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 89
ReForest: Interpretability Meets Reasoning in Isolation Forest
Statistical anomaly detection methods like Isolation Forest are efficient but suffer from a critical semantic gap.AnalogScout: Simulation-Efficient PVT-Aware Analog Sizing with LLM Agents
Analog device sizing must satisfy all specifications across process, voltage, and temperature (PVT) corners to ensure tape-out success, but simulating every candidate at all corners dominates the…WCC: A Learning-Based Framework for Deciding When to Apply Cooperative Coevolution in Large-Scale Optimization
Cooperative Coevolution (CC) and Non-Decomposition Algorithms (NDAs) are two primary approaches for large-scale global optimization (LSGO).CoS-VQ: Coordinated Shared-Scale Vector Quantization for Large Language Models
Large language models (LLMs) require substantial memory, motivating low-bit weight quantization for efficient deployment.Aligning Institutional Power and Operational Ability
To be socially sustainable, Agentic AI systems require normative designs that align responsibilities with the ability to fulfil them.Uncovering Why LLMs Fail: From Internal Mechanisms to Adversarial Reasoning Trajectories
Large language models (LLMs) are usually evaluated by final-output correctness or safety, yet an endpoint label does not reveal how a failure develops throughout the reasoning process.Agent-Conditioned Routing: Diverse Reasoning Paths Within One Frozen Model
Generating several solutions can expose alternative methods, but additional trajectories often paraphrase one dominant approach.Channel Selection for Mixture-of-Experts Decoding Needs No Calibration Data
Mixture-of-Experts (MoE) models are bottlenecked by loading routed-expert weights from memory during interactive decoding at small batch sizes.JEPA-Inspired Latent Field Prediction for Sparse-Pilot MIMO-OFDM Channel Reconstruction
Accurate acquisition of full channel state information (CSI) is essential for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless systems, while dense…Learning Quality-Controllable Robot Policies with Iterative Hindsight Relabeling
Robot policy evaluation often relies on a binary success signal for task completion.Clearing the FoG: Training KG Agents to Retrieve the Evidence Behind Their Answers
Large language models (LLMs) can answer many factual questions correctly, but a correct answer is not necessarily a supported one.Constraining the Commit Boundary: A DIO/ODI-Informed Authorization Guardrail for Agentic Entity Resolution
Agentic Entity Resolution (ER) systems iteratively plan actions, retrieve evidence, revise hypotheses, and propose graph mutations.Beyond the Readout: A Latent Numerical Frame Shared Across Modality and Operation
When a multimodal model computes 17+35 from text or 91-39 from a rendered image, it produces the same answer, 52.Bayes-Adaptive MDPs for Rollout Allocation to Enhance GRPO Training
Group Relative Policy Optimization (GRPO) has demonstrated strong performance in LLM post-training.FeatureArc: Benchmarking Coding Agents on Evolving Features
A software feature usually evolves over time: developers extend its first version as new use cases and requirements arrive, and each extension builds on code written earlier.Representing the Unseen: Rethinking Visual Imagination for Spatial Reasoning
Visual world models increasingly support spatial reasoning by generating RGB views of unobserved states.Test-Time Communication in Trading Agent Swarms
Communication between agents has mostly been studied on tasks with a shared objective and fast, clear feedback.M4Bench: Evaluating Procedural Specification for Clinical EHR Derivation Agents
Clinical research over electronic health records depends on derived variables whose definitions sit outside the schema, in item codes, time windows, missingness rules, and output-grain choices that…Concordat: Typed Sessions for LLM Agents
Agents built on large language models are increasingly allowed to take consequential actions: cancelling bookings, issuing refunds, changing accounts.PerceptRL: Making Visual Perception Rewardable in MLLMs from Answers to Interactions
Fine-grained visual perception remains a bottleneck during post-training of multimodal large language models (MLLMs), as perceptual errors can constrain gains from reasoning-focused optimization.