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Artificial Intelligence
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The newest 5,000 papers, from 11 October 2026 to 5 November 2026: 4,998 arXiv preprints and 2 OpenAlex records.
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Newest papers, page 70
DynaRev: Benchmarking Web Agents under Dynamic Goal Revision
Web agents are typically evaluated with goals fully specified before execution and fixed thereafter, whereas real users often revise their requirements during interaction.The generative paradox: the Anti-Prompt paradigm for human capability
Generative AI in education is usually judged by the output of human-AI teams.FARE: Factor-Aware Retrieval with Error-Guided Fusion for Retail Forecasting
Retail sales forecasting underpins inventory planning, yet promotions and calendar events reshape short-term demand.A Negotiation-Aware Hybrid LLM Framework for Intent Interpretation and Service Discovery
Increasingly, users express their needs as natural-language intents rather than structured requests.EventSeeColor: Learning to Reconstruct Color from Monochrome Events
Event cameras capture visual brightness changes under challenging lighting conditions.Locating and Closing Information Gaps in Multi-Agent Systems
Multi-step Web tasks often require several LLM agents to gather and combine evidence.Understanding axial attention in TSFMs through single location regression
Recent advances in Time Series Foundation Models (TSFMs) increasingly rely on axial attention mechanisms, but despite their empirical success, the theoretical properties of axial attention remain…Bridging Semantics and Structure: A Theoretically Grounded Framework for Link Prediction on TAGs
Link prediction on text-attributed graphs (TAGs) is a fundamental problem with applications in knowledge discovery, recommendation and beyond.EpiAgent: Epistemic Self-Play for LLM Agents
Epistemic game theory explains an agent's strategic behavior via the beliefs it holds about others and how these beliefs are updated as the interactions unfold.EnvOS: Auditing Computer-Use Agents When the World Moves On : Phenomenon-Based Task Construction, Certification, and Evidence-Based Evaluation
Computer-use agents built on large language models (LLMs) work in software that can change while they act: an appointment moves, a policy is revised, a record is updated.Reaction precedents extend retrosynthetic prediction beyond training coverage
Reaction precedents guide retrosynthesis, but how far they can compensate for gaps in task-specific training coverage remains unclear.Distributional Reward Calibration and Budget-Neutral Credit Assignment for Multimodal RLVR
We revisit advantage estimation in Group Relative Policy Optimization (GRPO) and observe that any token-level advantage admits a two-stage factorization: a \emph{sample-level baselining} step that…Learned Terminal Costs for Short-Horizon Model Predictive Control of Autonomous Trucks in Interactive Highway Traffic
Autonomous highway driving for heavy-duty trucks is a challenging control problem.TaskFrame: Connecting Task-Level Planning with Frame-Level Control for Game Agents
Building general game agents requires both high-level task reasoning and low-latency action control in rapidly changing visual environments.Neighborhood Elites: Quality-Diversity Search for Uncertainty Quantification in Question Answering
Large language models (LLMs) are increasingly capable, yet they produce wrong answers indistinguishable from correct ones.Latent Prediction Yields Cross-Subject Alignable Representations in Sensory-Evoked fMRI
Decoding sensory-evoked functional magnetic resonance imaging (fMRI) data is a central analytical tool in cognitive neuroscience.Only Accepted Tokens Count: An Energy Accounting of Speculative Decoding Under Latency Targets
Large language model serving is limited by facility power.Self-training for cross-centre coronary vessel segmentation in X-ray angiography using pre-contrast frame subtraction
Deep learning models for coronary vessel segmentation in X-ray angiography often perform poorly on data from centres that were not represented in training.Hammering with Vampire in Lean
We present Vampire-Hammer, a hammer for the Lean interactive theorem prover that discharges goals using the Vampire automated theorem prover and reconstructs proofs checked by the Lean kernel.Decision and System One models in rheumatology: accuracy and calibration on specialty-training examination questions and diagnostic case vignettes
To evaluate the accuracy and calibration of Decision and System One models on rheumatology examination questions and diagnostic vignettes.