PEEK: Context Map as an Orientation Cache for Long-Context LLM Agents
The paper introduces PEEK, a system designed to enhance long-context LLM agents by maintaining reusable orientation knowledge. This context map allows agents to interact with recurring external contexts more effectively and efficiently. PEEK demonstrates significant improvements in performance metrics compared to existing frameworks.
- ▪PEEK caches and maintains orientation knowledge as a context map, which is a small artifact in the agent's prompt.
- ▪The system improves long-context reasoning and information aggregation by 6.3-34.0% while using fewer iterations and incurring lower costs.
- ▪PEEK also enhances context learning, increasing solving rates and rubric accuracy by 6.0-14.0% and 7.8-12.1%, respectively.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.19932 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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| 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.
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Computer Science > Artificial Intelligence arXiv:2605.19932 (cs) [Submitted on 19 May 2026] Title:PEEK: Context Map as an Orientation Cache for Long-Context LLM Agents Authors:Zhuohan Gu, Qizheng Zhang, Omar Khattab, Samuel Madden View a PDF of the paper titled PEEK: Context Map as an Orientation Cache for Long-Context LLM Agents, by Zhuohan Gu and 3 other authors View PDF Abstract:Large language model (LLM) agents increasingly operate over long and recurring external contexts, like document corpora and code repositories. Across invocations, existing approaches preserve either the agent's trajectory, passive access to raw material, or task-level strategies.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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