GraphMind, a model that jointly processes micro- and macro-level structures for novelty prediction and rationale generation, is proposed that significantly outperforms baseline LLMs in both novelty score prediction and rationale generation.
Italo Luis da Silva, Hanqi Yan, Lin Gui et al.· Proceedings of the 32nd ACM...· 1 citation
Assessing scientific novelty is inherently complex, requiring evaluation of both a paper's internal structure and its contribution within the broader research landscape. Existing large language model (LLM) approaches often rely on surface-level similarity or citation retrieval, overlooking the integration between a paper's content and its contextual grounding in related literature. To address this gap, we introduce SciNova, a benchmark containing 3,063 papers from both ICLR and NeurIPS, with full content, bibliographies, and peer review scores for novelty prediction. Building on this benchmark, we propose GraphMind, a model that jointly processes micro- and macro-level structures for novelty prediction and rationale generation. It represents each paper as a hierarchical graph that captures its claims, methods, and experiments (micro-level), while its related paper graphs encode both cited and semantically similar works (macro-level). Experiments show that by incorporating this bi-level graph, GraphMind significantly outperforms baseline LLMs in both novelty score prediction and rationale generation.
Italo Luis da Silva, Hanqi Yan, Lin Gui et al.· Proceedings of the 32nd ACM...· 1 citation
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