💡 The paper introduces PEEK, a system designed to improve the performance of large language model agents operating over long and recurring external contexts, such as document corpora and code repositories. The problem with existing approaches is that they do not preserve reusable orientation knowledge about the recurring context itself, which includes information about what the context contains, how it is organized, and which entities, constants, and schemas have historically been useful.

To address this issue, PEEK uses a context map, a small and constant-sized artifact in the agent's prompt, to cache and maintain this orientation knowledge. The context map is maintained by a programmable cache policy consisting of three modules: a Distiller that extracts transferable knowledge from inference-time signals, a Cartographer that translates it into structured edits, and a priority-based Evictor that enforces a fixed token budget.

The results show that PEEK improves over strong baselines in long-context reasoning and information aggregation tasks by 6.3-34.0 percent, while using 93-145 fewer iterations and incurring 1.7-5.8 times lower cost than the state-of-the-art prompt-learning framework, ACE. Additionally, PEEK improves solving rate and rubric accuracy in context learning tasks by 6.0-14.0 percent and 7.8-12.1 percent, respectively, at 1.4 times lower cost than ACE. These gains generalize across different language models and agent architectures, including OpenAI Codex, a production-grade coding agent. Overall, the paper demonstrates that using a context map helps long-context language model agents interact with recurring external contexts more accurately and efficiently.