AI news archive.
19,861 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchScaling Laws for Looped Mixture of Experts
arXiv:2609.40316v1 Announce Type: cross Abstract: Looped transformers and Mixture-of-Experts (MoE) offer complementary routes to efficient scaling: recurrence increases computational depth at fixed parameters, while MoE…
- arXiv cs.AIresearchMatLoom: Layered Text-to-Material Generation in a Compact Program Space
arXiv:2609.40322v1 Announce Type: cross Abstract: Material generation should produce not only an appearance, but also the rules that construct it. We introduce MatLoom, a compact, layer-oriented language for text-to-mate…
- arXiv cs.AIresearchViTeX-Bench: Benchmarking High-Fidelity Video Scene Text Editing
arXiv:2609.40356v2 Announce Type: replace-cross Abstract: Recent video generation is increasingly realistic and controllable, yet video editing remains less developed, particularly for precise local edits that must prese…
- arXiv cs.AIresearchSemifactual Credit-Augmented Policy Optimization
arXiv:2609.40360v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has improved the reasoning capabilities of large language models (LLMs), yet their predictions remain sensitive to t…
- arXiv cs.AIresearchGeneralising from Self-Produced Data: Model Training Beyond Human Constraints
arXiv:2504.04711v3 Announce Type: replace Abstract: Current large language models (LLMs) are constrained by human-derived training data and limited by a single level of abstraction that impedes definitive truth judgments…
- arXiv cs.AIresearchHierarchical Reasoning Model
arXiv:2506.21734v4 Announce Type: replace Abstract: Reasoning, the process of devising and executing complex goal-oriented action sequences, remains a critical challenge in AI. Current large language models (LLMs) primar…
- arXiv cs.AIagentsA memory-based active inference model of DishBrain-like adaptive behaviour
arXiv:2508.06980v2 Announce Type: replace Abstract: Recent and rapid advances in artificial intelligence (AI) make it increasingly important to understand the foundations of adaptive behaviour in autonomous agents, espec…
- arXiv cs.AIresearchA Quantitative Study of Sustained Focus in Large Language Models via Repetitive Deterministic Prediction Tasks
arXiv:2511.00763v3 Announce Type: replace Abstract: We investigate the performance of large language models (LLMs) on repetitive deterministic prediction tasks and study how the sequence accuracy rate (SAR) scales with o…
- arXiv cs.AIagentsCo-Evolving Agents: Learning from Failures as Hard Negatives
arXiv:2511.22254v5 Announce Type: replace Abstract: Self-evolving agents improve their performance on long-horizon tasks by learning from their own interactions with an environment. A common approach uses the resulting f…
- arXiv cs.AIresearchSuperficial Reflection or Genuine Thought? A Fine-Grained Cognitive Analysis of Large Reasoning Models
arXiv:2512.00729v2 Announce Type: replace Abstract: Motivated by the observed human-like behaviours in Large Reasoning Models (LRMs), this paper introduces a comprehensive taxonomy to characterise atomic reasoning steps…
- arXiv cs.AIresearchAn Empirical Study of On-Device Translation for Real-Time Live-Stream Chat on Mobile Devices
arXiv:2601.02641v3 Announce Type: replace Abstract: Despite its efficiency, there has been little research on the practical aspects required for real-world deployment of on-device AI models, such as the device's CPU util…
- arXiv cs.AIresearchSurvival is the Only Reward: Sustainable Self-Training Through Environment-Mediated Selection
arXiv:2601.12310v3 Announce Type: replace Abstract: Self-training systems often degenerate due to the lack of an external criterion for judging data quality, leading to reward hacking and semantic drift. This paper provi…
- arXiv cs.AIagentsEvaluating Agents Across Runtime Contracts: When Mismatch Costs Efficiency or Quality
arXiv:2603.01209v3 Announce Type: replace Abstract: In CodeAct, language-model agents write Python that calls tools and use execution feedback to choose actions. Persistent runtimes preserve Python variables between acti…
- arXiv cs.AIresearchLSR-Ben: A Logical and Scientific Reasoning Benchmark for Evaluating Process Reward Models
arXiv:2605.01203v3 Announce Type: replace Abstract: Currently, process reward models (PRMs) have exhibited remarkable potential for test-time scaling. Since large language models (LLMs) regularly generate flawed intermed…
- arXiv cs.AIagentsSkillMaster: Toward Autonomous Skill Mastery in LLM Agents
arXiv:2605.08693v3 Announce Type: replace Abstract: Skills provide an effective mechanism for improving LLM agents on complex tasks, yet in existing agent frameworks, their creation, refinement, and selection are typical…
- arXiv cs.AIresearchSTEPS: Selective On-Policy Self-Distillation for Reasoning
arXiv:2605.10194v2 Announce Type: replace Abstract: On-policy self-distillation (OPSD) uses a model as its own teacher under privileged context, providing token-level supervision on the model's own reasoning trajectories…
- arXiv cs.AIagentsGeneralizing the Turing Test to Interactive Agents
arXiv:2605.10851v2 Announce Type: replace Abstract: We initiate the study of the Generalized Turing Test (GTT), a formal generalization of Turing's imitation game from humans to arbitrary interactive agents. For agents $…
- arXiv cs.AIagentsASH: Agents that Self-Hone in Long-Horizon Worlds
arXiv:2605.14211v5 Announce Type: replace Abstract: Long-horizon visuomotor tasks remain a fundamental challenge in AI, as current methods rely on hand-engineered rewards or action-labeled demonstrations, neither of whic…
- arXiv cs.AIagentsPAIR: Prefix-Aware Internal Reward Model for Multi-Turn Agent Optimization
arXiv:2605.17877v3 Announce Type: replace Abstract: A significant hurdle for current LLMs is the execution of complex, multi-stage tasks. Group Relative Policy Optimization (GRPO) has been emerging as a leading choice, b…
- arXiv cs.AIresearch$D^2$-Monitor: Dynamic Safety Monitoring for Diffusion LLMs via Hesitation Signals
arXiv:2605.25893v2 Announce Type: replace Abstract: Despite the emergence of diffusion large language models (D-LLMs) as an alternative to autoregressive large language models (AR-LLMs), safety monitoring for D-LLMs rema…
- arXiv cs.AIresearchSTAB: Specification-driven Testing for Algorithmic Bottlenecks
arXiv:2605.27981v2 Announce Type: replace Abstract: Evaluating the efficiency of algorithmic code requires test cases that expose runtime bottlenecks. Previous methods generate efficiency test cases either by increasing…
- arXiv cs.AIresearchEmergent alignment and the projectability of ethical personas
arXiv:2606.09475v3 Announce Type: replace Abstract: Work on `emergent misalignment' shows that finetuning LLMs on narrow tasks can induce broadly misaligned behavior. This supports the `persona selection' (PSM) hypothesi…
- arXiv cs.AIagentsPROJECTMEM: A Local-First, Event-Sourced Memory and Judgment Layer for AI Coding Agents
arXiv:2606.12329v2 Announce Type: replace Abstract: AI coding agents often lose project-specific rationale across sessions and may repeat approaches that previously failed. The bottleneck is often not model capability bu…
- arXiv cs.AIresearchCombEval: A Framework for Evaluating Combinatorial Counting in Large Language Models
arXiv:2606.19788v2 Announce Type: replace Abstract: We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models. CombEval represents each problem as a typed Cofola specificatio…
- arXiv cs.AIagentsSETA: Scaling Environments for Terminal Agents
arXiv:2607.10891v2 Announce Type: replace Abstract: Large language models (LLMs) are rapidly shifting toward agents that solve tasks through diverse interfaces, including web and graphical user interfaces (GUIs). Among t…
- arXiv cs.AIagentsORACLE: Agentic AI Orchestrator Routing Via Adaptive Verifier Calibration Feedback
arXiv:2607.22465v4 Announce Type: replace Abstract: Modern enterprise agent deployments consist of a heterogeneous pool of large language models (LLMs) having diverse capabilities and cost. Existing model routing strateg…
- arXiv cs.AIresearchGroup Preference Collapse in Personalized Multimodal Large Language Models
arXiv:2607.22603v2 Announce Type: replace Abstract: Personalized multimodal large language models (MLLMs) aim to generate user-specific responses, but existing methods mainly rely on profile-level information and overloo…
- arXiv cs.AIresearchBeyond Rephrasing: Book-Level Organization Improves Synthetic Textbook Data for Mid-Training
arXiv:2607.28109v2 Announce Type: replace Abstract: Synthetic textbook data has improved language model pre-training, but prior work largely treats the benefit as a property of generated content or local rewriting style.…
- arXiv cs.AIagentsEnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
arXiv:2608.06197v2 Announce Type: replace Abstract: Training large language model agents for long-horizon tool use typically relies on interactions with real or synthesized executable environments, whose construction and…
- arXiv cs.AIresearchFast LapSum: Exact Differentiable Top-$k$ at Million Scale
arXiv:2608.06912v3 Announce Type: replace Abstract: Selecting the top-$k$ elements is a fundamental operation for inducing sparsity in large-scale models and optimization problems, enabling robust expert activation, toke…
- arXiv cs.AIresearchWho Bridges Safety? Identifying and Targeting Cross-Lingual Shared Safety Pathways
arXiv:2608.09095v2 Announce Type: replace Abstract: Uncovering the internal mechanisms underlying the safety capabilities of large language models (LLMs) is crucial for developing trustworthy artificial intelligence. Cur…
- arXiv cs.AIresearchMAE I Trust Myself? Self-Evaluating VLA Action Generation with Markov Attention Entropy
arXiv:2608.16697v2 Announce Type: replace Abstract: Vision-Language-Action models (VLAs) integrate visual perception, language instruction, and action generation into end-to-end policies across heterogeneous architecture…
- arXiv cs.AIagentsSAPO: Single-Rollout Autoregressive Policy Optimization for Agentic Reinforcement Learning
arXiv:2608.19842v4 Announce Type: replace Abstract: Agentic reinforcement learning (RL) has emerged as an important post-training approach for enhancing the capabilities of Large Language Models (LLMs). However, existing…
- arXiv cs.AIagentsCONTRAMEM: Learning Self-Evolving Procedural Memory from Contrasting Multi-Model Trajectories
arXiv:2608.22533v2 Announce Type: replace Abstract: Autonomous computer-use agents are increasingly applied to long-horizon tasks requiring coordinated application calls, persistent state tracking, and verifier-sensitive…
- arXiv cs.AIagentsAtlasNav: Mitigating Evidence Blindness with Persistent Corpus Navigation
arXiv:2608.24764v2 Announce Type: replace Abstract: As language-model agents become more capable of iterative search, corpus access is shifting from retrieval toward interaction. Agents can explore the corpus, inspect do…
- arXiv cs.AIagentsTowards a Belief-Based World Model for LLM Agents
arXiv:2609.00455v2 Announce Type: replace Abstract: Large language models (LLMs) are being used as policies for autonomous decision-making and planning in many domains. Despite their strong reasoning capabilities, LLMs s…
- arXiv cs.AIresearchLearning What to Practice: Diagnosis-Guided Self-Evolution for Language Models
arXiv:2609.00768v2 Announce Type: replace Abstract: Self-play supports the self-evolution of language models, but solver performance can plateau or decline across rounds without guidance. Existing unguided methods typica…
- arXiv cs.AIresearchMolecular D\'ej\`a Vu: Digit-Level Retrieval of Molecular Properties in Frontier Language Models
arXiv:2609.05381v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly employed to predict molecular properties. However, prediction error alone cannot distinguish prediction from retrieval of…
- arXiv cs.AIagentsResidualAuth: What Authorization State Must Language Agents Preserve under Revocable Delegation?
arXiv:2609.08062v2 Announce Type: replace Abstract: With revocable delegation, two histories can yield identical current permissions yet require opposite decisions for the same query after the same revocation. We introdu…
- arXiv cs.AIresearchA polynomial time algebraic solution to exact marginal inference in Markov Random Field models
arXiv:1709.09051v3 Announce Type: replace-cross Abstract: This paper develops on algebraic grounds a polynomial time exact linear solution to the hard combinatorial problem of marginal inference in Markov random field (M…
- arXiv cs.AIresearchETHER: Aligning Emergent Communication for Hindsight Experience Replay
arXiv:2307.15494v4 Announce Type: replace-cross Abstract: Hindsight Experience Replay (HER) enhances sample efficiency in goal-conditioned reinforcement learning (RL) by relabelling failed trajectories with goals that we…
- arXiv cs.AIresearchSpatio-Temporal Partial Sensing Forecast for Long-term Traffic
arXiv:2408.02689v3 Announce Type: replace-cross Abstract: Traffic forecasting uses recent measurements by sensors installed at chosen locations to forecast the future road traffic. Existing work either assumes all locati…
- arXiv cs.AIresearchMitigating Memorization In Language Models
arXiv:2410.02159v3 Announce Type: replace-cross Abstract: Language models (LMs) can "memorize" information, i.e., encode training data in their weights in such a way that inference-time queries can lead to verbatim regur…
- arXiv cs.AIresearchBringing Generative Learning to Representation Learning: Self-Supervised Transfer Learning as Distribution Matching
arXiv:2502.14424v5 Announce Type: replace-cross Abstract: Most self-supervised learning objectives defend against collapse but leave the target representation law unspecified. We formulate representation learning as Dist…
- arXiv cs.AIresearchAneumoBench: A Source-Linked Benchmark for Synthetic-Geometry Transfer in Aneurysm CFD
arXiv:2505.14717v2 Announce Type: replace-cross Abstract: Scientific machine learning uses simulation data to train surrogate models for fast physical-field prediction across geometries. Local shape editing can expand li…
- arXiv cs.AIresearchCross-Layer Discrete Concept Discovery for Interpreting Language Models
arXiv:2506.20040v4 Announce Type: replace-cross Abstract: Interpreting language models remains challenging due to the existence of residual stream, which linearly mixes and duplicates features across adjacent layers, cau…
- arXiv cs.AIresearchNMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance
arXiv:2507.09601v3 Announce Type: replace-cross Abstract: Financial text embeddings must distinguish changes in event status, perspective, and obligations even when passages share similar wording. NMIXX adapts existing e…
- arXiv cs.AIresearch3D Software Synthesis Driven by Constraint-Expressive Intermediate Representation
arXiv:2507.18625v3 Announce Type: replace-cross Abstract: Graphical user interface (UI) software has undergone a fundamental transformation from traditional two-dimensional (2D) desktop/web/mobile interfaces to spatial t…
- arXiv cs.AIresearchOut-of-Distribution Detection using Counterfactual Distance
arXiv:2508.10148v2 Announce Type: replace-cross Abstract: Accurate and explainable out-of-distribution (OOD) detection is required to use machine learning systems safely. Previous work has shown that feature distance to…
- arXiv cs.AIresearchFrom Construction to Injection: Edit-Based Fingerprints for Large Language Models
arXiv:2509.03122v5 Announce Type: replace-cross Abstract: Reliable model fingerprints are essential for protecting large language models (LLMs) against unauthorized redistribution and commercial misuse. In black-box depl…