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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 12 October 2026 to 6 November 2026: 4,992 arXiv preprints and 8 OpenAlex records.
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Newest papers, page 245
Personalising Deformable Image Registration with Clinical Context and Deformation History for Adaptive Radiotherapy
Adaptive radiotherapy must account for anatomical changes during treatment, but doing so depends on reliably mapping targets, organs at risk and dose across serial images.Why Static Skill Inconsistencies Do Not Equate to Runtime Risks: A Behavioral Analysis of LLM Agent Execution
LLM agent frameworks heavily rely on external skill assets to standardize tool usage, encode operational workflows, and supply executable runtime resources.The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery
Modern coding agents are fundamentally reshaping the role of human input for computational research: they write code, launch in silico experiments, analyze their results, and autonomously operate…Beyond Equation Search: Conjecture-Guided Discovery with SCOPE and ClosureBench
Most symbolic regression methods optimize candidate equations, but improving an equation's fit does not establish whether its underlying scientific conjecture should be retained, revised, or rejected.Non-Abelian quantum Hall states at strong Landau level mixing: finding anyons with first-principles AI
Non-Abelian anyons, strange particles forming the basis of fault-tolerant topological quantum computing, are predicted to emerge in fractional quantum Hall states at even-denominator filling factors.Discovering Undisclosed Concepts in LoRA Adapters for Diffusion-Based AIGC Models
Low-Rank Adaptation (LoRA) has made diffusion model personalization affordable and widely shareable, but it also creates moderation risks when harmful or problematic content is not faithfully…Evaluating Large Language Models for Multilingual Abstractive Summarization: A Comprehensive Study of English and Bengali
Abstractive summarization remains challenging across languages, where linguistic quality and factual consistency may diverge, particularly in low-resource settings.CyberArena: Benchmarking AI Agents in Live Symmetric Attack-and-Defense Combat
AI agents are demonstrating increasingly strong offensive and defensive cybersecurity capabilities in the real world.Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction
Real measurements are too limited to train an accurate indoor pathloss predictor that works across a wide range of buildings.Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment
Pluralistic alignment needs to accommodate diverse preferences under limited human feedback.Loss Curvature as a Sampling-Free Proxy for Memorization in Diffusion Models
Understanding the mechanism behind why diffusion models memorize their training data remains an open question in generative modeling.Graph Coarsening Enables Escaping Local Optima in Compute-Constrained Nonconvex MAX-CUT
Because its binary optimization landscape contains exponentially many local optima, MAX-CUT is highly sensitive to initialization.Smart Alone, Blind Together: Why Accuracy Fails to CertifyTheory of Mind in Multi-Agent Teams
Coordinators that act on what they infer about teammates are usually certified by the accuracy of those inferences.RLPE: Teaching LLMs to Prompt with Reinforcement Learning
Prompting is a widely used method for adapting general-purpose LLMs to domain-specific tasks.How Inner Optimizers Shape Replica Scaling
Training large language models (LLMs) across geographically distributed and poorly interconnected compute pools or data centers has become a topic of significant interest.Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap
Generative retrieval (GR) has emerged as a strong candidate generator for sequential recommendation as language models and semantic-item identifiers have improved.Plan or Retrieve First? A Scale Study of Tool Discovery Architectures
Tool-augmented LLM agents discover relevant APIs from large registries before planning execution, yet finite context windows prohibit injecting all tool definitions into a single prompt.Beyond Aggregate Accuracy: Item-Level Transitions in Prompt Optimization
Automatic prompt optimization (APO) uses feedback from model responses to improve prompts automatically.DexMimic: A Generalist Dexterous Hand-Object Tracking Policy via Value-Guided Reset Allocation
Achieving human-like dexterous manipulation remains a central challenge in robotics.GroundGUI: GUI Grounding by High-Quality Data and Distillation-to-Reinforcement Learning
Constructing robust GUI agents relies on multimodal visual grounding: the ability to precisely map natural language intents to actionable on-screen components.