SOURCE-LINKED INTELLIGENCE
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 question using citation-normalized impact as a noisy but scalable proxy for scholarly uptake. We construct a large-scale dataset from over 100K computer science papers by extracting goal-conditioned idea des
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-24T13:37:59.000Z
First collected: 2026-09-25T06:12:46.948Z. This is not the publication date.