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.
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Newest papers, page 208
Tool-Augmented Large Language Models for Reliable Medical Calculation from Patient Notes
Medical calculators translate raw patient observations into clinically actionable values and scores.AI-Driven Disaster Response and Displacement Monitoring
ARTIFICIAL INTELLIGENCE (AI) has demonstrated its potential across a wide range of humanitarian tasks, offering scalable and data-driven solutions to some of the world's most pressing challenges.FlashAttention with Statistics: Low-Overhead Attention Features for Compact Hallucination Detectors
Large language models can produce fluent answers that the supplied context does not support.MetaExp: Learning to Self-Improve through Experience via Verifiable Cross-Task Environment Scaling
Language-model agents can accumulate interaction feedback, but reliably turning that feedback into reusable experience remains challenging.ISW-Agent: Integrating LLM-Driven Dialogue with n8n Workflow for Internal Solitary Wave Remote Sensing Image Processing
Internal solitary wave (ISW) remote sensing requires task parameters and spatial information to be transferred across image acquisition, detection, stripe extraction, and propagation prediction.Decomposing Brain-LLM Alignment with Interpretable Semantic Representations
Large language models (LLMs) predict neural responses during naturalistic language comprehension, but the linguistic information underlying this brain alignment remains difficult to interpret.QREF: Improving DeepSearch Agents by Aligning Search with Reasoning via Query Refinement and Evidence Filtering
DeepSearch agents achieve strong performance on complex information-seeking tasks through iterative search and reasoning.Poison Valleys: Diagnosing Path-Dependent Failure in LLM Code Repair
When code-repair agents fail, final tests reveal that a patch is wrong but not which earlier commitment caused it or which software obligation failed.SCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics
Understanding how the human brain constructs meaning from real-world language remains a fundamental challenge, as semantics depend on context that varies in natural settings.Hierarchical Interpretable Multitask Audio Representation Learning: An Analysis of Event-Scene Relationships through the Information Bottleneck
Joint Acoustic Scene Classification (ASC) and Audio Tagging (AT) models traditionally rely on unconstrained multihead architectures.CriticaLLM: Using LLMs as Autonomous Game Master for Tabletop Role-Playing Games
Large Language Models (LLMs) have shown impressive capabilities across many tasks, including processing large amounts of text, following instructions, and generating ideas.Functional Subnetwork Localization for Efficient Continual Learning in Large Language Models
Biological intelligence continually adapts to new information while retaining prior skills through localized plasticity.Agent Status Protocol (ASP): A Lightweight Specification for State Reporting in Multi-Agent Systems
The Agent Status Protocol (ASP) defines a lightweight way for heterogeneous AI agents to report their operational state to a status server, and for observers such as dashboards to receive that state…Reinforcement Learning-driven Diffusion Models: Language, Vision, and Beyond
Diffusion models (DMs) have steadily demonstrated outstanding performance in high-fidelity, controllable generation across diverse scenarios, following the "pretraining-finetuning" paradigm…When Is Muon Optimal? Radial Fragility and Minimax Geometry
Muon replaces a matrix gradient by an approximate polar factor and has shown strong empirical performance in neural-network training.Uncertainty Quantification in Pairwise Difference Learning for Classification
Instead of learning a mapping from instances to outcomes in the standard way, the key idea of pairwise difference learning (PDL) is to learn a function that takes two instances as input and predicts…When Does Muon Actually Win? A Paired Data-, Time-, and Tuning-Budget Benchmark
Muon's gains in data efficiency motivate evaluating its performance under explicit data, time, and tuning budgets.DialFork: Going Beyond Factuality to Evaluate Procedural Hallucination in Conversational Agents
Large language models (LLMs) deployed as conversational agents are increasingly expected to follow procedural logic.BiShield-TEE: On the (In-)Security of Unilateral Weight Obfuscation in On-Device TEE-Shielded LLM Partition
On-device deployment of large language models (LLMs) is increasingly adopted due to latency and privacy requirements, but exposes proprietary models to reverse engineering and intellectual property…Independent Data-Integrity Verification for LLM-Driven Functional Coverage Closure in Hardware Design
LLM agents can write directed tests that close functional coverage, but coverage only shows that a state was reached, not that the data on the way there was correct.