arXiv:2604.04383v1 Announce Type: new
Abstract: Service system performance depends on how participants respond to design choices, but modeling these responses is hard due to the complexity of human behavior. We introduce an LLM-powered multi-agent sim…
arXiv:2604.00449v2 Announce Type: replace
Abstract: We study distributed optimization over networks with Byzantine agents that may send arbitrary adversarial messages. We propose \emph{Gradient Tracking with Probabilistic Edge Dropout} (GT-PD), a stoc…
arXiv:2311.00855v3 Announce Type: replace
Abstract: Human immunodeficiency virus (HIV) is a major public health concern in the United States (U.S.), with about 1.2 million people living with it and about 35,000 newly infected each year. There are cons…
arXiv:2604.04604v1 Announce Type: cross
Abstract: AI agents – i.e. AI systems that autonomously plan, invoke external tools, and execute multi-step action chains with reduced human involvement – are being deployed at scale across enterprise functions …
arXiv:2604.04442v1 Announce Type: cross
Abstract: Autonomous agents are increasingly deployed in both offensive and defensive cyber operations, creating high-speed, closed-loop interactions in critical infrastructure environments. Advanced Persistent …
arXiv:2510.06800v3 Announce Type: replace-cross
Abstract: As large language models (LLMs) advance in role-playing (RP) tasks, existing benchmarks quickly become obsolete due to their narrow scope, outdated interaction paradigms, and limited adaptabili…
arXiv:2604.04503v1 Announce Type: new
Abstract: Deep research agents (DRAs) integrate LLM reasoning with external tools. Memory systems enable DRAs to leverage historical experiences, which are essential for efficient reasoning and autonomous evolutio…
arXiv:2604.03785v1 Announce Type: new
Abstract: Communication is essential for coordination in \emph{cooperative} multi-agent reinforcement learning under partial observability, yet \emph{cross-timestep} delays cause messages to arrive multiple timest…
arXiv:2604.03295v1 Announce Type: cross
Abstract: Large language model (LLM) multi-agent systems can scale along two distinct dimensions: by increasing the number of agents and by improving through accumulated experience over time. Although prior work…
arXiv:2604.03888v1 Announce Type: new
Abstract: This paper presents PolySwarm, a novel multi-agent large language model (LLM) framework designed for real-time prediction market trading and latency arbitrage on decentralized platforms such as Polymarke…