ClaimNov Cuts AI Novelty Check False Rejections to 1.5%
The finding reframes novelty filtering as a recall-versus-precision tradeoff: checking whole proposals punishes incremental builds on prior work, which is how most real papers actually happen. Anyone running an automated research-idea pipeline can adopt the claim-level scoring method directly, keeping proposals that extend one known result. The 96%-versus-1.5% rejection gap gives a concrete benchmark for auditing your own filter.
