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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 174
UtoMe: Observation-Uncertainty-Guided Token Merging for Weather Foundation Models
Weather transformers process dense reanalysis fields whose reliability varies across variables, locations, and weather regimes.UpLoop: Upcycling Large Language Models with Gated Loop
Looped large language models trade parameter storage for recurrent computation, but most are trained from scratch.ReTrace: Visual Backtracking for Efficient Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) encode images into dense visual token sequences, creating substantial attention and KV-cache overhead.IECLA: Interpretable Emotion Contribution Learning for Affective Image Editing
Affective image editing (AIE) aims to evoke a target emotion while preserving the content and structure of a source image.LLM Agents as Suppliers of Physical System Models: Calibration and Acceptance in Modelica
To procure a model of a device, a client hands a supplier a description, sometimes with recorded data or a target response, and accepts the model against this handover.Synergy-Aware Residual Flow Matching for Single-Cell Perturbation Prediction
Predicting single-cell responses to genetic and chemical perturbations is important for understanding regulatory mechanisms and prioritizing therapeutic interventions.One Head Away from Language Confusion: Single Attention Heads Across Model Families
Large language models sometimes reply in an unexpected language, a failure known as language confusion.VLA-Rolling: Fewer Denoising Steps, Better Control via Adaptive Action Reuse
Flow-matching vision-language-action (VLA) models generate an action chunk by iteratively denoising Gaussian noise, but typically execute only a short prefix before replanning with updated…SteadyThought: Mitigating LLM Under-Thinking via Thought-Level Preference Optimization
Flexible switching between reasoning trajectories (i.e., thought switching) has significantly enhanced the reasoning capabilities of Large Reasoning Models (LRMs).HCFM: Helmholtz Component Flow Matching for Time Series Anomaly Detection
Flow Matching (FM) is an efficient simulation-free approach for training flow-based generative models, making it a promising foundation for scalable time-series anomaly detection (TSAD).Policy Is Not Capability: Some Safety Benchmarks Measure Which Definition of "Unsafe" You Trained On
A guardrail classifier is reported as if unsafe were a property of text.One Skill Takes Over: Clashing Agent Skills and a One-File Fix
Agent Skills are instruction files that tell a coding agent how to do one job.Auto-MD-bench: Evaluating Autoresearch Agents for Real-World Medical Tasks
Autonomous agents are increasingly capable of developing machine learning (ML) models end to end.DAGLoop: Aligning Method Design with Experimental Feedback in Automated AI Research
Automated AI research agents aim to improve machine learning methods by iteratively proposing, implementing, and evaluating candidate methods.Toward Agentic Retrieval-Augmented Generation for Enterprise Retail Workflows over Heterogeneous Data Sources
Enterprise retail workflows require agents to chain evidence across heterogeneous sources, from structured product and order records to unstructured inquiries and reviews, yet existing agentic RAG…Pre-Query Retention with Bounded Bit and Word Retrieval
An agent can retain information before receiving a future question and later retrieve evidence from an archive.Explore or Confirm? LLM Agents Misallocate Measurements Under Noise
As model reasoning gets cheaper, the physical experiment becomes the slow and costly step in discovery.Self-organizing reinforcement learning: a learner-free swarm approaches the explore-exploit optimum
The explore/exploit trade-off is the core problem of reinforcement learning (RL).Compile Once, Run Cheap: Frozen, Portable Skills for Embodied Agents, and What Recursive Self-Improvement Adds
Embodied AI lacks the data-rich pretraining that gave language models their agents and skills, so new manipulation capability costs demonstrations per task or a frontier model in every episode.ClinSDT: LLM-Encoded Clinical Semantic Guidance for Sepsis Treatment via Decision Transformer
Offline sepsis treatment aims to learn treatment policies from retrospective ICU trajectories.