Silo-Bench: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems
Summary: arXiv:2603.01045v2 Announce Type: replace-cross
Abstract
Large language models (LLMs) are increasingly deployed in multi-agent systems to overcome context limitations by distributing information across agents. However, whether agents can reliably compute with distributed information, rather than merely exchange it, remains an open question in the field of artificial intelligence. To address this, we introduce SILO-BENCH, a role-agnostic benchmark consisting of 30 algorithmic tasks across three communication complexity levels. This benchmark evaluates a total of 54 configurations over 1,620 experiments.
Key Findings
Our experiments reveal a fundamental Communication-Reasoning Gap: while agents spontaneously form task-appropriate coordination topologies and actively exchange information, they systematically fail to synthesize distributed state into correct answers. This failure is particularly pronounced during the reasoning-integration stage, where agents often acquire sufficient information but struggle to integrate it effectively.
Challenges of Scaling
As the number of agents increases, the coordination overhead compounds, ultimately negating any potential gains from parallelization. This indicates that simply scaling the number of agents cannot overcome the inherent context limitations present in multi-agent systems. Our findings suggest a need for a more nuanced approach to designing collaborative systems, one that goes beyond mere communication and focuses on effective reasoning and integration.
Benchmark Components
SILO-BENCH consists of three main components:
- Algorithmic Tasks: The benchmark includes 30 distinct tasks that challenge agents to work together and utilize shared information effectively.
- Communication Complexity Levels: Tasks are categorized into three levels, allowing for a comprehensive evaluation of how communication impacts performance.
- Configurations: A total of 54 configurations across these tasks facilitate a robust analysis of agent performance under varying conditions.
Implications for Future Research
The results obtained from SILO-BENCH provide valuable insights into the current limitations of multi-agent systems powered by LLMs. Researchers can utilize this benchmark to track progress toward developing genuinely collaborative systems that can effectively integrate distributed information.
As the field of artificial intelligence continues to evolve, understanding the nuances of agent coordination and reasoning will be crucial. SILO-BENCH serves as a foundational tool for researchers aiming to bridge the gap between communication and reasoning.
Access the Code
For those interested in exploring SILO-BENCH further, the code is available at: https://github.com/jwyjohn/acl26-silo-bench.
Conclusion
SILO-BENCH highlights the complexities inherent in multi-agent systems and underscores the need for focused research on effective communication and reasoning integration. By addressing these challenges, the AI community can make significant strides toward more efficient and collaborative systems.