{"slug": "beyond-static-summarization-proactive-memory-extraction-for-llm-agents", "title": "Beyond Static Summarization: Proactive Memory Extraction for LLM Agents", "summary": "Researchers propose ProMem, a proactive memory extraction method for LLM agents that uses a recurrent feedback loop with self-questioning to iteratively probe dialogue history, addressing limitations of static summarization. The method improves memory completeness and QA accuracy while achieving a superior trade-off between extraction quality and token cost, according to a paper submitted on 8 Jan 2026.", "body_md": "# Computer Science > Computation and Language\n\n[Submitted on 8 Jan 2026]\n\n# Title:Beyond Static Summarization: Proactive Memory Extraction for LLM Agents\n\n[View PDF](/pdf/2601.04463)\n\n[HTML (experimental)](https://arxiv.org/html/2601.04463v1)\n\nAbstract: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. First, summarization is \"ahead-of-time\", acting as a blind \"feed-forward\" process that misses important details because it doesn't know future tasks. Second, extraction is usually \"one-off\", lacking a feedback loop to verify facts, which leads to the accumulation of information loss. To address these issues, we propose proactive memory extraction (namely ProMem). Unlike static summarization, ProMem treats extraction as an iterative cognitive process. We introduce a recurrent feedback loop where the agent uses self-questioning to actively probe the dialogue history. This mechanism allows the agent to recover missing information and correct errors. Our ProMem significantly improves the completeness of the extracted memory and QA accuracy. It also achieves a superior trade-off between extraction quality and token cost.\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/beyond-static-summarization-proactive-memory-extraction-for-llm-agents", "canonical_source": "https://arxiv.org/abs/2601.04463", "published_at": "2026-07-24 07:39:30+00:00", "updated_at": "2026-07-24 07:52:23.871924+00:00", "lang": "en", "topics": ["large-language-models", "ai-agents", "ai-research"], "entities": ["ProMem"], "alternates": {"html": "https://wpnews.pro/news/beyond-static-summarization-proactive-memory-extraction-for-llm-agents", "markdown": "https://wpnews.pro/news/beyond-static-summarization-proactive-memory-extraction-for-llm-agents.md", "text": "https://wpnews.pro/news/beyond-static-summarization-proactive-memory-extraction-for-llm-agents.txt", "jsonld": "https://wpnews.pro/news/beyond-static-summarization-proactive-memory-extraction-for-llm-agents.jsonld"}}