AI news archive.
19,861 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchPrism Attention: Proposal-Refined Index Sharing Mechanism for Efficient LLMs Inference
arXiv:2610.22985v1 Announce Type: cross Abstract: Long-context decoding in large language models (LLMs) is increasingly bottlenecked by the memory and computation required to attend over an ever-growing Key-Value (KV) ca…
- arXiv cs.AIagentsPersonaCUABench: Benchmarking Computer-Use Agents Serving Diverse Personas
arXiv:2610.22990v1 Announce Type: cross Abstract: Computer-use agents (CUAs) are commonly evaluated against fixed task specifications, but users may omit requirements, add constraints, or require approval before conseque…
- arXiv cs.AIresearchA Label Noise Robust Algorithm for Graph Data using Neighborhood Label Distribution
arXiv:2610.22998v1 Announce Type: cross Abstract: Graph Convolutional Networks (GCNs) have demonstrated exceptional performance on graph-structured data. However, their success heavily relies on the availability of clean…
- arXiv cs.AIresearchSpikeQ: Spike-Guided Post-Training Quantization for Spiking Neural Networks
arXiv:2610.23046v1 Announce Type: cross Abstract: On neuromorphic chips, spiking neural networks (SNNs) consume far less energy than artificial neural networks (ANNs), but the limited memory of these chips requires low-b…
- arXiv cs.AIresearchUnifying Embodied Foundation Models via a Model-to-Policy Formulation
arXiv:2610.23053v1 Announce Type: cross Abstract: Vision-language-action (VLA) models and world-action models (WAMs) have emerged as promising approaches to robot learning and control. These models integrate multimodal p…
- arXiv cs.AIresearchPath to Prior-Data Fitted Relational Models via Learned Latent Multi-table Representations
arXiv:2610.23073v1 Announce Type: cross Abstract: Prior-data fitted networks (PFNs) show that pretraining on large populations of synthetic tasks can enable generalization to unseen datasets with little or no downstream…
- arXiv cs.AIresearchLocal proxies for memory-efficient training
arXiv:2610.23145v1 Announce Type: cross Abstract: Local learning algorithms can reshape resource requirements of artificial intelligence. The memory bottleneck of standard end-to-end backpropagation lies in the need to s…
- arXiv cs.AIresearchExplaining Chess and Go in Natural Language
arXiv:2610.23166v1 Announce Type: cross Abstract: Modern chess engines play far beyond human ability, often choosing moves whose underlying ideas are difficult for humans to understand. We propose an automatic metric for…
- arXiv cs.AIagentsTree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning
arXiv:2610.23181v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards has become a scalable post-training paradigm for large language models. However, training agents over long-horizon interact…
- arXiv cs.AIresearchA Stronger Teacher Within: Strengthening MoE Models via On-Policy Bidirectional Distillation
arXiv:2610.23185v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has substantially improved Mixture-of-Experts (MoE) LLMs, but its outcome-level rewards provide only sparse…
- arXiv cs.AIresearchLangNCO: Compiling Natural-Language Constraints into Inference-Time Interventions for Neural Combinatorial Optimization
arXiv:2610.23190v1 Announce Type: cross Abstract: Neural combinatorial optimization (NCO) rapidly produces high-quality solutions for problem structures encountered during training, but operational requirements may chang…
- arXiv cs.AIresearchFraming Spatial Hearing within the Free Energy Principle: A Hierarchical Model of Perceptual Learning
arXiv:2610.23196v1 Announce Type: cross Abstract: Spatial hearing relies on dynamic internal representations to decode spatial information from ever-changing acoustic conditions. A fundamental example is sound localisati…
- arXiv cs.AIresearchValidating LLM-generated political networks with survey-reported partisan exposure
arXiv:2610.23199v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to simulate human behavior in social science research, including decisions, interactions, and social relationships. How…
- arXiv cs.AIresearchqGAMMA: A Framework for Optimal DNN Mapping on Multi-Precision Accelerators
arXiv:2610.23222v1 Announce Type: cross Abstract: This work describes qGAMMA, a flexible framework for exploring and optimizing the mapping of mixed-precision Deep Neural Network (DNN) models onto general-purpose spatial…
- arXiv cs.AIresearchMGE-PO: Entropy-Aware Credit Assignment for Mathematical Reasoning
arXiv:2610.23245v1 Announce Type: cross Abstract: GRPO assigns the same group-relative advantage to equally rewarded responses within a question, leaving their generation uncertainty unused. We introduce MGE-PO, centered…
- arXiv cs.AIagentsPreventing Data Hallucinations in Agentic AI for Optical Network Troubleshooting
arXiv:2610.23252v1 Announce Type: cross Abstract: An agentic AI system for automated troubleshooting in optical communication networks is demonstrated that integrates data references into the agent architecture to preven…
- arXiv cs.AIagentsBeyond Marginal Influence: Interaction-Aware Memory Selection for LLM Agents
arXiv:2610.23266v1 Announce Type: cross Abstract: LLM-agent memory selection often uses retrieval rankings based on record relevance or individual influence. Such rankings cannot identify sufficient subsets under redunda…
- arXiv cs.AIresearchFlashReFocus: Interactive Image Refocusing in Seconds
arXiv:2610.23297v1 Announce Type: cross Abstract: Single-image refocusing changes the focal plane and depth of field of a photograph after capture. It is inherently interactive: users try many focal points and blur stren…
- arXiv cs.AIagentsDesigning for Imperfect Prompts: A Simulation-Based Evaluation of an LLM-Based Enterprise Data Assistant Using Knowledge Graphs
arXiv:2610.23304v1 Announce Type: cross Abstract: Enterprise data assistants using large language models (LLMs) promise democratized data access in organizations. Yet in our real-world deployment within the CFO unit's da…
- arXiv cs.AIagentsMobileInject: Evaluating Reasoning-to-Action Security of MLLM-Based Mobile Agents under Cross-Modal Consistency Injection
arXiv:2610.23307v1 Announce Type: cross Abstract: Multimodal large language model (MLLM)-based mobile agents are rapidly evolving from conversational assistants into autonomous systems that perceive, reason over, and act…
- arXiv cs.AIresearchLanguage Models Learn to Improve Language Models
arXiv:2610.23344v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to automate AI research, including language-model (LM) development, offering a potential path toward recursive self-imp…
- arXiv cs.AIresearchAWAM: Active Trajectory-Conditioned View Imagination for Robust Robot Manipulation
arXiv:2610.23396v1 Announce Type: cross Abstract: Rapid progress in embodied intelligence has enabled increasingly capable robots to follow language instructions and manipulate diverse objects. However, strong results in…
- arXiv cs.AIagentsRECREG: Recovery-based Shielding for Regular Safety Properties in Reinforcement Learning
arXiv:2610.23445v1 Announce Type: cross Abstract: In many real-world scenarios we need to consider formal requirements, such as safety and constraint compliance, to deploy Reinforcement Learning (RL) agents. In this pape…
- arXiv cs.AIagentsQD-Rec: Open-Ended Evolution of Recommendations via Quality-Diversity
arXiv:2610.23470v2 Announce Type: replace-cross Abstract: Open-ended search combined with foundation models has produced advances such as faster algorithms, new results on open mathematical problems, and self-improving a…
- arXiv cs.AIresearchA Topological Signature of Learning under Noisy Labels
arXiv:2610.23491v1 Announce Type: cross Abstract: How does noisy supervision change the representations learned by a neural network? Training accuracy measures agreement with training labels but provides little informati…
- arXiv cs.AIresearchTruth in Contrast: Calibrated Factual Evidence from Language Model Likelihoods
arXiv:2610.23527v1 Announce Type: cross Abstract: Large Language Models (LLMs) encode substantial factual knowledge. Yet, estimating confidence in factual claims reliably remains challenging. We study likelihood-based fa…
- arXiv cs.AIagentsFrom Billion-Scale to Million-Scale Language Models: Operational Capabilities for Embedded Post-Exploitation
arXiv:2610.23534v1 Announce Type: cross Abstract: Small Language Models (SLMs) are increasingly attractive for resource-constrained environments due to their low computational footprint. These characteristics also make t…
- arXiv cs.AIresearchReason-guided Human Reward Modeling for Preference-based Reinforcement Learning
arXiv:2610.23550v1 Announce Type: cross Abstract: Reinforcement learning (RL) policies optimized for task rewards may produce behaviors that are efficient but misaligned with human expectations, safety preferences, or in…
- arXiv cs.AIagentsSIT-Eval: Situation-Based Interactive Turn-by-Turn Evaluation of Conversational Agents
arXiv:2610.23612v1 Announce Type: cross Abstract: The rapid proliferation of conversational AI agents necessitates evaluation methods that assess performance across diverse dialogue scenarios, utilizing multiple situatio…
- arXiv cs.AIresearchBeyond Action Prediction: Learning Search-Free Planning in Latent Space
arXiv:2610.23615v1 Announce Type: cross Abstract: Latent world models learn action-conditioned state transitions in a compact representation space, providing a predictive model for visual planning and control. However, p…
- arXiv cs.AIresearchPresenceBench: An Exploratory Evaluation of Large Language Model Adaptation to User State
arXiv:2610.23625v1 Announce Type: cross Abstract: Presence-aware intelligence is the capacity of an AI system's intelligence layer to adapt appropriately to the functional state of the person it serves (here readiness, f…
- arXiv cs.AIresearchTUPLE-Bench: Evaluating and Mitigating Privacy-Utility Trade-offs in Typed Decision Models
arXiv:2610.23668v1 Announce Type: cross Abstract: Typed decision models often return more than a selected category. They also expose probabilities for each candidate and, sometimes, a separate confidence score. These fie…
- arXiv cs.AIagentsOn the possibilities and limitations of LLM-based multi-agent systems for multi-criteria decision-making in healthcare operations management
arXiv:2610.23673v1 Announce Type: cross Abstract: Multi-criteria models for healthcare operations need weights that express how stakeholders trade off patient waiting time, provider workload, and operating cost, but thes…
- arXiv cs.AIagentsGLOBE: Evolving Structured Agentic Memory for E-commerce Product Optimization in Generative Search
arXiv:2610.23709v1 Announce Type: cross Abstract: Optimizing product listings for generative search requires language agents not only to rewrite effectively but also to continually self-improve by retaining successful st…
- arXiv cs.AIresearchNeural Expansion: A Unified Mechanism for How Deep Neural Networks Generalize
arXiv:2610.23716v1 Announce Type: cross Abstract: Deep neural networks demonstrate remarkable generalization despite being highly over-parameterized, challenging traditional statistical learning theories. While several t…
- arXiv cs.AIresearchBehavioral Couplings: From Formation to Behavioral Emergence in Language Models
arXiv:2610.23847v2 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) can induce behaviors beyond those explicitly targeted. For example, training a model to respond more warmly can make it m…
- arXiv cs.AIresearchRethinking the design space of Vanilla Pixel Diffusion Transformers
arXiv:2610.23854v1 Announce Type: cross Abstract: Pixel-space diffusion models have recently re-emerged as a compelling alternative to latent diffusion, avoiding the lossy latent bottleneck and separately trained autoenc…
- arXiv cs.AIresearchGVPO: Group Variance Policy Optimization for Large Language Model Post-Training
arXiv:2504.19599v4 Announce Type: replace Abstract: Post-training plays a crucial role in refining and aligning large language models to meet specific tasks and human preferences. While recent advancements in post-traini…
- arXiv cs.AIresearchA Survey of Explainable Reinforcement Learning: Targets, Methods and Needs
arXiv:2507.12599v2 Announce Type: replace Abstract: The success of recent Artificial Intelligence (AI) models has been accompanied by the opacity of their internal mechanisms, due notably to the use of deep neural networ…
- arXiv cs.AIagentsComputational Multi-Agents Society Experiments: Social Modeling Framework Based on Generative Agents
arXiv:2508.17366v3 Announce Type: replace Abstract: Computational social science has developed descriptive, interpretative, and predictive modeling, but comprehensive modeling combining in-world human intervention with o…
- arXiv cs.AIagentsTowards Unified World Models for Visual Navigation via Memory-Augmented Planning and Foresight
arXiv:2510.08713v4 Announce Type: replace Abstract: Enabling embodied agents to imagine future states is essential for robust and generalizable visual navigation. Yet, state-of-the-art systems typically rely on modular d…
- arXiv cs.AIresearchHow RLHF Amplifies Sycophancy
arXiv:2602.01002v2 Announce Type: replace Abstract: Large language models often exhibit increased sycophantic behavior after preference-based post-training, showing a stronger tendency to affirm a user's stated or implie…
- arXiv cs.AIresearchMoral Sycophancy in Vision Language Models
arXiv:2602.08311v2 Announce Type: replace Abstract: Sycophancy in Vision-Language Models (VLMs) refers to their tendency to align with user opinions, often at the expense of moral or factual accuracy. While prior studies…
- arXiv cs.AIresearchAccurate and Efficient Multi-Channel Time Series Forecasting via Sparse Attention Mechanism
arXiv:2603.18712v3 Announce Type: replace Abstract: The task of multi-channel time series forecasting is ubiquitous in numerous fields such as finance, supply chain management, and energy planning. It is critical to effe…
- arXiv cs.AIresearchTowards Reliable Truth-Aligned Uncertainty Estimation in Large Language Models
arXiv:2604.00445v2 Announce Type: replace Abstract: Uncertainty estimation (UE) aims to quantify uncertainty in large language model (LLM) outputs to flag potential hallucinations and support reliable deployment. However…
- arXiv cs.AIagentsNEXUS: Learning Safety Constraints and Action Models for Safe and Robust Embodied Planning
arXiv:2605.09387v2 Announce Type: replace Abstract: Large language models (LLMs) enable embodied agents to plan from naturallanguage instructions, yet their actions may be physically infeasible or hazardous. Existing neu…
- arXiv cs.AIresearchCrystalReasoner: Reasoning and RL for Property-Conditioned Crystal Structure Generation
arXiv:2605.14344v3 Announce Type: replace Abstract: Generative modeling has emerged as a promising approach for crystal structure discovery. However, existing LLM-based generative models struggle with low-level atomic pr…
- arXiv cs.AIresearchBrain Vascular Age Prediction Using Cerebral Blood Flow Velocity and Machine Learning Algorithms
arXiv:2605.16969v3 Announce Type: replace Abstract: Defining vascular age in terms of physiological function has become one focal point of the extensive studies to categorize and track chronological age. Transcranial Dop…
- arXiv cs.AIresearchHyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction
arXiv:2605.17355v2 Announce Type: replace Abstract: As a modern commodity, language has become a vast repository of socially and psychologically significant traits and concepts, reflecting the ways people encode pattern…
- arXiv cs.AIresearchExploration and Experimentation with a Misspecified Simulator
arXiv:2605.21458v2 Announce Type: replace Abstract: Randomized experiments are the standard for measuring causal effects that guide decisions, but are costly. Simulators calibrated to historical data offer a cheaper alte…