# EAGER: Multi-Hop Reasoning with LLMs: A Structure-Guided Reasoning Approach

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · University of Illinois at Urbana-Champaign (IL) · $300,000

## Abstract

Large language models (LLMs) have been revolutionizing recent artificial intelligence research and applications. To empower complex reasoning and avoid hallucinations in special domain-focused problem-solving, LLMs need to incorporate updated and specialized data wisely and effectively. This project proposes a structure-guided reasoning approach for multi-hop, complex reasoning with LLMs. More concretely, it proposes to develop a retrieving-structuring-reasoning framework: (1) retrieving task-focused data and information, (2) structuring the retrieved data and knowledge, and (3) reasoning on the structured data and knowledge. Each task will need to explore the power of LLMs and develop efficient and effective LLM-integrated methods. The project will lead to the development of new algorithms for information retrieval, data and knowledge structuring, and structure-guided reasoning. The new methodologies generated will be broadly applicable across the fields of data science. Moreover, this research will support the cross-disciplinary development of a diverse cohort of doctoral and undergraduate students on both research and education at the University of Illinois.


The technical aims of the project are divided into three thrusts. The first thrust, Retrieving, develops user- or task- query guided, theme-specific information retrieval to collect theme-specific documents, by developing effective methods for corpus-based, domain-specific multi-class text classification, knowledg

## Key facts

- **NSF award ID:** 2537827
- **Awardee organization:** University of Illinois at Urbana-Champaign (IL)
- **SAM.gov UEI:** Y8CWNJRCNN91
- **PI:** Jiawei Han
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** INFO INTEGRATION & INFORMATICS, EAGER
- **Estimated total:** $300,000
- **Funds obligated:** $300,000
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2027

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2537827

## Citation

> US National Science Foundation, Award 2537827, EAGER: Multi-Hop Reasoning with LLMs: A Structure-Guided Reasoning Approach. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2537827. Licensed CC0.

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