IE as Cache: Information Extraction Enhanced Agentic Reasoning

2026-04-16

AI Summary

Traditional information extraction is typically treated as an isolated end-task, limiting its utility in multi-step reasoning scenarios that require reuse of extracted knowledge. This work proposes IE-as-Cache, a novel framework that reframes information extraction as a dynamically maintainable and queryable cognitive cache—akin to hierarchical memory systems—to enable agents to efficiently leverage compact, denoised intermediate knowledge during reasoning. The framework integrates query-driven extraction with cache-aware reasoning and leverages large language models to facilitate contextualized knowledge retrieval and utilization. Experimental results demonstrate that IE-as-Cache significantly improves multi-step reasoning accuracy across several challenging benchmarks, thereby validating the effectiveness and novelty of treating information extraction as a reusable cognitive resource.

Technology Category

• Natural Language Processing: Information Extraction
• Knowledge Representation and Reasoning: Reasoning with Beliefs
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Content-based information diffusion

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.

Problem

• Information Extraction
• Agentic Reasoning
• Cognitive Cache
• Multi-step Inference
• Structured Information

Innovation

• Information Extraction
• Agentic Reasoning
• Cognitive Cache
• Query-driven Extraction
• Cache-aware Reasoning