What Is Novel? A Knowledge-Driven Framework for Bias-Aware Literature Originality Evaluation

• URL: http://arxiv.org/abs/2602.06054v2
• Date: Wed, 11 Feb 2026 12:35:37 GMT
• Title: What Is Novel? A Knowledge-Driven Framework for Bias-Aware Literature Originality Evaluation
• Authors: Abeer Mostafa, Thi Huyen Nguyen, Zahra Ahmadi,
• Abstract summary: We introduce a literature-aware novelty assessment framework that learns how humans judge novelty from peer-review reports.<n>Using nearly 80K novelty-annotated reviews from top-tier AI conferences, we fine-tune a large language model to capture reviewer-aligned novelty evaluation behavior.
• Score: 4.14197005718384
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Assessing research novelty is a core yet highly subjective aspect of peer review, typically based on implicit judgment and incomplete comparison to prior work. We introduce a literature-aware novelty assessment framework that explicitly learns how humans judge novelty from peer-review reports and grounds these judgments in structured comparison to existing research. Using nearly 80K novelty-annotated reviews from top-tier AI conferences, we fine-tune a large language model to capture reviewer-aligned novelty evaluation behavior. For a given manuscript, the system extracts structured representations of its ideas, methods, and claims, retrieves semantically related papers, and constructs a similarity graph that enables fine-grained, concept-level comparison to prior work. Conditioning on this structured evidence, the model produces calibrated novelty scores and human-like explanatory assessments, reducing overestimation and improving consistency relative to existing approaches.

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