MIR: Methodology Inspiration Retrieval for Scientific Research Problems

2025-05-30

AI Summary

Existing dense retrieval models lack explicit modeling of methodological lineage, hindering effective identification of prior works that offer methodological inspiration for novel scientific problems. Method: We propose Methodology-Inspired Retrieval (MIR), introducing the Methodological Adjacency Graph (MAG) to explicitly encode cross-paper method inheritance and evolution, and inject this graph-structured prior into a dense retriever. We further construct the first MIR-specific benchmark dataset and design an LLM-driven, inspiration-aware re-ranking module. Contribution/Results: Our approach outperforms strong baselines by +5.4% in Recall@3 and +7.8% in mAP; integrating the LLM-based re-ranker yields further gains of +4.5% and +4.8%, respectively. These results significantly alleviate the methodological inspiration gap in scientific problem solving.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Computer Vision: Image and Video Retrieval

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

There has been a surge of interest in harnessing the reasoning capabilities of Large Language Models (LLMs) to accelerate scientific discovery. While existing approaches rely on grounding the discovery process within the relevant literature, effectiveness varies significantly with the quality and nature of the retrieved literature. We address the challenge of retrieving prior work whose concepts can inspire solutions for a given research problem, a task we define as Methodology Inspiration Retrieval (MIR). We construct a novel dataset tailored for training and evaluating retrievers on MIR, and establish baselines. To address MIR, we build the Methodology Adjacency Graph (MAG); capturing methodological lineage through citation relationships. We leverage MAG to embed an"intuitive prior"into dense retrievers for identifying patterns of methodological inspiration beyond superficial semantic similarity. This achieves significant gains of +5.4 in Recall@3 and +7.8 in Mean Average Precision (mAP) over strong baselines. Further, we adapt LLM-based re-ranking strategies to MIR, yielding additional improvements of +4.5 in Recall@3 and +4.8 in mAP. Through extensive ablation studies and qualitative analyses, we exhibit the promise of MIR in enhancing automated scientific discovery and outline avenues for advancing inspiration-driven retrieval.

Problem

• Retrieving prior work to inspire solutions for research problems
• Building a Methodology Adjacency Graph for methodological lineage
• Improving retrieval performance with LLM-based re-ranking strategies

Innovation

• Constructs Methodology Adjacency Graph for lineage
• Embeds intuitive prior into dense retrievers
• Adapts LLM-based re-ranking for inspiration