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

• URL: http://arxiv.org/abs/2607.03709v1
• Date: Sat, 04 Jul 2026 05:30:01 GMT
• Title: GRASP: Graph-Reasoning Aided Survey Planning for High-Fidelity Related Work Generation
• Abstract summary: 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.<n>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.<n>Our citation analysis-based evaluation shows that GRASP generates related work sections that closely match human-written targets in terms of the discourse roles, intents, and grouping of citations.
• Score: 3.1561542616550518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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