GraphMind: Unveiling Scientific Reasoning through Contextual Graphs for Novelty Assessment

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.