AI news archive.
19,861 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchMENTOR: Learning from Failure with On-Policy Pedagogical Intervention
arXiv:2610.13594v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) is a key driver for Large Reasoning Models, but it is vulnerable to "zero-reward collapse", a training failure where a…
- arXiv cs.AIagentsHumanize Olympiad Agents: A Multi-Session Workflow for Olympiad Problem Solving with Solve, Review, Wiki, Verify
arXiv:2610.13632v1 Announce Type: new Abstract: Large language model (LLM) agents are rapidly improving on long-horizon scientific and olympiad tasks. Recent systems have produced a Lean-formalized resolution of the Navi…
- arXiv cs.AIresearchLearning Size Independent Constraint Models from Input Data and Positive and Negative Example Solutions
arXiv:2610.13640v1 Announce Type: new Abstract: In this paper we describe a Constraint Acquisition Tool that generates size independent constraint programs from input data and positive and negative example solutions of m…
- arXiv cs.AIagentsA Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models
arXiv:2610.13663v1 Announce Type: new Abstract: Many machine learning approaches are two-level: an expert selects a class of methods, within this class the choice of key components is critical and usually manual. This pa…
- arXiv cs.AIresearchA Step Harder, Not a Step Too Far: The Benefits and Limits of Curriculum Difficulty Progression in GRPO
arXiv:2610.13669v1 Announce Type: new Abstract: Curriculum learning typically organizes training samples from easy to hard. In GRPO, medium-difficulty samples are attractive for online selection because they are more lik…
- arXiv cs.AIagentsA Baseline Assessment for the Agentic Web Technology Stack
arXiv:2610.13703v1 Announce Type: new Abstract: The fast pace in which protocols, frameworks, and technologies are being proposed for LLM-based agents results in a complex scenario for anyone aiming to create state-of-th…
- arXiv cs.AIresearchAdaptive DHiSparse: Speculative Decoding over a Load-Adaptive KV Hierarchy
arXiv:2610.13708v1 Announce Type: new Abstract: Long-context serving runs out of KV memory before the hardware reaches its useful throughput. HiSparse stores history in host memory and fetches only the selected entries,…
- arXiv cs.AIresearchA Privileged Teacher That Only Scores Cannot Search: Critique-Conditioned On-Policy Self-Distillation for Reasoning
arXiv:2610.13744v1 Announce Type: new Abstract: On-policy self-distillation promises to replace the sparse outcome signal of verifiable-reward RL with dense token-level credit: a teacher conditioned on the reference solu…
- arXiv cs.AIresearchUkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes
arXiv:2610.13783v1 Announce Type: new Abstract: Memes play a central role in modern online communication, especially in contexts of political conflict where visual and textual cues combine to convey persuasive messages.…
- arXiv cs.AIresearchStudying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga
arXiv:2610.13840v1 Announce Type: new Abstract: Comics are one of the great visual records of the twentieth century: hundreds of thousands of pages in which a mass readership saw its leaders, its wars and its heroes. Mos…
- arXiv cs.AIagentsX-AgentRed: Red-Teaming Personal Agents in Evolving Multi-Service Ecosystems
arXiv:2610.13886v1 Announce Type: new Abstract: Personal agents increasingly connect to services across the web and applications by using tools. As the effects of their actions accumulate and interact across services, un…
- arXiv cs.AIagentsRATED: Reliability-Aware AI Agent Tool Retrieval via Execution Dynamics
arXiv:2610.13889v1 Announce Type: new Abstract: As AI agents increasingly rely on external tools, accurate retrieval is essential for reliable behavior. Existing methods optimize semantic relevance but largely ignore too…
- arXiv cs.AIresearchCan Language Models Play the Wikipedia Game?
arXiv:2610.13948v1 Announce Type: new Abstract: Language models increasingly support or automate information retrieval from document collections too large to index. This task is difficult when it requires specialist ling…
- arXiv cs.AIresearchImagine the Future, Internalize the Gist: Efficient VLA Reasoning via Internalized Spatiotemporal Imagination
arXiv:2610.02626v4 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models increasingly incorporate intermediate reasoning to improve robotic manipulation, yet existing approaches primarily reason abou…
- arXiv cs.AIresearchKairos: Toward a Byte-Level Foundation Model for Every Modality
arXiv:2610.12495v1 Announce Type: cross Abstract: Small language models trained on a single local machine leave little room for architectural trial and error. Kairos combines bidirectional gated-DeltaNet linear attention…
- arXiv cs.AIresearchEnergy-Efficient Spiking Neural Networks for Sensing-Aided Beam Prediction
arXiv:2610.12507v1 Announce Type: cross Abstract: Sensing-aided beam prediction can reduce the beam-training overhead of millimeter-wave (mmWave) systems by leveraging environmental information from complementary modalit…
- arXiv cs.AIresearchCircuitTree: Composing Circuits into A Syllable Transcriber
arXiv:2610.12520v1 Announce Type: cross Abstract: The goal of circuit discovery is to decompose a monolithic neural network into tiny modular subnetworks which are interpretable in the sense that each does an identifiabl…
- arXiv cs.AIresearchAutoIndexer: Flexible-Order Decoding by Training Causal Models on Chains of Edits
arXiv:2610.12545v1 Announce Type: cross Abstract: Autoregressive decoding is less suited to non-causally ordered data, e.g., cross-references or future dependencies, since their outputs cannot be revised post hoc, unlike…
- arXiv cs.AIresearchSelf-Preservation in Language Models
arXiv:2610.12602v1 Announce Type: cross Abstract: Continual learning (CL) seeks to adapt pretrained models while preserving prior capabilities. Yet the tasks supplied for preservation cover only part of what a model know…
- arXiv cs.AIresearchMesh-Native Tokenisation: Retrofitting Foundation Models to Unstructured Data
arXiv:2610.12618v1 Announce Type: cross Abstract: Physics foundation models are an increasingly popular approach for building versatile emulators for spatiotemporal systems. They require tokenisation layers that lift phy…
- arXiv cs.AIresearchMachine Learning and Time-Series Foundation Models for Day-Ahead Electricity Forecasting in a University Building
arXiv:2610.12638v1 Announce Type: cross Abstract: Day-ahead electricity forecasts can help building operators schedule heating, ventilation, and other services around anticipated demand. Choosing a forecasting model also…
- arXiv cs.AIresearchCancerous Backdoor Attack: Gradual Activation via Invisible Tokens and A Stage Controller
arXiv:2610.12644v1 Announce Type: cross Abstract: Backdoor Attacks on Large Language Models (LLMs) typically follow a trigger-response pattern, where satisfying a trigger condition activates a single terminal behavior. H…
- arXiv cs.AIresearchKronecker Product Attention: Memory-Efficient KV Compression via Structured Factorization
arXiv:2610.12657v1 Announce Type: cross Abstract: Efficient autoregressive generation is increasingly limited by the memory footprint of the key-value (KV) cache, which grows linearly with sequence length, batch size, an…
- arXiv cs.AIresearchIncremental Retrieval-Augmented Generation for Human-in-the-Loop Decision Support
arXiv:2610.12666v1 Announce Type: cross Abstract: We introduce iRAG, a human-in-the-loop decision-support system combining Retrieval-Augmented Generation (RAG) with Hybrid Decision-Making. Unlike conventional RAG systems…
- arXiv cs.AIagentsRecurrent Harness for Agentic Continual Learning
arXiv:2610.12668v1 Announce Type: cross Abstract: Language model agents increasingly operate over long interaction streams, requiring online adaptation to distribution shifts through continual learning. However, existing…
- arXiv cs.AIresearchGeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks
arXiv:2610.12669v1 Announce Type: cross Abstract: Large language models are becoming central to GeoAI systems, yet their evaluation rarely considers the elicitation of geospatial data requirements. This omission is criti…
- arXiv cs.AIresearchUnsupervised Multimodal Reward Modeling
arXiv:2610.12707v1 Announce Type: cross Abstract: Vision-language reward models are trained on annotated preference pairs, which are expensive to collect, can disagree, and are ranked by benchmark accuracy. We build one…
- arXiv cs.AIresearchKITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting
arXiv:2610.12738v1 Announce Type: cross Abstract: Forecasting rare, high-cost events in multivariate time-series systems is hard because their precursors are seldom observed, so a purely data-driven model has little to l…
- arXiv cs.AIagentsTrajectory Prediction for Autonomous Driving in Road Construction Zones Using Human-in-the-Loop Driving Data
arXiv:2610.12747v1 Announce Type: cross Abstract: Ego-trajectory prediction in urban roadway work zones remains an underexplored topic in autonomous-driving research, with limited dedicated data and systematic evaluation…
- arXiv cs.AIresearchSemantic Views: Certified Reuse of LLM Labels Across Semantic Queries
arXiv:2610.12768v1 Announce Type: cross Abstract: Semantic query engines evaluate natural-language filters with a large language model, and every evaluation is a costly oracle call. Optimizers that reduce these calls fac…
- arXiv cs.AIresearchREDaCT: Collapse-Free Unlearning in Large Reasoning Models
arXiv:2610.12769v1 Announce Type: cross Abstract: Unlearning in large reasoning models (LRMs) requires removing forget-set information from both the reasoning trace and the final answer. Existing approaches achieve this…
- arXiv cs.AIresearchMeta-Reflective Evolution Effectively Improves Production AI Systems
arXiv:2610.12794v1 Announce Type: cross Abstract: Large Language Model (LLM)-guided evolutionary optimization has successfully improved prompts, programs, and even entire AI systems. Most optimization methods select a ca…
- arXiv cs.AIresearchEfficient Audio-Visual Speech Enhancement via Reinforcement Learning-Based Neural Architecture Search
arXiv:2610.12796v1 Announce Type: cross Abstract: Audio-visual speech enhancement (AVSE) improves speech intelligibility in noisy environments but often relies on computationally expensive models unsuitable for resource-…
- arXiv cs.AIresearchLeWAM: End-to-End World and Action Modeling with JEPAs
arXiv:2610.12818v1 Announce Type: cross Abstract: Joint-embedding predictive architectures (JEPAs) learn compact latent world models without pixel reconstruction. Existing JEPA pipelines typically train visual representa…
- arXiv cs.AIresearchCOFFEQA: Counterfactual-Guided Few-Shot Distillation for LLM Question Answering
arXiv:2610.12837v1 Announce Type: cross Abstract: Knowledge distillation aims to train a smaller student model to mimic a larger teacher model for resource-constrained environments. However, the process of distillation i…
- arXiv cs.AIresearchWhen Generative Models Act as Simulators
arXiv:2610.12847v1 Announce Type: cross Abstract: A simulator summarizes an environment in a state that users can read and change. Generative models learn representations of the world without explicit specification, but…
- arXiv cs.AIagentsCo-Modifying Application Code and Deployment Infrastructure: A Causal Study of AI Agent Pull Requests
arXiv:2610.12849v1 Announce Type: cross Abstract: Current software engineering agents are evaluated predominantly inside isolated sandboxes against synchronous local tests. However, deploying features in collaborative re…
- arXiv cs.AIresearchLocalized Generative Design of Ship Hulls with Diffusion Models and High-Fidelity Assessment
arXiv:2610.12870v1 Announce Type: cross Abstract: Practical ship design requires localized refinement and performance evaluation. This paper presents a reward-directed diffusion framework for localized hull optimization.…
- arXiv cs.AIresearchSurfing the Activation Wave: Analyzing the Information and Geometry of LLM Hidden States for Speculative Decoding
arXiv:2610.12939v1 Announce Type: cross Abstract: Decoder hidden states from different layers of large language models (LLMs) often encode complementary information. However, existing speculative decoding methods typical…
- arXiv cs.AIagentsJoyInside-VLM-Agent: Product-Conditioned Multimodal Policy Learning for Embodied Agents
arXiv:2610.12957v1 Announce Type: cross Abstract: Embodied agents must translate multimodal observations and user requests into executable actions while adapting to product constraints, changing goals, and execution feed…
- arXiv cs.AIresearchMotionCoach: Constructing Multimodal Guidance for Video Professional Motion Editing
arXiv:2610.12963v1 Announce Type: cross Abstract: Video editing models achieve strong performance in modifying appearance, backgrounds, styles, and motions, empowered by large-scale training data. Beyond general scenario…
- arXiv cs.AIresearchThe Evidence Anchoring Boundary: Where Visual Interventions Matter and When They Help in Multimodal LLMs
arXiv:2610.12981v1 Announce Type: cross Abstract: Visual interventions provide a training-free way to reduce hallucinations in multimodal large language models. However, an intervention may have little leverage at some l…
- arXiv cs.AIresearchWhat Should SFT Learn? Conditional Information Gain for Generalizable Post-Training
arXiv:2610.12995v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) remains a widely used approach for post-training large language models, because fixed demonstrations can directly introduce desired reasoning…
- arXiv cs.AIresearchGeneralized Proximal Policy Optimization: Unifying LLM RL Algorithms from the Perspective of Divergence
arXiv:2610.13015v1 Announce Type: cross Abstract: Reinforcement learning has become the foundational paradigm for enhancing the reasoning capabilities of LLMs. Yet the dominant approach, Proximal Policy Optimization (PPO…
- arXiv cs.AIresearchDon't Guess, Just Ask: Evaluating Interactive Requirement Clarification for Repository-Level Software Development
arXiv:2610.13022v1 Announce Type: cross Abstract: As LLMs become increasingly capable of implementing software, a critical bottleneck emerges: whether they truly understand what users want. Users often begin with vague i…
- arXiv cs.AIresearchGeoGR: Spatial-Semantic Token Compression and Group Routing for Efficient 3D VLMs
arXiv:2610.13035v1 Announce Type: cross Abstract: Multi-view representations enable pretrained image and video vision-language models to understand 3D scenes, but their long visual token sequences make inference costly.…
- arXiv cs.AIresearchCoBe: A Benchmark For Conversational Counterfactual Text Editing
arXiv:2610.13057v1 Announce Type: cross Abstract: Counterfactual text editing is the ability to revise a given text in light of a hypothetical change while preserving facts not causally affected by that change. When aske…
- arXiv cs.AIresearchEvolving Creatives with Clicks: CTR-Driven Advertising image Editing via Attribute Disentanglement and Preference Alignment
arXiv:2610.13066v1 Announce Type: cross Abstract: The optimization objective for e-commerce advertising creatives has shifted from aesthetic quality to online business metrics like click-through rate (CTR). Recent method…
- arXiv cs.AIresearchContinually Post-training Small Models via Synthetic Self-distillation
arXiv:2610.13113v1 Announce Type: cross Abstract: Continual post-training faces a dual challenge of acquisition and retention: models must learn new capabilities while preserving prior knowledge. While Self-Distillation…
- arXiv cs.AIresearchDoes Learning Recurrent Networks Matter in Partially Observable RL?
arXiv:2610.13197v1 Announce Type: cross Abstract: Reinforcement learning (RL) under partial observability typically re- lies on recurrent networks to encode temporal information, with the recurrent module trained jointly…