AI news archive.
19,861 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsA Deterministic Security Runtime for LLM Agents on MPI and Batch-Scheduled HPC Systems: Consensus-Governed, Hardware-Rooted Supervision of Every Rank
arXiv:2610.33318v1 Announce Type: cross Abstract: Large language model agents launch simulations on high-performance computing (HPC) systems, where one sbatch or mpirun can commit thousands of node-hours and a forged res…
- arXiv cs.AIresearchFrom Hype to ROI: An Economic Model for LLM Adoption in Information Systems and Security Operations
arXiv:2610.33352v1 Announce Type: cross Abstract: Large Language Models (LLMs) promise significant efficiency gains across information-intensive operations by automating repetitive language-based tasks. However, adoption…
- arXiv cs.AIresearchMOSHO: A Native Discrete Shark-Inspired Metaheuristic for Hardware-Aware Neural Architecture Search
arXiv:2610.33358v1 Announce Type: cross Abstract: Hardware-aware neural architecture search (NAS) requires jointly optimizing predictive accuracy and device-specific latency over discrete search spaces. While multi-objec…
- arXiv cs.AIresearchExact Unlearning After Data Curation
arXiv:2610.33426v1 Announce Type: cross Abstract: Training pipelines often remove similar examples before fitting a model. Deleting a raw record can change that selection and admit an example that was excluded before. Wh…
- arXiv cs.AIresearchQuantifying Meaning with SCAPE-T: Semantic Complexity from Encoder-Only Transformer Attention
arXiv:2610.33441v1 Announce Type: cross Abstract: Automated semantic complexity estimation remains underexplored in NLP, despite important applications in linguistics and interpretability. This paper defines semantic com…
- arXiv cs.AIresearchLLMs in Cloud: A Discussion on Architecture, Orchestration, and Inference
arXiv:2610.33475v1 Announce Type: cross Abstract: The widespread deployment of foundational Large Language Models (LLM) across cloud datacenters marks a fundamental paradigm shift from stateless deep learning toward dist…
- arXiv cs.AIresearchA Unified Analysis of Importance-Corrected Momentum Actor-Critic over a Markovian Trajectory
arXiv:2610.33477v1 Announce Type: cross Abstract: We present a generic actor-critic method that combines a ridge-regularized pathwise least-squares temporal-difference (LSTD) critic with importance-corrected momentum (IC…
- arXiv cs.AIresearchMarineLife-16K: An Attribute-Rich Video-Text Dataset for Motion-Aware Marine Video Understanding
arXiv:2610.33478v2 Announce Type: replace-cross Abstract: Marine video understanding is crucial for a wide range of real-world applications. However, existing marine video datasets suffer from limitations, including the…
- arXiv cs.AIresearchEnergy Efficient Lung Tumor Segmentation with a modified CBAM ENet Skip Model
arXiv:2610.33479v1 Announce Type: cross Abstract: Lung tumor segmentation plays a vital role in early detection and treatment planning of lung cancer. Although state-of-the-art deep learning models achieve high dice scor…
- arXiv cs.AIresearchError-Subspace Calibration for Multi-Task Model Merging
arXiv:2610.33483v1 Announce Type: cross Abstract: Model merging combines task-specific expert models into a single model without joint training on their original datasets, but often incurs a performance gap relative to t…
- arXiv cs.AIagentsNovel or Not? PatEx-Gym: Agentic Prior-Art Search for Novelty Assessment in Patent Examination
arXiv:2610.33503v1 Announce Type: cross Abstract: Patent examination is a challenging real-world task that requires agents to discover sparse prior art, iteratively refine queries, inspect technical evidence, and produce…
- arXiv cs.AIagentsCan Agentic Code Review Catch Vulnerabilities Before They Are Merged?
arXiv:2610.33509v2 Announce Type: replace-cross Abstract: LLM agents have been increasingly adopted for security-focused code review, but existing benchmarks mostly derive the reviewed changes from their later fixes inst…
- arXiv cs.AIresearchPacifo: An Efficient Fully Homomorphic Encryption-based Lightweight ResNet for Trustworthy Machine Learning as a Service
arXiv:2610.33525v1 Announce Type: cross Abstract: Machine Learning as a Service (MLaaS) has become a popular paradigm for deploying AI applications. However, it raises significant privacy concerns that the raw client dat…
- arXiv cs.AIresearchSEDATE: Do Large Language Models Share an Aggression Circuit Across Languages?
arXiv:2610.33542v1 Announce Type: cross Abstract: As the applications of AI continue to expand across various multimodal and multilingual domains, ensuring safe and responsible deployment of these systems has become an i…
- arXiv cs.AIresearchHybrid-time PINNs: Discontinuity-Aware Physics-Informed Neural Networks for Learning Hybrid Dynamical Systems
arXiv:2610.33562v1 Announce Type: cross Abstract: Hybrid dynamical systems flow continuously but jump at discrete events. Physics-informed neural networks (PINNs) fit data while enforcing known dynamics, but a single net…
- arXiv cs.AIresearchWorldFailBench: Do World Models Understand Policy Failures?
arXiv:2610.33565v1 Announce Type: cross Abstract: Robot policy evaluation informs deployment decisions and policy improvement, but physical testing and simulator preparation require substantial effort. World models offer…
- arXiv cs.AIagentsReplicate What Varies: Resource Sharing for Domain-Randomized Embodied Simulation
arXiv:2610.33582v1 Announce Type: cross Abstract: Simulation provides a low-cost sandbox where embodied agents can attempt tasks, observe outcomes, and learn from experience. Running many diverse simulation environments…
- arXiv cs.AIresearchMycelium: A Meta-Study of Hallucination Detection Across Provenance, Typology, and Granularities
arXiv:2610.33608v1 Announce Type: cross Abstract: Hallucination detection is increasingly important for evaluating and deploying large language models. However, existing methods are often compared across benchmarks that…
- arXiv cs.AIagentsRSI Arena: Towards Recursive Self-Improvement with Human Feedback
arXiv:2610.33624v1 Announce Type: cross Abstract: Can AI agents autonomously train useful language models and improve them using feedback from users? We introduce RSI Arena, a two-stage study of autonomous post-training…
- arXiv cs.AIagentsProbeID: Verifying MCP Tool Identity for LLM Agents under Registry Drift
arXiv:2610.33633v1 Announce Type: cross Abstract: Agents select tools by copyable descriptions, which cannot establish which implementation runs. ProbeID compares canonicalized responses to schema-valid probes with a tru…
- arXiv cs.AIresearchDROA-CLIPSeg: Prompt-Guided Thin Crack Segmentation in Low-Light Conditions
arXiv:2610.33635v1 Announce Type: cross Abstract: Pavement crack segmentation under low light is degraded by reduced contrast, shadows, headlight glare, joints, stains, and wet seams. Existing crack segmentation methods…
- arXiv cs.AIagentsBuild or Adopt? Custom-Built vs. General-Purpose LLM Agent Harnesses for Automotive GUI Testing
arXiv:2610.33636v1 Announce Type: cross Abstract: As general-purpose LLM agent harnesses mature, organizations must decide whether to continue developing in-house systems or adopt existing alternatives. We study this dec…
- arXiv cs.AIresearchExtraction meets Distillation - cryptanalytic techniques to recover parameters from a Deep Neural Network
arXiv:2610.33671v1 Announce Type: cross Abstract: We study fully connected neural-network reconstruction by combining cryptanalytic weight-direction extraction with learning from predicted labels. We adapt a ReLU extract…
- arXiv cs.AIresearchEmbedding-Based Educational Taxonomies
arXiv:2610.33694v1 Announce Type: cross Abstract: Educational taxonomies organize educational content, such as exercise problems, into concepts, which are also called skills or knowledge components. These taxonomies defi…
- arXiv cs.AIresearchE3tereo: Improving Stereo Matching at Depth Boundaries with Learned Geometric Edges
arXiv:2610.33704v1 Announce Type: cross Abstract: Depth boundaries remain a major source of error in stereo matching. For IGEV on Scene Flow, geometric edge pixels account for only 6% of evaluated pixels but about half o…
- arXiv cs.AIagentsStrategic Calibration in LLM Multi-Agent Systems: Audited Pivot Scoring for Truthful Elicitation
arXiv:2610.33728v1 Announce Type: cross Abstract: In large language model (LLM)-based multi-agent systems, reported confidence increasingly determines the allocation of scarce downstream attention, computation, and decis…
- arXiv cs.AIresearchTrust Mechanisms for Offline Generative AI at the Edge: A Connectivity-Regime Framework and an Empirical Study on a Raspberry Pi 5
arXiv:2610.33732v1 Announce Type: cross Abstract: Generative AI and large language models (LLMs) are moving into the Internet of Things, yet most deployments assume cloud connectivity, and many IoT settings have intermit…
- arXiv cs.AIresearchA Distribution-Matching Perspective on LLM Training Algorithms
arXiv:2610.33740v1 Announce Type: cross Abstract: In this work, we propose a unified framework for training algorithms across different stages of modern large language model development, based on the perspective of distr…
- arXiv cs.AIresearchPOD-RoPE: Rotary Position Encoding over Physical Observation Distributions for Embodied AI
arXiv:2610.33745v2 Announce Type: replace-cross Abstract: Embodied perception requires positional representations that capture both physical geometry and the spatial and temporal extent of observations. We introduce POD-…
- arXiv cs.AIagentsProtAgent: Post-Training Compact Protein Tool Agents with Interaction-Level Credit Assignment
arXiv:2610.33746v1 Announce Type: cross Abstract: Protein annotation agents must choose suitable bioinformatics tools, interpret their outputs, and use the resulting evidence in the final answer. Most existing systems re…
- arXiv cs.AIagentsC-DFF: Concept-Level Diagnostics for Mechanism Parity in Simulation-Based Validation of Autonomous Driving Perception
arXiv:2610.33760v1 Announce Type: cross Abstract: Simulation is widely used in autonomous-driving validation, but matching task outputs between real and synthetic images does not guarantee that a system relies on the sam…
- arXiv cs.AIagentsStateWise: Teaching Language Models to Track and Obey Evolving Constraints in Multi-Turn Dialogue
arXiv:2610.33766v1 Announce Type: cross Abstract: Users of conversational assistants rarely state all requirements up front: they add constraints, amend them, withdraw them, digress, and return. Instruction-tuned LLMs fa…
- arXiv cs.AIresearchCFL-aware edge design for graph neural solvers of hyperbolic transport
arXiv:2610.33769v1 Announce Type: cross Abstract: Hyperbolic partial differential equations (PDEs) transport information at finite speed along characteristics, often producing shocks and discontinuities that are challeng…
- arXiv cs.AIresearchTriageRAG: Failure-Aware Self-Training for Adapting Retrieval-Augmented Language Models to Specialized Domains
arXiv:2610.33776v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) lets large language models (LLMs) answer knowledge-intensive questions from retrieved evidence, but adapting a general-purpose RAG sy…
- arXiv cs.AIresearchRoboReason: Boosting Robotics-Relevant VLM Reasoning Through Scalable Data Generation
arXiv:2610.33778v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated impressive performance when integrated into robot manipulation pipelines, but real-world evaluation of VLM capabilities on…
- arXiv cs.AIresearchOne Scan is Enough: Demonstration-Free Adaptation for Language-Guided Navigation
arXiv:2610.33794v1 Announce Type: cross Abstract: Deploying a language-guided navigation policy in a new building usually means watching it fail. Public-benchmark VLAs degrade scene by scene as layout, appearance, and la…
- arXiv cs.AIresearchProMo3D: Probing Motion Cues in Frozen 3D Foundation Models
arXiv:2610.33802v1 Announce Type: cross Abstract: Feed-forward 3D foundation models have recently reshaped multi-view reconstruction, yet dynamic 4D understanding still often relies on large-scale motion supervision or h…
- arXiv cs.AIagentsMemory or a Clock? Auditing a Memory Advantage in XLand-MiniGrid with Seeds, Maps and Evaluation Noise Apart
arXiv:2610.33816v1 Announce Type: cross Abstract: On a partially observable XLand-MiniGrid task, an LSTM agent beats a memory-free MLP by 25 percentage points. The gap looks like a clear case of memory. We ask two questi…
- arXiv cs.AIresearchLearning What to Share: Expert Specialization for Time-Series Forecasting
arXiv:2610.33822v1 Announce Type: cross Abstract: A central challenge in multivariate forecasting is deciding what to share across channels and where to specialize. Mixture-of-experts models offer flexible sharing, yet i…
- arXiv cs.AIresearchAdapting DNNs to Embrace Voltage Scaling Induced Errors via Accumulation-Aware Quantization
arXiv:2610.33824v1 Announce Type: cross Abstract: The pervasive deployment of Deep Neural Networks (DNNs) across the computing spectrum has brought growing attention to energy efficiency, alongside raw performance. While…
- arXiv cs.AIresearchEEGBiFormer: Real-Time EEG Attention Classification via Hybrid CNN-Transformer with EEG-Informed Auditory Protocol Validation
arXiv:2610.33832v1 Announce Type: cross Abstract: Sustained attention is critical for academic performance, yet existing auditory support tools are static and not grounded in real-time neural evidence. This paper present…
- arXiv cs.AIagentsFlashScript: Compiling Recurring Agent Workflows into Reusable Neuro-Symbolic Programs
arXiv:2610.33876v1 Announce Type: cross Abstract: Many agent tasks recur with new data or small changes, yet agents regenerate their reasoning and actions on each run, producing different results. Distilling a prior run…
- arXiv cs.AIresearchIntervention-Certified Amplification: A Validity Gate for $\sigma$-Weighted Post-Training
arXiv:2610.33882v1 Announce Type: cross Abstract: Hybrid post-training often mixes supervised fine-tuning (SFT) and reinforcement learning (RL), using training-side signals to set each sample's balance. Such signals char…
- arXiv cs.AIresearchCounterfactual Test-Time Augmentation for Shortcut Removal in Language Models
arXiv:2610.33904v1 Announce Type: cross Abstract: Large language models (LLMs) can exploit shortcuts, i.e., correlations present in training data that may not hold at test time. This can lead to poor predictions when cor…
- arXiv cs.AIresearchRank-1 Weight-Corrected Trainable Weight Averaging for Out-of-Distribution Detection
arXiv:2610.33909v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection identifies inputs outside the training distribution. Rank-based analyses such as RankFeat and RankWeight suggest that OOD overconfiden…
- arXiv cs.AIresearchMultitask Interactions in Recurrent Dynamics across 100 Cognitive Tasks
arXiv:2610.33926v1 Announce Type: cross Abstract: The brain continually learns tasks with very different computational demands. Recurrent neural networks (RNNs) have been used as dynamical surrogates for studying task so…
- arXiv cs.AIresearchWhat Makes a Question Curious? Measuring Model Curiosity in Human-LLM Interactions
arXiv:2610.33950v1 Announce Type: cross Abstract: Large language models increasingly act as partners in everyday communication. In these interactions, useful assistance may require not only answering, but also asking the…
- arXiv cs.AIagentsContinual Learning Exam: Benchmarking Long-Horizon RSI and Experiential Learning in LLM Agents
arXiv:2610.33956v1 Announce Type: cross Abstract: Continual learning is the ability of LLM agents to improve over time by acquiring, refining, and applying knowledge from experience while retaining useful prior knowledge…
- arXiv cs.AIresearchBeyond Belief: Evaluating and Mitigating LLM Susceptibility to Misinformation
arXiv:2610.33962v1 Announce Type: cross Abstract: Tool-using large language models (LLMs) must decide whether external information warrants action. We investigate whether their expressed credibility judgments reliably in…
- arXiv cs.AIresearchQECGBERT: A Scalable ECG Foundation Model Based on Self-Supervised Language Learning via VQ-VAE Waveform Tokenization
arXiv:2610.33964v1 Announce Type: cross Abstract: AI foundation models play a pivotal role in advancing heart disease diagnosis from electrocardiogram (ECG) data, particularly for rare diseases where the lack of sufficie…