PEEK: Give Your Agent an Orientation Cache (MIT CSAIL, Khattab group)
The article discusses the introduction of PEEK, a new approach to context management for language model agents. PEEK enables agents to cache a context map that retains learned information about external contexts, improving efficiency in repeated tasks. This method enhances performance while reducing costs compared to existing frameworks.
- ▪PEEK allows agents to maintain a compact context map that stores learned information across interactions.
- ▪The system includes a Distiller, Cartographer, and Evictor to manage knowledge extraction and storage.
- ▪PEEK has been shown to improve task quality and reduce iteration costs compared to state-of-the-art methods.
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| Original publisher | Zhuohan's Homepage |
| Canonical URL | https://zhuohangu.github.io/blog-post-peek/ |
| Publication time | Mon, 25 May 2026 12:21:01 +0000 |
| Retrieval time | 2026-05-25T12:37:36.725Z |
| Last seen | 2026-05-25T12:37:36.725Z |
| 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 | OIamicYvKUEx |
| 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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| 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
Paper: https://arxiv.org/abs/2605.19932 Code: github.com/zhuohangu/peektl;drLLM agents such as Claude Code , Codex , RLM , and Hermes Agent increasingly operate over long and recurring external contexts: document corpora, code repositories, and other resources that the agent queries again and again but live outside the LLM’s context window. This capability is now referred to as Grounded Reasoning . Existing approaches preserve the agent’s trajectory, passive access to raw materials, or task-level strategies.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Zhuohan's Homepage.
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