AMemGym: Interactive Memory Benchmarking for Assistants in Long-Horizon Conversations

Researchers introduce AMemGym, an interactive benchmarking environment for evaluating and optimizing memory management in long-horizon conversations with AI assistants. The framework addresses limitations in current memory evaluation methods by enabling on-policy testing with LLM-simulated users and revealing performance gaps in existing memory systems like RAG and long-context LLMs.

• →AMemGym provides an interactive environment for evaluating memory management in AI assistants during extended conversations.
• →Current memory benchmarks using static data have limitations in reliability and scalability for evaluation.
• →The framework uses structured data sampling and LLM-simulated users to generate high-quality evaluation interactions.
• →Experiments revealed significant performance gaps in existing memory systems including RAG and long-context LLMs.
• →The environment enables both assessment and optimization of memory management strategies in conversational agents.