OppoMem: An Opponent-Aware Memory Framework for LLM Agents Evolving in Strategic Games

Memory enables large language model (LLM) agents to evolve through experience without updating model parameters. However, strategic games pose three key challenges: uncertainty arising from opponent behavior, the limitations of naive similarity-based memory retrieval, and difficulty evaluating individual memories from final outcome feedback. Inspired by opponent modeling in game theory, We propose **OppoMem**: an **Oppo**nent-aware **Mem**ory framework for self-evolving LLM agents in strategic games. OppoMem represents strategic memories as associations between opponent types and response strategies. The agent uses online opponent modeling to guide the dynamic retrieval of relevant memories. To support memory evolution, OppoMem estimates individual memory utility through randomized activation and uses the resulting feedback to guide memory retention, revision, and removal. Extensive experiments show the effectiveness of our framework in enabling LLM agents to evolve in strategic games with an average relative performance improvement around 30%. Our code is available at https://anonymous.4open.science/r/Core_code-91FF/.