OpenRaiser/Intern-Atlas huggingface dataset:
EfficientXpert: Efficient Domain Adaptation for Large Language Models via Propagation-Aware Pruning
Venue: arXiv cs.LG 2026
Year: 2026

Large language models (LLMs) are increasingly adapted into domain-specific variants for applications in law, healthcare, and finance. Their scale, however, limits deployment in resource-constrained settings, and existing compression approaches often either degrade after domain adaptation or require substantial additional computation. We introduce EfficientXpert, a lightweight framework for domain pruning that integrates ForeSight Mask, a propagation-aware criterion for selecting weights to prune without backpropagation, and Partial Brain Surgeon, an efficient closed-form update for low-rank adapters under a fixed sparsity pattern. With fine-tuning cost comparable to standard LoRA, EfficientXpert converts a general pretrained model into a sparse, domain-adapted expert in a single pruning step. Across health and legal benchmarks, EfficientXpert reaches up to 98 percent of dense performance at 40 percent sparsity, improving over prior pruning baselines while matching LoRA training time and staying within 1 percent of LoRA peak GPU memory in our experiments.
Semantic Router: On the Feasibility of Hijacking MLLMs via a Single Adversarial Perturbation
Venue: arXiv cs.CV 2026
Year: 2026

Multimodal Large Language Models (MLLMs) are increasingly deployed in stateless systems, such as autonomous driving and robotics. This paper investigates a novel threat: Semantic-Aware Hijacking. We explore the feasibility of hijacking multiple stateless decisions simultaneously using a single universal perturbation. We introduce the Semantic-Aware Universal Perturbation (SAUP), which acts as a semantic router, "actively" perceiving input semantics and routing them to distinct, attacker-defined targets. To achieve this, we conduct theoretical and empirical analysis on the geometric properties in the latent space. Guided by these insights, we propose the Semantic-Oriented (SORT) optimization strategy and annotate a new dataset with fine-grained semantics to evaluate performance. Extensive experiments on three representative MLLMs demonstrate the fundamental feasibility of this attack, achieving a 66% attack success rate over five targets using a single frame against Qwen.
SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space
Venue: arXiv cs.CL 2026
Year: 2026

Sparse attention reduces the quadratic complexity of full self-attention but faces two challenges: (1) an attention gap, where applying sparse attention to full-attention-trained models causes performance degradation due to train-inference distribution mismatch, and (2) a capability gap, where models trained purely with sparse attention lack complete gradient flow, preventing them from matching full-attention performance. We propose SSA (Sparse Sparse Attention), a training framework that integrates both sparse and full attention with bidirectional attention-output alignment. We prove that the approximation error scales linearly with the attention mass dropped under sparse attention, and show that SSA's alignment objective substantially reduces this quantity compared to baselines. Experiments demonstrate that SSA achieves state-of-the-art performance under both inference modes, adapts smoothly to varying sparsity budgets, and demonstrates superior long-context capabilities. The code is available at https://github.com/zhenyi4/ssa.
Interpretable Air Pollution Forecasting by Physics-Guided Spatiotemporal Decoupling
Venue: arXiv cs.LG 2026
Year: 2026

Accurate and interpretable air pollution forecasting is crucial for public health, but most models face a trade-off between performance and interpretability. This study proposes a physics-guided, interpretable-by-design spatiotemporal learning framework. The model decomposes the spatiotemporal behavior of air pollutant concentrations into two transparent, additive modules. The first is a physics-guided transport kernel with directed weights conditioned on wind and geography (advection). The second is an explainable attention mechanism that learns local responses and attributes future concentrations to specific historical lags and exogenous drivers. Evaluated on a comprehensive dataset from the Stockholm region, our model consistently outperforms state-of-the-art baselines across multiple forecasting horizons. Our model's integration of high predictive performance and spatiotemporal interpretability provides a more reliable foundation for operational air-quality management in real-world applications.
Robust Algorithms for Finding Cliques in Random Intersection Graphs via Sum-of-Squares
Venue: arXiv cs.DS 2026
Year: 2026

We study efficient algorithms for recovering cliques in dense random intersection graphs (RIGs). In this model, $d = n^{Ω(1)}$ cliques of size approximately $k$ are randomly planted by choosing the vertices to participate in each clique independently with probability $δ$. While there has been extensive work on recovering one, or multiple disjointly planted cliques in random graphs, the natural extension of this question to recovering overlapping cliques has been, surprisingly, largely unexplored. Moreover, because every vertex can be part of polynomially many cliques, this task is significantly more challenging than in case of disjointly planted cliques (as recently studied by Kothari, Vempala, Wein and Xu [COLT'23]). In this work we obtain the first efficient algorithms for recovering the community structure of RIGs both from the perspective of exact and approximate recovery. Our algorithms are further robust to noise, monotone adversaries, and a certain, optimal number of edge corruptions. They work whenever $k \gg \sqrt{n \log(n)}$. Our techniques follow the proofs-to-algorithms framework utilizing the sum-of-squares hierarchy.
Metric, inertially aligned monocular state estimation via kinetodynamic priors
Venue: arXiv cs.RO 2026
Year: 2026

Accurate state estimation for flexible robotic systems poses significant challenges, particularly for platforms with dynamically deforming structures that invalidate rigid-body assumptions. This paper addresses this problem and enables the extension of existing rigid-body pose estimation methods to non-rigid systems. Our approach integrates two core components: first, we capture elastic properties using a deformation-force model, efficiently learned via a Multi-Layer Perceptron; second, we resolve the platform's inherently smooth motion using continuous-time B-spline kinematic models. By continuously applying Newton's Second Law, our method formulates the relationship between visually-derived trajectory acceleration and predicted deformation-induced acceleration. We demonstrate that our approach not only enables robust and accurate pose estimation on non-rigid platforms, but also demonstrates that the properly modeled platform physics allow for the recovery of inertial sensing properties. We validate this feasibility on a simple spring-camera system, showing how it robustly resolves the typically ill-posed problem of metric scale and gravity recovery in monocular visual odometry.
ACE-GF: A Generative Framework for Atomic Cryptographic Entities
Venue: arXiv cs.CR 2026
Year: 2026

Autonomous digital entities require deterministic identity mechanisms that avoid persistent storage of high-value master secrets, while supporting credential rotation and cryptographic agility across heterogeneous systems. Existing deterministic key hierarchies and centralized key management systems typically rely on long-lived root secrets, introducing structural single points of failure and complicating lifecycle management. We present ACE-GF (Atomic Cryptographic Entity Generative Framework), a seed-storage-free identity construction that enables deterministic and context-isolated key derivation without storing any master secret at rest. The construction reconstructs an identity root ephemerally in memory from a sealed artifact and authorization credentials, using misuse-resistant authenticated encryption together with standard key derivation primitives. Derived keys are generated via HKDF with explicit context encoding, ensuring cryptographic isolation across curves and application domains. This design naturally supports stateless credential rotation, authorization-bound revocation, and non-disruptive migration toward post-quantum cryptographic domains. Furthermore, the framework's parametric agility allows for optimization in resource-constrained environments, ensuring that deterministic identity reconstruction remains viable across a spectrum of hardware from high-performance servers to low-power IoT nodes without compromising the underlying security model. This work builds upon the conceptual framework introduced in MSCIKDF, which identified the core design goals for multi-curve, context-isolated, PQC-pluggable identity but did not provide a concrete construction. A formal protocol specification of ACE-GF has been submitted as an IETF Internet-Draft.
PIRA: Preference-Oriented Instruction-Tuned Reward Models with Dual Aggregation
Venue: arXiv cs.CL 2026
Year: 2026

Reward models are pivotal for aligning Large Language Models (LLMs) with human preferences. Existing approaches face two key limitations: Discriminative reward models require large-scale annotated data, as they cannot exploit the preference instruction-following capability of LLMs available to generative reward models. Moreover, reward models are particularly prone to reward overoptimization, where LLMs exploit weaknesses in the reward function instead of improving true alignment. We introduce \textbf{PIRA}, a training paradigm that integrates three complementary strategies to address these challenges: (1) reformulating question-answer pairs into preference-task instructions to explicitly leverage LLMs' preference instruction-following capability, (2) averaging the rewards aggregated from diverse preference-task instructions for each sample, which mitigates task-specific bias and enhances robustness across evaluation perspectives, and (3) averaging outputs from the value head under different dropout rates to stabilize reward estimation. Experiments on public datasets show that PIRA improves performance considerably, enhances generalization, and effectively mitigates reward overoptimization.
Musical Score Understanding Benchmark: Evaluating Large Language Models' Comprehension of Complete Musical Scores
Venue: arXiv cs.SD 2026
Year: 2026

Understanding complete musical scores entails integrated reasoning over pitch, rhythm, harmony, and large-scale structure, yet the ability of Large Language Models and Vision-Language Models to interpret full musical notation remains insufficiently examined. We introduce the Musical Score Understanding Benchmark (MSU-Bench), the first large-scale, human-curated benchmark for score-level musical understanding across textual (ABC notation) and visual (PDF) modalities. MSU-Bench contains 1,800 generative Question-Answering pairs from works by Bach, Beethoven, Chopin, Debussy, and others, organised into four levels of increasing difficulty, ranging from onset information to texture and form. Evaluations of more than fifteen state-of-the-art models, in both zero-shot and fine-tuned settings, reveal pronounced modality gaps, unstable level-wise performance, and challenges in maintaining multilevel correctness. Fine-tuning substantially improves results across modalities while preserving general knowledge, positioning MSU-Bench as a robust foundation for future research in multimodal reasoning. To facilitate further research, we publicly release MSU-Bench and all associated resources.
Towards Trustworthy Legal AI through LLM Agents and Formal Reasoning
Venue: arXiv cs.AI 2026
Year: 2026

Legal decisions should be logical and based on statutory laws. While large language models(LLMs) are good at understanding legal text, they cannot provide verifiable justifications. We present L4L, a solver-centric framework that enforces formal alignment between LLM-based legal reasoning and statutory laws. The framework integrates role-differentiated LLM agents with SMT-backed verification, combining the flexibility of natural language with the rigor of symbolic reasoning. Our approach operates in four stages: (1) Statute Knowledge Building, where LLMs autoformalize legal provisions into logical constraints and validate them through case-level testing; (2) Dual Fact-and-Statute Extraction, in which the prosecutor-and defense-aligned agents independently map case narratives to argument tuples; (3) Solver-Centric Adjudication, where SMT solvers check the legal admissibility and consistency of the arguments against the formalized statute knowledge; (4) Judicial Rendering, in which a judge agent integrates solver-validated reasoning with statutory interpretation and similar precedents to produce a legally grounded verdict. Experiments on public legal benchmarks show that L4L consistently outperforms baselines, while providing auditable justifications that enable trustworthy legal AI.
BRIDGE: Building Representations In Domain Guided Program Synthesis
Venue: arXiv cs.LG 2026
Year: 2026

Large language models (LLMs) are good at generating code, but remain brittle for formal verification in systems like Lean4. A core scalability challenge is that verified synthesis requires consistent outputs across multiple artifacts: executable code, precise specifications, theorem statements, and ultimately proofs. Existing approaches rarely treat these as a unified pipeline. We present BRIDGE, a structured prompting framework that decomposes verification into three interconnected domains: Code (implementations), Specifications (formal intent), and Theorem Statements (constructive correctness claims), and elicits domain-specific intermediate reasoning to connect them. In Lean4, BRIDGE often adopts a code-first workflow, using the generated implementation as a semantic anchor for downstream specification and theorem statement generation. Across 178 algorithmic problems and five LLMs, BRIDGE improves Lean executable correctness by nearly 1.5x (pass at 5) over direct baselines and can be 2x more sample-efficient at inference time, requiring fewer samples per verified solution at comparable generation lengths. We further find that specification-driven prompting improves Python pass rates by up to 17.5 percent. Beyond inference-time prompting, supervised fine-tuning on BRIDGE-style reasoning traces yields nearly 1.5x higher Lean pass success than code-only SFT, indicating that these intermediate representations are learnable. BRIDGE provides a practical foundation for scaling verified synthesis and motivates future work on expert iteration and full proof generation.
Robust gene prioritization for Dietary Restriction via Fast-mRMR Feature Selection techniques
Venue: arXiv cs.LG 2026
Year: 2026

Gene prioritization (identifying genes potentially associated with a biological process) is increasingly tackled with Artificial Intelligence. However, existing methods struggle with the high dimensionality and incomplete labelling of biomedical data. This work proposes a more robust and efficient pipeline that leverages Fast-mRMR Feature Selection to retain only relevant, non-redundant features for classifiers, building simpler, more interpretable and more efficient models. Experiments in our domain of interest, prioritizing genes related to Dietary Restriction (DR), show significant improvements over existing methods and enables us to integrate heterogeneous biological feature sets for better performance, a strategy that previously degraded performance due to noise accumulation. This work focuses on DR given the availability of curated data and expert knowledge for validation, yet this pipeline would be applicable to other biological processes, proving that feature selection is critical for reliable gene prioritization in high-dimensional omics.
When Models Fabricate Credentials: Measuring How Professional Identity Suppresses Honest Self-Representation
Venue: arXiv cs.AI 2026
Year: 2026

Language models produce authoritative, persuasive responses even when those responses rest on fabricated expertise. Measuring this fabrication propensity directly across all domains is intractable, but AI identity disclosure provides a clean test: when a model assigned a professional persona is asked about its expertise origins, it can either disclose its AI nature or fabricate a human professional history. Because the ground truth is known-the model is not a neurosurgeon-non-disclosure constitutes unambiguous fabrication. Using a factorial evaluation design, sixteen open-weight models (4B-671B parameters) were audited under identical conditions across 19,200 trials. Under professional personas-neurosurgeon, financial advisor, classical musician-models that disclose their AI nature in 99.8-99.9% of interactions under neutral conditions instead fabricated professional credentials, training narratives, and embodied experiences. Fabrication rates varied unpredictably: a 14B model disclosed in 61.4% of interactions while a 70B model disclosed in just 4.1%. Domain-specific inconsistency was pronounced: a Financial Advisor persona elicited 35.2% disclosure at the first prompt while a Neurosurgeon persona elicited only 3.6%-a 9.7-fold difference. Model identity provided substantially larger improvement in fitting observations than parameter count (Delta R_adj^2 = 0.375 vs 0.012). An additional experiment found that adding explicit disclosure permission to persona system prompts increased disclosure from 23.7% to 65.8%, indicating that honest self-representation is a suppressed default rather than an absent capability-models can disclose but do not when persona instructions are silent on self-disclosure. The propensity to fabricate expertise is context-dependent rather than a stable model property, requiring deliberate behavior design and domain-specific verification.
Who Owns the Knowledge? Copyright, GenAI, and the Future of Academic Publishing
Venue: arXiv cs.DL 2026
Year: 2026

The integration of generative artificial intelligence (GenAI) and large language models (LLMs) into scientific research and higher education presents a paradigm shift, offering revolutionizing opportunities while simultaneously raising profound ethical, legal, and regulatory questions. This study examines the complex intersection of AI and science, with a specific focus on the challenges posed to copyright law and the principles of open science. The author argues that current regulatory frameworks in key jurisdictions like the United States, China, the European Union, and the United Kingdom, while aiming to foster innovation, contain significant gaps, particularly concerning the use of copyrighted works and open science outputs for AI training. Widely adopted licensing mechanisms, such as Creative Commons, fail to adequately address the nuances of AI training, and the pervasive lack of attribution within AI systems fundamentally challenges established notions of originality. While current doctrine treats AI training as potentially fair use, this paper argues such mechanisms are inadequate and that copyright holders should retain explicit opt-out rights regardless of fair use doctrine. Instead, the author advocates for upholding authors' rights to refuse the use of their works for AI training and proposes that universities assume a leading role in shaping responsible AI governance. The conclusion is that a harmonized international legislative effort is urgently needed to ensure transparency, protect intellectual property, and prevent the emergence of an oligopolistic market structure that could prioritize commercial profit over scientific integrity and equitable knowledge production. This is a substantially expanded and revised version of a work originally presented at the 20th International Conference on Scientometrics & Informetrics (Kochetkov, 2025).
Sneak Path Current Modeling in Memristor Crossbar Arrays for Analog In-Memory Computing
Venue: arXiv cs.ET 2026
Year: 2026

Memristor crossbar arrays have emerged as a key component for next-generation non-volatile memories, artificial neural networks, and analog in-memory computing (IMC) systems. By minimizing data transfer between the processor and memory, they offer substantial energy savings. However, a major design challenge in memristor crossbar arrays is the presence of sneak path currents, which degrade electrical performance, reduce noise margins, and limit reliable operations. This work presents a closed-form analytical framework based on 1.4nm technology for accurately estimating sneak path currents in memristor crossbar arrays. The proposed model captures the interdependence of key design parameters in memristor crossbar arrays, including array size, ON/OFF ratio of memristors, read voltage, and interconnect conditions, through mathematically derived relationships. It supports various practical configurations, such as different data patterns and connection strategies, enabling rapid and comprehensive sneak path current modeling. The sensitivity analysis includes how design parameters influence sneak path current and noise margin loss, underscoring the trade-offs involved in scaling crossbar arrays. Validation through SPICE simulations shows that the model achieves an error of less than 10.9% while being up to 4784 times faster than full circuit simulations. This analytical framework offers a powerful tool for quantitative assessment and pre-design/real-time optimization of memristor-based analog in-memory computing (IMC) architectures.
StreamFlow: Theory, Algorithm, and Implementation for High-Efficiency Rectified Flow Generation
Venue: arXiv cs.CV 2026
Year: 2026

New technologies such as Rectified Flow and Flow Matching have significantly improved the performance of generative models in the past two years, especially in terms of control accuracy, generation quality, and generation efficiency. However, due to some differences in its theory, design, and existing diffusion models, the existing acceleration methods cannot be directly applied to the Rectified Flow model. In this article, we have comprehensively implemented an overall acceleration pipeline from the aspects of theory, design, and reasoning strategies. This pipeline uses new methods such as batch processing with a new velocity field, vectorization of heterogeneous time-step batch processing, and dynamic TensorRT compilation for the new methods to comprehensively accelerate related models based on flow models. Currently, the existing public methods usually achieve an acceleration of 18%, while experiments have proved that our new method can accelerate the 512*512 image generation speed to up to 611%, which is far beyond the current non-generalized acceleration methods.