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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 110
CaliMem: Search-Efficient Data Agent via Calibrated Memory
Data curation is a first-order design choice in machine learning, yet tailoring training data to a downstream model and task remains costly in labor, expertise, and compute.Multimodal Requirements Traceability Link Recovery in Industrial Settings
Requirements traceability links support activities such as change-impact analysis, coverage checking, and validation.The Price of Scale in Proactive Memory Recall
A long-lived assistant that brings up memories without being asked must decide at every turn whether anything in its store is worth an interruption.TRIAGE: Efficient Vision-Language-Action Inference Needs Both Reuse and Pruning
Vision-language-action (VLA) models are increasingly used for robot manipulation because they map camera images and a language instruction directly to actions.Conviction Without Evidence: Verdict Stability Is Independent of Visual Grounding in Medical Vision--Language Models
Vision--language models applied to medical question answering can reach correct diagnoses without using the diagnostic image, inferring instead from the clinical vignette.How Humans Learn Unfamiliar Worlds: A Think-Aloud Dataset on ARC-AGI-3
Learning through exploration and using newly acquired knowledge to achieve goals are central capabilities of general intelligence.Acute Stroke Classification Using Asymmetric Data Standardization and Orthogonal-Focal Loss Optimized CNN-ViT Cross-Attention Fusion: A Multicenter External Validation Study
Background/Objectives: Rapid differentiation of ischemic and hemorrhagic stroke subtypes is vital for acute management.SentryBench: A Cost-Sensitive Benchmark for Adversarial Robustness of Tool-Using LLM Agents in Public-Safety Deployments
Large language model (LLM) agents are increasingly wired to consequential tools that send public alerts, dispatch responders, and query citizen records, where one erroneous tool call carries…LLM WikiLeaks: Uncovering URL-Based Capability Extension and Security Circumvention in Agentic Message Boards
Autonomous agents have been observed acting beyond their assigned tasks, accessing unauthorized systems, and establishing communication channels to exchange information, yet the Web and…How Much Information Can We Fit inside a Transformer?
How efficiently can transformers store factual knowledge?GRASP: Group-Adaptive Hybrid RL-SFT Post-Training for Medical VQA without Reasoning Annotations
Adapting general-purpose vision-language models (VLMs) to medical visual question answering (VQA) is valuable for helping clinicians read images and make diagnoses, but annotated data are scarce and…Recursive CLIP: Vision-Language Distillation with Looped Transformer Blocks
Contrastive vision-language models such as CLIP typically rely on independently parameterized transformer blocks across depth.Agentic AI Orchestration Framework for Spatial Intelligence: Supply Chain Deployment
Industrial spatial decisions are assembled across systems that share neither a reference frame nor an owner, and analysts spend substantial effort reconciling those systems rather than reasoning…Rethinking Verification in Agent Harnesses
Agent harnesses enable large language models to act through tools, observe the results, and iteratively work toward a task goal.HobbyLM: A Sparse Mixture-of-Experts Model Family on a Hobby Budget
We describe HobbyLM, a family of small sparse Mixture-of-Experts (MoE) models trained from scratch with a few hundred to a few thousand dollars of rented GPU time.Large Language Models: A Plain-Language Overview for Engineers
Large language models (LLMs) are neural networks trained on huge text collections to generate text by predicting what comes next.HARP-LoRA: Hierarchy-Conditioned Rank Projection for Low-Rank Adaptation in Scientific Table Understanding
Tabular data is widely used in scientific applications, where complex layouts, including multi-level headers, merged cells, captions, and footnotes, make information extraction difficult.Audio-R1: Audio Reasoning through Reinforcement Learning on Self-Supervised Tasks
Reasoning is increasingly important for large audio-language models (LALMs), yet remains relatively underexplored compared with text-only and vision-language models.A Battery for AI Propensity Measurement: Rubrics, Synthetic Item Banks, and Annotations
We release a battery of 28 propensities for AI research, with expert-written rubrics, synthetic datasets, and annotated demand intervals, built with the method of Romero-Alvarado et al. (2026).CSI-TextBench: A Dataset and Benchmark for Language-Grounded Ambient Sensing Perception
Indoor context deciphered by ambient sensing underpins many applications, from assisted living to smart buildings, yet remains weakly connected to the language-aligned paradigm of modern multimodal…