Beyond Static Summarization: Proactive Memory Extraction for LLM Agents
Most research focuses on how to organize and use memory summary, but often overlooks the initial memory extraction stage. In this paper, we argue that existing summary-based methods have two major limitations based on the recurrent processing theory. First, summarization is "ahead-of-time", acting as a blind "feed-forward" process that misses important details because it doesn't know future tasks.
- ▪Most research focuses on how to organize and use memory summary, but often overlooks the initial memory extraction stage.
- ▪In this paper, we argue that existing summary-based methods have two major limitations based on the recurrent processing theory.
- ▪First, summarization is "ahead-of-time", acting as a blind "feed-forward" process that misses important details because it doesn't know future tasks.
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Computer Science > Computation and Language arXiv:2601.04463 (cs) [Submitted on 8 Jan 2026] Title:Beyond Static Summarization: Proactive Memory Extraction for LLM Agents Authors:Chengyuan Yang, Zequn Sun, Wei Wei, Wei Hu View a PDF of the paper titled Beyond Static Summarization: Proactive Memory Extraction for LLM Agents, by Chengyuan Yang and Zequn Sun and Wei Wei and Wei Hu View PDF HTML (experimental) Abstract:Memory management is vital for LLM agents to handle long-term interaction and personalization. Most research focuses on how to organize and use memory summary, but often overlooks the initial memory extraction stage. In this paper, we argue that existing summary-based methods have two major limitations based on the recurrent processing theory.
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