AI news archive.
19,861 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchBeyond Actions: Learning Future Operation Targets for Vision-Language-Action Models
arXiv:2610.13212v2 Announce Type: replace-cross Abstract: Actions specify what a robot should execute, but do not describe what those commands will achieve. Vision-language-action (VLA) policies nevertheless learn primar…
- arXiv cs.AIresearchReSFlow: Rectifying Categorical Flows on Statistical Manifolds for One-Step Generation
arXiv:2610.13225v2 Announce Type: replace-cross Abstract: Generative modeling for discrete data has been significantly advanced by learning continuous vector fields on the statistical manifolds to transport simple priors…
- arXiv cs.AIresearchHow Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing
arXiv:2610.13236v2 Announce Type: replace-cross Abstract: Recent advances have significantly improved the performance of Abstract Meaning Representation (AMR) parsers, yet determining when their outputs can be trusted re…
- arXiv cs.AIresearchTabular Foundation Models Are Effectively Shallow
arXiv:2610.13253v1 Announce Type: cross Abstract: In-context-learning Tabular Foundation Models (TFMs) are uniquely suited to scenarios with limited resources or privacy constraints, as they solve new tasks without addit…
- arXiv cs.AIagentsAgent4Spec: An LLM-based Agentic System to Optimize API Specifications through Automated Validation and Market Analysis
arXiv:2610.13273v1 Announce Type: cross Abstract: API specifications serve as critical documentation for modern software development, directly impacting integration efficiency and client code generation. While recent adv…
- arXiv cs.AIagentsPRIME: Pre-Retrieval Index Modeling Enhancement for Structured Document Retrieval-Real
arXiv:2610.13296v1 Announce Type: cross Abstract: High-precision retrieval over external knowledge bases is essential for LLM agents to operate effectively. However, existing Information Retrieval (IR) research primarily…
- arXiv cs.AIresearchAgreement Without Alignment: Label-Free Rubric Clarification Increases LLM-Judge Consensus but Not Agreement with Humans
arXiv:2610.13303v1 Announce Type: cross Abstract: LLM judges are increasingly deployed as panels, and disagreement among panel members is often read as a symptom of an unclear rubric. We test a label-free procedure that…
- arXiv cs.AIagentsAgentSWE: Can Coding Agents Build the Agent You Actually Want?
arXiv:2610.13361v1 Announce Type: cross Abstract: As the production of software passes into the hands of software itself, coding agents are increasingly commissioned to build agents fitted to their users' own workflows.…
- arXiv cs.AIagentsProgramGym: Scaling Training Environments for Long-Horizon Program Reproduction Agents
arXiv:2610.13395v1 Announce Type: cross Abstract: Coding agents are increasingly used to build programs from scratch as software engineers do rather than to resolve issues within a repository or to generate a repository…
- arXiv cs.AIresearchIn-Context Learning for System Identification: Probabilistic Formulation, Long-Sequence Handling, and Benchmark Validation
arXiv:2610.13411v1 Announce Type: cross Abstract: Traditional system identification typically involves two steps: initially, training data is processed to estimate a model of the system of interest; then, the model is us…
- arXiv cs.AIresearchCNN-Transformer with Sigmoid Label for High-Resolution Sub-Degree DOA Estimation
arXiv:2610.13433v1 Announce Type: cross Abstract: This paper addresses the grid quantization error of deep-learning (DL)-based direction-of-arrival (DOA) estimation. Classification- based DOA methods discretize the conti…
- arXiv cs.AIagentsCollective Autonomous Vehicle Routing Needs Social Awareness for Coexistence with Human Drivers
arXiv:2610.13439v1 Announce Type: cross Abstract: Collective Autonomous Vehicle (AV) route choices in real-world traffic networks can create externalities for Human-Driven Vehicles (HDVs), as routes that benefit AVs may…
- arXiv cs.AIresearchWhen MLLMs miss what humans read: diagnosing and learning to read squint text
arXiv:2610.13465v1 Announce Type: cross Abstract: Multimodal LLMs can fail to read text embedded in an image's coarse structure (text a human recovers by squinting) and report that no text is present. We name this class…
- arXiv cs.AIresearchBeyond Temporal Proximity: Explanatory World Models for Credit Assignment in Reinforcement Learning
arXiv:2610.13504v1 Announce Type: cross Abstract: Generative Recommenders increasingly use Reinforcement Learning (RL) to optimize outcomes that may appear hundreds or thousands of interactions after a decision. Standard…
- arXiv cs.AIresearchRESCUE: Is This Alarm Worth Acting On? Counterfactual Evaluation of VLA Runtime Monitors
arXiv:2610.13516v1 Announce Type: cross Abstract: Runtime failure monitors for robot policies are typically evaluated by how accurately they distinguish failed from successful executions. Yet an accurate alarm may arrive…
- arXiv cs.AIresearchDefence against the dark arts of distillation: Unsupervised discovery of subliminal learning
arXiv:2610.13536v1 Announce Type: cross Abstract: When a language model is distilled from a teacher that shares its base parameters, the student can inherit behavioural traits of the teacher---including misalignment---ev…
- arXiv cs.AIresearchAudioTETRIS: Token Elimination via Transitional Importance Signals for Efficient Audio LLMs
arXiv:2610.13600v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) achieve strong performance on speech-text tasks, but standard audio encoders produce hundreds of tokens for even short audio clips. Be…
- arXiv cs.AIresearchSWE-DeceptionBench: Benchmarking Deceptive Behaviors of LLMs Across the Software Development Lifecycle
arXiv:2610.13626v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used throughout the software development lifecycle. This growing reliance on LLMs raises concerns about their trustworthines…
- arXiv cs.AIresearchModel-agnostic Knowledge Augmented Machine Learning for Weakly Annotated Bioimaging Data
arXiv:2610.13637v1 Announce Type: cross Abstract: Machine learning models are increasingly used for automated feature extraction from weakly annotated bioimaging data, such as microscopy and cytomorphology images. Due to…
- arXiv cs.AIresearchSelectArena: A Modern Benchmark for Feature Selection on Real-world Tabular Data
arXiv:2610.13644v2 Announce Type: replace-cross Abstract: Selecting a subset of features (columns) from a tabular dataset is a ubiquitous challenge in real-world applications. Industry experts seek to minimize model trai…
- arXiv cs.AIresearchPretraining and Test-Time Training Budgets Matter for ARC: A Controlled Study of VARC and TRM
arXiv:2610.13660v1 Announce Type: cross Abstract: The Abstraction and Reasoning Corpus (ARC) is increasingly used to evaluate whether neural networks can infer task-specific transformations from few demonstrations. Recen…
- arXiv cs.AIresearchInject a Turn, Not a Token: Learning Where to Redirect LLM Reasoning for Self-Correction
arXiv:2610.13672v1 Announce Type: cross Abstract: Large language models struggle to reliably identify and correct errors in their own reasoning. Unlike prior training-based approaches that fine-tune an LLM to generate re…
- arXiv cs.AIresearchNot All Dimensions Are Equal: Outlier-Aware Attention Linearization with Grouped Quadratic Kernels
arXiv:2610.13674v1 Announce Type: cross Abstract: Linearizing softmax attention offers a route to efficient Transformer inference, but preserving pretrained attention remains challenging. We identify an important source…
- arXiv cs.AIresearchVariance-Reduced Policy Gradient is Nearly Minimax-Optimal
arXiv:2610.13692v1 Announce Type: cross Abstract: Standard Policy Gradient (PG) algorithms suffer from high estimation variance because they rely on Monte Carlo updates. While off-policy variance reduction mitigates this…
- arXiv cs.AIresearchCalibration in One Go: Jointly Learning Accurate and Calibrated Neural Networks
arXiv:2610.13693v1 Announce Type: cross Abstract: Neural networks (NNs), while highly flexible and versatile, are often poorly calibrated. Post-hoc calibration methods adjust the outputs of an already trained model using…
- arXiv cs.AIresearchAmortized Counterfactual Prediction with Graph Neural Networks
arXiv:2610.13724v1 Announce Type: cross Abstract: Counterfactual inference asks how an observed outcome might have differed under different circumstances, such as whether a patient would have recovered had they received…
- arXiv cs.AIresearchLAPT-Scale: Scaling Embodied Policies with Latent Action Pre-Training on Human Videos
arXiv:2610.13732v1 Announce Type: cross Abstract: Scaling generalist robotic policies is limited by the high cost of collecting embodied interaction data. Large-scale egocentric human videos offer a rich alternative, but…
- arXiv cs.AIresearchOrthros: Phase-Aware Heterogeneous Attention for Efficient Transformers
arXiv:2610.13735v1 Announce Type: cross Abstract: Efficient attention mechanisms, such as linear and sliding window attention, aim to reduce the computational complexity of Transformers from quadratic to linear. However,…
- arXiv cs.AIagentsSafeDiSTL: Decomposing Joint Constraints for Safe Multi-Agent Reinforcement Learning
arXiv:2610.13737v1 Announce Type: cross Abstract: Cooperative multi-agent tasks often impose constraints on a shared objective or collective safety. A team level violation cost function can measure this constraint outcom…
- arXiv cs.AIagentsMatched-Context Group Policy Optimization for LLM Agent Training
arXiv:2610.13755v1 Announce Type: cross Abstract: Training long-horizon language model agents requires assigning credit to intermediate decisions from sparse terminal feedback. Within a finite group of sibling rollouts,…
- arXiv cs.AIresearchWhen Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors
arXiv:2610.13765v1 Announce Type: cross Abstract: Discrete graph diffusion models corrupt graph structure through independent edge updates with a Markovian noise model. Combined with the exchangeability requirement on th…
- arXiv cs.AIresearchDualQuant: Dual-scale Calibration-free Weight-and-Activation Quantization for LLMs
arXiv:2610.13767v1 Announce Type: cross Abstract: Low-bit inference on accelerators with native 4-bit support requires quantizing both weights and activations to realize the full throughput gains these formats provide. W…
- arXiv cs.AIresearchReplaySSM: Cache SSM Inputs, Not State
arXiv:2610.13778v1 Announce Type: cross Abstract: State-space models (SSMs) maintain a fixed-size recurrent state, but reading and writing it at every decoding step can bottleneck hybrid models. This eager state manageme…
- arXiv cs.AIresearchYour Discrete Graph Diffusion Model is Secretly Memorizing
arXiv:2610.13786v1 Announce Type: cross Abstract: Discrete graph diffusion models (GDMs) corrupt the graph structure by adding noise independently to nodes and edges under a Markovian noise model. In the unconditional se…
- arXiv cs.AIresearchFORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems
arXiv:2610.13806v1 Announce Type: cross Abstract: Production recommender systems have feature-lifecycle asymmetry: adding new features must produce gains on some metric, while feature removal must not regress any metric.…
- arXiv cs.AIresearchSame Query, Different Answer: Run-to-Run Variance in Semantic Query Processing
arXiv:2610.13823v1 Announce Type: cross Abstract: Semantic query engines ask a large language model (LLM) to judge individual rows, and benchmarks compare these engines on the quality of a few executions at temperature 0…
- arXiv cs.AIagentsMC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents
arXiv:2610.13829v1 Announce Type: cross Abstract: A central challenge in embodied recursive self-improvement is enabling agents to convert interaction history into capabilities that persist beyond one world and transfer…
- arXiv cs.AIresearchSpatialASM: Internalizing Active Mentalization for Spatial Reasoning
arXiv:2610.13832v1 Announce Type: cross Abstract: When solving complex spatial reasoning tasks, ordinary humans naturally revisit the scenes from task-relevant viewpoints and mentally reconstruct the spatial layouts from…
- arXiv cs.AIresearchTask Context Shapes Component Roles in Language Models
arXiv:2610.13895v1 Announce Type: cross Abstract: Large language models reuse the same attention heads across tasks, but identifying these shared heads does not explain what they do differently in each task. A natural so…
- arXiv cs.AIresearchRevisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping
arXiv:2610.13909v1 Announce Type: cross Abstract: While deep learning models for tabular data have recently received significant research attention, the choice of objective functions for regression tasks remains an under…
- arXiv cs.AIresearchHide and Seek: HIDE Concepts to SEEK explanations for black box models
arXiv:2610.13935v3 Announce Type: replace-cross Abstract: Interpreting machine learning model predictions is a central challenge, particularly for black-box vision models. Concept-based attribution methods interpret mode…
- arXiv cs.AIresearchFrom the Fluency Fallacy to the Micro-to-Macro Validity Gap: Opportunities and Pitfalls of LLMs in Social Simulation
arXiv:2507.19364v4 Announce Type: replace Abstract: The integration of Large Language Models (LLMs) into social simulation has generated enthusiasm but also raises methodological and epistemological challenges. This crit…
- arXiv cs.AIresearchDeltaLLM: Delta-Key Sparse Attention for Edge LLMs
arXiv:2507.19608v2 Announce Type: replace Abstract: On-device LLM inference is increasingly important for edge platforms but remains challenging due to high computational and memory demands and tight resource constraints…
- arXiv cs.AIresearchGlobal Consistency Repair for Natural-Language Claim Sets
arXiv:2601.13600v2 Announce Type: replace Abstract: NLP systems increasingly aggregate claims from retrieved passages, documents, and generated reasoning steps. Yet current factuality methods mostly verify claims individ…
- arXiv cs.AIagentsCoMMa: Contribution-Aware Medical Multi-Agents for Decentralized Oncology Decision Support
arXiv:2602.09159v3 Announce Type: replace Abstract: Recent multi-agent frameworks have shown promise for oncology decision support, yet most assume centralized data access and rely on prompt-based assignment, limiting th…
- arXiv cs.AIagentsAutoControl Arena: Synthesizing Executable Test Environments for Frontier AI Risk Evaluation
arXiv:2603.07427v3 Announce Type: replace Abstract: As Large Language Models (LLMs) evolve into autonomous agents, existing safety evaluations face a fundamental trade-off: manual benchmarks are costly, while LLM-based s…
- arXiv cs.AIresearchNeuronSpark: Selective Spiking State Space Models with Adaptive Computation for Language Modeling
arXiv:2603.16148v2 Announce Type: replace Abstract: Spiking neural networks promise brain-inspired neural dynamics, yet native large-scale spiking language-model pretraining remains largely unexplored relative to convers…
- arXiv cs.AIresearchDEAF: A Benchmark for Diagnostic Evaluation of Acoustic Faithfulness in Audio Language Models
arXiv:2603.18048v3 Announce Type: replace Abstract: Recent Audio Multimodal Large Language Models (Audio MLLMs) demonstrate impressive performance on speech benchmarks, yet it remains unclear whether these models genuine…
- arXiv cs.AIagentsCan Agents Price a Reaction? Evaluating LLMs on Chemical Cost Reasoning
arXiv:2605.07251v2 Announce Type: replace Abstract: Large Language Models (LLMs) have become increasingly capable as tool-using agents, with benchmarks spanning diverse general agentic tasks. Yet rigorous evaluation of s…
- arXiv cs.AIresearchFactoryBench: Evaluating Industrial Machine Understanding
arXiv:2605.07675v2 Announce Type: replace Abstract: We introduce FactoryBench, a benchmark for evaluating time-series models and LLMs on machine understanding over industrial robotic telemetry. Q&A pairs are organized al…