Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2603.07955v4 Announce Type: replace-cross Abstract: We develop a reinforcement learning pipeline for simplifying knot diagrams. A trained agent learns move proposals and a value heuristic for n...
arXiv:2609.05749v1 Announce Type: new Abstract: Work on the risks of artificial intelligence has focused predominantly on capability risk: the danger that systems become too powerful, too autonomous,...
arXiv:2609.08275v1 Announce Type: new Abstract: Recent multi-shot audio-video generators can produce increasingly coherent and cinematic outputs, but coherence does not imply the ability to execute e...
arXiv:2609.08599v1 Announce Type: new Abstract: Large Language Model (LLM) agents are evolving from single-session tools toward long-term personal assistants that must adapt to individual users acros...
arXiv:2609.09133v1 Announce Type: new Abstract: Execution feedback can guide coding agents toward correct repository repairs, but only when the tests capture the behavior requested by the issue. Agen...
arXiv:2609.05591v1 Announce Type: cross Abstract: Safety evaluations typically focus on individual agents, but interacting agents can spread harmful information and influence the environment in which...
arXiv:2609.05593v1 Announce Type: cross Abstract: Autonomous robot navigation failures differ not only in categorical severity but also in the physical context in which they occur. A near-miss at low...
arXiv:2609.06816v1 Announce Type: new Abstract: Decision-time search in perfect and imperfect information games with enumerable belief states are effective methods for game AI. Collectible card games...
arXiv:2605.12957v2 Announce Type: replace Abstract: Recent developments in generative models and large-scale datasets have substantially advanced 3D world generation, facilitating a broad range of do...
arXiv:2609.05975v1 Announce Type: cross Abstract: We introduce Chain of Intent, a governance framework for agentic AI at small regulated firms, and validate it against a failure it was built to addre...
arXiv:2609.07128v1 Announce Type: new Abstract: Reliable risk assessment remains a central challenge for Autonomous Vehicles (AVs). Despite advances in automation, passenger cognition provides a non-...
arXiv:2609.07984v1 Announce Type: new Abstract: Process mining has long turned event logs into process knowledge: discovered models, conformance evidence, bottleneck diagnoses, and runtime prediction...
The paper presents a distributed primal‑dual algorithm for quadratic Generalized Nash Equilibrium Problems (GNEPs) that eliminates the requirement for multiplier consensus. By removing shared multipliers, the method reduces communication overhead and enhances privacy, allowing different initializations to converge to distinct GNEs, including non‑variational equilibria. Convergence is proven under sufficient conditions using an input‑to‑state stability (ISS) framework.
arXiv:2609.08015v1 Announce Type: new Abstract: Long-running AI agents may read state, reason, wait for tools or human approval, and perform an external action much later. The state that justified th...
arXiv:2609.08025v1 Announce Type: new Abstract: Reasoning agents increasingly rely on external tools such as web search to answer complex queries. Reinforcement learning (RL) finetuning algorithms su...
arXiv:2609.06467v1 Announce Type: new Abstract: In performative reinforcement learning the deployed policy shapes the environment that generates the learner's future data, and the natural solution co...
arXiv:2609.08258v1 Announce Type: new Abstract: Long-running language-model agents depend on persistent memory. Many agent-memory systems preserve history through soft revocation: a contradicted fact...
arXiv:2609.06586v1 Announce Type: cross Abstract: A shared reward gives agents a common objective, but leaves open when, how and even whether they must cooperate to succeed. We address these question...
arXiv:2609.08572v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems (MAS) achieve strong performance by employing specialized multiple agents, yet their performance d...
arXiv:2507.19364v3 Announce Type: replace Abstract: The integration of Large Language Models (LLMs) into social simulation has generated considerable enthusiasm, but also raises substantial methodolo...