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About this paper
Yuwei Zhang, Chengyu Dong, Shuowei Jin, Changlong Yu, Hejie Cui, Hongye Jin, Xinyang Zhang, Hamed Bonab and 6 more
COMEM introduces a decoupled architecture for large language model agents, employing a lightweight memory model for asynchronous history summarization and a separate agent model for policy generation. This framework significantly reduces inference latency and GPU KV cache usage, achieving up to 2.08x speedups while preserving or improving task performance on long-horizon agentic tasks.
Key insights
- Decoupling context summarization from agent policy generation enables the main agent model to operate with a smaller, bounded effective context, thus mitigating memory-bound inference bottlenecks.
- Asynchronous execution of the memory model is critical for achieving net latency reductions, as it allows for the overlapping of memory processing with agent execution cycles.
- Optimizing memory model training with an action-consistency reward ensures the compressed summaries are functionally equivalent to full context for agent decision-making, offering a practical alternative to generic summarization or online environment interactions.
Summary by alphaXiv.
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