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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: 4,993 arXiv preprints and 7 OpenAlex records.
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Newest papers, page 18
Swapping the Judge Changes the Ranking: A Preregistered Study of Cross-Provider Replication in LLM-Judged System Comparisons
Practitioners who compare systems with an LLM judge and have no human labels often check a conclusion by repeating it with a judge from another provider.The Agent Board: Kanban for Machines; Durable Coordination for Agents That Outlive the Conversation
Harnesses for large language models have converged on a single-threaded shape: one agent, one history, one machine, one sitting.Structured Clinical Memory as Context for Probabilistic Decision Models: A Synthetic Evaluation with Jev
Longitudinal clinical context is often distributed across noisy, repetitive forms and narrative records.Separating Common and Unique Directions for Model Merging
Model merging combines multiple fine-tuned models without additional training, and arithmetic methods have emerged as a simple yet effective way to merge models without data.Generalizable AI-Generated Image Detection with Embedding-Space Explanations
AI-generated images have reached unprecedented realism, while instruction-driven AI editors re-synthesize images for local changes, leaving no splice boundary to localize.The Evolution Strategies Paradox: Better Models from Worse Perturbations
How is it possible for perturbation-based methods like Evolution Strategies (ES) to optimize billion-parameter large language models (LLMs)?GlucoAgent: Personalized Glucose Forecasting from Multimodal Personal Context
Continuous glucose monitors, smartwatches, meal logs, and health profiles form a rich multimodal personal context that can significantly enhance glucose forecasting.Interpretable Early Termination of Web Navigation Agents via Closed Sequential Pattern Mining
Web navigation agents built on open-weight language models fail often and fail late, with most of the tokens spent after the trajectory has become unrecoverable.Palimpsestic Accountability in the US Federal AI Inventories
An AI inventory is a structured, often public record in which a government or organization lists and describes the AI systems it uses or plans to use.Improving PI-RADS Prediction and Prostate Cancer Detection from Multiparametric MRI with Anatomical Guidance
Accurate prediction of Prostate Imaging Reporting and Data System (PI-RADS) scores from multiparametric MRI (mpMRI) remains challenging, more so than clinically significant prostate cancer (csPCa)…What Remains in Sight? Autoregressive Video Decoding as Representation-Guided Context Rewriting
Long-horizon video generation is increasingly moving toward autoregressive rollout, where each newly generated segment becomes part of the evidence used for future prediction.Length Is Cheap, Competition Is Not: Attention Predicts Context Rot in Retrieval
Why do language models fail on long inputs?Validation-Gated ML Pipeline Synthesis with Expert-Grounded Self-Evolving Skills
Recent advances in large language models have enabled agentic systems that automate machine learning pipeline construction from minimal user input.Clocks all the way down: does sequence-processing AI need endogenous temporal structure?
Mainstream sequence-processing AI is temporally reactive: each update is driven by input and learned state transitions, rather than by an independently evolving internal temporal reference.When the Consumer Is an Agent: Execution-Grounded Service-Level Agreements
Large language model (LLM) agents increasingly act as service consumers.Agentic Path Maps: Exposing an Agent's Traversal Through the Garden of Forking Paths
When an AI agent executes an underspecified task, it embeds choices that may contradict unarticulated user context and preferences.DoGNAVY-Exploit: Harnessing Parallel Open-Weight Agents for Stage-Aware Vulnerability Exploitation
Frontier LLM agents can turn known vulnerabilities into working exploits, but are costly and often access-restricted due to their potential for malicious use.Impact of Input Resolution in Mammographic Mass Detection: A Controlled Multi-Architecture Study
Deep-learning-based mass detectors usually downsample mammograms, trading fine image detail for lower computational cost.Toward User-Centered Agentic Recommendation: When Does Platform Competition Benefit Users?
Conventional recommender systems operate within platform-specific catalogs, limiting access to potentially valuable resources across platforms.GC-MoE: Overlapping Expert-Parallel Token Dispatch with FP8/FP4 Grouped GEMM via CUDA Green Contexts
Under expert parallelism, mixture-of-experts (MoE) layers pay all-to-all token dispatch, grouped GEMMs, and a combine step in every layer.