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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 141
Asking a Multimodal LLM Whether Someone Fell: Zero-Shot Screening of Low-Resolution Thermal Video in a Laboratory and an Occupied Home
Thermal cameras can watch over older people living alone without capturing faces, and multimodal large language models (LLMs) can judge a short video without task-specific training.Unintended Side Effects of Value Training in LLMs
As LLMs are increasingly deployed in value-sensitive contexts, aligning them with human values has emerged as a post-training objective in NLP, aimed at instilling additional values and guiding…Privacy in Context Is Being Overlooked in the Age of General-Purpose LLMs
As large language models (LLMs) continue to grow in scope and capability, privacy concerns are on the rise.Your Benchmark Is an Empirical Measure Over Difficulty
A benchmark is not merely a collection of test questions; it naturally induces an empirical measure over difficulty.Graph Topology Meets Energy-Driven Pathfinding: Preserving Contextual Consistency in Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) frequently integrates knowledge graphs (KGs) for complex multi-hop question answering.ToM2Vec: Continuous Theory-of-Mind Tokens for Multi-Agent Coordination
Forming an accurate theory-of-mind (ToM) is key to successful multi-agent collaboration and requires reasoning under uncertainty to capture multiple possible partner states.Commit or Abstain? System One Decision Models, LLMs, and Missing Legal Authority
Given the enormous number of claims to be checked, verifiable legal AI requires scalable, cost-effective models that answer the practical needs of law.CulturalRiddles: A Multicultural Benchmark of Riddles
As LLMs expand their language coverage, evaluating how well they understand and represent diverse cultures becomes increasingly important.FaultGene: LLM-Assisted Fault-Augmented Test Generation for Hardware-in-the-Loop Validation of Autonomous Driving Systems
Reliable validation of autonomous driving cyber-physical systems requires testing not only nominal driving behavior but also failures arising from faults in sensing and embedded computation.TIDE: Tree-Induced Fine-Grained Diagnostic Evaluation from Model Behaviors
Benchmark performance is the dominant standard for comparing large language models (LLMs), typically summarized by a single aggregate score, such as accuracy, pass rate, or rubric score.HiddenLens: A Multi-Layer LLM Framework for Detecting and Resolving Hidden Assumptions in Software Requirements
Natural-language software requirements often omit assumptions about performance, operating conditions, user behaviour, and domain constraints, affecting architecture, testing, and deployment.Every Alarm Has a Cause: An Open Generator of Telecom Alarm Storms with Exact Root Causes, Bound to NORIA-O, RFC 8632 and TMF642
Alarm root-cause analysis is studied on data that cannot be shared.Building Auditable AI Text Detectors with Sparse Autoencoders
AI-generated text detectors are increasingly deployed in consequential settings---peer review, education, admissions, journalism, and content moderation---where their outputs directly affect…Agent Behavior Maps: Locating Agents by What They Would Do, Without Running Them
LLM agents with similar success rates can follow different action sequences.Code Comprehensor: Hierarchical Documentation and Cross-Modal Retrieval for Large Repositories
Understanding large, complex, and rapidly evolving code bases remains a core challenge for software engineering at scale, hindering onboarding, maintenance, refactoring, and impact analysis.GuideEvolve: Self-Evolving Agent Framework with Expert Guidance and 3Ts Mechanisms
This study introduces GuideEvolve, a self-evolving agent framework incorporating expert guidance and three autonomous regulatory mechanisms, collectively termed the 3Ts: Think Fast and Slow…Stationarity Constraints Improve Both Energies and Densities of Machine-Learned Exchange-Correlation Functionals
Empirical exchange-correlation functionals are fitted on fixed orbitals but used self-consistently, so better energies often come with worse densities.What the Author Wrote, What the Agent Reads: Silent Cue Loss in Enterprise Document Ingestion
Enterprise agents act on documents.Robust Soft Skill Adaptation via Behavioral Localization in Tool-Using Agents
Text skills can extend the capabilities of tool-using agents, but long skill descriptions increase computational costs.HGM-Dual: Lineage-Aware Guided Agent Harness Optimization for Long-Horizon Planning
Harness optimization, which improves an agent's prompts, tools, and workflows, is an increasingly effective way to enhance agents built on frozen or closed-source LLMs.