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.
Get the few papers worth your time in Artificial Intelligence every morning.
Follow Artificial Intelligence and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 94
Assessing Language Models for Scientific Applications: Inertial Confinement Fusion as a Use Case
Advancing language models for specialized scientific applications requires evaluations of scientific fluency, physical reasoning, and quantitative analyses.Making Sense of Audience Change: Causal Attribution and Boundary Management on Xiaohongshu
Recommendation-driven platforms can reshape not only what users see, but also who becomes visible and how community boundaries are negotiated.METATRON: A Promptable Whole-Body Execution Layer for Humanoids
Humanoid controllers often require task-specific training despite sharing the need for balance and whole-body coordination.Measuring Deep Learning Performance - An Empirical Study of Performance Distributions Across Architectures and Tasks
Non-determinism in deep learning algorithm design and implementation leads to performance variation, meaning model performance is not a single value, but rather a distribution.EARS-Info: Information-Aware Event-Driven Reallocation for Multi-Robot Exploration
We introduce EARS-Info, an information-aware extension of Event-Driven Ant Reallocation Search for multi-robot exploration on OpenStreetMap-derived grids.Can Event Detectors Leave the Broadcast Camera? Evidence from Tennis
Ball bounces and racket hits are the events from which tennis statistics are derived, and on monocular video the two are easy to confuse.PromptSense: Sentiment-Aware Mental Health Chatbot with Dynamic Prompting
Current Large Language Models (LLMs)-based mental health chatbots often lack sufficient empathy, contextual awareness, and safety mechanisms, potentially leading to ineffective or harmful…From Error Prediction to Causal Influence: Auditing SAE Features in Multiple-Choice QA
Sparse autoencoders (SAEs) can predict internal features associated with model errors, but association alone does not establish causal influence.Prompt Lottery in LLM-as-a-Judge: Measuring and Mitigating Sensitivity to Syntactic Rephrasing
LLM-as-a-Judge has become a standard for evaluating large language models, yet most approaches implicitly assume that a single judge prompt functions as a stable evaluation instrument.Interpretable Feature Discovery from Unstructured Data Using LLMs and Statistical Choice Models
Feature extraction from unstructured data typically relies on opaque embeddings unsuited to interpretable decision-making.SlowLab: Benchmarking Autonomous AI Agents on a Year of Experiments They Cannot Undo
AI agents are increasingly proposed as autonomous experimenters, yet the benchmarks that test them mostly offer cheap experiments that answer at once.RobotAgentBench: Towards Open-world Robot Autonomy
Recent advances in vision-language-action (VLA) and world-action models (WAMs) have significantly improved robotic manipulation, yet deploying robots in the open world demands a higher level of…Thinking-in-Bets at Test Time Reduces Sycophantic Revisions in LLM Agents
Language model agents often abandon correct work when a user challenges it, yet simply telling them to stand firm makes them ignore real mistakes.AdaTutor: Adaptive Tutoring through Pedagogical Strategy Selection
Effective tutoring requires determining not only what information to provide, but also which form of assistance to deliver.Video World Models Need Predictable Latents
Video world models trained on large-scale robot data promise a safe and scalable approach for learning and evaluating policies that act in the physical world.Hierarchical Task Network planning in the Goal Lifecycle
Goal reasoning (GR) is the process by which intelligent agents continually reason about the goals they are pursuing, which may lead to goal change, for instance when opportunities arise, or when…Agent Memory as Recommendation: Recommender-System Design for Long-Term Memory of LLM Agents
Large language model (LLM) agents that serve users across many sessions must choose, for every request, the few stored memories that enter a limited context window.Benchmarking Obstacle-Conditioned Safety in Household Robot Navigation
Safe household navigation requires robots to regulate clearance and speed according to what an obstacle is and how close it is, without unnecessary conservatism.Curriculum-Guided Acquisition of Hierarchical Task Knowledge from LLM-Generated Experience
Hierarchical task knowledge enables efficient problem solving by allowing previously acquired skills to be reused across tasks and problem instances.Topic-Level Fairness in Retrieval-Augmented Generation
Group fairness in retrieval-augmented generation (RAG) systems is typically evaluated using metrics aggregated across queries.