AI news archive.
19,861 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchWhen Log Parsing Removes What Matters: Detecting Failures in Distributed GPU Workloads
arXiv:2610.14009v2 Announce Type: replace-cross Abstract: Large AI models increasingly rely on distributed GPU execution, yet it remains unclear whether established anomaly detection methods transfer effectively to this…
- arXiv cs.AIresearchPhysics-Informed State Space Models for Day-Ahead Photovoltaic Power Forecasting
arXiv:2610.14014v1 Announce Type: cross Abstract: Accurate day-ahead photovoltaic (PV) power forecasting requires both strong temporal modeling and adherence to physical PV system constraints. Purely data-driven models o…
- arXiv cs.AIresearchBias Robustness of Genetic Multi-Criteria Sorting versus Learned Rankers under Proxy Skew and Label Contamination
arXiv:2610.14015v1 Announce Type: cross Abstract: Learning-to-rank (LTR) models can align rankings with undesignated signals when those signals appear as features or leak into training labels. We compare label-free genet…
- arXiv cs.AIresearchBarycentric Anchored Weight Search for Robust Model Merging
arXiv:2610.14017v1 Announce Type: cross Abstract: We propose Barycentric Anchored Search, a post-hoc weight-space procedure for improving fine-tuned vision models under distribution shift without additional gradient trai…
- arXiv cs.AIresearchRoboHarness: A Simple Harness Outperforms VLA and World Action Models
arXiv:2610.14018v2 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has opened a path toward generalist embodied AI, raising the question of how LLMs can act in the physical world. One stra…
- arXiv cs.AIresearchLabInstruct: Benchmarking Situated Instructional Video Generation for Lab Procedures
arXiv:2610.14020v1 Announce Type: cross Abstract: Self-driving laboratories (SDLs) aim to automate the full experimental loop, from scientific decision-making to physical execution. Ideally, AI-generated plans could be c…
- arXiv cs.AIagentsCoEvoke: Evoking Multi-Agent Collaboration for Trajectory Collection
arXiv:2610.14021v1 Announce Type: cross Abstract: Training multi-agent collaboration requires trajectories of checking, feedback, and repair, yet task-solving rollouts may end in immediate approval or repeat unsuccessful…
- arXiv cs.AIresearchOPMD: On-Policy Mechanism Distillation Beyond Output Supervision
arXiv:2610.14022v1 Announce Type: cross Abstract: On-policy distillation (OPD) improves language-model by supervising a student on prefixes sampled from its own policy, but its supervision remains confined to the output…
- arXiv cs.AIresearchContrastive Covariance Subspaces for Activation Steering
arXiv:2610.14025v1 Announce Type: cross Abstract: Activation steering controls large language model behavior by adding a direction to hidden activations during inference. For steering to work well, that direction must be…
- arXiv cs.AIresearchPick the Mixture, Not the Samples: Combinatorial Data Selection for LLMs
arXiv:2610.14028v2 Announce Type: replace-cross Abstract: Data selection for LLM fine-tuning faces two facts about real training: data pools draw from many distinct domains, and evaluation spans diverse tasks. As a resul…
- arXiv cs.AIresearchCan We Trust This Evaluation? Instance-Level Reliability of LLM-as-a-Judge
arXiv:2610.14029v1 Announce Type: cross Abstract: LLM-as-a-judge evaluation commonly aggregates multiple judges' decisions into a majority vote, yet this panel decision process does not guarantee that the evaluation of a…
- arXiv cs.AIresearchRotate the Loop: Blockwise Conformality for Looped Transformers
arXiv:2610.14030v1 Announce Type: cross Abstract: Looped transformers reuse one shared block across many recurrent steps, so depth becomes a quantity that can be spent at runtime rather than a stack of distinct layers. T…
- arXiv cs.AIresearchPLDiv: Measuring Dataset Diversity and Semantic Collapse via Persistence Landscapes
arXiv:2610.14031v1 Announce Type: cross Abstract: Modern data pipelines increasingly combine human-created and human-curated data with AI-generated data for model training and evaluation. As datasets grow, they often add…
- arXiv cs.AIresearchA Geometric Perspective on Reward Function Updates in Inverse Reinforcement Learning
arXiv:2610.14041v1 Announce Type: cross Abstract: Inverse Reinforcement Learning (IRL) is classically solved using Lagrangian optimisation, by jointly optimising a primal-dual problem with gradient descent to obtain the…
- arXiv cs.AIresearchReinforcement Learning from Real-Robot Interaction without High-Quality State Estimates
arXiv:2610.14042v1 Announce Type: cross Abstract: A longstanding goal for robot reinforcement learning (RL) is that robots learn from their own physical experience, as humans and animals do. A key obstacle to learning di…
- arXiv cs.AIresearchThe Future of AI-assisted Physics: Discovering, Verifying and Organizing Results via Formalization
arXiv:2610.14059v1 Announce Type: cross Abstract: Rapid advances in AI have opened a new era for physics, enabling unprecedented capabilities in research and discovery. However, these advances also bring challenges in ve…
- arXiv cs.AIagentsAuditing LLM Program Search for Bermudan Stopping with Primal--Dual Certificates
arXiv:2610.14063v1 Announce Type: cross Abstract: LLM program-search agents are usually judged by the score that drives their search. We study LLM-driven search over Python exercise policies for Bermudan options, an opti…
- arXiv cs.AIagentsLoose Lips: Understanding Privacy Leakage in Agentic Dialogue
arXiv:2610.14071v1 Announce Type: cross Abstract: As LLM agents are increasingly deployed in high-stakes enterprise settings, understanding how they handle sensitive information in agentic dialogue has become critical. P…
- arXiv cs.AIresearchIs the Future Predictable? Modeling Future Predictability for Reliable Temporal Reasoning
arXiv:2610.14073v1 Announce Type: cross Abstract: Large language models (LLMs) remain unreliable when reasoning about the future. Existing models often overlook the difference among future states, committing to specific…
- arXiv cs.AIresearchOne Model, Multiple Compute Budgets: Multi-Ratio ConceptMoE for Flexible Compute Across Turns
arXiv:2610.14074v1 Announce Type: cross Abstract: Large language models typically support different inference budgets through separate model sizes or fixed compute configurations. In multi-turn interactions, however, com…
- arXiv cs.AIagentsTwo-Stage Multi-modal Learning for Dynamic Audio-Visual Navigation: From Physical Alignment to Scene Aggregation
arXiv:2610.14077v1 Announce Type: cross Abstract: Embodied Audio-Visual Navigation (AVN) requires an agent to locate sound targets by jointly leveraging visual and auditory cues. While prior work has achieved notable suc…
- arXiv cs.AIagentsSAGE: Efficient Protected Vision-Language Inference on Smart Glasses
arXiv:2610.14089v1 Announce Type: cross Abstract: Smart glasses are a promising platform for vision-language assistants, but private split inference remains expensive: protecting visual representations before transmissio…
- arXiv cs.AIresearchEmbodied Zero-Shot Task Planning via Hardware-Aware LLM-VLM Integration for Grounded Robotic Manipulation
arXiv:2610.14106v1 Announce Type: cross Abstract: Large language models can translate natural-language instructions into plausible manipulation plans, but robotic execution requires physically grounded decisions that res…
- arXiv cs.AIresearchBeyond Discrimination: Evaluating Probability Calibration in Radiomics-Image Fusion for Renal Cell Carcinoma Classification
arXiv:2610.14151v1 Announce Type: cross Abstract: Reliable probability estimates are important for the clinical interpretation of machine-learning classifiers, yet radiomics--image fusion models for renal cell carcinoma…
- arXiv cs.AIresearchBeyond Gate Weights: Decision-Level Interpretation of Radiomics-Image Feature Fusion for RCC Classification
arXiv:2610.14152v1 Announce Type: cross Abstract: Gate coefficients describe how a fusion layer weights intermediate features, but they do not necessarily reflect the magnitude of branch contributions at the classifier o…
- arXiv cs.AIresearchNext-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation
arXiv:2610.14162v1 Announce Type: cross Abstract: Diffusion language models (DLMs) offer parallel sampling for faster generation than autoregressive models, an increasingly important advantage as inference becomes a domi…
- arXiv cs.AIresearchDo you "see" what I "see"? A Multi-panel Visual Question Answer Dataset for Large Language Model Chart Analysis
arXiv:2610.14173v1 Announce Type: cross Abstract: Digital libraries increasingly host scientific publications whose figures encode primary research findings, yet making this visual content searchable, accessible, and reu…
- arXiv cs.AIagentsCortex: Measurement-Gated Evolution and Pruning of Coding-Agent Skills and Rules
arXiv:2610.14216v1 Announce Type: cross Abstract: Coding agents such as Claude Code also follow text files that developers write for them: a project file, skills the agent may choose to open, and rules that load automati…
- arXiv cs.AIresearchLearnable Low-Rank Polynomial Sketch for Effective Linear Attention
arXiv:2610.14219v1 Announce Type: cross Abstract: Softmax attention in Transformers suffers from quadratic complexity in sequence length, making it impractical for long-context applications. Linear attention alleviates t…
- arXiv cs.AIresearchCAPTOR: a vision-language foundation model for Chest-Abdomen-Pelvis Oncologic CT
arXiv:2610.14226v1 Announce Type: cross Abstract: Purpose : To develop and evaluate CAPTOR, a deep-learning vision-language foundation model for oncologic chest-abdomen-pelvis (CAP) CT interpretation. Materials & Methods…
- arXiv cs.AIagentsLatent World Action Model for GUI Agents
arXiv:2610.14228v1 Announce Type: cross Abstract: Pixel-space world models have recently emerged as a promising approach for GUI agents, predicting future screens prior to execution and subsequently re-perceiving them fo…
- arXiv cs.AIagentsAgentic Data Engineering for LLMs: System Design and a Controlled Empirical Study
arXiv:2610.14238v1 Announce Type: cross Abstract: LLM data engineering requires iterative experimentation to determine how data selection, rewards, and curricula affect model learning, making it a natural setting for AI-…
- arXiv cs.AIresearchAkule: Fast and Scalable Multi-Robot Motion Planning via Sparse Interaction Diffusion
arXiv:2610.14248v1 Announce Type: cross Abstract: Diffusion models provide expressive trajectory priors for multi-robot motion planning (MRMP), but scaling them to large teams requires coordinating individual motions wit…
- arXiv cs.AIresearchThe RNN Homunculus: Learning to Gate Long-Term Memory
arXiv:2610.14279v1 Announce Type: cross Abstract: Learning long-range dependencies with recurrent neural networks (RNNs) is difficult because of the vanishing gradient problem. Gated recurrent neural networks like GRUs a…
- arXiv cs.AIresearchGeoFix: Geometry Fixing for 4D Consistent Generation
arXiv:2610.14293v1 Announce Type: cross Abstract: Video diffusion models increasingly serve as priors for 4D generation, but the generated videos often lack geometric consistency. Recent approaches address this through p…
- arXiv cs.AIresearchRUBATO: Decoupled Clocks Allow Full-Duplex Models to Think as Fast as They Can
arXiv:2610.14299v1 Announce Type: cross Abstract: Recent Speech Full-Duplex (FDX) models are capable of engaging in naturalistic conversation by listening and speaking at the same time. Building on this foundation, recen…
- arXiv cs.AIresearchMvFFN: Multi-view Floor plan Feed-forward Network for Unposed Wide-Baseline Panorama Layout Reconstruction
arXiv:2610.14315v2 Announce Type: replace-cross Abstract: Reconstructing accurate camera poses and floor-plan layouts from sparse, unposed, wide-baseline RGB panoramas remains a challenging open problem. Direct multi-roo…
- arXiv cs.AIresearchEV-JEPA: Elastic V-JEPA for Any Device and Cross-Device Inference
arXiv:2610.14324v2 Announce Type: replace-cross Abstract: Foundation models like V-JEPA are increasingly deployed across a heterogeneous edge-cloud continuum spanning smart glasses, phones, robots, and servers. Yet, they…
- arXiv cs.AIresearchTraceSampler: Making Sampling Errors Predictable and Correctable for Accelerated Generative Sampling
arXiv:2610.14332v1 Announce Type: cross Abstract: Accurate sampling from continuous-time generative models can require many model evaluations. Low-step sampling reduces cost, but coarse updates introduce endpoint errors…
- arXiv cs.AIagentsNatural Code Transformation Confuses Agents in Vulnerability Analysis
arXiv:2610.14350v2 Announce Type: replace-cross Abstract: Having demonstrated a strong potential for automating vulnerability discovery, AI agents can also be used to attack software systems at scale. Today's software sy…
- arXiv cs.AIresearchDexFLEX: Contact-Aware Foundation Controller for Command-Guided Dexterity
arXiv:2610.14372v1 Announce Type: cross Abstract: Dexterous robot hands can receive useful motion intent from teleoperation or learned policies, but successful execution also depends on contact decisions that these comma…
- arXiv cs.AIresearchDeep learning from a control vantage point
arXiv:2610.14385v1 Announce Type: cross Abstract: This article offers an overview of recent developments in deep learning from a control vantage point. It reviews several classes of models including feedforward and resid…
- arXiv cs.AIagentsRealityBench: Simulated Environments to Evaluate a Vision Language Model's Ability to Document Human Physical Work
arXiv:2610.14397v2 Announce Type: replace-cross Abstract: Augmenting worker capabilities in the physical world through guidance has been a decades-long goal for AI agents. One key supporting capability is to understand w…
- arXiv cs.AIresearchMemory Without Curation: Learning Multi-Stream Latent Memory for Robot Policies
arXiv:2610.14406v1 Announce Type: cross Abstract: It is widely appreciated that robotic policies require long-term memory to perform complex tasks. However, directly conditioning policies on all past observations leaves…
- arXiv cs.AIresearchCoarse-Graining Recursive Expert Computation: Continuation-Relative Interfaces and Failure Modes
arXiv:2610.14413v1 Announce Type: cross Abstract: Computational coarse-graining aims to replace a sequence of updates with a cheaper module while preserving task performance. We present a continuation-relative evaluation…
- arXiv cs.AIresearchSceneForesee: Learning Scene Evolution for Spatiotemporal Reasoning
arXiv:2610.14439v1 Announce Type: cross Abstract: Large multimodal models (LMMs) need to understand how entities move and interact as a scene develops. This requires following the same entities over time and relating the…
- arXiv cs.AIresearchTarget-ESS Tempered Policy Optimization (TETPO): Smooth Variance Control via Bridge Importance Sampling
arXiv:2610.14440v1 Announce Type: cross Abstract: Asynchronous reinforcement-learning post-training of large language models is computationally efficient but inherently off-policy: the learner updates on rollouts generat…
- arXiv cs.AIresearchSpecification-Based Testing of Large Language Models
arXiv:2610.14443v1 Announce Type: cross Abstract: Model cards, system cards, and safety reports increasingly document LLM safety behaviors, yet safety evaluations typically start from predefined benchmarks rather than as…
- arXiv cs.AIresearchKV4-RL: Unlocking Faster Asynchronous Rollouts for RLVR with INT4 KV Cache Quantization
arXiv:2610.14448v1 Announce Type: cross Abstract: Asynchronous rollout improves the efficiency of reinforcement learning with verifiable rewards (RLVR) by reducing under-utilization during long-tail generation. Sustained…
- arXiv cs.AIagentsJevolution: A Framework for Studying Communication and Collective Dynamics in AI Agents
arXiv:2610.14449v1 Announce Type: cross Abstract: Experiments with remote decision-making agents depend on more than the policy: the world may change during inference, the simulator determines which actions are available…