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Cache Engineering for Agents

107 upvotes · 20 OCT 2026 · Tianyu Fu, Ruoxi Wang, Zihan Min et al.

This paper proposes a new method called cache engineering that allows large language model agents to control their active cache without modifying the trajectory or re-prefilling retained context, which can improve task accuracy and reduce costs. Practitioners might care about this because it can help them optimize their LLM agents for complex tasks.

continual learningactive memory managementagent optimizationcache optimizationlanguage model efficiency