IE as Cache: Information Extraction Enhanced Agentic Reasoning

arXiv cs.CL / 4/17/2026

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Key Points

The paper argues that information extraction (IE) is often used only as a final output and not maintained for reuse during multi-step inference.
It introduces “IE-as-Cache,” which repurposes IE as a cognitive cache to support agentic reasoning across intermediate steps.
The proposed method uses query-driven extraction and cache-aware reasoning to keep compact intermediate facts and reduce irrelevant noise.
Experiments on multiple LLMs and challenging benchmarks show improved reasoning accuracy, suggesting IE can function as a reusable cognitive resource.
The authors position this as a promising research direction for leveraging downstream uses of IE beyond standalone extraction.

Abstract

Information Extraction aims to distill structured, decision-relevant information from unstructured text, serving as a foundation for downstream understanding and reasoning. However, it is traditionally treated merely as a terminal objective: once extracted, the resulting structure is often consumed in isolation rather than maintained and reused during multi-step inference. Moving beyond this, we propose \textit{IE-as-Cache}, a framework that repurposes IE as a cognitive cache to enhance agentic reasoning. Drawing inspiration from hierarchical computer memory, our approach combines query-driven extraction with cache-aware reasoning to dynamically maintain compact intermediate information and filter noise. Experiments on challenging benchmarks across diverse LLMs demonstrate significant improvements in reasoning accuracy, indicating that IE can be effectively repurposed as a reusable cognitive resource and offering a promising direction for future research on downstream uses of IE.