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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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2601.04463 |
| Publication time | Fri, 24 Jul 2026 07:39:30 +0000 |
| Retrieval time | 2026-07-24T07:45:57.516Z |
| Last seen | 2026-07-24T07:45:57.516Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | JmSql_abc4cq |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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