AI & Machine Learning · Computer Science & AI
Artificial Intelligence
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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 188
Generative Policy Improvement from Observational Data
We study how to improve decision policies from observational data to generate better actions when actions are complex, structured objects, such as text messages or images.ScoutRoute: Efficient Mixture-of-Experts Routing Using Internal Language Model Signals
Efficient expert routing is a fundamental challenge in Mixture-of-Experts (MoE) systems, particularly for heterogeneous collections of independently trained Large Language Mod els (LLMs).RECREG: Recovery-based Shielding for Regular Safety Properties in Reinforcement Learning
In many real-world scenarios we need to consider formal requirements, such as safety and constraint compliance, to deploy Reinforcement Learning (RL) agents.GatewayBench: Unified Capability and Security Evaluation Platform for MCP Gateways
AI agents have increasingly interacted with external environments by invoking tools, with Model Context Protocol (MCP) emerging as a standard interface for connecting agents to heterogeneous tools…S-GAP: Stealthy Graph Attack Primitives for Graph Large Language Models
Graph Large Language Models (GraphLLMs) enable powerful semantic understanding and reasoning over Text-Attributed Graphs (TAGs), while their semantic reasoning may exhibit vulnerabilities to…QD-Rec: Open-Ended Evolution of Recommendations via Quality-Diversity
Open-ended search combined with foundation models has produced advances such as faster algorithms, new results on open mathematical problems, and self-improving agents.A Topological Signature of Learning under Noisy Labels
How does noisy supervision change the representations learned by a neural network?LocalIntent: Outcome-Specific Structured Generation for Backend-Compatible Smart Building Automation with Local LLMs
Locally deployed large language models (LLMs) can reduce reliance on remote cloud services for natural language smart building automation, but backend-facing processing requires correct outcome…Trapped in Your Story: Evaluating Narrative-Induced Sycophancy in Large Language Models
Large language models (LLMs) are known to exhibit sycophancy by adjusting their responses toward users' expressed views.Truth in Contrast: Calibrated Factual Evidence from Language Model Likelihoods
Large Language Models (LLMs) encode substantial factual knowledge.From Billion-Scale to Million-Scale Language Models: Operational Capabilities for Embedded Post-Exploitation
Small Language Models (SLMs) are increasingly attractive for resource-constrained environments due to their low computational footprint.Reason-guided Human Reward Modeling for Preference-based Reinforcement Learning
Reinforcement learning (RL) policies optimized for task rewards may produce behaviors that are efficient but misaligned with human expectations, safety preferences, or intent.SkillBrowser: Selective Skill Exposure for Scalable Skill-Using Agents
Large language model agents increasingly rely on skills to extend their capabilities in complex tasks.Darwin-Claw: Environment-Adaptive Experience Consolidation for Recursively Self-Evolving Agents
While Large Language Model (LLM) agents have demonstrated remarkable capabilities across complex tasks, they predominantly operate in isolated sessions, lacking the ability to continually learn from…RL-Guided Temporal Localization for Dual-Channel Retrieval in Long-Horizon Agent Memory
Time-aware retrieval is crucial for long-horizon LLM agents to effectively leverage ever-growing long-term memory.From Trajectories to Differentiated Skills: Cognitive Differentiation for Self-Improving Agents
Large language model (LLM) agents tackle complex tasks through reasoning and interaction with external environments.SIT-Eval: Situation-Based Interactive Turn-by-Turn Evaluation of Conversational Agents
The rapid proliferation of conversational AI agents necessitates evaluation methods that assess performance across diverse dialogue scenarios, utilizing multiple situational criteria, as appropriate…Beyond Action Prediction: Learning Search-Free Planning in Latent Space
Latent world models learn action-conditioned state transitions in a compact representation space, providing a predictive model for visual planning and control.GEMS: Molecular Structure Elucidation as Learned Search over Isomer Graphs
Identifying small-molecule structures from molecular formulas and tandem mass spectra remains a bottleneck in untargeted metabolomics and natural-product discovery.PresenceBench: An Exploratory Evaluation of Large Language Model Adaptation to User State
Presence-aware intelligence is the capacity of an AI system's intelligence layer to adapt appropriately to the functional state of the person it serves (here readiness, flow, and stability) while…