EGRD: Assessing Research Novelty through Evidence-Grounded Residual Decomposition

Assessing the novelty of a research idea requires identifying what remains beyond prior work, even when coverage is dispersed across several papers or the contribution lies in a new combination of familiar components. We propose Evidence-Grounded Residual Decomposition (EGRD), a supervised framework for idea-level novelty prediction, assigning ordered grades relative to an explicit set of prior works published before the target idea. Given temporally admissible prior evidence, EGRD first estimates its joint coverage of the target idea, providing an evidence basis for subsequent novelty assessment. It then decomposes the target idea into latent semantic units and learns a support matrix aligning each unit with each prior. Aggregating support across priors yields unit residuals for insufficiently covered content, while within-prior co-support yields interaction residuals for potentially novel combinations. The model predicts novelty from these residuals together with coverage and contextual representations. To support training and evaluation, we construct IdeaDelta, which pairs research ideas with temporally admissible priors and provides coverage and novelty annotations across graph machine learning, computer vision, and time series. On temporally held-out ideas, EGRD outperforms the evaluated baselines across these domains and shows strong transfer performance on RINoBench. Ablations show that predictions depend on matched prior evidence and benefit from aggregating support across priors and representing residual contributions.