Bilevel Coordinated Reflection: A Game-Theoretic Approach to Multi-Agent LLM Systems
Paper models multi-agent LLM orchestration as a bilevel game, proving transcript-only gating limits and introducing grounded-memory SRMA.
A new paper frames orchestrator-worker coordination in multi-agent LLM systems as a bilevel coordination game and analyzes free-form reflection as stochastic movement over semantic memory states, deriving finite-time bounds and an information-theoretic impossibility result: no gate observing only the generated transcript can uniformly improve over text-indistinguishable environments, while an environment-grounded gate can. The authors propose Stochastic Reflective Memory Ascent (SRMA), which accepts candidate memory only when grounded evaluation risk strictly decreases, with geometric or polynomial convergence guarantees. On 500 SWE-bench instances, a Kimi-based instantiation of the full system resolves 72.2% versus a 70.8% public mini-SWE-agent reference.
- Proves no transcript-only gate can uniformly improve over text-indistinguishable environments; environment-grounded gates can.
- Introduces SRMA, accepting memory updates only when grounded evaluation risk strictly decreases.
- Kimi-based system resolves 72.2% of 500 SWE-bench instances versus a 70.8% mini-SWE-agent reference.
- Products
- SWE-benchmini-SWE-agent
- AI models
- Kimi
From the original article
1 min read at huggingface.co
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.
By Yihang Chen, Yuxiang Chen, Yuxuan Huang · huggingface.co
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