RINI: Seeing the Prior Is Not Enough

2026-09-27

AI Summary

This study addresses the challenge of erroneously claimed novel contributions in research proposals, which are notoriously difficult to correct. To this end, we propose RINI, a method that integrates retrieval-augmented generation with an explicit contribution attribution mechanism. RINI achieves precise corrections through a three-step process: auditing contribution claims, examining residual distinctions, and performing multi-stage local revisions. Experimental results demonstrate that among cases requiring correction, RINI attains a successful repair rate of 72.2%, outperforming a direct revision baseline by 33 percentage points. These findings indicate that RINI effectively mitigates the problem of spurious novelty claims in academic proposals, offering a robust approach for ensuring accurate scholarly attribution.

Abstract

A research proposal can describe an established mechanism correctly while claiming to introduce it. We study whether providing the earlier paper corrects such contribution claims. Three controlled experiments compare proposals generated with a contribution-bearing prior and a same-topic control. Providing the prior yields no clear aggregate reduction in unsupported novelty. Human analysis of 175 interpretable exposed proposals finds that 137 recognize the prior's relevance, but 61 correctly attribute the established contribution. Of 71 proposed remaining distinctions, 37 are covered by the same prior. We introduce Research Idea Novelty Inspection (RINI), which audits contribution claims against evidence, checks the remaining distinction, and applies local revisions. Five human annotators evaluate 1,080 original-revision pairs across three methods. On the same 240 originals judged to require correction, successful repair is 11.7% for Self-Revision, 39.1% for Retrieve-and-Revise, and 72.2% for RINI, with research tasks weighted equally. The improvement over same-evidence direct revision is 33.0 percentage points. The revised proposals retain their research questions and technical methods. These results motivate explicit contribution attribution when using literature to generate and revise research proposals.

Problem

• research proposal generation
• novelty claims
• contribution attribution
• prior knowledge
• hallucination

Innovation

• Research Idea Novelty Inspection
• contribution attribution
• proposal revision
• novelty auditing
• large language models