Bilevel Coordinated Reflection: A Game-Theoretic Approach to Multi-Agent LLM Systems

📅 2026-09-02
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🤖 AI Summary
本文通过建模协调者-工作者的双层协调博弈及引入基于环境验证的记忆改进方法SRMA,解决了多智能体LLM系统中任务分解、协调和记忆改进的问题。
📝 Abstract
Multi-agent LLM systems commonly use an orchestrator to decompose a task for a team of workers and then improve through textual reflection. Despite strong empirical results, these systems lack a unified account of coordination, memory improvement, and the role of external verification. We model orchestrator-worker interaction as a bilevel coordination game: under bounded coupling, the workers' local-update game is an approximate potential game whose equilibrium slack is controlled by decomposition quality. We then analyse reflection as stochastic movement over semantic memory states. For free-form reflection, we derive a finite-time upper bound, prove worst-case tightness, and give a positive lower bound under a falsifiable persistent-harm condition. We further prove an information-theoretic impossibility result: no gate that observes only the generated transcript can improve uniformly over text-indistinguishable environments, whereas an environment-grounded gate can. Motivated by this separation, we introduce Stochastic Reflective Memory Ascent (SRMA), which accepts a candidate memory only after a grounded evaluation risk strictly decreases. Under calibration and non-degenerate corrective mass, SRMA converges exactly, geometrically or polynomially; matching constructions show that both rate regimes are order-tight. We also provide confidence gating for stochastic evaluation and re-anchoring guarantees for piecewise-stationary environments. Experiments instantiate these objects with environment-grounded metrics and test the predicted coordination and drift laws. On 500 SWE-bench instances, the complete Kimi-based system resolves 72.2% versus a 70.8% public mini-SWE-agent reference. Code: https://github.com/YihangChen9/Bilevel-Coordinated-Reflection
Problem

Research questions and friction points this paper is trying to address.

multi-agent LLM systems
coordination
memory improvement
external verification
Innovation

Methods, ideas, or system contributions that make the work stand out.

bilevel coordination game
stochastic reflective memory ascent (SRMA)
environment-grounded evaluation