AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsThe Compiler May Read It, the Agent May Not: Keeping Part of a Research Code Away from a Coding Agent
arXiv:2609.35557v2 Announce Type: replace-cross Abstract: The compiler must read modules a physics-based solver cannot build without; the coding agent must not read that intellectual property. The harness, the software a…
- arXiv cs.AIresearchAlmieyar: A Culturally Grounded Benchmark for Multi-Dialect Arabic Speech Recognition
arXiv:2609.35564v1 Announce Type: cross Abstract: Arabic speech technology has largely focused on Modern Standard Arabic, leaving the living dialects spoken by hundreds of millions under-served. We introduce ALMIEYAR, a…
- arXiv cs.AIresearchF4R: Failure-Driven Recognition, Reconstruction, Refinement, and Redeployment for Continual Robot Self-Improvement
arXiv:2609.35575v2 Announce Type: replace-cross Abstract: The real-world performance of current vision-language-action models is fundamentally constrained by the limited coverage of expert demonstrations and their insuff…
- arXiv cs.AIresearchFactorEngram: Factorized N-gram Memory with Basis-Level Gating for Language Models
arXiv:2609.35578v1 Announce Type: cross Abstract: Lookup-based memory has been a promising way to scale the parameters of large language models (LLMs). It retrieves learned representations of local token patterns, such a…
- arXiv cs.AIresearchQC-Stark: A Multi-Task Benchmark Revealing Capability Dissociations in LLMs Evaluated on Quantum Computing Tasks
arXiv:2609.35581v1 Announce Type: cross Abstract: We introduce QC-Stark, a benchmark for evaluating large language models (LLMs) on 11 quantum computing (QC) tasks, spanning circuit construction, debugging, compilation,…
- arXiv cs.AIagentsSEABench: Benchmarking Endogenous Misalignment In Self-Evolving Agents
arXiv:2609.35596v2 Announce Type: replace-cross Abstract: Self-evolving LLM agents have gained prominence for their ability to improve after deployment by modifying their harness, including their controller instructions,…
- arXiv cs.AIresearchTwist, Don't Tilt: Trajectory-Exact Constrained Decoding for Masked Diffusion Models
arXiv:2609.35609v1 Announce Type: cross Abstract: Constrained decoding for Masked Diffusion Language Models (MDLMs) aims to ensure that generated outputs satisfy a specified structure or syntax constraint. MDLMs generate…
- arXiv cs.AIresearchBehavioral Foundation Models for Quality Diversity
arXiv:2609.35615v1 Announce Type: cross Abstract: Behavioral Foundation Models (BFMs) are an emerging paradigm in reinforcement learning, playing a role analogous to large language models in natural language processing:…
- arXiv cs.AIresearchDR-net-Mamba: Selective State-Space Modeling for Long-Range ECG Time-Series Denoising
arXiv:2609.35634v1 Announce Type: cross Abstract: Electrocardiogram (ECG) recordings are corrupted by non-stationary noise sources that degrade diagnostic reliability, particularly in ambulatory and long-duration recordi…
- arXiv cs.AIagentsGPUPhysBench: Benchmarking Coding Agents for Correct and Efficient GPU Physics Simulation
arXiv:2609.35639v1 Announce Type: cross Abstract: Writing fast GPU code for physical simulation is difficult: implementations must preserve numerical accuracy while handling irregular data access, synchronization, and it…
- arXiv cs.AIresearchCMDO: A Cognitive Memory-Driven Optimization Algorithm for Adaptive Population-Based Search
arXiv:2609.35657v1 Announce Type: cross Abstract: Population-based optimization methods often use previous search information through successful solutions, parameter adaptation, or operator performance, but they rarely r…
- arXiv cs.AIresearchMS-GLA: Multi-Scale Gated Linear Attention for Addressing Representational Bottlenecks via Multi-Temporal Resolution
arXiv:2609.35664v2 Announce Type: replace-cross Abstract: Gated Linear Attention (GLA) Transformers advance linear recurrent models through data-dependent gating, but face a core limitation: the fixed-capacity memory mat…
- arXiv cs.AIresearchRethinking Circuit Evaluation: Do Circuits Explain Model Errors?
arXiv:2609.35686v1 Announce Type: cross Abstract: Mechanistic interpretability (MI) aims to explain a model's behaviour through analyzing its internal computations; circuit-based explanations aim to isolate these computa…
- arXiv cs.AIresearchDistillation Defenses Easily Break After Reinforcement Learning
arXiv:2609.35699v1 Announce Type: cross Abstract: Distillation attacks copy the reasoning capabilities of closed-source large language models, allowing bad actors to replicate state-of-the-art performance at low cost. At…
- arXiv cs.AIresearchA Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion
arXiv:2609.35703v1 Announce Type: cross Abstract: Reliable deployment of graph neural networks requires calibration, out-of-distribution (OOD) detection, and robustness to distribution shift, yet existing methods address…
- arXiv cs.AIresearchX-Reset: Scaling Object-Centric Reinforcement Learning via Cross-Embodiment Resets
arXiv:2609.35715v1 Announce Type: cross Abstract: Reinforcement learning (RL) in simulation can train dexterous manipulation policies without robot demonstrations, but training a single generalist policy with task-agnost…
- arXiv cs.AIresearchCopy the Same, Distill the Difference: Initializing Linear Vision Transformers
arXiv:2609.35745v1 Announce Type: cross Abstract: Linear Vision Transformers (ViTs) are designed to replace the attention in Softmax ViTs with the linear-complexity attention operator for more efficient token routing, bu…
- arXiv cs.AIagentsKV-streams for Efficient Compaction in Agentic Reinforcement Learning
arXiv:2609.35750v3 Announce Type: replace-cross Abstract: Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory. Context compaction has been the most popular mech…
- arXiv cs.AIresearchHow to Loop MoE: Flatten the Experts, Untie the Attention
arXiv:2609.35751v1 Announce Type: cross Abstract: Looped Transformers reuse one block of layers several times: by spending extra computation they push a model of fixed size further, and so use its parameters more fully;…
- arXiv cs.AIagentsTokenCast: Forecasting Token Consumption During LLM Agent Execution
arXiv:2609.35760v2 Announce Type: replace-cross Abstract: When a large language model (LLM) agent executes the same task, token consumption can vary by over an order of magnitude across runs. The agent chooses its next s…
- arXiv cs.AIresearchLearning Native Reflection in Unified Models with Interleaved Reinforcement Learning
arXiv:2609.35767v2 Announce Type: replace-cross Abstract: Unified multimodal models can both look at and render images, so in principle they can repair their own generations: diagnose what an image gets wrong, revise it,…
- arXiv cs.AIresearchTelescopic Language Models
arXiv:2609.35769v1 Announce Type: cross Abstract: One deployed language model must often serve many compute budgets, yet serving each budget still means a separate training or compression run per point. We train a Telesc…
- arXiv cs.AIresearchFurE: Efficient Instance-Specific 3D Fur Reconstruction without Animal-Fur Datasets
arXiv:2609.35770v1 Announce Type: cross Abstract: Realistic and editable animal fur reconstruction from multi-view images is challenging due to fine-scale detail, self-occlusion and obfuscation, and, unlike human hair, t…
- arXiv cs.AIresearchCategorical Approach to Conflict Resolution:A Corrected Correspondence between Category Theory and the Graph Model for Conflict Resolution
arXiv:2306.13961v3 Announce Type: replace Abstract: This note is a substantially revised version of the author's earlier preprint (version~1, 2023), which proposed a ``Categorical Graph Model for Conflict Resolution'' (C…
- arXiv cs.AIresearchFlexQuant: Elastic Quantization Framework for Locally Hosted LLM on Edge Devices
arXiv:2501.07139v2 Announce Type: replace Abstract: Deploying LLMs on edge devices presents serious technical challenges. Memory elasticity is crucial for edge devices with unified memory, where memory is shared and fluc…
- arXiv cs.AIagentsBuilding Intelligent Agents with Neuro-Symbolic Concepts
arXiv:2505.06191v2 Announce Type: replace Abstract: This article presents a concept-centric paradigm for building agents that can learn continually and reason flexibly. The concept-centric agent utilizes a vocabulary of…
- arXiv cs.AIresearchFrom Reasoning to Generalization: Knowledge-Augmented LLMs for ARC Benchmark
arXiv:2505.17482v2 Announce Type: replace Abstract: Recent reasoning-oriented LLMs have demonstrated strong performance on challenging tasks such as mathematics and science examinations. However, core cognitive faculties…
- arXiv cs.AIresearchSearching for Actual Causes: Approximate Algorithms with Adjustable Precision
arXiv:2507.07857v2 Announce Type: replace Abstract: Causality has gained increasing attention in recent years, notably for improving the interpretability of machine learning models. Yet the field of explainable artificia…
- arXiv cs.AIresearchRethinking Prospect Theory for LLMs: Revealing the Instability of Decision-Making under Epistemic Uncertainty
arXiv:2508.08992v5 Announce Type: replace Abstract: Real-world decision-making often involves uncertainty expressed in linguistic rather than numerical terms, and Prospect Theory (PT) provides a classic framework for mod…
- arXiv cs.AIagentsEnabling Regulatory Multi-Agent Collaboration: Architecture, Challenges, and Solutions
arXiv:2509.09215v3 Announce Type: replace Abstract: Large language models (LLMs)-empowered autonomous agents are transforming both digital and physical environments by enabling adaptive, multi-agent collaboration. While…
- arXiv cs.AIresearchAI-driven ionic liquid discovery with unified chemical intelligence
arXiv:2511.11257v2 Announce Type: replace Abstract: The rational discovery of functional solvents remains challenging because macroscopic solvent behaviors emerge from complex many-body interactions across vast chemical…
- arXiv cs.AIagentsOn Memory: A comparison of memory mechanisms in world models
arXiv:2512.06983v2 Announce Type: replace Abstract: World models enable agents to plan within imagined environments by predicting future states conditioned on past observations and actions. However, their ability to plan…
- arXiv cs.AIresearchWhat If TSF: Reframing Time Series Forecasting as Scenario-Guided Multimodal Forecasting
arXiv:2601.08509v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have enabled time series forecasting to move beyond numerical observations and incorporate external information in multi…
- arXiv cs.AIresearchSTEP-LLM: Generating CAD STEP Models from Natural Language with Large Language Models
arXiv:2601.12641v2 Announce Type: replace Abstract: Computer-aided design (CAD) is vital to modern manufacturing, yet model creation remains labor-intensive and expertise-heavy. To enable non-experts to translate intuiti…
- arXiv cs.AIresearchGLOVE: Global Verifier for LLM Memory-Environment Realignment
arXiv:2601.19249v2 Announce Type: replace Abstract: Most existing memory-enhanced Large Language Model (LLM) approaches implicitly assume that memory validity can be established either through external evaluators that pr…
- arXiv cs.AIresearchBayesian-LoRA: Probabilistic Low-Rank Adaptation of Large Language Models
arXiv:2601.21003v4 Announce Type: replace Abstract: Large Language Models usually put more emphasis on accuracy and therefore, will guess even when not certain about the prediction, which is especially severe when fine-t…
- arXiv cs.AIresearchLatent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalization
arXiv:2601.21358v3 Announce Type: replace Abstract: Chain-of-Thought (CoT) empowers Large Language Models (LLMs) to tackle complex problems, but remains constrained by the computational cost and early token commitments i…
- arXiv cs.AIresearchDo Latent-CoT Models Think Step-by-Step? A Mechanistic Study on Sequential Reasoning Tasks
arXiv:2602.00449v2 Announce Type: replace Abstract: Latent Chain-of-Thought aims to enable step-by-step computation without emitting long rationales, yet its internal mechanisms remain unclear. We study CODI, a continuou…
- arXiv cs.AIresearchAdversarial Reward Auditing for Active Detection and Mitigation of Reward Hacking
arXiv:2602.01750v2 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) remains vulnerable to reward hacking, where models exploit spurious correlations in learned reward models to achieve h…
- arXiv cs.AIresearchKANFIS: A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware Learning
arXiv:2602.03034v3 Announce Type: replace Abstract: Adaptive Neuro-Fuzzy Inference System (ANFIS) was designed to combine the learning capabilities of neural network with the reasoning transparency of fuzzy logic. Howeve…
- arXiv cs.AIresearchSemantic Purification for Conditional Representation Learning
arXiv:2602.05464v2 Announce Type: replace Abstract: Conditional representation learning aims to extract criterion-specific features for customized tasks. Recent methods construct conditional subspaces spanned by criterio…
- arXiv cs.AIresearchSelective Fine-Tuning for Targeted and Robust Concept Unlearning
arXiv:2602.07919v2 Announce Type: replace Abstract: Text guided diffusion models are used by millions of users, but can be easily exploited to produce harmful content. Concept unlearning methods aim at reducing the model…
- arXiv cs.AIresearchGUI-GenBench: Evaluating Image Generation Models as Interactive GUI Environments
arXiv:2602.09007v3 Announce Type: replace Abstract: An image generation model used as a graphical user interface (GUI) environment must produce not only a plausible screen but also the correct response to an action. Exis…
- arXiv cs.AIagentsTSR: Trajectory-Search Rollouts for Multi-Turn RL of LLM Agents
arXiv:2602.11767v4 Announce Type: replace Abstract: Advances in large language models (LLMs) are driving a shift toward using reinforcement learning (RL) to train agents from iterative, multi-turn interactions across tas…
- arXiv cs.AIagentsActionEngine: From Reactive to Programmatic Web Agents via State Machine Memory
arXiv:2602.20502v2 Announce Type: replace Abstract: Many web agents operate through a reactive execution loop: they observe the current interface, reason about the next action, execute it, and repeat. This design incurs…
- arXiv cs.AIresearchCausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation
arXiv:2602.20571v3 Announce Type: replace Abstract: Many benchmarks for automated causal inference evaluate a system's performance based on a single numerical output, such as an Average Treatment Effect (ATE). This appro…
- arXiv cs.AIagentsMASRubric: Auditing Information Flow in Multi-Agent Systems with Failure-Distilled Pitfall Rubrics
arXiv:2602.23258v3 Announce Type: replace Abstract: While multi-agent systems (MAS) excel at complex reasoning, they are vulnerable to errors that intermediate agents introduce and downstream agents build upon. Auditing…
- arXiv cs.AIresearchWords & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation
arXiv:2603.01375v2 Announce Type: replace Abstract: Test-time policy adaptation for multi-turn interactions (T2PAM) is essential for aligning Large Language Models (LLMs) with dynamic user needs during inference time. Ho…
- arXiv cs.AIagentsAgentic Service Markets Across the Computing Continuum: A Polymatroidal Architecture
arXiv:2603.05614v3 Announce Type: replace Abstract: Autonomous AI agents are becoming economic actors. They compose deadline-bound services across the device-edge-cloud continuum and contend for capacity no single operat…
- arXiv cs.AIresearchEveryQuery: A Promptable Foundation Model for Clinical Prediction Tasks over Electronic Health Records
arXiv:2603.07900v3 Announce Type: replace Abstract: Autoregressive foundation models for electronic health records can make predictions for diverse clinical tasks without finetuning. These methods work by generating poss…