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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 207
Learning to Reason with Selective Internal Feedback
Reinforcement learning from internal feedback can improve language models without ground-truth answers or external verifiers.Self-Guided LLM Reasoning for Error Diagnosis in Vehicle Routing Problems
Vehicle Routing Problems (VRPs) are fundamental to many real-world applications.Matoi: Selective Revalidation of Consequence Evidence for Agent Authorization
Matoi separates reusable consequence evidence from current execution authority and durable occurrence history.When do quantum views help? Complementary classical-quantum representations in multi-view fusion
Recent studies have reported promising results from quantum machine learning in drug discovery, healthcare, materials science and cybersecurity.Watts If? A Benchmark for Counterfactual Reasoning About the AI Data Center Buildout
An AI data center is committed years before it draws a watt: a few discrete choices (grid or on-site gas, evaporative or dry cooling, which county to file in) fix its capital, water draw, and carbon…Multi-Reward Hacking: From Aggregation-Induced Drift to Trust-Anchored Defense
To improve task performance while encouraging desirable response properties, LLM post-training often combines a primary reward (e.g., correctness) with auxiliary rewards (e.g., brevity or efficiency).MemCut: Every Secret Leaves a Trail in the Long-Term Memory of LLM Agents
Long-term memory has become a crucial component of large language model (LLM) agents, with most research focusing on retrieving relevant information accurately and efficiently.Jev-LiteMAS: Separating Decisions from Generation in Multi-Agent Workflows
Multi-agent LLM systems rely on generative models for expert execution, workflow planning, and artifact review.JevAgentHarness: Benchmarking, Deploying and Training Decision Models That Share Control with LLMs
Large language model (LLM) agents now fix software, operate computers and serve customers, but their harnesses, the programs that run them, call a large LLM at every step, which makes long tasks…Where, How, and When: Query-Guided Grounding for Video Understanding
Understanding video content requires reasoning at multiple levels of granularity, from identifying objects to interpreting their interactions and localizing when specific actions occur.Do Agents Need a New Kind of Paper? SPRA: Structured Paper Representation for Agents
Scientific agents must connect research problems, methods, and findings dispersed through narrative articles.Learning from Interaction Experience via Localized Context Distillation
Learning from interaction experience is essential for enabling large language models to continually improve during deployment.V-SHARE: Provable Convergence Guarantees for Advantage-based Algorithms
In reinforcement learning, advantage-based value-learning methods, unlike standard Q-learning, share information across actions by decomposing the action-value function into a state-dependent value…Incentivizing Tool-Use Reinforcement Learning for Post-Arrival Emergency Care Prediction
Large language models (LLMs) can reason over both structured measurements and free-text records, which makes them attractive for emergency department (ED) prediction.X-Fly: Learning to Reach and Stop in Full-Chunk Aerial Vision-Language Navigation
Aerial vision-language navigation (VLN) agents built on large vision-language models are expensive to query.Full-Duplex Interaction with Speech, Expressions and Gestures
Human face-to-face conversation requires coordinating speech, gestures and expressions across both speaking and listening.Can Visual Generation Expand What Unified Multimodal Models Know?
Unified multimodal models (UMMs) receive image-generation supervision that exposes them to visual information not fully documented in text.Trajectory-Level Speculation with Semantic Verification for Tool-Using Agents
Large language model agents solve tasks through long sequences of tool calls, and the prevailing design has a single strong model generate every step, although many steps are routine enough for a…Ontology Refinement with Embeddings and LLMs: A Case Study on CASTON
Ontologies are widely used to represent and organize complex knowledge in domains such as biomedicine and healthcare.Can LLMs Tell Stories Out of Order? A Corpus and Benchmark for Nonlinear Narrative Generation
Every story has two orders: the order in which its events happen and the order in which they are told.