Paper page - Bilevel Coordinated Reflection: A Game-Theoretic Approach to Multi-Agent LLM Systems
2026-09-07 08:00Models🔥 42.2 heat score
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Yihang Chen et al. proposed the Bilevel Coordinated Reflection method, modeling the multi-agent large language model system as a two-layer coordinated game. The study demonstrated that the worker-local update game belongs to an approximate potential game, and its equilibrium relaxation degree depends on the quality of task decomposition; it was also confirmed that a gateway that only observes generated text cannot uniformly improve memory in an indistinguishably textured environment, whereas a grounded gateway can. The proposed SRMA (Stochastic Reflective Memory Ascent) algorithm accepts candidate memories only when the risk assessment strictly decreases in a grounded environment, featuring precise convergence and geometric/polynomial rate characteristics. In 500 SWE-bench instance tests, the system's solution rate based on the Kimi framework was 72.2%, higher than the 70.8% of public mini-SWE-agent benchmarks.
Yihang Chen et al. proposed the Bilevel Coordinated Reflection method, modeling multi-agent LLM systems as a two-layer coordinated game. The study demonstrated that worker-local updates in the game are approximately potential games, and their equilibrium relaxation degree depends on the quality of task decomposition; it also showed that gateways that only observe generated texts cannot uniformly improve memory in an indistinguishably textured environment, whereas gateways with a grounded environment can. The proposed SRMA (Stochastic Reflective Memory Ascent) accepts candidate memories only when the risk assessment strictly decreases in a grounded environment, achieving accurate convergence and geometric/polynomial rates. On 500 SWE-bench instances, the system's success rate based on the Kimi framework was 72.2%, higher than 70.8% of public mini-SWE-agent benchmarks.