双层协调反思：多智能体LLM系统的博弈论方法

AI 摘要

该论文将多智能体LLM系统的协调形式化为双层博弈和随机记忆反思，引入环境接地评估门和SRMA算法，并证明其收敛性。在SWE-bench基准上，基于Kimi的完整系统解决了72.2%的问题，而公开的mini-SWE-agent参考为70.8%。

正文节选

Bilevel Coordinated Reflection: A Game-Theoretic Approach to Multi-Agent LLM Systems Abstract The study formalizes multi-agent LLM coordination via bilevel games and stochastic memory reflection, introducing a grounded evaluation gate and SRMA algorithm with convergence guarantees, validated on SWE-bench. 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

发布时间：—

抓取时间：2026-09-07 20:51

来源机构：Hugging Face