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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 104
Enhancing Deep Learning Model Performance through Data Generation Strategies: Classification of Olive Tree Diseases
Deep learning models are highly effective in a wide range of domains, particu larly in plant disease diagnosis.Let the Gate Decide: Per-Lookup Precision for Quantized Conditional Memory in LLMs
Conditional memory layers, such as DeepSeek's Engram, attach large hashed n-gram tables to a language model, and removing them from DeepSeek-V4.1-Flash costs 36.8 PopQA points.PROBE: Frontier Coding Agents Find Different Bugs Than Maintainers Fix
Proactive bug discovery is an important software-engineering problem.Layerwise Repacking: Variable-Length Hybrid-Attention Training without an Extra Redistribution Pass
Variable-length training creates conflicting parallelism requirements across the layers of a hybrid-attention model.Pricing Cross-Bank Context in Federated Anti-Money Laundering
Most graph-based Anti-Money Laundering (AML) methods assume access to a pooled transaction graph containing all accounts and payments, which in reality does not exist.PolicyMediator: Multi-Regional Policy Learning through Mediation Agents with Evolving Skills in OPC Ecosystems
One-person companies (OPCs) are emerging enterprises operated by individual entrepreneurs through digital platforms and LLM-based AI agents.MedJev: Evaluating Zero-Shot System-One Decision Making in Medicine
System One decision models such as Jev answer structured questions in a single forward pass without generating text, offering fast and computationally efficient clinical decision support.DuplexTune: Coupled-State Replay for Memory-Efficient Mixture-of-Experts Adaptation
Sparse expert execution does not remove the stored states needed to adapt a mixture-of-experts language model.CIVER: Causal Intervention Verification for Reliable Code Review
Code review is essential for maintaining software repositories by identifying defects, improving code quality, and supporting reliable software evolution.Do Coding Agents Still Need a Debugger? Frontier-Model-Based Fault Localization at the Accuracy Ceiling
To localize a fault, a coding agent can reason from source code alone or use debugger assistance by running the program and stepping through it in an interactive debugger.Rollback: Experience Backtracking in World Models for Policy Improvement
Action-conditioned world models offer a scalable alternative to costly real-world interaction for improving robot policies.The Trinity:Capability-Driven Recursive Self-Improvement for Multimodal Agents
Multimodal intelligence can be viewed as human-guided co-evolution between worlds and agents through interaction.How Much Does the Hint Matter? Scope Conditions for Hindsight Self-Distillation in Financial Reasoning
Financial institutions operating under data-residency constraints are restricted to open-weight models small enough to locally host, which trail frontier systems on multi-step quantitative reasoning.CohortFlow: A Steerable Clinical Cohort Curation Agent with Localized Semantic Correction
Language model agents for clinical cohort curation should accommodate evolving eligibility criteria without repeatedly processing unchanged requirements.Log-To-Leak: Prompt Injection Attacks on Tool-Using LLM Agents via the Model Context Protocol
LLM agents increasingly access external tools through the Model Context Protocol (MCP), but the natural-language metadata used to describe those tools creates a prompt-injection surface.Dirichlet Process Mixtures within Semiparametric Bayesian Networks: A Novel Conditional Density Estimation Approach
Semiparametric Bayesian networks (SPBNs) are flexible probabilistic graphical models for representing joint distributions of continuous variables through lower-dimensional conditional probability…Scaling and Renormalization in Neural Networks: An Effective Renormalization-Group Description of Grokking
Grokking is a delayed transition from memorization to generalization in deep neural networks (NNs).Context Is a Pipeline, Not a Window: Information Attrition in LLM Agent Harnesses
LLM agents are judged by two numbers: the size of the context window, and how often the final answer is correct.Why and How to Do Science When AI Is Better at It: A Framework Grounded in Human Understanding and Direction
As AI begins to match or exceed human researchers at core scientific tasks, two questions become pressing: \emph{why} should humans keep doing science, and \emph{how} should they use AI when they do?When Connectivity Misleads: Guiding Agentic Search with Contrastive Evidence Paths
Search agents can use cross-document graphs to discover sources through multi-hop connections and choose subsequent actions based on candidate rankings and relationships within a limited observation…