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 12 October 2026 to 6 November 2026: 4,992 arXiv preprints and 8 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 234
Train for Many, Update with One: Improving Multi-Sample Reasoning via Fenchel-Bregman Posterior Learning
Multi-sample reasoning measures candidate coverage, the probability that at least one of $K$ candidates is correct.DuplexAct: How Interactive Are Frontier Full-Duplex Spoken Language Models?
End-to-end (E2E) full-duplex spoken language models (FD-SLMs) enable simutanous listening and speaking continously, requiring models to take appropriate interaction actions as conversations unfold.HiSTOR: Structured Treatment-outcome Representation Learning for High-Dimensional Longitudinal Data
Estimating potential outcomes under alternative treatment sequences is essential for personalized decision-making, but remains challenging with high-dimensional covariates and time-varying…Coupled Harness--Model Evolution for Scientific Equation Discovery
Large language models (LLMs) are increasingly used to discover scientific equations from experimental data.Auditing Failure Attribution: Label Predictability and Judge Behavior
Failure-attribution benchmarks ask an LLM judge which step or agent caused a failure.I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation
Policies trained offline from teleoperated demonstrations can degrade outside their training distribution.Degrees of Freedom in Multi-Agent Coordination: Between Centralized and Decentralized Regimes
Large language model (LLM)-based multi-agent systems (MAS) are commonly organized around two coordination regimes.Instructions Are Evidence, Not Objectives: Measuring Outcome Alignment of Agents
Large language model agents can follow complex human instructions to solve real-world tasks.Gaussian Process Bandits for AI System Optimization with Selectively Verified Feedback
Gaussian process bandits exploit similarities among configurations to optimize prompts, retrieval systems, and agent workflows.Autonomous 3D Visual-Thermal Inspection for Utility-Scale Solar Farms
Utility-scale photovoltaic (PV) inspection is dominated by aerial surveys, which are effective for identifying module-level hot cells but provide limited visibility of critical components located…Memory Echo in Single-Agent Long-Term Memory: Lifecycle Evaluation and Provenance-Guided Mitigation
Long-term memory supports agents across sessions.Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking
Large reasoning models (LRMs) achieve strong performance by externalizing explicit reasoning traces before producing the answer, yet suffer from overthinking challenge that allocates uniformly heavy…Agentic4PDM: Host-Governed Evidence Admissibility for Industrial Fault Diagnosis
We study industrial fault diagnosis as a classification task that assigns sensor time-series windows to predefined health states or fault types.Posterior-Averaged Bellman Flow Matching for Distributional Critics
Distributional critics model the full return distribution, providing richer signals for exploration and risk-sensitive control.GIFT-Eval-CTX: A Benchmark For Contextual Forecasting Model Evaluation
Contextual forecasting seeks to improve time series forecasting by incorporating forward-looking information, such as planned events, external conditions, and expert expectations.Learning from fracture: Teaching language models to design through physical experience
How can language models learn to optimize physical designs through experience?Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution
Edge-cloud collaboration combines the lower inference overhead and greater data locality of edge-side execution with the stronger capabilities of cloud-hosted models.Same Attack, Different Judge: A First Controlled Adversarial Comparison of Jev, LLM Judges, and Purpose-Built Safety Classifiers
TypeSafe AI's Jev is a new kind of safety judge.Checking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR
Reinforcement learning with verifiable rewards (RLVR) has become a principal recipe for improving the reasoning of large language models (LLMs).Benchmarking and Training Reward Models for Cross-Source Evidence Grounding in Search Agents
Existing approaches to training and evaluating search agents focus heavily on final-answer quality, largely neglecting how generated answers map back to retrieved evidence.