arXiv:2603.27304v1 Announce Type: new
Abstract: General-purpose technologies reshape economies less by improving individual tools than by enabling new ways to organize production and coordination. We believe AI agents are approaching a similar inflect…
arXiv:2603.20182v2 Announce Type: replace
Abstract: Although robot-to-robot (R2R) communication improves indoor scene understanding beyond what a single robot can achieve, R2R alone cannot overcome partial observability without substantial exploration…
arXiv:2603.27803v1 Announce Type: new
Abstract: We provide a distributed online algorithm for multi-agent submodular maximization under communication delays. We are motivated by the future distributed information-gathering tasks in unknown and dynamic…
arXiv:2603.27910v1 Announce Type: new
Abstract: AI agents that interact with users across multiple sessions require persistent long-term memory to maintain coherent, personalized behavior. Current approaches either rely on flat retrieval-augmented gen…
arXiv:2603.28573v1 Announce Type: new
Abstract: Offline multi-agent reinforcement learning (MARL) faces a critical challenge: the joint action space grows exponentially with the number of agents, making dataset coverage exponentially sparse and out-of…
arXiv:2410.09134v2 Announce Type: replace-cross
Abstract: The need for autonomous and adaptive defense mechanisms has become paramount in the rapidly evolving landscape of cyber threats. Multi-Agent Deep Reinforcement Learning (MADRL) presents a promi…
arXiv:2603.26993v1 Announce Type: cross
Abstract: This technical note studies the reliability limits of LLM-based multi-agent planning as a delegated decision problem. We model the LLM-based multi-agent architecture as a finite acyclic decision networ…
arXiv:2603.28361v1 Announce Type: new
Abstract: With the advancement of large language models (LLMs) in their knowledge base and reasoning capabilities, their interactive modalities have evolved from pure text to multimodality and further to agentic t…
arXiv:2512.04653v2 Announce Type: replace-cross
Abstract: Multi-agent reinforcement learning (MARL) has emerged as a promising paradigm for adaptive traffic signal control (ATSC) of multiple intersections. Existing approaches typically follow either a…
arXiv:2510.08847v2 Announce Type: replace
Abstract: We introduce the Agent GPA (Goal-Plan-Action) framework, driven by the fundamental insight that critical agent failures emerge at the intersections of setting goals, devising plans, and executing act…