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Principled, scalable multi-agent coordination for LLMs

Develop a principled and scalable framework for coordinating multiple large language model agents in multi-agent settings to enable effective collaboration at scale.

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Background

The paper surveys limitations of current multi-agent LLM frameworks, highlighting heavy inter-agent communication costs, context window saturation, and the absence of convergence guarantees. These issues hinder practical deployment and scalability of multi-agent debate and coordination methods.

Motivated by these gaps, the work proposes ECON, a belief-driven approach that frames multi-LLM interaction as an incomplete-information game and seeks Bayesian Nash Equilibrium. The explicit open challenge they state concerns the development of a principled and scalable coordination framework for multi-agent LLMs.

References

Thus, developing a principled, scalable framework for multi-agent coordination is necessary but remains an open challenge.

— From Debate to Equilibrium: Belief-Driven Multi-Agent LLM Reasoning via Bayesian Nash Equilibrium (2506.08292 - Yi et al., 9 Jun 2025) in Section 1 (Introduction)