conditions on the number of nearest neighbors.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.07255v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.07055v1", "url": "http://arxiv.org/abs/2603.07055v1", "pdf_url": "https://arxiv.org/pdf/2603.07055v1", "title": "Integrating Heterogeneous Information in Randomized Experiments: A Unified Calibration Framework", "authors": ["Wei Ma", "Zeqi Wu", "Zheng Zhang"], "annotation": "In modern randomized experiments, large-scale data collection increasingly yields rich baseline covariates and auxiliary information from multiple sources. Such information offers opportunities for more precise treatment effect estimation, but it also raises the challenge of integrating heterogeneous information coherently without compromising validity. Covariate-adaptive randomization (CAR) is widely used to improve covariate balance at the design stage, but it typically balances only a small set of covariates used to form strata, making covariate adjustment at the analysis stage essential for more efficient estimation of treatment effects. Beyond standard covariate adjustment, it is often desirable to incorporate auxiliary information, including cross-stratum information, predictions from various machine learning models, and external data from historical trials or real-world sources. While this auxiliary information is widely available, existing covariate adjustment methods under CAR primarily exploit within-stratum covariates and do not provide a coherent mechanism for integrating it. We propose a unified calibration framework that integrates such information through an information proxy vector and calibration weights defined by a convex optimization problem. The resulting estimator recovers many recent covariate adjustment procedures as special cases while providing a systematic mechanism for both internal and external information borrowing within a single framework. We establish large-sample validity and a no-harm efficiency guarantee, showing that incorporating additional information sources cannot increase asymptotic variance, and we extend the theory to settings in which both the number of strata and the number of information sources grow with the sample size.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.07055v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.07018v1", "url": "http://arxiv.org/abs/2603.07018v1", "pdf_url": "https://arxiv.org/pdf/2603.07018v1", "title": "TEA-Time: Transporting Effects Across Time", "authors": ["Harsh Parikh", "Gabriel Levin-Konigsberg", "Dominique Perrault-Joncas", "Alexander Volfovsky"], "annotation": "Treatment effects estimated from randomized controlled trials are local not only to the study population but also to the time at which the trial was conducted. We develop a framework for temporal transportation: extrapolating treatment effects to time periods where no experiment was conducted. We target the transported average treatment effect (TATE) and show that under a separable temporal effects assumption, the TATE decomposes into an observed average treatment effect and a temporal ratio. We provide two identification strategies -- one using replicated trials comparing the same treatments at different times, another using common treatment arms observed across time -- and develop doubly robust, semiparametrically efficient estimators for each. Monte Carlo simulations confirm that both estimators achieve nominal coverage, with the common arm strategy yielding substantial efficiency gains when its stronger assumptions hold. We apply our methods to A/B tests from the Upworthy Research Archive, demonstrating that the two strategies exhibit a variance-bias tradeoff: the common arm approach offers greater precision but may incur bias when treatments interact heterogeneously with temporal factors.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.07018v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.06820v1", "url": "http://arxiv.org/abs/2603.06820v1", "pdf_url": "https://arxiv.org/pdf/2603.06820v1", "title": "Hippocratic Utility", "authors": ["Tomasz Strzalecki"], "annotation": "A utility function has been proposed that values more those lives that are saved by not imposing a harmful treatment and values less those lives that could be saved by treating people who would otherwise die. I do not dispute the ethical motivation behind this kind of asymmetry. However, as my example illustrates, the scope of applicability of such a decision criterion may be limited.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.06820v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.15652v1", "url": "http://arxiv.org/abs/2603.15652v1", "pdf_url": "https://arxiv.org/pdf/2603.15652v1", "title": "P vs NP Problem in Portfolio Optimization: Integrating the Markowitz-CAPM Framework with Cardinality Constraints and Black-Scholes Derivative Pricing", "authors": ["Davit Gondauri"], "annotation": "This paper makes the Millennium Prize problem P vs NP operational in quantitative finance by studying cardinality-constrained portfolio selection. Starting from the convex Markowitz mean-variance program with CAPM-based expected returns (Rf plus beta times ERP), we impose a hard sparsity rule that limits the portfolio to K assets out of approximately 94 industry portfolios (Damodaran). The constraint couples discrete subset selection with continuous weight optimization, yielding a mixed-integer quadratic program and an NP-hard search space that grows combinatorially with n and K. We therefore evaluate scalable approximation schemes (greedy screening, Monte Carlo sampling, and genetic algorithms) under a replication-oriented protocol with random-seed control, distributional performance summaries (median and quantiles), runtime profiling, and convergence diagnostics. Dependence structure is documented via correlation and covariance diagnostics and positive-semidefinite checks to link algorithm behavior to the geometry implied by the risk matrix. To support the title's derivatives component, we add a European call option priced by the Black-Scholes model and map it into CAPM-consistent moments using delta-based linearization, validated with a bump test and moneyness/maturity sensitivity. Results highlight how the cardinality constraint reshapes the attainable efficient frontier, why stability and computational-cost trade-offs matter more than single-best runs, and how common-factor dependence can limit diversification in K-sparse solutions. The study provides a reproducible template for NP-hard portfolio optimization with transparent inputs and extensible derivative overlays.", "category": "econ.EM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.15652v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29986v1", "url": "http://arxiv.org/abs/2603.29986v1", "pdf_url": "https://arxiv.org/pdf/2603.29986v1", "title": "ParetoEnsembles.jl: A Julia Package for Multiobjective Parameter Estimation Using Pareto Optimal Ensemble Techniques", "authors": ["Jeffrey D. Varner"], "annotation": "Mathematical models of natural and man-made systems often have many adjustable parameters that must be estimated from multiple, potentially conflicting datasets. Rather than reporting a single best-fit parameter vector, it is often more informative to generate an ensemble of parameter sets that collectively map out the trade-offs among competing objectives. This paper presents ParetoEnsembles.jl, an open-source Julia package that generates such ensembles using Pareto Optimal Ensemble Techniques (POETs), a simulated-annealing-based algorithm that requires no gradient information. The implementation corrects the original dominance relation from weak to strict Pareto dominance, reduces the per-iteration ranking cost from $O(n^2 m)$ to $O(nm)$ through an incremental update scheme, and adds multi-chain parallel execution for improved front coverage. We demonstrate the package on a cell-free gene expression model fitted to experimental data and a blood coagulation cascade model with ten estimated rate constants and three objectives. A controlled synthetic-data study reveals parameter identifiability structure, with individual rate constants off by several-fold yet model predictions accurate to 7%. A five-replicate coverage analysis confirms that timing features are reliably covered while peak amplitude is systematically overconfident. Validation against published experimental thrombin generation data demonstrates that the ensemble predicts held-out conditions to within 10% despite inherent model approximation error. By making ensemble generation lightweight and accessible, ParetoEnsembles.jl aims to lower the barrier to routine uncertainty characterization in mechanistic modeling.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29986v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29977v1", "url": "http://arxiv.org/abs/2603.29977v1", "pdf_url": "https://arxiv.org/pdf/2603.29977v1", "title": "Quantifying Cross-Modal Interactions in Multimodal Glioma Survival Prediction via InterSHAP: Evidence for Additive Signal Integration", "authors": ["Iain Swift", "JingHua Ye", "Ruairi O'Reilly"], "annotation": "Multimodal deep learning for cancer prognosis is commonly assumed to benefit from synergistic cross-modal interactions, yet this assumption has not been directly tested in survival prediction settings. This work adapts InterSHAP, a Shapley interaction index-based metric, from classification to Cox proportional hazards models and applies it to quantify cross-modal interactions in glioma survival prediction. Using TCGA-GBM and TCGA-LGG data (n=575), we evaluate four fusion architectures combining whole-slide image (WSI) and RNA-seq features. Our central finding is an inverse relationship between predictive performance and measured interaction: architectures achieving superior discrimination (C-index 0.64$\\to$0.82) exhibit equivalent or lower cross-modal interaction (4.8\\%$\\to$3.0\\%). Variance decomposition reveals stable additive contributions across all architectures (WSI${\\approx}$40\\%, RNA${\\approx}$55\\%, Interaction${\\approx}$4\\%), indicating that performance gains arise from complementary signal aggregation rather than learned synergy. These findings provide a practical model auditing tool for comparing fusion strategies, reframe the role of architectural complexity in multimodal fusion, and have implications for privacy-preserving federated deployment.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29977v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29916v1", "url": "http://arxiv.org/abs/2603.29916v1", "pdf_url": "https://arxiv.org/pdf/2603.29916v1", "title": "Growth-rate distributions at stationarity", "authors": ["Edgardo Brigatti"], "annotation": "We propose new analytical tools for describing growth-rate distributions generated by stationary time-series. Our analysis shows how deviations from normality are not pathological behaviour, as suggested by some traditional views, but instead can be accounted for by clean and general statistical considerations. In contrast, strict normality is the effect of specific modelling choices. Systems characterized by stationary Gamma or heavy-tailed abundance distributions produce log-growth-rate distributions well described by a generalized logistic distribution, which can describe tent-shaped or nearly normal datasets and serves as a useful null model for these observables. These results prove that, for large enough time lags, in practice, growth-rate distributions cease to be time-dependent and exhibit finite variance. Based on this analysis, we identify some key stylized macroecological patterns and specific stochastic differential equations capable of reproducing them. A pragmatic workflow for heuristic selection between these models is then introduced. This approach is particularly useful for systems with limited data-tracking quality, where applying sophisticated inference methods is challenging.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29916v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29793v1", "url": "http://arxiv.org/abs/2603.29793v1", "pdf_url": "https://arxiv.org/pdf/2603.29793v1", "title": "Multimodal Machine Learning for Early Prediction of Metastasis in a Swedish Multi-Cancer Cohort", "authors": ["Franco Rugolon", "Korbinian Randl", "Braslav Jovanovic", "Ioanna Miliou", "Panagiotis Papapetrou"], "annotation": "Multimodal Machine Learning offers a holistic view of a patient's status, integrating structured and unstructured data from electronic health records (EHR). We propose a framework to predict metastasis risk one month prior to diagnosis, using six months of clinical history from EHR data. Data from four cancer cohorts collected at Karolinska University Hospital (Stockholm, Sweden) were analyzed: breast (n = 743), colon (n = 387), lung (n = 870), and prostate (n = 1890). The dataset included demographics, comorbidities, laboratory results, medications, and clinical text. We compared traditional and deep learning classifiers across single modalities and multimodal combinations, using various fusion strategies and a Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) 2a design, with an 80-20 development-validation split to ensure a rigorous, repeatable evaluation. Performance was evaluated using AUROC, AUPRC, F1 score, sensitivity, and specificity. We then employed a multimodal adaptation of SHAP to analyze the classifiers' reasoning. Intermediate fusion achieved the highest F1 scores on breast (0.845), colon (0.786), and prostate cancer (0.845), demonstrating strong predictive performance. For lung cancer, the intermediate fusion achieved an F1 score of 0.819, while the text-only model achieved the highest, with an F1 score of 0.829. Deep learning classifiers consistently outperformed traditional models. Colon cancer, the smallest cohort, had the lowest performance, highlighting the importance of sufficient training data. SHAP analysis showed that the relative importance of modalities varied across cancer types. Fusion strategies offer distinct strengths and weaknesses. Intermediate fusion consistently delivered the best results, but strategy choices should align with data characteristics and organizational needs.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29793v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29684v1", "url": "http://arxiv.org/abs/2603.29684v1", "pdf_url": "https://arxiv.org/pdf/2603.29684v1", "title": "FcsIT: An Open-Source, Cross-Platform Tool for Correlation and Analysis of Fluorescence Correlation Spectroscopy Data", "authors": ["Tomasz Kalwarczyk"], "annotation": "FcsIT is a platform-independent, open-source tool for calculating the correlation and fitting fluorescence correlation spectroscopy data. The software is written in Python and uses a powerful Dear PyGUI engine for its interface. It provides reading and correlating the TTTR data, as well as TCSPC filtering of the photon time-trace data. The circular-block bootstrap method applied to the calculation of correlation data and its variance results in data quality comparable to that obtained with commercially available software. An intuitive fitting interface provides efficient analysis of large datasets and includes nine predefined mathematical models for fitting correlation curves. Moreover, it allows users to add their own models in a user-friendly manner. Validation of the FcsIT tool against simulated FCS data and real FCS experiments confirms its usability and potential appeal to a wide variety of FCS users.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29684v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.29546v1", "url": "http://arxiv.org/abs/2603.29546v1", "pdf_url": "https://arxiv.org/pdf/2603.29546v1", "title": "Sampling from the Solution Space and Metabolic Environments of Genome-Scale Metabolic Models", "authors": ["Haris Zafeiropoulos", "Daniel Rios Garza"], "annotation": "Flux sampling is an analysis that, based on a distribution, picks randomly an efficient number of points from the solution space of a metabolic model. Unlike most constraint-based analyses, flux sampling does not require an objective function to optimize, allowing for the exploration of the whole spectrum of the phenotypes a species can exhibit. However, sampling can also be restricted to a subspace where a chosen objective reaches at least a specified fraction of its optimum. This targeted approach adds value when investigating phenotypes that are optimal for a specific function. Contrary to Flux Balance Analysis, which returns a single solution, sampling leverages statistical power to uncover phenotypes that otherwise would be masked. This can be especially useful when changing the conditions (medium) in which a species lives. Here, we highlight some state-of-the-art methods for applying flux sampling at Genome-Scale Metabolic Models in different scenarios, and we showcase flux sampling applications", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.29546v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.28930v1", "url": "http://arxiv.org/abs/2603.28930v1", "pdf_url": "https://arxiv.org/pdf/2603.28930v1", "title": "Retrospective Economic Evaluation of Group Testing in the COVID-19 Pandemic", "authors": ["Michael Balzer", "Kainat Khowaja", "Christiane Fuchs"], "annotation": "Surveillance of diseases in a pandemic is an important part of public health policy. Diagnostic testing at the individual level is often infeasible due to resource constraints. To circumvent these constraints, group testing can be applied. The economic cost evaluation from the payer's perspective typically focuses only on deterministic costs which overlooks the substantial economic impact of productivity losses resulting from quarantine and workplace disruptions. The objective of this article is to develop a mathematical model for a retrospective economic evaluation of group testing that incorporates both deterministic costs and income-based economic loss. Group testing algorithms are revisited and simulated at optimized pool sizes to determine the required number of tests. Income data from the German Socio-Economic Panel are integrated into a mathematical model to capture the economic loss. Afterward, hybrid Monte Carlo experiments are conducted by evaluating the economic cost in the Coronavirus disease 2019 pandemic in Germany. Monte Carlo experiments show that the optimal choice of group testing algorithms changes substantially when income-based economic losses are included. Evaluations considering only deterministic costs systematically underestimate the total economic cost. Algorithms with a longer quarantine duration are less attractive than shorter quarantine duration if income-based economic loss is accounted for. The findings show that current evaluations underestimate the true economic cost. Group testing algorithms with shorter duration and fewer stages are preferred, even when they require a larger number of tests. These results underscore the importance of incorporating income-based economic loss into a mathematical model.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.28930v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27787v1", "url": "http://arxiv.org/abs/2603.27787v1", "pdf_url": "https://arxiv.org/pdf/2603.27787v1", "title": "Cardiovascular-Kidney-Metabolic Health: Insights from Wearables and Blood Biomarkers", "authors": ["Zeinab Esmaeilpour", "A. Ali Heydari", "Daniel McDuff", "Anthony Z Faranesh", "Conor Heneghan", "Shwetak Patel", "Mark Malhotra", "Cathy Speed", "Javier L. Prieto", "Ahmed A. Metwally"], "annotation": "Cardiovascular-Kidney-Metabolic (CKM) syndrome represents a growing public health crisis, yet the subclinical heterogeneity of its component systems remains underexplored. Early detection of physiological deviation is critical for preventing irreversible organ damage and mortality. Here, we characterize the prevalence and interplay of CKM impairment in a US cohort (N=841) by integrating continuous wearable data with clinical biomarkers. We assessed cardiovascular, kidney via clinical biomarkers, namely Chol/HDL, eGFR, as well as metabolic health risk through Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). We show that while metabolic and cardiovascular disruptions are significantly associated (r=0.26, p<0.001), early-stage kidney impairment manifests independently. Utilizing a normalized deviance score, we identified significant health impairments in 29.0% of the cohort. Cardiovascular deviation was the most prevalent singular phenotype (13.3%), followed by metabolic (9.1%) and renal (6.25%) deviations, with dual metabolic-cardiovascular impairment occurring in only 2.2% of participants. These findings suggest that high system-specific deviance may serve as an indicator for accelerated physiological aging within the respective organ system. Furthermore, feature ablation analysis revealed that step count, Active Zone Minutes, and resting heart rate are the most potent wearable-derived predictors of cardiovascular and metabolic decline. These findings underscore the necessity of a multi-system subtyping approach, demonstrating that wearable-derived phenotypes can facilitate the early, targeted interventions required to manage the complex landscape of CKM syndrome.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27787v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27484v1", "url": "http://arxiv.org/abs/2603.27484v1", "pdf_url": "https://arxiv.org/pdf/2603.27484v1", "title": "Quantitative mapping of dynamic 3D transport in growing cells via volumetric spatio-temporal image correlation spectroscopy (vSTICS)", "authors": ["Ahmad Mahmood", "Paul W. Wiseman"], "annotation": "Quantitatively mapping three-dimensional (3D) flow, diffusion, and particle density in crowded living cells remains challenging because most dynamic optical microscopy measurements are effectively planar and existing analysis methods struggle with dense, noisy volumetric data. We introduce volumetric spatio-temporal image correlation spectroscopy (vSTICS), a framework that recovers voxel-resolved flow, diffusion coefficients, and particle densities from 3D fluorescence time series. Growing Camellia japonica pollen tubes were imaged with field-synthesis lattice light-sheet microscopy, and localized 3D spatio-temporal correlation analysis was applied to overlapping volumetric samples to generate maps of velocity, diffusion, and density. Validation with synthetic flow-diffusion simulations showed accurate recovery of seeded transport parameters, including velocities near $3$ $μ$m s$^{-1}$ and diffusion near $10^{-3}$ $μ$m$^2$ s$^{-1}$. Fluorescent microsphere experiments verified particle number and point spread function readouts and measured diffusion coefficients of $0.3 \\pm 0.1$ $μ$m$^2$ s$^{-1}$ in gel, consistent with imaging-FCS measurements of $0.5 \\pm 0.2$ $μ$m$^2$ s$^{-1}$. Applied to mitochondria in pollen tubes, vSTICS resolved a bidirectional reverse-fountain pattern with slower anterograde transport ($0.1$-$1$ $μ$m s$^{-1}$) and faster retrograde motion peaking near $3$ $μ$m s$^{-1}$, plus a retrograde corridor about $2$ $μ$m wide. Density and diffusion maps indicated a denser, more advective core and higher peripheral diffusion. High-density sub-diffraction vesicle mapping produced similar velocity landscapes with about ten-fold higher particle densities. These results establish vSTICS as a practical method for quantitative 3D mapping of intracellular transport and refines the reverse-fountain model by revealing asymmetric, predominantly transverse circulation.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27484v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27303v1", "url": "http://arxiv.org/abs/2603.27303v1", "pdf_url": "https://arxiv.org/pdf/2603.27303v1", "title": "Self-evolving AI agents for protein discovery and directed evolution", "authors": ["Yang Tan", "Lingrong Zhang", "Mingchen Li", "Yuanxi Yu", "Bozitao Zhong", "Bingxin Zhou", "Nanqing Dong", "Liang Hong"], "annotation": "Protein scientific discovery is bottlenecked by the manual orchestration of information and algorithms, while general agents are insufficient in complex domain projects. VenusFactory2 provides an autonomous framework that shifts from static tool usage to dynamic workflow synthesis via a self-evolving multi-agent infrastructure to address protein-related demands. It outperforms a set of well-known agents on the VenusAgentEval benchmark, and autonomously organizes the discovery and optimization of proteins from a single natural language prompt.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27303v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27104v1", "url": "http://arxiv.org/abs/2603.27104v1", "pdf_url": "https://arxiv.org/pdf/2603.27104v1", "title": "Autonomous Agent-Orchestrated Digital Twins (AADT): Leveraging the OpenClaw Framework for State Synchronization in Rare Genetic Disorders", "authors": ["Hongzhuo Chen", "Zhanliang Wang", "Quan M. Nguyen", "Gongbo Zhang", "Chunhua Weng", "Kai Wang"], "annotation": "Background: Medical Digital Twins (MDTs) are computational representations of individual patients that integrate clinical, genomic, and physiological data to support diagnosis, treatment planning, and outcome prediction. However, most MDTs remain static or passively updated, creating a critical synchronization gap, especially in rare genetic disorders where phenotypes, genomic interpretations, and care guidelines evolve over time.   Methods: We propose an agent-orchestrated digital twin framework using OpenClaw's proactive \"heartbeat\" mechanism and modular Agent Skills. This Autonomous Agent-orchestrated Digital Twin (AADT) system continuously monitors local and external data streams (e.g., patient-reported phenotypes and updates in variant classification databases) and executes automated workflows for data ingestion, normalization, state updates, and trigger-based analysis.   Results: A prototype implementation demonstrates that agent orchestration can continuously synchronize MDT states with both longitudinal phenotype updates and evolving genomic knowledge. In rare disease settings, this enables earlier diagnosis and more accurate modeling of disease progression. We present two case studies, including variant reinterpretation and longitudinal phenotype tracking, highlighting how AADTs support timely, auditable updates for both research and clinical care.   Conclusion: The AADT framework addresses the key bottleneck of real-time synchronization in MDTs, enabling scalable and continuously updated patient models. We also discuss data security considerations and mitigation strategies through human-in-the-loop system design.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27104v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.27017v1", "url": "http://arxiv.org/abs/2603.27017v1", "pdf_url": "https://arxiv.org/pdf/2603.27017v1", "title": "Beyond BMI: Smartphone Body Composition Phenotyping for Cardiometabolic Risk Assessment", "authors": ["Dr Menglian Zhou", "Mr Arno Charton", "Ms Emily Blanchard", "Mr Lawrence Cai", "Dr Tracy Giest", "Dr Herschel Watkins", "Dr Mohamed Bouterfa", "Ms Jackie Wasson", "Dr Keerthana Natarajan", "Mr Aniket Deshpande", "Dr Jiening Zhan", "Dr Shelten Yuen", "Dr Xavi Prieto", "Jacqueline Shreibati", "Mark Malhotra", "Shwetak Patel", "Ms Lindsey Sunden", "Dr Cathy Speed", "Ms Alicia Kokoszka", "Dr Aravind Natarajan", "Dr Alexandros Pantelopoulos", "Dr Ahmed Metwally"], "annotation": "Body Mass Index (BMI) is a widely accessible but imprecise proxy of cardiometabolic health. While assessing true body composition is superior, gold-standard methods like Dual-Energy X-ray Absorptiometry (DXA) are not scalable. We address this gap by developing and validating \"PhotoScan,\" a method to estimate body composition from smartphone imagery. We pretrained a deep learning model on UK Biobank participants (N=35,323) and fine-tuned on a newly recruited clinical cohort (PhotoBIA cohort, N=677) with diverse ethnicity, age, and body fat distribution, achieving high accuracy against DXA for total body fat percentage (BF%, MAE = 2.15%), Android-to-Gynoid fat ratio (A/G, MAE = 0.11), and visceral-to-subcutaneous fat area ratio (V/S, MAE = 0.09). Generalizability of the model was demonstrated on an independent metabolic health study cohort (MetabolicMosaic cohort, N=132 participants), achieving MAEs of 2.13% for BF%, 0.09 for A/G, and 0.09 for V/S. We then evaluated the clinical utility of these metrics in the MetabolicMosaic cohort by predicting insulin resistance (IR). Adding PhotoScan-derived body composition metrics to baseline demographics model (Age, Sex, BMI) significantly improved insulin resistance classification (Area Under the Receiver Operating Characteristic Curve \"AUROC\" 76.0% vs 69.2%, DeLong test p=0.002, Net Reclassification Index \"NRI\" 0.593). Crucially, this accessible smartphone method achieved performance nearly equivalent to adding clinical-grade DXA data to baseline demographics model (AUROC 77.3% vs 69.2%, DeLong test p=0.004, NRI 0.748). These findings demonstrate that smartphone-based phenotyping captures clinically meaningful risk signals missed by BMI and anthropometrics, offering a scalable alternative to DXA for cardiometabolic risk stratification.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.27017v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26994v1", "url": "http://arxiv.org/abs/2603.26994v1", "pdf_url": "https://arxiv.org/pdf/2603.26994v1", "title": "ImmSET: Sequence-Based Predictor of TCR-pMHC Specificity at Scale", "authors": ["Marco Garcia Noceda", "Matthew T Noakes", "Andrew FigPope", "Daniel E Mattox", "Bryan Howie", "Harlan Robins"], "annotation": "T cells are a critical component of the adaptive immune system, playing a role in infectious disease, autoimmunity, and cancer. T cell function is mediated by the T cell receptor (TCR) protein, a highly diverse receptor targeting specific peptides presented by the major histocompatibility complex (pMHCs). Predicting the specificity of TCRs for their cognate pMHCs is central to understanding adaptive immunity and enabling personalized therapies. However, accurate prediction of this protein-protein interaction remains challenging due to the extreme diversity of both TCRs and pMHCs. Here, we present ImmSET (Immune Synapse Encoding Transformer), a novel sequence-based architecture designed to model interactions among sets of variable-length biological sequences. We train this model across a range of dataset sizes and compositions and study the resulting models' generalization to pMHC targets. We describe a failure mode in prior sequence-based approaches that inflates previously reported performance on this task and show that ImmSET remains robust under stricter evaluation. In systematically testing the scaling behavior of ImmSET with training data, we show that performance scales consistently with data volume across multiple data types and compares favorably with the pre-trained protein language model ESM2 fine-tuned on the same datasets. Finally, we demonstrate that ImmSET can outperform AlphaFold2 and AlphaFold3-based pipelines on TCR-pMHC specificity prediction when provided sufficient training data. This work establishes ImmSET as a scalable modeling paradigm for multi-sequence interaction problems, demonstrated in the TCR-pMHC setting but generalizable to other biological domains where high-throughput sequence-driven reasoning complements structure prediction and experimental mapping.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26994v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26544v1", "url": "http://arxiv.org/abs/2603.26544v1", "pdf_url": "https://arxiv.org/pdf/2603.26544v1", "title": "Development of a European Union Time-Indexed Reference Dataset for Assessing the Performance of Signal Detection Methods in Pharmacovigilance using a Large Language Model", "authors": ["Maria Kefala", "Jeffery L. Painter", "Syed Tauhid Bukhari", "Maurizio Sessa"], "annotation": "Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets. Existing datasets do not capture when adverse events (AEs) are officially recognized by regulatory authorities, preventing restriction of analyses to pre-confirmation periods and limiting evaluation of early detection performance. This study addresses this gap by developing a time-indexed reference dataset for the European Union (EU), incorporating the timing of AE inclusion in product labels along with regulatory metadata. Methods: Current and historical Summaries of Product Characteristics (SmPCs) for all centrally authorized products (n=1,513) were retrieved from the EU Union Register of Medicinal Products (data lock: 15 December 2025). Section 4.8 was extracted and processed using DeepSeek V3 to identify AEs. Regulatory metadata, including labelling changes, were programmatically extracted. Time indexing was based on the date of AE inclusion in the SmPC. Results: The database includes 17,763 SmPC versions spanning 1995-2025, comprising 125,026 drug-AE associations. The time-indexed reference dataset, restricted to active products, included 1,479 medicinal products and 110,823 drug-AE associations. Most AEs were identified pre-marketing (74.5%) versus post-marketing (25.5%). Safety updates peaked around 2012. Gastrointestinal, skin, and nervous system disorders were the most represented System Organ Classes. Drugs had a median of 48 AEs across 14 SOCs. Conclusions: The proposed dataset addresses a critical gap in pharmacovigilance by incorporating temporal information on AE recognition for the EU, supporting more accurate assessment of signal detection performance and facilitating methodological comparisons across analytical approaches.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26544v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26370v1", "url": "http://arxiv.org/abs/2603.26370v1", "pdf_url": "https://arxiv.org/pdf/2603.26370v1", "title": "Multi-scale Metabolic Modeling and Simulation", "authors": ["Peter E. Carstensen", "Teddy Groves", "Lars K. Nielsen", "Ulrich Krühne", "Krist V. Gernaey", "John B. Jørgensen"], "annotation": "Biological systems are governed by coupled interactions between intracellular metabolism and bioreactor operation that span multiple time scales. Constraint-based metabolic models are widely used to describe intracellular metabolism, but repeatedly solving the optimization problem at each time step in dynamic models introduces numerical challenges related to infeasibility and computational efficiency. This work presents a multi-scale modeling framework that integrates genome-scale, constraint-based metabolic models with dynamic bioreactor simulations. Intracellular metabolism is described using positive flux variables in a parsimonious flux balance analysis, and the resulting embedded optimization problem is replaced by a neural network surrogate. The surrogate provides a smooth approximation of the embedded optimization mapping and eliminates repeated linear program solves during simulation. The approach is demonstrated for fed-batch fermentation of Escherichia coli, in which the surrogate model yields intracellular fluxes under substrate-limited conditions, whereas the underlying linear program would otherwise be infeasible. The framework provides a continuous representation of intracellular metabolism suitable for dynamic simulation of genome-scale models in bioreactor configurations.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26370v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26110v1", "url": "http://arxiv.org/abs/2603.26110v1", "pdf_url": "https://arxiv.org/pdf/2603.26110v1", "title": "TurboESM: Ultra-Efficient 3-Bit KV Cache Quantization for Protein Language Models with Orthogonal Rotation and QJL Correction", "authors": ["Yue Hu", "Junqing Wang", "Yingchao Liu"], "annotation": "The rapid scaling of Protein Language Models (PLMs) has unlocked unprecedented accuracy in protein structure prediction and design, but the quadratic memory growth of the Key-Value (KV) cache during inference remains a prohibitive barrier for single-GPU deployment and high-throughput generation. While 8-bit quantization is now standard, 3-bit quantization remains elusive due to severe numerical outliers in activations. This paper presents TurboESM, an adaptation of Google's TurboQuant to the PLM domain. We solve the fundamental incompatibility between Rotary Position Embeddings (RoPE) and orthogonal transformations by deriving a RoPE-first rotation pipeline. We introduce a head-wise SVD calibration method tailored to the amino acid activation manifold, a dual look-up table (LUT) strategy for asymmetric K/V distributions, and a 1-bit Quantized Johnson-Lindenstrauss (QJL) residual correction. All experiments are conducted on ESM-2 650M, where our implementation achieves a 7.1x memory reduction (330 MB to 47 MB) while maintaining cosine similarity > 0.96 in autoregressive decoding across diverse protein families, including short peptides, transmembrane helices, enzyme active site fragments, and intrinsically disordered regions. We further implement a Triton-based fused decode attention kernel that eliminates intermediate dequantization memory allocations, achieving a 1.96x speedup over the PyTorch two-step path for the KV fetch operation alone; however, TurboESM incurs a prefill overhead of 21-27 ms relative to the original model due to KV quantization and packing, making it most suitable for memory-bound scenarios rather than latency-critical short-sequence workloads. Analysis reveals that PLMs exhibit sharper outlier profiles than large language models (LLMs) due to amino acid vocabulary sparsity, and our method effectively addresses these distributions.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26110v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.25986v1", "url": "http://arxiv.org/abs/2603.25986v1", "pdf_url": "https://arxiv.org/pdf/2603.25986v1", "title": "Evaluating Phylogenetic Comparative Methods under Reticulate Evolutionary Scenarios", "authors": ["Lydia Morley", "Emma Lehmberg", "Sungsik Kong"], "annotation": "Phylogenetic comparative methods (PCMs) are widely used to study trait evolution. However, many evolutionary histories involve reticulate evolutionary scenarios, such as hybridization, that violate core assumptions of these methods. In this study, we evaluate how such violations affect the performance of PCMs. In particular, we focus on the ancestral character estimation, evolutionary rate estimation, and model selection. We simulate continuous trait evolution on various phylogenetic network topologies and assess the performance of PCMs that assume a bifurcating tree (i.e., major tree of the network) as the underlying model of evolution. We found that the performance of the tested PCMs was suboptimal. Using random forest, generalized linear models, and model-based clustering, we identified key factors contributing to these inaccuracies. Our results show that frequent and/or recent hybridization accompanied by one ore more transgressive events and rapidly evolving traits (i.e., high evolutionary rate) lead to significant estimation error, especially with respect to rate estimation and model choice. These factors substantially shift trait values away from tree-based model expectations, leading to overall increased error in parameter estimates. Our study demonstrates cases in which PCMs that rely on trees are likely to misinterpret biological histories and offers recommendations for researchers studying systems with complex evolutionary histories.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.25986v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.25880v1", "url": "http://arxiv.org/abs/2603.25880v1", "pdf_url": "https://arxiv.org/pdf/2603.25880v1", "title": "Spectral Coherence Index: A Model-Free Metric for Protein Structural Ensemble Quality Assessment", "authors": ["Yuda Bi", "Huaiwen Zhang", "Jingnan Sun", "Vince D Calhoun"], "annotation": "Protein structural ensembles from NMR spectroscopy capture biologically important conformational heterogeneity, but it remains difficult to determine whether observed variation reflects coordinated motion or noise-like artifacts. We evaluate the Spectral Coherence Index (SCI), a model-free, rotation-invariant summary derived from the participation-ratio effective rank of the inter-model pairwise distance-variance matrix. Under grouped primary analysis of a Main110 cohort of 110 NMR ensembles (30--403 residues; 10--30 models per entry), SCI separated experimental ensembles from matched synthetic incoherent controls with AUC-ROC $= 0.973$ and Cliff's $δ= -0.945$. Relative to an internal 27-protein pilot, discrimination softened modestly, showing that pilot-era thresholds do not transfer perfectly to a larger, more heterogeneous cohort: the primary operating point $τ= 0.811$ yielded 95.5\\% sensitivity and 89.1\\% specificity. PDB-level sensitivity remained nearly unchanged (AUC $= 0.972$), and an independent 11-protein holdout reached AUC $= 0.983$. Across 5-fold grouped stratified cross-validation and leave-one-function-class-out testing, SCI remained strong (AUC $= 0.968$ and $0.971$), although $σ_{R_g}$ was the stronger single-feature discriminator and a QC-augmented multifeature model generalized best (AUC $= 0.989$ and $0.990$). Residue-level validation linked SCI-derived contributions to experimental RMSF across 110 proteins and showed broad concordance with GNM-based flexibility patterns. Rescue analyses showed that Main110 softening arose mainly from size and ensemble normalization rather than from loss of spectral signal. Together, these results establish SCI as an interpretable, bounded coherence summary that is most useful when embedded in a multimetric QC workflow for heterogeneous protein ensembles.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.25880v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.25713v1", "url": "http://arxiv.org/abs/2603.25713v1", "pdf_url": "https://arxiv.org/pdf/2603.25713v1", "title": "Compiling molecular ultrastructure into neural dynamics", "authors": ["Konrad P. Kording", "Anton Arkhipov", "Davy Deng", "Sean Escola", "Seth G. N. Grant", "Gal Haspel", "Michał Januszewski", "Narayanan Kasthuri", "Nina Khera", "Richie E. Kohman", "Grace Lindsay", "Jeantine Lunshof", "Adam Marblestone", "David A. Markowitz", "Jordan Matelsky", "Brett Mensh", "Patrick Mineault", "Andrew Payne", "Joanne Peng", "Xaq Pitkow", "Philip Shiu", "Gregor Schuhknecht", "Sven Truckenbrodt", "Joshua T. Vogelstein", "Edward S. Boyden"], "annotation": "High-resolution brain imaging can now capture not just synapse locations but their molecular composition, with the cost of such mapping falling exponentially. Yet such ultrastructural data has so far told us little about local neuronal physiology - specifically, the parameters (e.g., synaptic efficacies, local conductances) that govern neural dynamics. We propose to translate molecularly annotated ultrastructure into physiology, introducing the concept of an ultrastructure-to-dynamics compiler: a learned mapping from molecularly annotated ultrastructure to simulator-ready, uncertainty-aware physiological parameters. The requirement is paired training data, with jointly acquired ultrastructure from imaging, and dynamical responses to perturbations from physiological experiments. With this data we can train models that predict local physiology directly from structure. Such a compiler would support biophysical simulations by turning anatomical maps into models of circuit dynamics, shifting structure-to-function from a descriptive program to a predictive one and opening routes to understanding neural computation and forecasting intervention effects.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.25713v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.26809v1", "url": "http://arxiv.org/abs/2603.26809v1", "pdf_url": "https://arxiv.org/pdf/2603.26809v1", "title": "Dictionary-based Pathology Mining with Hard-instance-assisted Classifier Debiasing for Genetic Biomarker Prediction from WSIs", "authors": ["Ling Zhang", "Boxiang Yun", "Ting Jin", "Qingli Li", "Xinxing Li", "Yan Wang"], "annotation": "Prediction of genetic biomarkers, e.g., microsatellite instability in colorectal cancer is crucial for clinical decision making. But, two primary challenges hamper accurate prediction: (1) It is difficult to construct a pathology-aware representation involving the complex interconnections among pathological components. (2) WSIs contain a large proportion of areas unrelated to genetic biomarkers, which make the model easily overfit simple but irrelative instances. We hereby propose a Dictionary-based hierarchical pathology mining with hard-instance-assisted classifier Debiasing framework to address these challenges, dubbed as D2Bio. Our first module, dictionary-based hierarchical pathology mining, is able to mine diverse and very fine-grained pathological contextual interaction without the limit to the distances between patches. The second module, hard-instance-assisted classfier debiasing, learns a debiased classifier via focusing on hard but task-related features, without any additional annotations. Experimental results on five cohorts show the superiority of our method, with over 4% improvement in AUROC compared with the second best on the TCGA-CRC-MSI cohort. Our analysis further shows the clinical interpretability of D2Bio in genetic biomarker diagnosis and potential clinical utility in survival analysis. Code will be available at https://github.com/DeepMed-Lab-ECNU/D2Bio.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.26809v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.25455v1", "url": "http://arxiv.org/abs/2603.25455v1", "pdf_url": "https://arxiv.org/pdf/2603.25455v1", "title": "A Bayesian Gamma-power-mixture survival regression model: predicting the recurrence of prostate cancer post-prostatectomy", "authors": ["Tommy Walker Mackay", "Mingtong Xu", "Shahrokh F. Shariat", "Roger Sewell"], "annotation": "In a dataset of 423 patients who had had radical prostatectomy for localised prostate cancer we estimated the apparent Shannon information (ASI) about time to biochemical recurrence in various subsets of the available pre-op variables using a Bayesian Gamma-power-mixture survival regression model.   In all the subsets examined the ASI was positive with posterior probability greater than 0.975 .   Using only age and results of pre-operative blood tests (PSA and biomarkers) we achieved 0.232 (0.180 to 0.290) nats ASI (0.335 (0.260 to 0.419) bits) (posterior mean and equitailed 95% posterior confidence intervals). This is more than double the mean posterior ASI previously achieved on the same dataset by a subset of the current authors using a log-skew-Student-mixture model, and is greater than that previous value with posterior probability greater than 0.99 . Additionally using pre- or post-operative Gleason grades, operative findings, clinical stage, and presence or absence of extraprostatic extension or seminal vesicle invasion did not increase the ASI extracted. However removing the blood-based biomarkers and replacing them with either pre-operative Gleason grades or findings available from MRI scanning greatly reduced the available ASI to respectively 0.077 (0.038 to 0.120) and 0.088 (0.045 to 0.132) nats (both less than the values using blood-based biomarkers with posterior probability greater than 0.995). A greedy approach to selection of the best biomarkers gave TGFbeta1, VCAM1, IL6sR, and uPA in descending order of importance from those examined.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.25455v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.25283v1", "url": "http://arxiv.org/abs/2603.25283v1", "pdf_url": "https://arxiv.org/pdf/2603.25283v1", "title": "A Gait Foundation Model Predicts Multi-System Health Phenotypes from 3D Skeletal Motion", "authors": ["Adam Gabet", "Sarah Kohn", "Guy Lutsker", "Shira Gelman", "Anastasia Godneva", "Gil Sasson", "Arad Zulti", "David Krongauz", "Rotem Shaulitch", "Assaf Rotem", "Ohad Doron", "Yuval Brodsky", "Adina Weinberger", "Eran Segal"], "annotation": "Gait is increasingly recognized as a vital sign, yet current approaches treat it as a symptom of specific pathologies rather than a systemic biomarker. We developed a gait foundation model for 3D skeletal motion from 3,414 deeply phenotyped adults, recorded via a depth camera during five motor tasks. Learned embeddings outperformed engineered features, predicting age (Pearson r = 0.69), BMI (r = 0.90), and visceral adipose tissue area (r = 0.82). Embeddings significantly predicted 1,980 of 3,210 phenotypic targets; after adjustment for age, BMI, VAT, and height, gait provided independent gains in all 18 body systems in males and 17 of 18 in females, and improved prediction of clinical diagnoses and medication use. Anatomical ablation revealed that legs dominated metabolic and frailty predictions while torso encoded sleep and lifestyle phenotypes. These findings establish gait as an independent multi-system biosignal, motivating translation to consumer-grade video and its integration as a scalable, passive vital sign.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.25283v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.25240v1", "url": "http://arxiv.org/abs/2603.25240v1", "pdf_url": "https://arxiv.org/pdf/2603.25240v1", "title": "Lingshu-Cell: A generative cellular world model for transcriptome modeling toward virtual cells", "authors": ["Han Zhang", "Guo-Hua Yuan", "Chaohao Yuan", "Tingyang Xu", "Tian Bian", "Hong Cheng", "Wenbing Huang", "Deli Zhao", "Yu Rong"], "annotation": "Modeling cellular states and predicting their responses to perturbations are central challenges in computational biology and the development of virtual cells. Existing foundation models for single-cell transcriptomics provide powerful static representations, but they do not explicitly model the distribution of cellular states for generative simulation. Here, we introduce Lingshu-Cell, a masked discrete diffusion model that learns transcriptomic state distributions and supports conditional simulation under perturbation. By operating directly in a discrete token space that is compatible with the sparse, non-sequential nature of single-cell transcriptomic data, Lingshu-Cell captures complex transcriptome-wide expression dependencies across approximately 18,000 genes without relying on prior gene selection, such as filtering by high variability or ranking by expression level. Across diverse tissues and species, Lingshu-Cell accurately reproduces transcriptomic distributions, marker-gene expression patterns and cell-subtype proportions, demonstrating its ability to capture complex cellular heterogeneity. Moreover, by jointly embedding cell type or donor identity with perturbation, Lingshu-Cell can predict whole-transcriptome expression changes for novel combinations of identity and perturbation. It achieves leading performance on the Virtual Cell Challenge H1 genetic perturbation benchmark and in predicting cytokine-induced responses in human PBMCs. Together, these results establish Lingshu-Cell as a flexible cellular world model for in silico simulation of cell states and perturbation responses, laying the foundation for a new paradigm in biological discovery and perturbation screening.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.25240v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.24745v1", "url": "http://arxiv.org/abs/2603.24745v1", "pdf_url": "https://arxiv.org/pdf/2603.24745v1", "title": "Learning relationships in epidemiological data using graph neural networks", "authors": ["Anthony J Wood", "Aeron R Sanchez", "Rowland R Kao"], "annotation": "When designing control strategies for an infectious disease it is critical to identify the key pathways of transmission. Data on infected hosts - when they were born, where they lived and with whom they interacted - can help infer sources of infection and transmission clusters. However such data are generally not powerful enough to identify infector-infectee pairs with any certainty.   Whole-genome sequencing data of the underlying pathogen, on the other hand, can serve as a powerful adjoint to these data as they can be used to estimate a time to a most recent common ancestor between two infected hosts. and in turn their relative proximity in the transmission tree. A statistical model that explains the genetic distance between different host pathogens and associated risk factors can therefore inform key risk factors for transmission itself.   We show how graph neural networks (GNNs) are a powerful and natural modelling architecture for such a problem. By treating the epidemiological dataset as a graph where infected hosts are nodes and edges are weighted by the genetic distance between different host pairs, we show how a GNN can be fit to predict the genetic distance between known hosts and new, unsequenced hosts. Comparisons with other established approaches show that GNNs have useful performance advantages albeit with greater computational cost.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.24745v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.24733v1", "url": "http://arxiv.org/abs/2603.24733v1", "pdf_url": "https://arxiv.org/pdf/2603.24733v1", "title": "OpenCap Monocular: 3D Human Kinematics and Musculoskeletal Dynamics from a Single Smartphone Video", "authors": ["Selim Gilon", "Emily Y. Miller", "Scott D. Uhlrich"], "annotation": "Quantifying human movement (kinematics) and musculoskeletal forces (kinetics) at scale, such as estimating quadriceps force during a sit-to-stand movement, could transform prediction, treatment, and monitoring of mobility-related conditions. However, quantifying kinematics and kinetics traditionally requires costly, time-intensive analysis in specialized laboratories, limiting clinical translation. Scalable, accurate tools for biomechanical assessment are needed. We introduce OpenCap Monocular, an algorithm that estimates 3D skeletal kinematics and kinetics from a single smartphone video. The method refines 3D human pose estimates from a monocular pose estimation model (WHAM) via optimization, computes kinematics of a biomechanically constrained skeletal model, and estimates kinetics via physics-based simulation and machine learning. We validated OpenCap Monocular against marker-based motion capture and force plate data for walking, squatting, and sit-to-stand tasks. OpenCap Monocular achieved low kinematic error (4.8° mean absolute error for rotational degrees of freedom; 3.4 cm for pelvis translations), outperforming a regression-only computer vision baseline by 48% in rotational accuracy (p = 0.036) and 69% in translational accuracy (p < 0.001). OpenCap Monocular also estimated ground reaction forces during walking with accuracy comparable to, or better than, our prior two-camera OpenCap system. We demonstrate that the algorithm estimates important kinetic outcomes with clinically meaningful accuracy in applications related to frailty and knee osteoarthritis, including estimating knee extension moment during sit-to-stand transitions and knee adduction moment during walking. OpenCap Monocular is deployed via a smartphone app, web app, and secure cloud computing (https://opencap.ai), enabling free, accessible single-smartphone biomechanical assessments.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.24733v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.24009v1", "url": "http://arxiv.org/abs/2603.24009v1", "pdf_url": "https://arxiv.org/pdf/2603.24009v1", "title": "Analyzing animal movement using deep learning", "authors": ["Thibault Fronville", "Maximilian Pichler", "Johannes Signer", "Marius Grabow", "Stephanie Kramer-Schadt", "Viktoriia Radchuk", "Florian Hartig"], "annotation": "Understanding how animals move through heterogeneous landscapes is central to ecology and conservation. In this context, step selection functions (SSFs) have emerged as the main statistical framework to analyze how biotic and abiotic predictors influence movement paths observed by radio tracking, GPS tags, or similar sensors. A traditional SSF consists of a generalized linear model (GLM) that infers the animal's habitat preferences (selection coefficients) by comparing each observed movement step to random steps. Such GLM-SSFs, however, cannot flexibly consider non-linear or interacting effects, unless those have been specified a priori. To address this problem, generalized additive models have been integrated in the SSF framework, but those GAM-SSFs are still limited in their ability to represent complex habitat preferences and inter-individual variability. Here we explore the utility of deep neural networks (DNNs) to overcome these limitations. We find that DNN-SSFs, coupled with explainable AI to extract selection coefficients, offer many advantages for analyzing movement data. In the case of linear effects, they effectively retrieve the same effect sizes and p-values as conventional GLMs. At the same time, however, they can automatically detect complex interaction effects, nonlinear responses, and inter-individual variability if those are present in the data. We conclude that DNN-SSFs are a promising extension of traditional SSF. Our analysis extends previous research on DNN-SSF by exploring differences and similarities of GLM, GAM and DNN-based SSF models in more depth, in particular regarding the validity of statistical indicators that are derived from the DNN. We also propose new DNN structures to capture inter-individual effects that can be viewed as a nonlinear random effect. All methods used in this paper are available via the 'citoMove' R package.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.24009v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.25755v1", "url": "http://arxiv.org/abs/2603.25755v1", "pdf_url": "https://arxiv.org/pdf/2603.25755v1", "title": "KANEL: Kolmogorov-Arnold Network Ensemble Learning Enables Early Hit Enrichment in High-Throughput Virtual Screening", "authors": ["Pavel Koptev", "Nikita Krainov", "Konstantin Malkov", "Alexander Tropsha"], "annotation": "Machine learning models of chemical bioactivity are increasingly used for prioritizing a small number of compounds in virtual screening libraries for experimental follow-up. In these applications, assessing model accuracy by early hit enrichment such as Positive Predicted Value (PPV) calculated for top N hits (PPV@N) is more appropriate and actionable than traditional global metrics such as AUC. We present KANEL, an ensemble workflow that combines interpretable Kolmogorov-Arnold Networks (KANs) with XGBoost, random forest, and multilayer perceptron models trained on complementary molecular representations (LillyMol descriptors, RDKit-derived descriptors, and Morgan fingerprints).", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.25755v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.22477v1", "url": "http://arxiv.org/abs/2603.22477v1", "pdf_url": "https://arxiv.org/pdf/2603.22477v1", "title": "Subspace Tensor Orthogonal Rotation Model (STORM) for Batch Alignment, Cell Type Deconvolution, and Gene Imputation in Spatial Transcriptomic Data", "authors": ["Sean Cottrell", "Guo-Wei Wei", "Longxiu Huang"], "annotation": "Spatial transcriptomics data analysis integrates cellular transcriptional activity with spatial coordinates to identify spatial domains, infer cell-type dynamics, and characterize gene expression patterns within tissues. Despite recent advances, significant challenges remain, including the treatment of batch effects, the handling of mixed cell-type signals, and the imputation of poorly measured or missing gene expression. This work addresses these challenges by introducing a novel Subspace Tensor Orthogonal Rotation Model (STORM) that aligns multiple slices which vary in their spatial dimensions and geometry by considering them at the level of physical patterns or microenvironments. To this end, STORM presents an irregular tensor factorization technique for decomposing a collection of gene expression matrices and integrating them into a shared latent space for downstream analysis. In contrast to black-box deep learning approaches, the proposed model is inherently interpretable. Numerical experiments demonstrate state-of-the-art performance in vertical and horizontal batch integration, cell-type deconvolution, and unmeasured gene imputation for spatial transcriptomics data.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.22477v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.21743v2", "url": "http://arxiv.org/abs/2603.21743v2", "pdf_url": "https://arxiv.org/pdf/2603.21743v2", "title": "CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning", "authors": ["Dongxia Wu", "Shiye Su", "Yuhui Zhang", "Elaine Sui", "Emma Lundberg", "Emily B. Fox", "Serena Yeung-Levy"], "annotation": "Building virtual cells with generative models to simulate cellular behavior in silico is emerging as a promising paradigm for accelerating drug discovery. However, prior image-based generative approaches can produce implausible cell images that violate basic physical and biological constraints. To address this, we propose to post-train virtual cell models with reinforcement learning (RL), leveraging biologically meaningful evaluators as reward functions. We design seven rewards spanning three categories-biological function, structural validity, and morphological correctness-and optimize the state-of-the-art CellFlux model to yield CellFluxRL. CellFluxRL consistently improves over CellFlux across all rewards, with further performance boosts from test-time scaling. Overall, our results present a virtual cell modeling framework that enforces physically-based constraints through RL, advancing beyond \"visually realistic\" generations towards \"biologically meaningful\" ones.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.21743v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.21020v1", "url": "http://arxiv.org/abs/2603.21020v1", "pdf_url": "https://arxiv.org/pdf/2603.21020v1", "title": "Characterizing Long-Range Dependencies in Knee Joint Contact Mechanics: A Comparison of Topology Diffusion, Global Routing, and Hybrid Graph Neural Networks", "authors": ["Zhengye Pan", "Jianwei Zuo", "Jiajia Luo"], "annotation": "Finite element analysis of knee joint contact mechanics is computationally expensive, which has motivated the development of graph neural network surrogate models. However, effectively representing long-range dependencies in joint mechanical responses remains challenging. This study systematically compared topology diffusion, global routing, and their hybridization for surrogate modeling of knee joint contact mechanics. Using kinematic and force data from nine soccer players performing change-of-direction maneuvers, finite element simulations were used to generate graph-structured samples for training and evaluation under a grouped three-fold cross-subject evaluation framework. Five architectures were compared: standard MeshGraphNet, hierarchical MeshGraphNet, a routing-only transformer, a topology-biased routing transformer, and a hybrid model. The hybrid model achieved the best overall performance, yielding the lowest full-field error and peak stress error, together with the highest spatial agreement for high-risk regions. Among the non-hybrid models, the standard topology-diffusion model performed best overall, whereas routing-only strategies were less effective. These findings indicate that topology diffusion provides a robust basis for surrogate modeling of knee joint contact mechanics within the present benchmark, while the addition of global routing can further improve reconstruction of clinically relevant high-stress patterns.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.21020v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.20420v1", "url": "http://arxiv.org/abs/2603.20420v1", "pdf_url": "https://arxiv.org/pdf/2603.20420v1", "title": "CERN: Correcting Errors in Raw Nanopore Signals Using Hidden Markov Models", "authors": ["Simon Ambrozak", "Ulysse McConnell", "Bhargav Srinivasan", "Burak Ozkan", "Can Firtina"], "annotation": "Nanopore sequencing can read substantially longer sequences of nucleic acid molecules than other sequencing methods, which has led to advances in genomic analysis such as the gapless human genome assembly. By analyzing the raw electrical signal reads that nanopore sequencing generates from molecules, existing works can map these reads without translating them into DNA characters (i.e., basecalling), allowing for quick and efficient analysis of sequencing data. However, raw signals often contain errors due to noise and mistakes when processing them, which limits the overall accuracy of raw signal analysis.   Our goal in this work is to detect and correct errors in raw signals to improve the accuracy of raw signal analyses. To this end, we propose CERN, a mechanism that trains and utilizes a Hidden Markov Model (HMM) to accurately correct signal errors. Our extensive evaluation on various datasets including E. coli, Fruit Fly, and Human genomes shows that CERN 1) consistently improves the overall mapping accuracy of the underlying raw signal analysis tools, 2) minimizes the burden on segmentation algorithm optimization with newer nanopore chemistries, and 3) functions without causing substantial computational overhead. We conclude that CERN provides an effective mechanism to systematically identify and correct the errors in raw nanopore signals before further analysis, which can enable the development of a new class of error correction mechanisms purely designed for raw nanopore signals.   CERN is available at https://github.com/STORMgroup/CERN. We also provide the scripts to fully reproduce our results on our GitHub page.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.20420v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.20115v1", "url": "http://arxiv.org/abs/2603.20115v1", "pdf_url": "https://arxiv.org/pdf/2603.20115v1", "title": "Conditioning Protein Generation via Hopfield Pattern Multiplicity", "authors": ["Jeffrey D. Varner"], "annotation": "Protein sequence generation via stochastic attention produces plausible family members from small alignments without training, but treats all stored sequences equally and cannot direct generation toward a functional subset of interest. We show that a single scalar parameter, added as a bias to the sampler's attention logits, continuously shifts generation from the full family toward a user-specified subset, with no retraining and no change to the model architecture. A practitioner supplies a small set of sequences (for example, hits from a binding screen) and a multiplicity ratio that controls how strongly generation favors them. The method is agnostic to what the subset represents: binding, stability, specificity, or any other property. We find that the conditioning is exact at the level of the sampler's internal representation, but that the decoded sequence phenotype can fall short because the dimensionality reduction used to encode sequences does not always preserve the residue-level variation that defines the functional split. We term this discrepancy the calibration gap and show that it is predicted by a simple geometric measure of how well the encoding separates the functional subset from the rest of the family. Experiments on five Pfam families (Kunitz, SH3, WW, Homeobox, and Forkhead domains) confirm the monotonic relationship between separation and gap across a fourfold range of geometries. Applied to omega-conotoxin peptides targeting a calcium channel involved in pain signaling, curated seeding from 23 characterized binders produces over a thousand candidates that preserve the primary pharmacophore and all experimentally identified binding determinants. These results show that stochastic attention enables practitioners to expand a handful of experimentally characterized sequences into diverse candidate libraries without retraining a generative model.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.20115v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.20345v1", "url": "http://arxiv.org/abs/2603.20345v1", "pdf_url": "https://arxiv.org/pdf/2603.20345v1", "title": "Towards Improved Short-term Hypoglycemia Prediction and Diabetes Management based on Refined Heart Rate Data", "authors": ["Vaibhav Gupta", "Florian Grensing", "Beyza Cinar", "Louisa van den Boom", "Maria Maleshkova"], "annotation": "Hypoglycemia is a severe condition of decreased blood glucose, specifically below 70 mg/dL (3.9 mmol/L). This condition can often be asymptomatic and challenging to predict in individuals with type 1 diabetes (T1D). Research on hypoglycemic prediction typically uses a combination of blood glucose readings and heart rate data to predict hypoglycemic events. Given that these features are collected through wearable sensors, they can sometimes have missing values, necessitating efficient imputation methods. This work makes significant contributions to the current state of the art by introducing two novel imputation techniques for imputing heart rate values over short-term horizons: Controlled Weighted Rational Bézier Curves (CRBC) and Controlled Piecewise Cubic Hermite Interpolating Polynomial with mapped peaks and valleys of Control Points (CMPV). In addition to these imputation methods, we employ two metrics to capture data patterns, alongside a combined metric that integrates the strengths of both individual metrics with RMSE scores for a comprehensive evaluation of the imputation techniques. According to our combined metric assessment, CMPV outperforms the alternatives with an average score of 0.33 across all time gaps, while CRBC follows with a score of 0.48. These findings clearly demonstrate the effectiveness of the proposed imputation methods in accurately filling in missing heart rate values. Moreover, this study facilitates the detection of abnormal physiological signals, enabling the implementation of early preventive measures for more accurate diagnosis.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.20345v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.19761v1", "url": "http://arxiv.org/abs/2603.19761v1", "pdf_url": "https://arxiv.org/pdf/2603.19761v1", "title": "Multimodal branched transport infers anatomically aligned brain reaction maps", "authors": ["Cristian Mendico"], "annotation": "How external stimulation is transformed into distributed reaction patterns remains unresolved at the level of propagation architecture. Existing large-scale control models quantify transition costs on prescribed networks but do not infer the routing map itself from source and target activity. Here we combine task-related blood-oxygen-level-dependent responses, source-reconstructed electrophysiology and tractography-derived anisotropy to estimate stimulation and reaction measures, define an anatomical transport cost, and infer a branched propagation architecture by variational optimisation. Unlike standard transport formulations, branched transport favours aggregation of signal into shared neural highways before redistribution. We further attach a stochastic graph-induced dynamics to the inferred map and quantify the trade-off between geometric efficiency and dynamical controllability. We show that multimodal data generate anatomically aligned brain reaction maps, that anisotropic costs qualitatively reshape routing backbones relative to isotropic baselines, and that hybrid geometric--dynamical optimisation reveals non-trivial rank reversals across branching regimes.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.19761v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.19751v1", "url": "http://arxiv.org/abs/2603.19751v1", "pdf_url": "https://arxiv.org/pdf/2603.19751v1", "title": "Branched Optimal Transport for Stimulus to Reaction Brain Mapping", "authors": ["Cristian Mendico"], "annotation": "A central problem in systems neuroscience is to determine how an external stimulation is propagated through the brain so as to produce a reaction. Current deterministic and stochastic control models quantify transition costs between brain states on a prescribed network, but do not treat the transport network itself as an unknown. Here we propose a variational framework in which the inferred object is a graph/current connecting a stimulation source measure to a reaction target measure. The model is posed as an anisotropic branched optimal transport problem, where concavity of the flux cost promotes aggregation and branching. The support of an optimal current defines a stimulus-to-reaction routing architecture, interpreted as a brain reaction map. We prove existence of minimizers in discrete and continuous formulations and introduce a hybrid stochastic extension combining ramified transport with a path-space Kullback--Leibler control cost on the induced graph dynamics. This approach provides a mathematical mechanism for inferring propagation architectures rather than controlling trajectories on fixed substrates.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.19751v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.19577v1", "url": "http://arxiv.org/abs/2603.19577v1", "pdf_url": "https://arxiv.org/pdf/2603.19577v1", "title": "Stochastic Averaging and Statistical Inference of Glycolytic Pathway", "authors": ["Arnab Ganguly", "Hye-Won Kang"], "annotation": "Many biological processes exhibit oscillatory behavior. Among these, glycolytic oscillations have been extensively studied due to their well-characterized biochemical reaction networks. However, the complexity of these networks necessitates low-dimensional ordinary differential equation (ODE) models to identify core mechanisms and perform stability analysis. While previous studies proposed reduced ODE models, these were typically introduced from deterministic descriptions rather than the underlying stochastic dynamics, which more accurately represent discrete reaction events occurring at random times. In this paper, we develop a rigorous probabilistic framework for deriving a reduced Othmer-Aldridge model of the glycolytic pathway from its stochastic formulation. The full system is modeled as a multiscale continuous-time Markov chain with different time and abundance scales. Under an appropriate scaling regime and specific structural conditions, we prove that the dynamics of the slow components are approximated by a two-dimensional ODE. The proof is technically involved due to the network's complexity and strong coupling between its components. We further consider the problem of parameter estimation when observations are limited to the slow species: fructose-6-phosphate and ADP. The reduced system yields a tractable loss function depending solely on these variables. We prove that the resulting estimators are statistically consistent when the data originate from the full stochastic reaction network. Together, these results provide a mathematically rigorous framework linking stochastic biochemical reaction networks, reduced deterministic dynamics, and statistically reliable parameter estimation.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.19577v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.19341v1", "url": "http://arxiv.org/abs/2603.19341v1", "pdf_url": "https://arxiv.org/pdf/2603.19341v1", "title": "Assessing 3D tree model quality and species classification using imbalance indices", "authors": ["Sophie J. Kersting", "Mareike Fischer"], "annotation": "We investigate the use of additional 3D and phylogenetic non-3D tree balance indices for analyzing and monitoring forests using an exemplary \"virtual forest\" dataset from the Wytham Woods, Oxford, UK. This study assesses 3D model quality, species classification performance, and the relevance of these indices. Our study shows that indices stemming from the study of ancestry trees of species can be successfully applied to 3D models of organic trees and, accompanied with recently introduced 3D imbalance indices, offer a complementary perspective on 3D tree models and improve the detection of deviations. Their computational efficiency combined with the simple and reproducible workflow presented in this manuscript form a computationally feasible quality control step in the 3D model construction.   Species classification models reached an estimated accuracy of up to 81.8% and allowed to make confident species predictions for a large portion of the unlabeled trees in the dataset. While conventional tree metrics can already provide strong predictive performance, the addition of filtered 3D and non-3D statistics improved results consistently, particularly for minority species classes.   Alongside this manuscript, we provide updated functionality in the R package treeDbalance to include the necessary functionalities and release the derived index datasets and species predictions.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.19341v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.18571v1", "url": "http://arxiv.org/abs/2603.18571v1", "pdf_url": "https://arxiv.org/pdf/2603.18571v1", "title": "CAPSUL: A Comprehensive Human Protein Benchmark for Subcellular Localization", "authors": ["Yicheng Hu", "Xinyu Lin", "Shulin Li", "Wenjie Wang", "Fengbin Zhu", "Fuli Feng"], "annotation": "Subcellular localization is a crucial biological task for drug target identification and function annotation. Although it has been biologically realized that subcellular localization is closely associated with protein structure, no existing dataset offers comprehensive 3D structural information with detailed subcellular localization annotations, thus severely hindering the application of promising structure-based models on this task. To address this gap, we introduce a new benchmark called $\\mathbf{CAPSUL}$, a $\\mathbf{C}$omprehensive hum$\\mathbf{A}$n $\\mathbf{P}$rotein benchmark for $\\mathbf{SU}$bcellular $\\mathbf{L}$ocalization. It features a dataset that integrates diverse 3D structural representations with fine-grained subcellular localization annotations carefully curated by domain experts. We evaluate this benchmark using a variety of state-of-the-art sequence-based and structure-based models, showcasing the importance of involving structural features in this task. Furthermore, we explore reweighting and single-label classification strategies to facilitate future investigation on structure-based methods for this task. Lastly, we showcase the powerful interpretability of structure-based methods through a case study on the Golgi apparatus, where we discover a decisive localization pattern $α$-helix from attention mechanisms, demonstrating the potential for bridging the gap with intuitive biological interpretability and paving the way for data-driven discoveries in cell biology.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.18571v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.18497v1", "url": "http://arxiv.org/abs/2603.18497v1", "pdf_url": "https://arxiv.org/pdf/2603.18497v1", "title": "Recovering Sparse Neural Connectivity from Partial Measurements: A Covariance-Based Approach with Granger-Causality Refinement", "authors": ["Quilee Simeon"], "annotation": "Inferring the connectivity of neural circuits from incomplete observations is a fundamental challenge in neuroscience. We present a covariance-based method for estimating the weight matrix of a recurrent neural network from sparse, partial measurements across multiple recording sessions. By accumulating pairwise covariance estimates across sessions where different subsets of neurons are observed, we reconstruct the full connectivity matrix without requiring simultaneous recording of all neurons. A Granger-causality refinement step enforces biological constraints via projected gradient descent. Through systematic experiments on synthetic networks modeling small brain circuits, we characterize a fundamental control-estimation tradeoff: stimulation aids identifiability but disrupts intrinsic dynamics, with the optimal level depending on measurement density. We discover that the ``incorrect'' linear approximation acts as implicit regularization -- outperforming the oracle estimator with known nonlinearity at all operating regimes -- and provide an exact characterization via the Stein--Price identity.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.18497v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.18249v1", "url": "http://arxiv.org/abs/2603.18249v1", "pdf_url": "https://arxiv.org/pdf/2603.18249v1", "title": "RAFT-UP: Robust Alignment for Spatial Transcriptomics with Explicit Control of Spatial Distortion", "authors": ["Yaqi Wu", "Jingfeng Wang", "Xin Maizie Zhou", "Yanxiang Zhao", "Zixuan Cang"], "annotation": "Spatial transcriptomics (ST) profiles gene expression across a tissue section while preserving the spatial coordinates. Because current ST technologies typically profile two-dimensional tissue slices, integrating and aligning slices from different regions of the same three-dimensional tissue or from samples under different conditions enables analyses that reveal 3D organization and condition-associated spatial patterns. Two major challenges remain. First, interpretable and flexible control over spatial distortion is needed because rigid transformations can be overly restrictive, whereas highly deformable mappings may arbitrarily distort spatial proximity. Second, biologically plausible matching is also needed, especially when the slices overlap partially. Here, we introduce RAFT-UP, a tool for robust ST alignment that provides explicit control over spatial distance preservation through a fused supervised Gromov-Wasserstein (FsGW) optimal transport framework. FsGW combines expression and spatial information, incorporates spot-wise constraints to discourage biologically implausible matches, and enforces a pairwise distance-consistency constraint that prevents mapping two pairs of spots when their spatial distances differ beyond a specified tolerance. We demonstrate that RAFT-UP accurately aligns slices from different regions of the same tissue and slices from different samples. Benchmarking shows that RAFT-UP improves spatial distance preservation while achieving spot label matching accuracy comparable to state-of-the-art methods. Finally, we demonstrate RAFT-UP on two spatially constrained downstream applications, including spatiotemporal mapping of developing mouse midbrain and comparative cross-slice analysis of cell-cell communication. RAFT-UP is available as open-source software.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.18249v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.18239v1", "url": "http://arxiv.org/abs/2603.18239v1", "pdf_url": "https://arxiv.org/pdf/2603.18239v1", "title": "Impact of automatic speech recognition quality on Alzheimer's disease detection from spontaneous speech: a reproducible benchmark study with lexical modeling and statistical validation", "authors": ["Himadri Samanta"], "annotation": "Early detection of Alzheimer's disease from spontaneous speech has emerged as a promising non-invasive screening approach. However, the influence of automatic speech recognition (ASR) quality on downstream clinical language modeling remains insufficiently understood. In this study, we investigate Alzheimer's disease detection using lexical features derived from Whisper ASR transcripts on the ADReSSo 2021 diagnosis dataset. We evaluate interpretable machine-learning models, including Logistic Regression and Linear Support Vector Machines, using TF-IDF text representations under repeated 5x5 stratified cross-validation.   Our results demonstrate that transcript quality has a statistically significant impact on classification performance. Models trained on Whisper-small transcripts consistently outperform those using Whisper-base transcripts, achieving balanced accuracy above 0.7850 with Linear SVM. Paired statistical testing confirms that the observed improvements are significant. Importantly, classifier complexity contributes less to performance variation than ASR transcription quality. Feature analysis reveals that cognitively normal speakers produce more semantically precise object- and scene-descriptive language, whereas Alzheimer's speech is characterized by vagueness, discourse markers, and increased hesitation patterns.   These findings suggest that high-quality ASR can enable simple, interpretable lexical models to achieve competitive Alzheimer's detection performance without explicit acoustic modeling. The study provides a reproducible benchmark pipeline and highlights ASR selection as a critical modeling decision in clinical speech-based artificial intelligence systems.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.18239v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.17765v1", "url": "http://arxiv.org/abs/2603.17765v1", "pdf_url": "https://arxiv.org/pdf/2603.17765v1", "title": "Grounded Multimodal Retrieval-Augmented Drafting of Radiology Impressions Using Case-Based Similarity Search", "authors": ["Himadri Samanta"], "annotation": "Automated radiology report generation has gained increasing attention with the rise of deep learning and large language models. However, fully generative approaches often suffer from hallucinations and lack clinical grounding, limiting their reliability in real-world workflows. In this study, we propose a multimodal retrieval-augmented generation (RAG) system for grounded drafting of chest radiograph impressions. The system combines contrastive image-text embeddings, case-based similarity retrieval, and citation-constrained draft generation to ensure factual alignment with historical radiology reports. A curated subset of the MIMIC-CXR dataset was used to construct a multimodal retrieval database. Image embeddings were generated using CLIP encoders, while textual embeddings were derived from structured impression sections. A fusion similarity framework was implemented using FAISS indexing for scalable nearest-neighbor retrieval. Retrieved cases were used to construct grounded prompts for draft impression generation, with safety mechanisms enforcing citation coverage and confidence-based refusal. Experimental results demonstrate that multimodal fusion significantly improves retrieval performance compared to image-only retrieval, achieving Recall@5 above 0.95 on clinically relevant findings. The grounded drafting pipeline produces interpretable outputs with explicit citation traceability, enabling improved trustworthiness compared to conventional generative approaches. This work highlights the potential of retrieval-augmented multimodal systems for reliable clinical decision support and radiology workflow augmentation", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.17765v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.17380v2", "url": "http://arxiv.org/abs/2603.17380v2", "pdf_url": "https://arxiv.org/pdf/2603.17380v2", "title": "SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction", "authors": ["Shuizhou Chen", "Lang Yu", "Kedu Jin", "Songming Zhang", "Hao Wu", "Wenxuan Huang", "Sheng Xu", "Quan Qian", "Qin Chen", "Lei Bai", "Siqi Sun", "Zhangyang Gao"], "annotation": "Virtual cell models aim to enable in silico experimentation by predicting how cells respond to genetic, chemical, or cytokine perturbations from single-cell measurements. In practice, however, large-scale perturbation prediction remains constrained by three coupled bottlenecks: inefficient training and inference pipelines, unstable modeling in high-dimensional sparse expression space, and evaluation protocols that overemphasize reconstruction-like accuracy while underestimating biological fidelity. In this work we present a specialized large-scale foundation model SCALE for virtual cell perturbation prediction that addresses the above limitations jointly. First, we build a BioNeMo-based training and inference framework that substantially improves data throughput, distributed scalability, and deployment efficiency, yielding 12.51* speedup on pretrain and 1.29* on inference over the prior SOTA pipeline under matched system settings. Second, we formulate perturbation prediction as conditional transport and implement it with a set-aware flow architecture that couples LLaMA-based cellular encoding with endpoint-oriented supervision. This design yields more stable training and stronger recovery of perturbation effects. Third, we evaluate the model on Tahoe-100M using a rigorous cell-level protocol centered on biologically meaningful metrics rather than reconstruction alone. On this benchmark, our model improves PDCorr by 12.02% and DE Overlap by 10.66% over STATE. Together, these results suggest that advancing virtual cells requires not only better generative objectives, but also the co-design of scalable infrastructure, stable transport modeling, and biologically faithful evaluation.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.17380v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.19326v1", "url": "http://arxiv.org/abs/2603.19326v1", "pdf_url": "https://arxiv.org/pdf/2603.19326v1", "title": "Mathematical Modeling of Cancer-Bacterial Therapy: Analysis and Numerical Simulation via Physics-Informed Neural Networks", "authors": ["Ayoub Farkane", "David Lassounon"], "annotation": "Bacterial cancer therapy exploits anaerobic bacteria's ability to target hypoxia tumor regions, yet the interactions among tumor growth, bacterial colonization, oxygen levels, immunosuppressive cytokines, and bacterial communication remain poorly quantified. We present a mathematical model of five coupled nonlinear reaction-diffusion equations in a two-dimensional tissue domain. We proved the global well-posedness of the model and identified its steady states to analyze stability. Furthermore, a physics-informed neural network (PINN) solves the system without a mesh and without requiring extensive data. It provides convergence guarantees by combining residual stability and Sobolev approximation error bounds. This results in an overall error rate of O(n^-2 ln^4(n) + N^-1/2), which depends on the network width n and the number of collocation points N. We conducted several numerical experiments, including predicting the tumor's response to therapy. We also performed a sensitivity analysis of certain parameters. The results suggest that long-term therapeutic efficacy may require the maintenance of hypoxia regions in the tumor, or using bacteria that tolerate oxygen better, may be necessary for long-lasting tumor control.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.19326v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.17247v1", "url": "http://arxiv.org/abs/2603.17247v1", "pdf_url": "https://arxiv.org/pdf/2603.17247v1", "title": "Binary Latent Protein Fitness Landscapes for Quantum Annealing Optimization", "authors": ["Truong-Son Hy"], "annotation": "We propose Q-BIOLAT, a framework for modeling and optimizing protein fitness landscapes in binary latent spaces. Starting from protein sequences, we leverage pretrained protein language models to obtain continuous embeddings, which are then transformed into compact binary latent representations. In this space, protein fitness is approximated using a quadratic unconstrained binary optimization (QUBO) model, enabling efficient combinatorial search via classical heuristics such as simulated annealing and genetic algorithms.   On the ProteinGym benchmark, we demonstrate that Q-BIOLAT captures meaningful structure in protein fitness landscapes and enables the identification of high-fitness variants. Despite using a simple binarization scheme, our method consistently retrieves sequences whose nearest neighbors lie within the top fraction of the training fitness distribution, particularly under the strongest configurations. We further show that different optimization strategies exhibit distinct behaviors, with evolutionary search performing better in higher-dimensional latent spaces and local search remaining competitive in preserving realistic sequences.   Beyond its empirical performance, Q-BIOLAT provides a natural bridge between protein representation learning and combinatorial optimization. By formulating protein fitness as a QUBO problem, our framework is directly compatible with emerging quantum annealing hardware, opening new directions for quantum-assisted protein engineering.   Our implementation is publicly available at: https://github.com/HySonLab/Q-BIOLAT", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.17247v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.17191v1", "url": "http://arxiv.org/abs/2603.17191v1", "pdf_url": "https://arxiv.org/pdf/2603.17191v1", "title": "Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data", "authors": ["Sophie Kearney", "Shu Yang", "Zixuan Wen", "Weimin Lyu", "Bojian Hou", "Duy Duong-Tran", "Tianlong Chen", "Jason H. Moore", "Marylyn D. Ritchie", "Chao Chen", "Li Shen"], "annotation": "Accurate diagnosis of Alzheimer's disease (AD) requires handling tabular biomarker data, yet such data are often small and incomplete, where deep learning models frequently fail to outperform classical methods. Pretrained large language models (LLMs) offer few-shot generalization, structured reasoning, and interpretable outputs, providing a powerful paradigm shift for clinical prediction. We propose TAP-GPT Tabular Alzheimer's Prediction GPT, a domain-adapted tabular LLM framework built on TableGPT2 and fine-tuned for few-shot AD classification using tabular prompts rather than plain texts. We evaluate TAP-GPT across four ADNI-derived datasets, including QT-PAD biomarkers and region-level structural MRI, amyloid PET, and tau PET for binary AD classification. Across multimodal and unimodal settings, TAP-GPT improves upon its backbone models and outperforms traditional machine learning baselines in the few-shot setting while remaining competitive with state-of-the-art general-purpose LLMs. We show that feature selection mitigates degradation in high-dimensional inputs and that TAP-GPT maintains stable performance under simulated and real-world missingness without imputation. Additionally, TAP-GPT produces structured, modality-aware reasoning aligned with established AD biology and shows greater stability under self-reflection, supporting its use in iterative multi-agent systems. To our knowledge, this is the first systematic application of a tabular-specialized LLM to multimodal biomarker-based AD prediction, demonstrating that such pretrained models can effectively address structured clinical prediction tasks and laying the foundation for tabular LLM-driven multi-agent clinical decision-support systems. The source code is publicly available on GitHub: https://github.com/sophie-kearney/TAP-GPT.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.17191v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.16789v1", "url": "http://arxiv.org/abs/2603.16789v1", "pdf_url": "https://arxiv.org/pdf/2603.16789v1", "title": "Conservative Continuous-Time Treatment Optimization", "authors": ["Nora Schneider", "Georg Manten", "Niki Kilbertus"], "annotation": "We develop a conservative continuous-time stochastic control framework for treatment optimization from irregularly sampled patient trajectories. The unknown patient dynamics are modeled as a controlled stochastic differential equation with treatment as a continuous-time control. Naive model-based optimization can exploit model errors and propose out-of-support controls, so optimizing the estimated dynamics may not optimize the true dynamics. To limit extrapolation, we add a consistent signature-based MMD regularizer on path space that penalizes treatment plans whose induced trajectory distribution deviates from observed trajectories. The resulting objective minimizes a computable upper bound on the true cost. Experiments on benchmark datasets show improved robustness and performance compared to non-conservative baselines.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.16789v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.16984v1", "url": "http://arxiv.org/abs/2603.16984v1", "pdf_url": "https://arxiv.org/pdf/2603.16984v1", "title": "Intermitotic timing and motility patterns in the cell division of the diatom Seminavis robusta", "authors": ["Jonas Ziebarth", "Thomas Fuhrmann-Lieker"], "annotation": "Many diatoms follow a size diminuation - size restoration cycle in their vegetative phase, leading to daughter cells that differ in size. For the diatom Seminavis robusta, we investigated by cell tracking over several generations whether the size difference reflects also in different intermitotic times or in the mobility of the cells. A tracking setup and machine-learning based detection algorithm was developed that revealed no significant difference in intermitotic times, a weak coupling to the day- night cycle, and a higher motility of the hypothecal, smaller daughter cell.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.16984v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.16741v1", "url": "http://arxiv.org/abs/2603.16741v1", "pdf_url": "https://arxiv.org/pdf/2603.16741v1", "title": "Bayesian Inference of Psychometric Variables From Brain and Behavior in Implicit Association Tests", "authors": ["Christian A. Kothe", "Sean Mullen", "Michael V. Bronstein", "Grant Hanada", "Marcelo Cicconet", "Aaron N. McInnes", "Tim Mullen", "Marc Aafjes", "Scott R. Sponheim", "Alik S. Widge"], "annotation": "Objective. We establish a principled method for inferring mental health related psychometric variables from neural and behavioral data using the Implicit Association Test (IAT) as the data generation engine, aiming to overcome the limited predictive performance (typically under 0.7 AUC) of the gold-standard D-score method, which relies solely on reaction times.   Approach. We propose a sparse hierarchical Bayesian model that leverages multi-modal data to predict experiences related to mental illness symptoms in new participants. The model is a multivariate generalization of the D-score with trainable parameters, engineered for parameter efficiency in the small-cohort regime typical of IAT studies. Data from two IAT variants were analyzed: a suicidality-related E-IAT ($n=39$) and a psychosis-related PSY-IAT ($n=34$).   Main Results. Our approach overcomes a high inter-individual variability and low within-session effect size in the dataset, reaching AUCs of 0.73 (E-IAT) and 0.76 (PSY-IAT) in the best modality configurations, though corrected 95% confidence intervals are wide ($\\pm 0.18$) and results are marginally significant after FDR correction ($q=0.10$). Restricting the E-IAT to MDD participants improves AUC to 0.79 $[0.62, 0.97]$ (significant at $q=0.05$). Performance is on par with the best reference methods (shrinkage LDA and EEGNet) for each task, even when the latter were adapted to the task, while the proposed method was not. Accuracy was substantially above near-chance D-scores (0.50-0.53 AUC) in both tasks, with more consistent cross-task performance than any single reference method.   Significance. Our framework shows promise for enhancing IAT-based assessment of experiences related to entrapment and psychosis, and potentially other mental health conditions, though further validation on larger and independent cohorts will be needed to establish clinical utility.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.16741v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.16587v1", "url": "http://arxiv.org/abs/2603.16587v1", "pdf_url": "https://arxiv.org/pdf/2603.16587v1", "title": "HistoAtlas: A Pan-Cancer Morphology Atlas Linking Histomics to Molecular Programs and Clinical Outcomes", "authors": ["Pierre-Antoine Bannier"], "annotation": "We present HistoAtlas, a pan-cancer computational atlas that extracts 38 interpretable histomic features from 6,745 diagnostic H&E slides across 21 TCGA cancer types and systematically links every feature to survival, gene expression, somatic mutations, and immune subtypes. All associations are covariate-adjusted, multiple-testing corrected, and classified into evidence-strength tiers. The atlas recovers known biology, from immune infiltration and prognosis to proliferation and kinase signaling, while uncovering compartment-specific immune signals and morphological subtypes with divergent outcomes. Every result is spatially traceable to tissue compartments and individual cells, statistically calibrated, and openly queryable. HistoAtlas enables systematic, large-scale biomarker discovery from routine H&E without specialized staining or sequencing. Data and an interactive web atlas are freely available at https://histoatlas.com .", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.16587v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.16562v1", "url": "http://arxiv.org/abs/2603.16562v1", "pdf_url": "https://arxiv.org/pdf/2603.16562v1", "title": "Understanding Cell Fate Decisions with Temporal Attention", "authors": ["Florian Bürger", "Martim Dias Gomes", "Adrián E. Granada", "Noémie Moreau", "Katarzyna Bozek"], "annotation": "Understanding non-genetic determinants of cell fate is critical for developing and improving cancer therapies, as genetically identical cells can exhibit divergent outcomes under the same treatment conditions. In this work, we present a deep learning approach for cell fate prediction from raw long-term live-cell recordings of cancer cell populations under chemotherapeutic treatment. Our Transformer model is trained to predict cell fate directly from raw image sequences, without relying on predefined morphological or molecular features. Beyond classification, we introduce a comprehensive explainability framework for interpreting the temporal and morphological cues guiding the model's predictions. We demonstrate that prediction of cell outcomes is possible based on the video only, our model achieves balanced accuracy of 0.94 and an F1-score of 0.93. Attention and masking experiments further indicate that the signal predictive of the cell fate is not uniquely located in the final frames of a cell trajectory, as reliable predictions are possible up to 10 h before the event. Our analysis reveals distinct temporal distribution of predictive information in the mitotic and apoptotic sequences, as well as the role of cell morphology and p53 signaling in determining cell outcomes. Together, these findings demonstrate that attention-based temporal models enable accurate cell fate prediction while providing biologically interpretable insights into non-genetic determinants of cellular decision-making. The code is available at https://github.com/bozeklab/Cell-Fate-Prediction.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.16562v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.16185v1", "url": "http://arxiv.org/abs/2603.16185v1", "pdf_url": "https://arxiv.org/pdf/2603.16185v1", "title": "Sample-Efficient Adaptation of Drug-Response Models to Patient Tumors under Strong Biological Domain Shift", "authors": ["Camille Jimenez Cortes", "Philippe Lalanda", "German Vega"], "annotation": "Predicting drug response in patients from preclinical data remains a major challenge in precision oncology due to the substantial biological gap between in vitro cell lines and patient tumors. Rather than aiming to improve absolute in vitro prediction accuracy, this work examines whether explicitly separating representation learning from task supervision enables more sample-efficient adaptation of drug-response models to patient data under strong biological domain shift. We propose a staged transfer-learning framework in which cellular and drug representations are first learned independently from large collections of unlabeled pharmacogenomic data using autoencoder-based representation learning. These representations are then aligned with drug-response labels on cell-line data and subsequently adapted to patient tumors using few-shot supervision. Through a systematic evaluation spanning in-domain, cross-dataset, and patient-level settings, we show that unsupervised pretraining provides limited benefit when source and target domains overlap substantially, but yields clear gains when adapting to patient tumors with very limited labeled data. In particular, the proposed framework achieves faster performance improvements during few-shot patient-level adaptation while maintaining comparable accuracy to single-phase baselines on standard cell-line benchmarks. Overall, these results demonstrate that learning structured and transferable representations from unlabeled molecular profiles can substantially reduce the amount of clinical supervision required for effective drug-response prediction, offering a practical pathway toward data-efficient preclinical-to-clinical translation.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.16185v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.16963v1", "url": "http://arxiv.org/abs/2603.16963v1", "pdf_url": "https://arxiv.org/pdf/2603.16963v1", "title": "Topology-Guided Biomechanical Profiling: A White-Box Framework for Opportunistic Screening of Spinal Instability on Routine CT", "authors": ["Zanting Ye", "Xuanbin Wu", "Guoqing Zhong", "Shengyuan Liu", "Jiashuai Liu", "Ge Song", "Zhisong Wang", "Jing Hao", "Xiaolong Niu", "Yefeng Zheng", "Yu Zhang", "Lijun Lu"], "annotation": "Routine oncologic computed tomography (CT) presents an ideal opportunity for screening spinal instability, yet prophylactic stabilization windows are frequently missed due to the complex geometric reasoning required by the Spinal Instability Neoplastic Score (SINS). Automating SINS is fundamentally hindered by metastatic osteolysis, which induces topological ambiguity that confounds standard segmentation and black-box AI. We propose Topology-Guided Biomechanical Profiling (TGBP), an auditable white-box framework decoupling anatomical perception from structural reasoning. TGBP anchors SINS assessment on two deterministic geometric innovations: (i) canal-referenced partitioning to resolve posterolateral boundary ambiguity, and (ii) context-aware morphometric normalization via covariance-based oriented bounding boxes (OBB) to quantify vertebral collapse. Integrated with auxiliary radiomic and large language model (LLM) modules, TGBP provides an end-to-end, interpretable SINS evaluation. Validated on a multi-center, multi-cancer cohort ($N=482$), TGBP achieved 90.2\\% accuracy in 3-tier stability triage. In a blinded reader study ($N=30$), TGBP significantly outperformed medical oncologists on complex structural features ($κ=0.857$ vs.\\ $0.570$) and prevented compounding errors in Total Score estimation ($κ=0.625$ vs.\\ $0.207$), democratizing expert-level opportunistic screening.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.16963v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.16957v1", "url": "http://arxiv.org/abs/2603.16957v1", "pdf_url": "https://arxiv.org/pdf/2603.16957v1", "title": "Non-perturbative Bacterial Identification Directly from Solid Agar Plates Using Raman", "authors": ["Jeong Hee Kim", "Jia Dong", "Marissa Morales", "Loza Tadesse"], "annotation": "Raman spectroscopy is a promising tool for microbial identification, yet its implementation in microbiology and clinical workflow is still restricted due to the accompanying additional preparation required to focus on microbial signals. Here, we demonstrate Raman-based bacterial identification directly from unopened, inverted agar plates, the same conditions used during incubation. Our approach enabled identification with single gene-level sensitivity using two Escherichia coli variants, differing only in green fluorescent protein (GFP) expression, across diverse media and substrate material conditions, despite the interrogation path traversing 3-4 mm thick background material. We integrated traditional density functional theory (DFT)-based material computation with machine learning analysis, achieving over 97.7% classification accuracy, surpassing the performance of standard measurements from opened plates by 10.8% higher mean accuracy and 0.76% less variance. We further demonstrated Raman mapping-based colony identification via Raman peaks characteristic to GFPmut3 chromophore structure generated by DFT. Our approach is robust to changes in algorithms or substrate materials and promises real-time, non-perturbative monitoring of bacterial growth, biofilm formation, and antimicrobial resistance development.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.16957v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.15711v1", "url": "http://arxiv.org/abs/2603.15711v1", "pdf_url": "https://arxiv.org/pdf/2603.15711v1", "title": "Knowledge Graph Extraction from Biomedical Literature for Alkaptonuria Rare Disease", "authors": ["Giang Pham", "Rebecca Finetti", "Caterina Graziani", "Bianca Roncaglia", "Asma Bendjeddou", "Linda Brodo", "Sara Brunetti", "Moreno Falaschi", "Stefano Forti", "Silvia Giulia Galfré", "Paolo Milazzo", "Corrado Priami", "Annalisa Santucci", "Ottavia Spiga", "Alina Sîrbu"], "annotation": "Alkaptonuria (AKU) is an ultra-rare autosomal recessive metabolic disorder caused by mutations in the HGD (Homogentisate 1,2-Dioxygenase) gene, leading to a pathological accumulation of homogentisic acid (HGA) in body fluids and tissues. This leads to systemic manifestations, including premature spondyloarthropathy, renal and prostatic stones, and cardiovascular complications. Being ultra-rare, the amount of data related to the disease is limited, both in terms of clinical data and literature. Knowledge graphs (KGs) can help connect the limited knowledge about the disease (basic mechanisms, manifestations and existing therapies) with other knowledge; however, AKU is frequently underrepresented or entirely absent in existing biomedical KGs. In this work, we apply a text-mining methodology based on PubTator3 for large-scale extraction of biomedical relations. We construct two KGs of different sizes, validate them using existing biochemical knowledge and use them to extract genes, diseases and therapies possibly related to AKU. This computational framework reveals the systemic interactions of the disease, its comorbidities, and potential therapeutic targets, demonstrating the efficacy of our approach in analyzing rare metabolic disorders.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.15711v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.15217v2", "url": "http://arxiv.org/abs/2603.15217v2", "pdf_url": "https://arxiv.org/pdf/2603.15217v2", "title": "A multiscale discrete-to-continuum framework for structured population models", "authors": ["Eleonora Agostinelli", "Keith L. Chambers", "Helen M. Byrne", "Mohit P. Dalwadi"], "annotation": "Mathematical models of biological populations commonly use discrete structure classes to capture trait variation among individuals (e.g. age, size, phenotype, intracellular state). Upscaling these discrete models into continuum descriptions can improve analytical tractability and scalability of numerical solutions. Common upscaling approaches based solely on Taylor expansions may, however, introduce ambiguities in truncation order, uniform validity and boundary conditions. To address this, here we introduce a discrete multiscale framework to systematically derive continuum approximations of structured population models. Using the method of multiple scales and matched asymptotic expansions applied to discrete systems, we identify regions of structure space for which a continuum representation is appropriate and derive the corresponding partial differential equations. The leading-order dynamics are given by a nonlinear advection equation in the bulk domain and advection-diffusion processes in small inner layers about the leading wavefronts and stagnation point. We further derive discrete boundary layer descriptions for regions where a continuum representation is fundamentally inappropriate. Finally, we demonstrate the method on a simple lipid-structured model for early atherosclerosis and verify consistency between the discrete and continuum descriptions. The multiscale framework we present can be applied to other heterogeneous systems with discrete structure in order to obtain appropriate upscaled dynamics with asymptotically consistent boundary conditions.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.15217v2.pdf", "pdf_downloaded": true}
+{"slug": "2603.15702v1", "url": "http://arxiv.org/abs/2603.15702v1", "pdf_url": "https://arxiv.org/pdf/2603.15702v1", "title": "Whole slide and microscopy image analysis with QuPath and OMERO", "authors": ["Léo Leplat", "Alan O'Callaghan", "Peter Bankhead"], "annotation": "QuPath is open-source software for bioimage analysis. As a desktop application that is flexible and easy to install, QuPath is used by labs worldwide to visualise and analyse large and complex images. However, relying only on images stored only on a local file system limits QuPath's use for larger studies.   This paper describes a new extension that enables QuPath to access pixels and metadata from an OMERO server. This enhances the software by allowing it to work efficiently with images stored remotely, while also serving as a template for developers who want to connect QuPath to other image management systems.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.15702v1.pdf", "pdf_downloaded": true}
+{"slug": "2603.15080v3", "url": "http://arxiv.org/abs/2603.15080v3", "pdf_url": "https://arxiv.org/pdf/2603.15080v3", "title": "Open Biomedical Knowledge Graphs at Scale: Construction, Federation, and AI Agent Access with Samyama Graph Database", "authors": ["Madhulatha Mandarapu", "Sandeep Kunkunuru"], "annotation": "Biomedical knowledge is fragmented across siloed databases -- Reactome for pathways, STRING for protein interactions, ClinicalTrials.gov for study registries, DrugBank for drug vocabularies, DGIdb for drug-gene interactions, SIDER for side effects. We present three open-source biomedical knowledge graphs -- Pathways KG (118,686 nodes, 834,785 edges from 5 sources), Clinical Trials KG (7,774,446 nodes, 26,973,997 edges from 5 sources), and Drug Interactions KG (32,726 nodes, 191,970 edges from 3 sources) -- built on Samyama, a high-performance graph database written in Rust.   Our contributions are threefold. First, we describe a reproducible ETL pattern for constructing large-scale KGs from heterogeneous public data sources, with cross-source deduplication, batch loading (Python Cypher and Rust native loaders), and portable snapshot export. Second, we demonstrate cross-KG federation: loading all three snapshots into a single graph tenant enables property-based joins across datasets. Third, we introduce schema-driven MCP server generation for LLM agent access, evaluated on a new BiomedQA benchmark (40 pharmacology questions): domain-specific MCP tools achieve 98% accuracy vs. 85% for schema-aware text-to-Cypher and 75% for standalone GPT-4o, with zero schema errors.   All data sources are open-license. The combined federated graph (7.9M nodes, 28M edges) loads in approximately 3 minutes on commodity cloud hardware, with single-KG queries completing in 80-100ms and cross-KG federation joins in 1-4s", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.15080v3.pdf", "pdf_downloaded": true}
+{"slug": "2603.15006v1", "url": "http://arxiv.org/abs/2603.15006v1", "pdf_url": "https://arxiv.org/pdf/2603.15006v1", "title": "Empowering Chemical Structures with Biological Insights for Scalable Phenotypic Virtual Screening", "authors": ["Xiaoqing Lian", "Pengsen Ma", "Tengfeng Ma", "Zhonghao Ren", "Xibao Cai", "Zhixiang Cheng", "Bosheng Song", "He Wang", "Xiang Pan", "Yangyang Chen", "Sisi Yuan", "Chen Lin"], "annotation": "Motivation: The scalable identification of bioactive compounds is essential for contemporary drug discovery. This process faces a key trade-off: structural screening offers scalability but lacks biological context, whereas high-content phenotypic profiling provides deep biological insights but is resource-intensive. The primary challenge is to extract robust biological signals from noisy data and encode them into representations that do not require biological data at inference. Results: This study presents DECODE (DEcomposing Cellular Observations of Drug Effects), a framework that bridges this gap by empowering chemical representations with intrinsic biological semantics to enable structure-based in silico biological profiling. DECODE leverages limited paired transcriptomic and morphological data as supervisory signals during training, enabling the extraction of a measurement-invariant biological fingerprint from chemical structures and explicit filtering of experimental noise. Our evaluations demonstrate that DECODE retrieves functionally similar drugs in zero-shot settings with over 20% relative improvement over chemical baselines in mechanism-of-action (MOA) prediction. Furthermore, the framework achieves a 6-fold increase in hit rates for novel anti-cancer agents during external validation. Availability and implementation: The codes and datasets of DECODE are available at https://github.com/lian-xiao/DECODE.", "category": "q-bio.QM", "dataset_lang": "en", "local_pdf": "dataset_arxiv_en\\pdfs\\2603.15006v1.pdf", "pdf_downloaded": true}
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+{"year": "2015", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2016/03/15-2015.pdf", "slug": "fian_15-2015", "title": "Скачать препринт", "local_pdf": "dataset_fian\\pdfs\\fian_15-2015.pdf"}
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+{"year": "2014", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2014/02/1-2014.pdf", "slug": "fian_1-2014", "title": "Скачать препринт", "local_pdf": "dataset_fian\\pdfs\\fian_1-2014.pdf"}
+{"year": "2014", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2014/05/02-2014color.pdf", "slug": "fian_02-2014color", "title": "Скачать препринт|Аннотация|Проведено исследование лазерных треков в вязкой желатиновой пленке под действием непрерывного лазерного излучения и белого континуума от фемтосекундного лазера---------------------------", "local_pdf": "dataset_fian\\pdfs\\fian_02-2014color.pdf"}
+{"year": "2014", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2014/02/3-2014.pdf", "slug": "fian_3-2014", "title": "Скачать препринт|Аннотация|В настоящей работе построены системы отсчёта координат как прямолинейно ускоряемого, так и неподвижного наблюдателей, и получены преобразования пространственно-временных координат события, измеряемых в этих системах. Вывод преобразований проведён на основе концепции 4-мерного пространства компактифицированного до трёх измерений, а также вытекающего из этой концепции представления о римановом пространстве событий с косоугольными пространственно-временными локальными базисами. Полученные преобразования являются более общими, чем известные до сих пор, и при определённых значениях координат переходят либо в преобразования и метрику Мёллера, либо Подосенова, либо в преобразования Ву и Ли, а также в обобщённые преобразования Мёллера-Ву-Ли, полученные Ксу и Клефф и усовершенствованные Эрнстом.---------------------------", "local_pdf": "dataset_fian\\pdfs\\fian_3-2014.pdf"}
+{"year": "2014", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2014/02/5-2014.pdf", "slug": "fian_5-2014", "title": "Скачать препринт|Аннотация|В работе представлены современное состояние и структура локальной вычислительной сети Пущинской Радиоастрономической Обсерватории АКЦ ФИАН и каналы передачи данных проекта «Радиоастрон». Рассмотреныразличные уровни архитектуры опорной сети, ее конфигурация, а также представлено оборудование и технологии, используемые для реализации научных исследований ПРАО---------------------------", "local_pdf": "dataset_fian\\pdfs\\fian_5-2014.pdf"}
+{"year": "2014", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2014/02/6-2014.pdf", "slug": "fian_6-2014", "title": "Скачать препринт|Аннотация|На Московской площадке ФИАН, в Лаборатории электронов высоких энергий (ЛЭВЭ) на протяжении многих лет действует циклический электронный ускоритель (синхротрон) С-60.---------------------------", "local_pdf": "dataset_fian\\pdfs\\fian_6-2014.pdf"}
+{"year": "2014", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2014/05/07-2014.pdf", "slug": "fian_07-2014", "title": "Скачать препринт|Аннотация|Проведён сравнительный анализ преобразований пространственновременных координат события при прямолинейном релятивистски-ускоренном движении, полученных в предыдущей работе на основе концепции 4-мерного пространства компактифицированного до трёх измерений, с известными до сих пор преобразованиями. Показано, что преобразования Мёллера, Подосенова, Парди, Ву и Ли, а также обобщённые преобразования Мёллера-Ву-Ли, полученные Ксу и Клефф и усовершенствованные Эрнстом являются предельными случаями этих новых преобразований. Наибольшее соответствие имеет место с обобщёнными преобразованиями Мёллера-Ву-Ли в форме Эрнста. Наибольшее различие – с преобразованием времени Логунова. Проанализированы факторы, обуславливающие отличие новых преобразований от известных.---------------------------", "local_pdf": "dataset_fian\\pdfs\\fian_07-2014.pdf"}
+{"year": "2014", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2014/05/08-2014.pdf", "slug": "fian_08-2014", "title": "Скачать препринт|Аннотация|В настоящее время в астрономии и астрофизике наблюдается значительный рост объёмов экспериментальных данных. В данной работе рассматриваются крупные астрономические проекты с точки зрения передачи, хранения и обработки больших научных данных. Рассмотрена актуальность этихпроблем в настоящее время и в будущем.Currently in astronomy and astrophysics has seen significant growth in the experimental data. This paper discusses the major astronomical projects in terms of communication, storage and processing of big scientific data. We consider the relevance of these issues now and in the future.---------------------------", "local_pdf": "dataset_fian\\pdfs\\fian_08-2014.pdf"}
+{"year": "2014", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2014/05/10-2014color.pdf", "slug": "fian_10-2014color", "title": "Скачать препринт|Аннотация|Препринт посвящен комплексным установкам на Тянь-Шаньской станции ФИАН. Дано подробное описание прежней установки, созданной в 60-х годах прошлого века и подбор текстов о новых, современных установках и новых исследованиях. Подробно описывается назначение прежней установки, ее детекторы, основные научные результаты и жизнь на станции с многочисленными фотографиями участников. Над выпуском работали — Н.М. Нестерова, В.П. Павлюченко, С.К. Мачавариани, Е.Н. Гудкова---------------------------", "local_pdf": "dataset_fian\\pdfs\\fian_10-2014color.pdf"}
+{"year": "2014", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2014/05/11-2014.pdf", "slug": "fian_11-2014", "title": "Скачать препринт|Аннотация|Формирование и доставка криогенных топливных мишеней с высокой частотой является непременным условием построения фабрики мишеней для обеспечения работы реактора на основе управляемого инерциального термоядерного синтеза (ИТС). Одним из ключевых моментов при построении такой фабрики является выбор эффек-тивного метода частотного формирования криогенных топливных мишеней и построение соответствующего устройства для их производства (так называемый, модуль формирования). При этом, безусловно, мишени должны быть бесподвесными (т.е. свободными от какого-либо подвеса). Кроме того, крайне остро стоит проблема получения криогенного слоя топлива с заданной микроструктурой, позволяющей удовлетворить высоким критериям качества, а именно: отклонения от сферичности и концентричности должны составлять не более 1%, а локальные неоднородности на поверхности топливного слоя не должны превышать 1 мкм во всехмодах. Поэтому, в программе ИТС проведение широкого спектра исследований в области формирования криогенного слоя с различной структурой топлива, изучение отклика этой структуры на вариацию тепловых и механических нагрузок, включая отклик на прохождение ударной волны, является ключевым моментом при выборе реакторных технологий. Перспективным путем решения поставленных задач является реализация метода FST,предложенного и разработанного в Физическом институте им. П.Н.Лебедева (ФИАН). В настоящей работе будут представлены основные результаты, достигнутые ФИАН на данный момент развития программы по FST- формированию криогенных мишеней, включая мишени реакторного класса.---------------------------", "local_pdf": "dataset_fian\\pdfs\\fian_11-2014.pdf"}
+{"year": "2014", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2014/05/12-2014.pdf", "slug": "fian_12-2014", "title": "Скачать препринт", "local_pdf": "dataset_fian\\pdfs\\fian_12-2014.pdf"}
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+{"year": "2012", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2012/04/stoilov_0212.pdf", "slug": "fian_stoilov_0212", "title": "pdf — 328 kb", "local_pdf": "dataset_fian\\pdfs\\fian_stoilov_0212.pdf"}
+{"year": "2012", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2012/12/03-2012.pdf", "slug": "fian_03-2012", "title": "pdf — 4,6Mb", "local_pdf": "dataset_fian\\pdfs\\fian_03-2012.pdf"}
+{"year": "2012", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2012/06/preprint_05-12.pdf", "slug": "fian_preprint_05-12", "title": "pdf — 254 kb", "local_pdf": "dataset_fian\\pdfs\\fian_preprint_05-12.pdf"}
+{"year": "2012", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2012/06/preprint_06-12.pdf", "slug": "fian_preprint_06-12", "title": "pdf — 357 kb", "local_pdf": "dataset_fian\\pdfs\\fian_preprint_06-12.pdf"}
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+{"year": "2009", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2009_26.pdf", "slug": "fian_2009_26", "title": "pdf — 309 K", "local_pdf": "dataset_fian\\pdfs\\fian_2009_26.pdf"}
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+{"year": "2008", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2008_3.pdf", "slug": "fian_2008_3", "title": "pdf — 3.39M", "local_pdf": "dataset_fian\\pdfs\\fian_2008_3.pdf"}
+{"year": "2008", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2008_4.pdf", "slug": "fian_2008_4", "title": "pdf — 4.19M", "local_pdf": "dataset_fian\\pdfs\\fian_2008_4.pdf"}
+{"year": "2008", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2008_5.pdf", "slug": "fian_2008_5", "title": "pdf — 1.54M", "local_pdf": "dataset_fian\\pdfs\\fian_2008_5.pdf"}
+{"year": "2008", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2008_6.pdf", "slug": "fian_2008_6", "title": "pdf — 260Kb", "local_pdf": "dataset_fian\\pdfs\\fian_2008_6.pdf"}
+{"year": "2008", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2008_8.pdf", "slug": "fian_2008_8", "title": "pdf — 2.96M", "local_pdf": "dataset_fian\\pdfs\\fian_2008_8.pdf"}
+{"year": "2008", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2008_9.pdf", "slug": "fian_2008_9", "title": "pdf — 549Kb", "local_pdf": "dataset_fian\\pdfs\\fian_2008_9.pdf"}
+{"year": "2008", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2008_11.pdf", "slug": "fian_2008_11", "title": "pdf — 723Kb", "local_pdf": "dataset_fian\\pdfs\\fian_2008_11.pdf"}
+{"year": "2008", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2008_12.pdf", "slug": "fian_2008_12", "title": "pdf — 414Kb", "local_pdf": "dataset_fian\\pdfs\\fian_2008_12.pdf"}
+{"year": "2008", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2008_13.pdf", "slug": "fian_2008_13", "title": "pdf — 860Kb", "local_pdf": "dataset_fian\\pdfs\\fian_2008_13.pdf"}
+{"year": "2008", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2008_14.pdf", "slug": "fian_2008_14", "title": "pdf — 4.09M", "local_pdf": "dataset_fian\\pdfs\\fian_2008_14.pdf"}
+{"year": "2008", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2008_15.pdf", "slug": "fian_2008_15", "title": "pdf — 8.04M", "local_pdf": "dataset_fian\\pdfs\\fian_2008_15.pdf"}
+{"year": "2008", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2008_19.pdf", "slug": "fian_2008_19", "title": "pdf — 295Kb", "local_pdf": "dataset_fian\\pdfs\\fian_2008_19.pdf"}
+{"year": "2008", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2008_20.pdf", "slug": "fian_2008_20", "title": "pdf — 3M", "local_pdf": "dataset_fian\\pdfs\\fian_2008_20.pdf"}
+{"year": "2008", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2008_21.pdf", "slug": "fian_2008_21", "title": "pdf — 343Kb", "local_pdf": "dataset_fian\\pdfs\\fian_2008_21.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_2.pdf", "slug": "fian_2007_2", "title": "pdf — 393k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_2.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_4.pdf", "slug": "fian_2007_4", "title": "pdf — 1.38M", "local_pdf": "dataset_fian\\pdfs\\fian_2007_4.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_6.pdf", "slug": "fian_2007_6", "title": "pdf — 3.14M", "local_pdf": "dataset_fian\\pdfs\\fian_2007_6.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_7.pdf", "slug": "fian_2007_7", "title": "pdf — 368k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_7.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_8.pdf", "slug": "fian_2007_8", "title": "pdf — 1.14M", "local_pdf": "dataset_fian\\pdfs\\fian_2007_8.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_9.pdf", "slug": "fian_2007_9", "title": "pdf — 274k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_9.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_10.pdf", "slug": "fian_2007_10", "title": "pdf — 405k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_10.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_11.pdf", "slug": "fian_2007_11", "title": "pdf — 662k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_11.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_12_1.pdf", "slug": "fian_2007_12_1", "title": "pdf (стр. 01-20) — 1.66M", "local_pdf": "dataset_fian\\pdfs\\fian_2007_12_1.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_12_2.pdf", "slug": "fian_2007_12_2", "title": "pdf (стр. 21-40) — 1.54M", "local_pdf": "dataset_fian\\pdfs\\fian_2007_12_2.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_12_3.pdf", "slug": "fian_2007_12_3", "title": "pdf (стр. 41-60) — 1.68M", "local_pdf": "dataset_fian\\pdfs\\fian_2007_12_3.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_12_4.pdf", "slug": "fian_2007_12_4", "title": "pdf (стр. 61-80) — 1.54M", "local_pdf": "dataset_fian\\pdfs\\fian_2007_12_4.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_12_5.pdf", "slug": "fian_2007_12_5", "title": "pdf (стр. 81-100) — 1.72M", "local_pdf": "dataset_fian\\pdfs\\fian_2007_12_5.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_12_6.pdf", "slug": "fian_2007_12_6", "title": "pdf (стр. 101-121) — 1.75M", "local_pdf": "dataset_fian\\pdfs\\fian_2007_12_6.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_14_ru.pdf", "slug": "fian_2007_14_ru", "title": "pdf (рус.) — 301k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_14_ru.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_14_eng.pdf", "slug": "fian_2007_14_eng", "title": "pdf (eng.) — 215k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_14_eng.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_14_tables.pdf", "slug": "fian_2007_14_tables", "title": "pdf (таблицы / tables) — 296k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_14_tables.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_15.pdf", "slug": "fian_2007_15", "title": "pdf — 207k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_15.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_16.pdf", "slug": "fian_2007_16", "title": "pdf — 1.18M", "local_pdf": "dataset_fian\\pdfs\\fian_2007_16.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_other.pdf", "slug": "fian_2007_other", "title": "pdf — 291k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_other.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_18.pdf", "slug": "fian_2007_18", "title": "pdf — 384k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_18.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_19.pdf", "slug": "fian_2007_19", "title": "pdf — 443k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_19.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_20.pdf", "slug": "fian_2007_20", "title": "pdf — 651k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_20.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_21.pdf", "slug": "fian_2007_21", "title": "pdf — 479k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_21.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_22.pdf", "slug": "fian_2007_22", "title": "pdf — 1.94M", "local_pdf": "dataset_fian\\pdfs\\fian_2007_22.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_23.pdf", "slug": "fian_2007_23", "title": "pdf — 335k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_23.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_24.pdf", "slug": "fian_2007_24", "title": "pdf — 743k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_24.pdf"}
+{"year": "2007", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2007_25.pdf", "slug": "fian_2007_25", "title": "pdf — 510k", "local_pdf": "dataset_fian\\pdfs\\fian_2007_25.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_1.pdf", "slug": "fian_2006_1", "title": "pdf — 590k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_1.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_3.pdf", "slug": "fian_2006_3", "title": "pdf — 622k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_3.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_4.pdf", "slug": "fian_2006_4", "title": "pdf — 525k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_4.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_5.pdf", "slug": "fian_2006_5", "title": "pdf — 227k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_5.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_6.pdf", "slug": "fian_2006_6", "title": "pdf — 1.0M", "local_pdf": "dataset_fian\\pdfs\\fian_2006_6.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_7.pdf", "slug": "fian_2006_7", "title": "pdf — 426k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_7.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_8.pdf", "slug": "fian_2006_8", "title": "pdf — 441k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_8.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_9.pdf", "slug": "fian_2006_9", "title": "pdf — 4.7M", "local_pdf": "dataset_fian\\pdfs\\fian_2006_9.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_10.pdf", "slug": "fian_2006_10", "title": "pdf — 1.12M", "local_pdf": "dataset_fian\\pdfs\\fian_2006_10.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_11.pdf", "slug": "fian_2006_11", "title": "pdf — 788k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_11.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_12.pdf", "slug": "fian_2006_12", "title": "pdf — 419k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_12.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_13.pdf", "slug": "fian_2006_13", "title": "pdf — 531k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_13.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_14.pdf", "slug": "fian_2006_14", "title": "pdf — 857k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_14.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_15.pdf", "slug": "fian_2006_15", "title": "pdf — 245k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_15.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_16.pdf", "slug": "fian_2006_16", "title": "pdf — 812k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_16.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_17.pdf", "slug": "fian_2006_17", "title": "pdf — 282k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_17.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_18.pdf", "slug": "fian_2006_18", "title": "pdf — 806k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_18.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_19.pdf", "slug": "fian_2006_19", "title": "pdf — 378k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_19.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_20.pdf", "slug": "fian_2006_20", "title": "pdf — 302", "local_pdf": "dataset_fian\\pdfs\\fian_2006_20.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_21.pdf", "slug": "fian_2006_21", "title": "pdf — 594k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_21.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_22.pdf", "slug": "fian_2006_22", "title": "pdf — 444k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_22.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_23.pdf", "slug": "fian_2006_23", "title": "pdf — 234k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_23.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_23g.pdf", "slug": "fian_2006_23g", "title": "графики — 730k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_23g.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_25.pdf", "slug": "fian_2006_25", "title": "pdf — 683k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_25.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_26.pdf", "slug": "fian_2006_26", "title": "pdf — 282k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_26.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_27.pdf", "slug": "fian_2006_27", "title": "pdf — 400k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_27.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_28.pdf", "slug": "fian_2006_28", "title": "pdf — 659k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_28.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_29.pdf", "slug": "fian_2006_29", "title": "pdf — 626k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_29.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_31.pdf", "slug": "fian_2006_31", "title": "pdf — 226k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_31.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_31pic.pdf", "slug": "fian_2006_31pic", "title": "Рисунки — 171k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_31pic.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_32.pdf", "slug": "fian_2006_32", "title": "pdf — 1.97M", "local_pdf": "dataset_fian\\pdfs\\fian_2006_32.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_33.pdf", "slug": "fian_2006_33", "title": "pdf — 358k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_33.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_34.pdf", "slug": "fian_2006_34", "title": "pdf — 310k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_34.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_35.pdf", "slug": "fian_2006_35", "title": "pdf — 1.17M", "local_pdf": "dataset_fian\\pdfs\\fian_2006_35.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_36.pdf", "slug": "fian_2006_36", "title": "pdf — 6.19M", "local_pdf": "dataset_fian\\pdfs\\fian_2006_36.pdf"}
+{"year": "2006", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2006_37.pdf", "slug": "fian_2006_37", "title": "pdf — 197k", "local_pdf": "dataset_fian\\pdfs\\fian_2006_37.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_2.pdf", "slug": "fian_2005_2", "title": "pdf — 445k", "local_pdf": "dataset_fian\\pdfs\\fian_2005_2.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_3.pdf", "slug": "fian_2005_3", "title": "pdf — 280k", "local_pdf": "dataset_fian\\pdfs\\fian_2005_3.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_4.pdf", "slug": "fian_2005_4", "title": "pdf — 294k", "local_pdf": "dataset_fian\\pdfs\\fian_2005_4.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_6.pdf", "slug": "fian_2005_6", "title": "pdf — 323k", "local_pdf": "dataset_fian\\pdfs\\fian_2005_6.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_7.pdf", "slug": "fian_2005_7", "title": "pdf — 511k", "local_pdf": "dataset_fian\\pdfs\\fian_2005_7.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_8.pdf", "slug": "fian_2005_8", "title": "pdf — 317k", "local_pdf": "dataset_fian\\pdfs\\fian_2005_8.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_9.pdf", "slug": "fian_2005_9", "title": "pdf — 249k", "local_pdf": "dataset_fian\\pdfs\\fian_2005_9.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_10.pdf", "slug": "fian_2005_10", "title": "pdf — 631k", "local_pdf": "dataset_fian\\pdfs\\fian_2005_10.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_12.pdf", "slug": "fian_2005_12", "title": "pdf — 3.89M", "local_pdf": "dataset_fian\\pdfs\\fian_2005_12.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_13.pdf", "slug": "fian_2005_13", "title": "pdf — 944k", "local_pdf": "dataset_fian\\pdfs\\fian_2005_13.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_14.pdf", "slug": "fian_2005_14", "title": "pdf — 242k", "local_pdf": "dataset_fian\\pdfs\\fian_2005_14.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_15.pdf", "slug": "fian_2005_15", "title": "pdf — 617k", "local_pdf": "dataset_fian\\pdfs\\fian_2005_15.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_16.pdf", "slug": "fian_2005_16", "title": "pdf — 4.72M", "local_pdf": "dataset_fian\\pdfs\\fian_2005_16.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_17.pdf", "slug": "fian_2005_17", "title": "pdf — 16.9M", "local_pdf": "dataset_fian\\pdfs\\fian_2005_17.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_18.pdf", "slug": "fian_2005_18", "title": "pdf — 465k", "local_pdf": "dataset_fian\\pdfs\\fian_2005_18.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_19.pdf", "slug": "fian_2005_19", "title": "pdf — 474k", "local_pdf": "dataset_fian\\pdfs\\fian_2005_19.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_20.pdf", "slug": "fian_2005_20", "title": "pdf — 1.04M", "local_pdf": "dataset_fian\\pdfs\\fian_2005_20.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_21.pdf", "slug": "fian_2005_21", "title": "pdf — 985k", "local_pdf": "dataset_fian\\pdfs\\fian_2005_21.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_22.pdf", "slug": "fian_2005_22", "title": "pdf — 437k", "local_pdf": "dataset_fian\\pdfs\\fian_2005_22.pdf"}
+{"year": "2005", "pdf_url": "http://preprints.lebedev.ru/wp-content/uploads/2011/12/2005_23.pdf", "slug": "fian_2005_23", "title": "pdf — 1.38M", "local_pdf": "dataset_fian\\pdfs\\fian_2005_23.pdf"}
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new file mode 100644
index 0000000000000000000000000000000000000000..14150743536a562952ee26a939aa65daba6b23ec
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