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AI: post transformers
AI: post transformers

NeurIPS 2025: Agentic Plan Caching: Test-Time Memory for Fast and Cost-Efficient LLM Agents

25 February 2026 18:37 mcgrof

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About this episode

The January 26, 2026 Stanford research paper introduces Agentic Plan Caching (APC), a novel framework designed to reduce the high operational costs of Large Language Model (LLM) agents. Traditional caching methods often fail because agent workflows are highly dynamic and dependent on external environments, making simple input-output storage ineffective. The APC framework solves this by extracting generalized plan templates from successful task executions, which are then indexed by high-level intent keywords. When a similar task is encountered, a small, cost-effective planner LM adapts these cached templates to the new context, significantly reducing the need for expensive, high-reasoning models. Experiments demonstrate that this approach can cut financial and computational costs by over 75% while maintaining high accuracy across complex benchmarks. Ultimately, this system offers a scalable way to deploy sophisticated AI agents by minimizing redundant reasoning through intelligent plan reuse.


Source:


January 26, 2026

Agentic Plan Caching: Test-Time Memory for Fast and Cost-Efficient LLM Agents

Stanford University

Qizheng Zhang, Michael Wornow, Gerry Wan, Kunle Olukotun

https://arxiv.org/pdf/2506.14852

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