伊利诺伊大学论文提出 ClaimNov，改进 AI 科研流程中的新颖性检查
伊利诺伊大学团队提出 ClaimNov，将研究提案拆分为逐条 claim 与既有论文对比后交给评审，抓抄袭率保持 97%，误杀率从 96% 降至 1.5%。现有按整体提案做新颖性检查的方法虽能抓住全部 380 份伪装抄袭，却拒绝了 96% 后来被人类学者发表的想法。作者建议过滤思路的团队按 claim 单独打分，保留基于已知成果做增量构建的提案。
Rohan Paul· @rohanpaul_ai · X·2026-11-01 04:00· 38 分钟前AI 评分51
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伊利诺伊大学团队发现，AI scientist 流程中按整体提案做新颖性检查的现有方法，虽能抓住全部 380 份伪装抄袭，却拒绝了 96% 后来被人类学者发表的想法。论文提出 ClaimNov，将提案拆分为逐条 claim 与既有论文对比后交给评审，抓抄袭率保持 97%，误杀率降至 1.5%。作者建议过滤思路的团队按 claim 单独打分，保留基于已知成果做增量构建的提案。
正文 · 原文
New University of Illinois paper finds that when AI decides if a research idea is new enough to work on, it often says no to ideas that human scientists later publish.
AI scientist pipelines run a novelty check to decide which generated ideas get compute. These checks judge a whole proposal at once, though most real papers build on some earlier contributions.
They built ClaimNov, which splits a proposal into a few claims, matches each against claims from prior papers, and gives a judge the claim-by-claim evidence.
A deployed check caught all 380 disguised copies but also rejected 96% of ideas researchers later published. ClaimNov caught 97% of copies while rejecting only 1.5% of those ideas.
If your pipeline filters ideas for novelty, score claims separately and keep proposals that build on 1 known result instead of discarding them.
来源：Rohan Paul · x.com
来源：RadarAI · aihot.news