MEMO: Memory-Augmented Model Context Optimization for Robust Multi-Turn Multi-Agent LLM Games

Abstract

Multi-turn, multi-agent LLM game evaluations exhibit substantial run-to-run variance. In long-horizon interactions, small deviations compound across turns and are amplified by multi-agent coupling, biasing win rate estimates and destabilizing comparative rankings across repeated tournaments. Prompt choice exacerbates this by inducing different effective policies and interaction dynamics. We address both instability and underperformance in games with MEMO: (Memory-augmented MOdel context optimization), a self-play framework that treats inference-time context as an optimizable, agentic object by coupling retention and exploration. Retention distills self-play trajectories into a persistent memory bank of structured insights that act as priors; exploration combines tournament-style prompt evolution under TrueSkill with prioritized replay over decisive states. Across five text-based games and a 2,000-game budget per task, MEMO raises mean win rate from 25.1% to 49.5% for GPT-4o-mini and from 20.9% to 45.0% for Qwen-2.5-7B, while sharply reducing run-to-run dispersion. These results suggest substantial headroom in multi-agent LLM game performance and robustness can be unlocked through memory, with MEMO achieving gains in negotiation and imperfect-information settings, while RL remains more effective in perfect-information settings. Project website: https://yunfeixie233.github.io/MEMO/.