AI & Machine Learning · Computer Science & AI
Artificial Intelligence
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The newest 5,000 papers, from 12 October 2026 to 6 November 2026: 4,992 arXiv preprints and 8 OpenAlex records.
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Newest papers, page 228
Player-1: Learning to Play from Synthetic Experience
Computer-use agents have demonstrated strong capabilities in interpreting screens and executing keyboard and mouse actions.Continuum Attention
We introduce Continuum Attention (CA), a causal Transformer attention operator that replaces discrete key and value lookup with integration over a continuous, piecewise linear memory curve.Price of Anarchy of First-Price Auctions in Agentic Markets
An agentic market is a decentralized serving-time market where AI agents bid to claim each user prompt.Your Transformer Is Secretly Elastic
Scaling up transformers has produced models with broad capabilities, deployed across heterogeneous hardware and fluctuating workloads.Co-Evolving Acting and Forecasting for Language Agents
Training language agents is bottlenecked by the high cost of long-horizon rollouts, yet standard agentic reinforcement learning (RL) learns only from a terminal scalar reward, leaving the…Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion
Recent advances in robot learning have driven rapid progress in humanoid control, enabling increasingly agile behaviors in the real world.Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance
Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tuning remains a formidable challenge because of high-dimensional action spaces and intricate coupling…The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock
Large language model (LLM) agents can solve complex interactive tasks, yet reasoning and tool calls take time.Evaluating Model Capabilities After Benchmark Saturation
Rapid advances in AI capabilities are causing benchmarks to saturate.Value-Informed Schedule Trimming for Accelerated Sequential Monte Carlo Guidance in Discrete Diffusion
Reward-guided discrete diffusion enables test-time alignment with downstream objectives, but applying Sequential Monte Carlo (SMC) guidance at every denoising step is computationally expensive.AlphaDiana: A Measurement System for Agents in Verifiable Reasoning Tasks
Agents pair reasoning models with harnesses that organize model calls, execute tools, load skill instructions, and retain history in memory.When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus
Correctness feedback suppresses the very evidence that specification-completeness auditing is designed to uncover: programs that satisfy a written contract but violate intended behavior.Counterfactual Neighborhood Risk Fields: Evidence-Grounded Selective Safety for Vision-Language Models
Representation-based safety defenses for vision-language models (VLMs) often learn to separate harmful from benign inputs, but separation alone does not establish what a risk score means or when it…IneqAgent: Agentic Inequality Proving in Lean with Symbolic Computation
Formal inequality proving requires mathematical reasoning and exact computation, often connected through intermediate lemmas and goal reformulations.L2G-SR: Learning to Guide LLM-based Symbolic Regression
Symbolic regression searches for an equation that explains observed data.Open Benchmark of AI Impact on Humans (ImpactBench): an expert-guided, open platform for the holistic evaluation of AI impact on humans
The present work is concerned with how to measure, at scale, the multi-dimensional impact of generative AI models on the individuals who use them.WorldCast: Exploring Intelligent Visual Generation in World State Prediction
Humans interpret multimodal observations of the world and forecast how it will evolve.Interpreting Language Model Parameters
Mechanistic interpretability aims to uncover how neural networks use their parameters to implement their impressive neural algorithms.Optimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets
Large language models (LLMs) trained on vast amounts of data have demonstrated impressive performance over a range of tasks, but future performance gains will require increasing amounts of data and…Reference-Guided Turn-Taking Style Transfer for Full-Duplex Spoken Language Models
Full-duplex spoken language models decide at every frame whether to take the floor, defining a conversational personality -- yet each set of weights encodes exactly one, which human speakers bracket…