OpenRaiser/Intern-Atlas huggingface dataset:
Support Recovery in One-bit Compressed Sensing with Near-Optimal Measurements and Sublinear Time
Venue: arXiv cs.IT 2025
Year: 2025

The problem of support recovery in one-bit compressed sensing (1bCS) aim to recover the support of a signal $x\in \mathbb{R}^n$, denoted as supp$(x)$, from the observation $y=\text{sign}(Ax)$, where $A\in \mathbb{R}^{m\times n}$ is a sensing matrix and $|\text{supp}(x)|\leq k, k \ll n$. Under this setting, most preexisting works have a recovery runtime $Ω(n)$. In this paper, we propose two schemes that have sublinear $o(n)$ runtime. (1): For the universal exact support recovery, a scheme of $m=O(k^2\log(n/k)\log n)$ measurements and runtime $D=O(km)$. For the universal $ε$-approximate support recovery, the same scheme with $m=O(kε^{-1}\log(n/k)\log n)$ and runtime $D=O(ε^{-1}m)$, improving the runtime significantly with an extra $O(\log n)$ factor in the number of measurements compared to the current optimal (Matsumoto et al., 2023). (2): For the probabilistic exact support recovery in the sublinear regime, a scheme of $m:=O(k\frac{\log k}{\log\log k}\log n)$ measurements and runtime $O(m)$, with vanishing error probability, improving the recent result of Yang et al., 2025.
Peer Code Review in Research Software Development: The Research Software Engineer Perspective
Venue: arXiv cs.SE 2025
Year: 2025

Background: Research software is crucial for enabling research discoveries and supporting data analysis, simulation, and interpretation across domains. However, evolving requirements, complex inputs, and legacy dependencies hinder the software quality and maintainability. While peer code review can improve software quality, its adoption by research software engineers (RSEs) remains unexplored. Aims: This study explores RSE perspectives on peer code review, focusing on their practices, challenges, and potential improvements. Building on prior work, it aims to uncover how RSEs insights differ from those of other research software developers and identify factors that can enhance code review adoption in this domain. Method: We surveyed RSEs to gather insights into their perspectives on peer code review. The survey design aligned with previous research to enable comparative analysis while including additional questions tailored to RSEs. Results: We received 61 valid responses from the survey. The findings align with prior research while uncovering unique insights about the challenges and practices of RSEs compared to broader developer groups. Conclusions: Peer code review is vital in improving research software's quality, maintainability, and reliability. Despite the unique challenges RSEs face, addressing these through structured processes, improved tools, and targeted training can enhance peer review adoption and effectiveness in research software development.
Sabiá: Um Chatbot de Inteligência Artificial Generativa para Suporte no Dia a Dia do Ensino Superior
Venue: arXiv cs.CL 2025
Year: 2025

Students often report difficulties in accessing day-to-day academic information, which is usually spread across numerous institutional documents and websites. This fragmentation results in a lack of clarity and causes confusion about routine university information. This project proposes the development of a chatbot using Generative Artificial Intelligence (GenAI) and Retrieval-Augmented Generation (RAG) to simplify access to such information. Several GenAI models were tested and evaluated based on quality metrics and the LLM-as-a-Judge approach. Among them, Gemini 2.0 Flash stood out for its quality and speed, and Gemma 3n for its good performance and open-source nature.
GFT: Graph Feature Tuning for Efficient Point Cloud Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Parameter-efficient fine-tuning (PEFT) significantly reduces computational and memory costs by updating only a small subset of the model's parameters, enabling faster adaptation to new tasks with minimal loss in performance. Previous studies have introduced PEFTs tailored for point cloud data, as general approaches are suboptimal. To further reduce the number of trainable parameters, we propose a point-cloud-specific PEFT, termed Graph Features Tuning (GFT), which learns a dynamic graph from initial tokenized inputs of the transformer using a lightweight graph convolution network and passes these graph features to deeper layers via skip connections and efficient cross-attention modules. Extensive experiments on object classification and segmentation tasks show that GFT operates in the same domain, rivalling existing methods, while reducing the trainable parameters. Code is available at https://github.com/manishdhakal/GFT.
HARNESS: Human-Agent Risk Navigation and Event Safety System for Proactive Hazard Forecasting in High-Risk DOE Environments
Venue: arXiv cs.AI 2025
Year: 2025

Operational safety at mission-critical work sites is a top priority given the complex and hazardous nature of daily tasks. This paper presents the Human-Agent Risk Navigation and Event Safety System (HARNESS), a modular AI framework designed to forecast hazardous events and analyze operational risks in U.S. Department of Energy (DOE) environments. HARNESS integrates Large Language Models (LLMs) with structured work data, historical event retrieval, and risk analysis to proactively identify potential hazards. A human-in-the-loop mechanism allows subject matter experts (SMEs) to refine predictions, creating an adaptive learning loop that enhances performance over time. By combining SME collaboration with iterative agentic reasoning, HARNESS improves the reliability and efficiency of predictive safety systems. Preliminary deployment shows promising results, with future work focusing on quantitative evaluation of accuracy, SME agreement, and decision latency reduction.
Dynamically Extensible and Retractable Robotic Leg Linkages for Multi-task Execution in Search and Rescue Scenarios
Venue: arXiv cs.RO 2025
Year: 2025

Search and rescue (SAR) robots are required to quickly traverse terrain and perform high-force rescue tasks, necessitating both terrain adaptability and controlled high-force output. Few platforms exist today for SAR, and fewer still have the ability to cover both tasks of terrain adaptability and high-force output when performing extraction. While legged robots offer significant ability to traverse uneven terrain, they typically are unable to incorporate mechanisms that provide variable high-force outputs, unlike traditional wheel-based drive trains. This work introduces a novel concept for a dynamically extensible and retractable robot leg. Leveraging a dynamically extensible and retractable five-bar linkage design, it allows for mechanically switching between height-advantaged and force-advantaged configurations via a geometric transformation. A testbed evaluated leg performance across linkage geometries and operating modes, with empirical and analytical analyses conducted on stride length, force output, and stability. The results demonstrate that the morphing leg offers a promising path toward SAR robots that can both navigate terrain quickly and perform rescue tasks effectively.
Towards Universal Neural Operators through Multiphysics Pretraining
Venue: arXiv cs.LG 2025
Year: 2025

Although neural operators are widely used in data-driven physical simulations, their training remains computationally expensive. Recent advances address this issue via downstream learning, where a model pretrained on simpler problems is fine-tuned on more complex ones. In this research, we investigate transformer-based neural operators, which have previously been applied only to specific problems, in a more general transfer learning setting. We evaluate their performance across diverse PDE problems, including extrapolation to unseen parameters, incorporation of new variables, and transfer from multi-equation datasets. Our results demonstrate that advanced neural operator architectures can effectively transfer knowledge across PDE problems.
Enhancing Demand-Oriented Regionalization with Agentic AI and Local Heterogeneous Data for Adaptation Planning
Venue: arXiv cs.AI 2025
Year: 2025

Conventional planning units or urban regions, such as census tracts, zip codes, or neighborhoods, often do not capture the specific demands of local communities and lack the flexibility to implement effective strategies for hazard prevention or response. To support the creation of dynamic planning units, we introduce a planning support system with agentic AI that enables users to generate demand-oriented regions for disaster planning, integrating the human-in-the-loop principle for transparency and adaptability. The platform is built on a representative initialized spatially constrained self-organizing map (RepSC-SOM), extending traditional SOM with adaptive geographic filtering and region-growing refinement, while AI agents can reason, plan, and act to guide the process by suggesting input features, guiding spatial constraints, and supporting interactive exploration. We demonstrate the capabilities of the platform through a case study on the flooding-related risk in Jacksonville, Florida, showing how it allows users to explore, generate, and evaluate regionalization interactively, combining computational rigor with user-driven decision making.
Generative Artificial Intelligence Adoption Among Bangladeshi Journalists: Exploring Journalists' Awareness, Acceptance, Usage, and Organizational Stance on Generative AI
Venue: arXiv cs.CY 2025
Year: 2025

Newsrooms and journalists across the world are adopting Generative AI (GenAI). Drawing on in-depth interviews with 23 journalists, this study identifies Bangladeshi journalists' awareness, acceptance, usage patterns, and their media organizations' stance toward GenAI. This study finds Bangladeshi journalists' high reliance on GenAI like their Western colleagues despite limited institutional support and the near absence of AI policy. Despite this contrast, concerns over GenAI's implications in journalism between the West and non-West were mostly identical. Moreover, this study contributes to the Unified Theory of Acceptance and Use of Technology (UTAUT) by proposing two changes regarding GenAI adoption among journalists in non-Western settings. First, this study identifies the non-contribution of facilitating conditions in shaping behavioral intent in GenAI adoption in non-Western contexts. Second, social influence works in a horizontal order through informal peer pressure or professional motivation in the absence of formal institutional hierarchical pressure. Voluntariness in the context of Bangladeshi journalists is underpinned by their professional compulsion. Therefore, this study contributes to understanding how contextual factors shape technology adoption trajectories in non-Western journalism.
Towards a Human-in-the-Loop Framework for Reliable Patch Evaluation Using an LLM-as-a-Judge
Venue: arXiv cs.SE 2025
Year: 2025

Reliable evaluation is crucial for advancing Automated Program Repair (APR), but prevailing benchmarks rely on execution-based evaluation methods (unit test pass@k), which fail to capture true patch validity. Determining validity can require costly manual annotation. To reduce this cost, we introduce a human-in-the-loop approach to LLM-based patch validity judgment. Inspired by the observation that human judgment is better aligned when using a shared rubric, we first employ an LLM to generate a per-bug rubric, followed by a one-time human review and optional refinement to this rubric, and then employ an LLM to judge patches using the refined rubric. We apply this approach to assign binary validity labels to patches for issues found by Google sanitizer tools. Our results show that this approach yields substantial agreement with human consensus (Cohen's kappa 0.75), high recall (0.94) and high precision (0.80), when considering patches that have unanimous agreement from 3 human raters on the validity labels. On the full dataset including patches where human raters disagree, we find this approach can still be further improved (Cohen's kappa 0.57, recall 0.93, precision 0.65) and identify possible future directions.
Architecting software monitors for control-flow anomaly detection through large language models and conformance checking
Venue: arXiv cs.SE 2025
Year: 2025

Context: Ensuring high levels of dependability in modern computer-based systems has become increasingly challenging due to their complexity. Although systems are validated at design time, their behavior can be different at run-time, possibly showing control-flow anomalies due to "unknown unknowns". Objective: We aim to detect control-flow anomalies through software monitoring, which verifies run-time behavior by logging software execution and detecting deviations from expected control flow. Methods: We propose a methodology to develop software monitors for control-flow anomaly detection through Large Language Models (LLMs) and conformance checking. The methodology builds on existing software development practices to maintain traditional V&V while providing an additional level of robustness and trustworthiness. It leverages LLMs to link design-time models and implementation code, automating source-code instrumentation. The resulting event logs are analyzed via conformance checking, an explainable and effective technique for control-flow anomaly detection. Results: We test the methodology on a case-study scenario from the European Railway Traffic Management System / European Train Control System (ERTMS/ETCS), which is a railway standard for modern interoperable railways. The results obtained from the ERTMS/ETCS case study demonstrate that LLM-based source-code instrumentation can achieve up to 84.775% control-flow coverage of the reference design-time process model, while the subsequent conformance checking-based anomaly detection reaches a peak performance of 96.610% F1-score and 93.515% AUC. Conclusion: Incorporating domain-specific knowledge to guide LLMs in source-code instrumentation significantly allowed obtaining reliable and quality software logs and enabled effective control-flow anomaly detection through conformance checking.
Multi-Joint Physics-Informed Deep Learning Framework for Time-Efficient Inverse Dynamics
Venue: arXiv cs.LG 2025
Year: 2025

Time-efficient estimation of muscle activations and forces across multi-joint systems is critical for clinical assessment and assistive device control. However, conventional approaches are computationally expensive and lack a high-quality labeled dataset for multi-joint applications. To address these challenges, we propose a physics-informed deep learning framework that estimates muscle activations and forces directly from kinematics. The framework employs a novel Multi-Joint Cross-Attention (MJCA) module with Bidirectional Gated Recurrent Unit (BiGRU) layers to capture inter-joint coordination, enabling each joint to adaptively integrate motion information from others. By embedding multi-joint dynamics, inter-joint coupling, and external force interactions into the loss function, our Physics-Informed MJCA-BiGRU (PI-MJCA-BiGRU) delivers physiologically consistent predictions without labeled data while enabling time-efficient inference. Experimental validation on two datasets demonstrates that PI-MJCA-BiGRU achieves performance comparable to conventional supervised methods without requiring ground-truth labels, while the MJCA module significantly enhances inter-joint coordination modeling compared to other baseline architectures.
MedPath: Multi-Domain Cross-Vocabulary Hierarchical Paths for Biomedical Entity Linking
Venue: arXiv cs.CL 2025
Year: 2025

Progress in biomedical Named Entity Recognition (NER) and Entity Linking (EL) is currently hindered by a fragmented data landscape, a lack of resources for building explainable models, and the limitations of semantically-blind evaluation metrics. To address these challenges, we present MedPath, a large-scale and multi-domain biomedical EL dataset that builds upon nine existing expert-annotated EL datasets. In MedPath, all entities are 1) normalized using the latest version of the Unified Medical Language System (UMLS), 2) augmented with mappings to 62 other biomedical vocabularies and, crucially, 3) enriched with full ontological paths -- i.e., from general to specific -- in up to 11 biomedical vocabularies. MedPath directly enables new research frontiers in biomedical NLP, facilitating training and evaluation of semantic-rich and interpretable EL systems, and the development of the next generation of interoperable and explainable clinical NLP models.
MCN-CL: Multimodal Cross-Attention Network and Contrastive Learning for Multimodal Emotion Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal emotion recognition plays a key role in many domains, including mental health monitoring, educational interaction, and human-computer interaction. However, existing methods often face three major challenges: unbalanced category distribution, the complexity of dynamic facial action unit time modeling, and the difficulty of feature fusion due to modal heterogeneity. With the explosive growth of multimodal data in social media scenarios, the need for building an efficient cross-modal fusion framework for emotion recognition is becoming increasingly urgent. To this end, this paper proposes Multimodal Cross-Attention Network and Contrastive Learning (MCN-CL) for multimodal emotion recognition. It uses a triple query mechanism and hard negative mining strategy to remove feature redundancy while preserving important emotional cues, effectively addressing the issues of modal heterogeneity and category imbalance. Experiment results on the IEMOCAP and MELD datasets show that our proposed method outperforms state-of-the-art approaches, with Weighted F1 scores improving by 3.42% and 5.73%, respectively.
From Proof to Program: Characterizing Tool-Induced Reasoning Hallucinations in Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Tool-augmented Language Models (TaLMs) can invoke external tools to solve problems beyond their parametric capacity. However, it remains unclear whether these tool-enabled gains reflect trustworthy reasoning. Focusing on the Code Interpreter tool, we show that even when tools are selected and executed correctly, TaLMs treat tool outputs as substitutes for reasoning, producing solutions that appear correct but lack coherent justification. We term this failure mode Tool-Induced Myopia (TIM), and study it using PYMATH, a benchmark of 1,679 competition-level mathematical problems for which Python code is helpful but not sufficient. We further develop a multi-dimensional evaluation suite to quantify reasoning degradation in TaLMs relative to their non-tool counterparts. Our findings reveal that while TaLMs achieve up to a 19.3 percentage point gain in final-answer accuracy, their reasoning behavior consistently deteriorates (e.g., non-tool LLMs win up to 41.5% more often in pairwise comparisons of the reasoning process). This degradation intensifies with tool use; the more frequently a model invokes tools, the less coherent its reasoning becomes. Moreover, tool use shifts errors from arithmetic mistakes toward global reasoning failures (logic, assumption, creativity); with TIM present in ~55% of high-risk cases. Finally, we propose a preference-optimization-based framework that realigns TaLMs to use tools as assistive evidence, improving both final-answer accuracy and reasoning depth under tool use. Codes and data are available at: https://github.com/megagonlabs/TIM.
Evaluating Large Language Models on Rare Disease Diagnosis: A Case Study using House M.D
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have demonstrated capabilities across diverse domains, yet their performance on rare disease diagnosis from narrative medical cases remains underexplored. We introduce a novel dataset of 176 symptom-diagnosis pairs extracted from House M.D., a medical television series validated for teaching rare disease recognition in medical education. We evaluate four state-of-the-art LLMs such as GPT 4o mini, GPT 5 mini, Gemini 2.5 Flash, and Gemini 2.5 Pro on narrative-based diagnostic reasoning tasks. Results show significant variation in performance, ranging from 16.48% to 38.64% accuracy, with newer model generations demonstrating a 2.3 times improvement. While all models face substantial challenges with rare disease diagnosis, the observed improvement across architectures suggests promising directions for future development. Our educationally validated benchmark establishes baseline performance metrics for narrative medical reasoning and provides a publicly accessible evaluation framework for advancing AI-assisted diagnosis research.
Synthetic Voices, Real Threats: Evaluating Large Text-to-Speech Models in Generating Harmful Audio
Venue: arXiv cs.SD 2025
Year: 2025

Modern text-to-speech (TTS) systems, particularly those built on Large Audio-Language Models (LALMs), generate high-fidelity speech that faithfully reproduces input text and mimics specified speaker identities. While prior misuse studies have focused on speaker impersonation, this work explores a distinct content-centric threat: exploiting TTS systems to produce speech containing harmful content. Realizing such threats poses two core challenges: (1) LALM safety alignment frequently rejects harmful prompts, yet existing jailbreak attacks are ill-suited for TTS because these systems are designed to faithfully vocalize any input text, and (2) real-world deployment pipelines often employ input/output filters that block harmful text and audio. We present HARMGEN, a suite of five attacks organized into two families that address these challenges. The first family employs semantic obfuscation techniques (Concat, Shuffle) that conceal harmful content within text. The second leverages audio-modality exploits (Read, Spell, Phoneme) that inject harmful content through auxiliary audio channels while maintaining benign textual prompts. Through evaluation across five commercial LALMs-based TTS systems and three datasets spanning two languages, we demonstrate that our attacks substantially reduce refusal rates and increase the toxicity of generated speech. We further assess both reactive countermeasures deployed by audio-streaming platforms and proactive defenses implemented by TTS providers. Our analysis reveals critical vulnerabilities: deepfake detectors underperform on high-fidelity audio; reactive moderation can be circumvented by adversarial perturbations; while proactive moderation detects 57-93% of attacks. Our work highlights a previously underexplored content-centric misuse vector for TTS and underscore the need for robust cross-modal safeguards throughout training and deployment.
PhaseWin Search Framework Enable Efficient Object-Level Interpretation
Venue: arXiv cs.CV 2025
Year: 2025

Attribution is essential for interpreting object-level foundation models. Recent methods based on submodular subset selection have achieved high faithfulness, but their efficiency limitations hinder practical deployment in real-world scenarios. To address this, we propose PhaseWin, a novel phase-window search algorithm that enables faithful region attribution with near-linear complexity. PhaseWin replaces traditional quadratic-cost greedy selection with a phased coarse-to-fine search, combining adaptive pruning, windowed fine-grained selection, and dynamic supervision mechanisms to closely approximate greedy behavior while dramatically reducing model evaluations. Theoretically, PhaseWin retains near-greedy approximation guarantees under mild monotone submodular assumptions. Empirically, PhaseWin achieves over 95% of greedy attribution faithfulness using only 20% of the computational budget, and consistently outperforms other attribution baselines across object detection and visual grounding tasks with Grounding DINO and Florence-2. PhaseWin establishes a new state of the art in scalable, high-faithfulness attribution for object-level multimodal models.