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GRASP: Graph-Reasoning Aided Survey Planning for High-Fidelity Related Work Generation

2026-07-04 05:30:01
Haoming Li, Jessica Ouyang
arXiv_CL
         
Abstract

Writing a literature review requires a deep understanding of the relationships among cited papers: how they build on, challenge, or offer alternative perspectives to one another. We present Graph-Reasoning Aided Survey Planning (GRASP), a framework combining LLM planning for related work generation with graph algorithms to extract key relationships among cited papers. Our two-layer graph structure consists of a Graph of Thoughts and an Argument-Counterargument Planning Network, representing the cited papers at different levels of granularity, and we apply topology-aware pruning via a Steiner tree to identify the core inter-paper relationships captured in our graph. Our citation analysis-based evaluation shows that GRASP generates related work sections (RWS) that closely match human-written targets in terms of the discourse roles, intents, and grouping of citations.

Abstract (translated)

URL

https://arxiv.org/abs/2607.03709

PDF

https://arxiv.org/pdf/2607.03709.pdf

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