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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 209
Rethinking the Role of Tool Graph Memory in Multi-Turn Tool Use
Large language model (LLM) agents solve complex tasks by calling external tools over multiple turns.Learning to Search for Executable Patternmaking from Certified Programs
Recent models generate sewing patterns from images or text, and program-based representations make these patterns executable.ClaimScope: Label-Blind Witness Verification for Mathematical Claim Auditing
Mathematical agents are commonly evaluated by answer accuracy or proof acceptance.AVALON: Zero-Shot Indoor Navigation with Human-Seeded Topological Memory
Indoor service robots often enter unmapped buildings whose layouts and task constraints are already familiar to users.LOADBench: A Large Open Analog Dataset and Benchmark for Machine Learning in IC Design
Machine learning (ML) for analog IC design is constrained by limited open data and the absence of standardized evaluation: results reported in different papers are typically obtained on bespoke…LoopAhead: Speculating Ahead for Faster Decoding in Looped Language Models
Looped Transformers improve parameter efficiency by repeatedly applying the same Transformer layers.Decoding What iOS Location Services Send to Apple: An LLM Is an Unreliable Analyst but a Useful Toolmaker
Reverse engineering the data formats used by mobile software is skilled and slow, and using AI to assist with it is attractive.Evaluating Inquiry Learning in LLM-Based Agents: Hypothesizing, Reasoning, and Exploration
Scientific inquiry involves iterative cycles of exploration, hypothesis generation, and reasoning over observations to uncover relationships in complex systems.Learning Intrinsic Dynamics in Latent World Models
Learning intrinsic dynamics---the relationship between actions and their consequences---is essential for world models.Qwen-Audio-Jev: A Unified Interface for Audio Decisions
Large audio language models (LALMs) have made substantial progress in audio understanding.AgentPerturb: Evaluating LLM Agent Robustness under Controlled Interaction Perturbations
Real-world tasks are often complex and noisy, posing substantial challenges for LLM agents.Efficient Pairwise Difference Learning: A Comparative Study of Encoding Strategies and Training Pair Selection
Pairwise Difference Learning for Classification (PDC) enhances supervised learning in low-data regimes by training a binary similarity classifier on pairs of instances rather than individual…The Self-Improvement Problem
With the rise of large language models, agentic systems are becoming increasingly autonomous, as they write code, call tools, revise prompts, and propose modifications to their own components.Data World: Scaling Data Environments from Scratch
Data agents use code to process heterogeneous files and derive analytical insights.Router Drift Control: Mitigating Off-Policy Degradation in Mixture-of-Experts Reinforcement Learning
Reinforcement learning (RL) can become off-policy due to training-inference mismatch, mini-batch updates, and asynchronous training.Human Interventions as Labels: Localizing, Steering, and (Sometimes) Editing the Computations Behind LLM Agent Failures
When an LLM agent misbehaves, a human steps in, and that intervention is usually discarded or replayed as training data.Encoded but Exposed: Language Embeddings can Reveal More Than They Should in Recommender Systems
Language embeddings are increasingly used in recommenders to represent item content, but their privacy risks remain poorly understood.FIRM: Fisher-Informed Retention Merging for Federated LLM Unlearning
Federated LLM unlearning addresses targeted knowledge removal when forget and retain data remain distributed across private clients and are inaccessible to the server.Retrieved but Not Read: Understanding Why Search-Augmented LLMs Fail to Use Available Evidence
Web search allows LLMs to access up-to-date information, but it remains unclear why they sometimes produce outdated answers even when relevant evidence is available.In-Context Preference-Conditioned Pareto Set Learning from Limited Offline Data
Multi-objective optimization involves diverse preferences over conflicting objectives.