[2603.01045] Silo-Bench: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems

Computer Science > Multiagent Systems arXiv:2603.01045 (cs) [Submitted on 1 Mar 2026] Title:Silo-Bench: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems Authors:Yuzhe Zhang, Feiran Liu, Yi Shan, Xinyi Huang, Xin Yang, Yueqi Zhu, Xuxin Cheng, Cao Liu, Ke Zeng, Terry Jingchen Zhang, Wenyuan Jiang View a PDF of the paper titled Silo-Bench: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems, by Yuzhe Zhang and 10 other authors View PDF HTML (experimental) Abstract:Large language models are increasingly deployed in multi-agent systems to overcome context limitations by distributing information across agents. Yet whether agents can reliably compute with distributed information -- rather than merely exchange it -- remains an open question. We introduce Silo-Bench, a role-agnostic benchmark of 30 algorithmic tasks across three communication complexity levels, evaluating 54 configurations over 1,620 experiments. Our experiments expose a fundamental Communication-Reasoning Gap: agents spontaneously form task-appropriate coordination topologies and exchange information actively, yet systematically fail to synthesize distributed state into correct answers. The failure is localized to the reasoning-integration stage -- agents often acquire sufficient information but cannot integrate it. This coordination overhead compounds with scale, eventually eliminating parallelization gains entirely. These findings dem...