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Silo-Bench: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems
arXiv – CS AI|Yuzhe Zhang, Feiran Liu, Yi Shan, Xinyi Huang, Xin Yang, Yueqi Zhu, Xuxin Cheng, Cao Liu, Ke Zeng, Terry Jingchen Zhang, Wenyuan Jiang||12 views
🤖AI Summary
Researchers introduce Silo-Bench, a benchmark revealing that multi-agent LLM systems can exchange information effectively but fail to integrate distributed data for correct reasoning. The study shows coordination overhead increases with scale, challenging the assumption that adding more agents can solve context limitations.
Key Takeaways
- →Multi-agent LLM systems exhibit a Communication-Reasoning Gap where agents exchange information but fail to synthesize it correctly.
- →Agents can form appropriate coordination topologies and communicate actively but struggle with the reasoning-integration stage.
- →Coordination overhead compounds with scale, eventually eliminating any parallelization benefits.
- →Simply increasing agent count cannot effectively circumvent context limitations in LLMs.
- →Silo-Bench provides a standardized framework for evaluating distributed coordination in multi-agent AI systems.
#multi-agent-systems#llm#benchmark#distributed-computing#ai-coordination#reasoning#scalability#research
Read Original →via arXiv – CS AI
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