# IE as Cache: Information Extraction Enhanced Agentic Reasoning

arXiv: https://arxiv.org/abs/2604.14930 · HTML: https://mlsanity.com/paper/2604.14930/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 41 of its 62 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/)

## Builds on (most cited first, 30 of 41)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://mlsanity.com/paper/2302.00093/) (2023)
- [Long Range Arena: A Benchmark for Efficient Transformers](https://mlsanity.com/paper/2011.04006/) (2020)
- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models](https://mlsanity.com/paper/2501.09686/) (2025)
- [Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding](https://mlsanity.com/paper/2401.04398/) (2024)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [Does Prompt Formatting Have Any Impact on LLM Performance?](https://mlsanity.com/paper/2411.10541/) (2024)
- [NATURAL PLAN: Benchmarking LLMs on Natural Language Planning](https://mlsanity.com/paper/2406.04520/) (2024)
- [Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools](https://mlsanity.com/paper/2502.04644/) (2025)
- [Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction](https://mlsanity.com/paper/2404.03868/) (2024)
- [Relation Extraction : A Survey](https://mlsanity.com/paper/1712.05191/) (2017)
- [Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2406.03085/) (2024)
- [RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery](https://mlsanity.com/paper/2503.00751/) (2025)
- [Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent](https://mlsanity.com/paper/2506.23485/) (2025)
- [Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation](https://mlsanity.com/paper/2502.13783/) (2025)
- [Killing Two Birds with One Stone: Unifying Retrieval and Ranking with a Single Generative Recommendation Model](https://mlsanity.com/paper/2504.16454/) (2025)
- [CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering](https://mlsanity.com/paper/2507.04756/) (2025)
- [FuXi-α: Scaling Recommendation Model with Feature Interaction Enhanced Transformer](https://mlsanity.com/paper/2502.03036/) (2025)
- [Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy](https://mlsanity.com/paper/2412.00430/) (2024)
- [SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment](https://mlsanity.com/paper/2509.03934/) (2025)
- [The Next Paradigm Is User-Centric Agent, Not Platform-Centric Service](https://mlsanity.com/paper/2602.15682/) (2026)
- [Why Thinking Hurts: Diagnosing and Rectifying Linguistic Inertia in Large Language Models for Recommendation](https://mlsanity.com/paper/2602.16587/) (2026)
- [Table as Thought: Exploring Structured Thoughts in LLM Reasoning](https://mlsanity.com/paper/2501.02152/) (2025)
- [Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.08690/) (2025)
- [Enhancing CTR Prediction with De-correlated Expert Networks](https://mlsanity.com/paper/2505.17925/) (2025)

## Built on by (most cited first, 3 of 3)

- [AsymSpec: Context-Asymmetric Speculative Decoding for Agentic LLMs](https://mlsanity.com/paper/2608.26004/) (2026)
- [Multi-Image Visual Token Pruning in Large Visual Language Models](https://mlsanity.com/paper/2608.26806/) (2026)
- [InsightChain: Optimized Chain-of-Insight Analytics for LLM-driven Data Visualization](https://mlsanity.com/paper/2609.06438/) (2026)

## Abstract (arXiv)

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 textitIE-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.