arXiv:2606.08068v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems often fail to reliably outperform a single strong model equipped with best-of-N sampling. We argue that a core source of this instability is ill-posed equilibrium selection: current systems specify what information agents share, but not which coordination convention should be selected. We formalize a broad class of such systems as discounted incomplete-information Markov games and show that two common pathologies, oscillation between competing conventions and drift across them, can both induce unstab
The proliferation of multi-agent LLM systems highlights the critical need for robust coordination mechanisms, as current approaches struggle with stability and performance benchmarks.
Achieving stable coordination in multi-agent LLM systems is crucial for unlocking their full potential, moving beyond single-model limitations, and enabling more complex autonomous applications.
This research introduces a formal framework to address equilibrium selection in multi-agent LLM systems, proposing a method to improve stability and reliability in their collaborative operations.
- · AI developers
- · Organizations deploying multi-agent systems
- · Academic AI research
- · Inefficient multi-agent LLM orchestration platforms
- · Systems reliant on uncontrolled agent interaction
Improved performance and reliability of multi-agent LLM systems for complex tasks.
Acceleration in the development and deployment of autonomous AI agents across various sectors.
Enhanced trust in AI systems leading to broader adoption in critical decision-making processes.
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Read at arXiv cs.LG