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 213
Decentralized LLM Workflows via Sequential Auctions
We present a scalable framework for solving multi-step tasks through a decentralized market of self-interested agents.Beyond Downstream QA: Rethinking How We Evaluate Agent Memory
Agent memory benchmarks evaluate whether an agent's responses reflect correct knowledge, measuring downstream question-answering accuracy as a proxy for memory quality.TRACE: Topology-Aware Reconnection via Prediction-Adaptive Connectivity Enforcement for Thin-Structure Segmentation
Thin, elongated structures are particularly sensitive to connectivity errors, as even a few local segmentation mistakes can disrupt otherwise continuous paths.GNN-Guided Local Refinement for Efficient Layout Decomposition with DSA-MP
Multiple patterning lithography (MPL) has been widely used to improve lithography resolution in advanced nodes, but it faces cost limits as feature sizes shrink.Skill-Decoupled On-Policy Expert Distillation for Unified Vision-Language Models
Reinforcement learning (RL) can elicit specialized visual capabilities in vision-language models (VLMs), yet jointly optimizing heterogeneous objectives often introduces cross-capability gradient…OS-Helix: Unifying GUI Interaction and Code Execution in Native Computer-Using Agents
Real desktop automation is neither purely visual nor purely programmatic.Training-Free On-Policy Distillation
On-policy distillation consolidates specialist models into one student at the price of rollouts, teacher passes and gradient steps for every teacher.Shortcut Sensitivity in Agent-Safety Evaluations
AI agents take actions through tools.Language Is a Security Surface for Agent Guards
Step-level guards decide, before each tool call, whether an agent's next action is safe given the user's request and the trajectory so far.Disagreement-Guided Weak-to-Strong Routing
Small language models are increasingly deployed on edge devices and robotic systems to meet cost and latency constraints, yet their limited capabilities can make reliable deployment challenging.X-RIC: An Agentic RAN-Intelligent Controller Benchmark for Enhancing Reinforcement Learning
The O-RAN ALLIANCE introduces architectural flexibility through disaggregation and AI-driven control.Librarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future Queries
Reasoning over long document contexts remains a challenge for large language models (LLMs).LEASH: Uncertainty-Aware Weak-to-Strong Escalation and Oversight for Computer-Use Agents
Computer-use agents increasingly tackle long-horizon tasks, where running a frontier model at every step is expensive and a small open-weight model alone often fails.Asking Smarter Questions: Targeted Active Preference Learning for Underspecified Rewards
Reward learning typically assumes the human's input covers everything that matters for the task.Improving Environment-Agnostic Agents: The Role of Scaffolding
Agents built on stronger language models may increasingly construct useful procedures for themselves, reducing the advantage of developer-supplied scaffolding.EliteReplay: Preserving Diverse Solutions in RL Post-Training via Quality-Diversity Search
Reinforcement learning has become a common post-training technique to improve the accuracy of LLMs on verifiable tasks.Towards Continual Learning: Mitigating Forgetting With Subspace Tuning
Large language models (LLMs) acquire new skills through fine-tuning, but it can destroy capabilities they already have: catastrophic forgetting remains a central obstacle to continual learning in…Private Retrieval Augmented Generation via Random Projection
Retrieval-Augmented Generation (RAG) enhances the capabilities of large language models (LLMs) by querying external structured knowledge.AI Consilium: a Benchmark of LLM Clinical Multidisciplinary Second Opinions
Large language models (LLMs) can generate differential diagnoses for complex cases, but a useful second opinion must also be factually grounded, clinically coherent, actionable, and safe.Knowledge That Is Linear Before Fine-Tuning Generalizes After It
Not all facts a language model can recall support further learning equally well.