University of Illinois 论文 ClaimNov 改进 AI 新颖性审查，误杀率降至 1.5%
这篇论文挺有实操价值：如果你的流水线在筛 idea，逐条拆开评 claim 能把误杀率从 96% 降到 1.5%，方法可直接照搬。
University of Illinois 的论文指出，现有 AI scientist 流水线把整个提案整体做新颖性检查，导致大量后来被人类科学家正式发表的思路被拒。团队提出 ClaimNov，把提案拆成多条 claim，逐条与已有论文匹配后交给 judge 评估。在一个已部署的检查中，其拦截了全部 380 个伪装抄袭样本，但拒绝了 96% 后续被发表的 idea；ClaimNov 同样拦截 97% 的抄袭，误杀率只有 1.5%。
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.
