Learning to Ideate for Scientific Impact
Scientific ideation is increasingly mediated by large language models, but current ideation systems are usually trained and evaluated on immediately judgeable proxies such as novelty, clarity, and feasibility. This leaves open whether delayed signals of scientific uptake can be used as feedback for steering models toward research directions with higher expected \emph{impact}. We study this…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
