AI & Machine Learning · Computer Science & AI
Artificial Intelligence
The newest papers in Artificial Intelligence that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
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 113
Replicate What Varies: Resource Sharing for Domain-Randomized Embodied Simulation
Simulation provides a low-cost sandbox where embodied agents can attempt tasks, observe outcomes, and learn from experience.Auditing evaluation artifacts and data leakage in large language model systems for rare disease diagnosis
Augmented large language model (LLM) systems have reported high benchmark accuracies in rare disease diagnosis, yet their real-world clinical utility remains unproven.Counterfactual Model Testing Under Distribution Shift
Deployed machine learning (ML) models can degrade as the distribution of their input data shifts.Data Governance for Generative and Agentic AI : Technology, Law, Economics, and Security
This book develops a unified governance framework for systems that retrieve, generate, infer, remember and act on information from large language models and retrieval augmented pipelines to…Mycelium: A Meta-Study of Hallucination Detection Across Provenance, Typology, and Granularities
Hallucination detection is increasingly important for evaluating and deploying large language models.When Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems
Large language models (LLMs) increasingly produce online articles from material that later becomes input to other models.Graph-dependent pooling: adaptive granularity in hierarchical graph neural networks
Graph Neural Networks are widely used to learn structural representations of relational data.RSI Arena: Towards Recursive Self-Improvement with Human Feedback
Can AI agents autonomously train useful language models and improve them using feedback from users?RAQ-MLA: Role-Aware Quantization for Multi-Head Latent Attention Caches
Multi-Head Latent Attention (MLA) has been adopted by recent frontier large language models.ProbeID: Verifying MCP Tool Identity for LLM Agents under Registry Drift
Agents select tools by copyable descriptions, which cannot establish which implementation runs.DROA-CLIPSeg: Prompt-Guided Thin Crack Segmentation in Low-Light Conditions
Pavement crack segmentation under low light is degraded by reduced contrast, shadows, headlight glare, joints, stains, and wet seams.Build or Adopt? Custom-Built vs. General-Purpose LLM Agent Harnesses for Automotive GUI Testing
As general-purpose LLM agent harnesses mature, organizations must decide whether to continue developing in-house systems or adopt existing alternatives.AlphaIdeaBench: Benchmarking Automated Research Idea Generation under Feasibility Constraints with Peer Review Alignment
Research ideas are always proposed under resource constraints: a plan must fit the available compute, API credit, and time.TAD: Distilling Vision-Language-Action Policies from Teacher Trajectories Alone
Online distillation of vision-language-action (VLA) policies commonly relies on teacher action scores that an inference-only interface may not expose.Jointly Modeling Preferences and Imperfect Decision-Making in Large Language Models
Do preferences Large Language Models (LLMs) express in standard elicitation tasks predict their strategic behaviour?Physics-Informed Graph-Based Safe RL with Constraint-Specific Critics for Community Battery Coordination in Distribution Network
High penetrations of distributed energy resources (DERs) and fast-varying electric vehicle (EV) charging intensify voltage and line-limit violations in distribution networks (DNs), making safe…Power-of-Two Leaky Integrate-and-Fire Neurons for Event-Driven Vision-Language-Action Inference
Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, yet their reliance on dense, continuous-valued computation presents a fundamental challenge for…SWE-Ladder: Learning to Localize, Reproduce, and Patch via Progressive Privileged Self-Distillation
Resolving a repository-level issue typically requires a software-engineering (SWE) agent to localize the faulty code, reproduce the failure, and patch the code.Extraction meets Distillation - cryptanalytic techniques to recover parameters from a Deep Neural Network
We study fully connected neural-network reconstruction by combining cryptanalytic weight-direction extraction with learning from predicted labels.Meta-CLIP: Metadata-Conditioned Contrastive Learning for Zero-Shot Disease Inference
Medical vision--language pretraining learns transferable representations from paired images and radiology reports, yet differences in scanners and acquisition protocols can impair transfer.