Evidence-Based Scientific Question Discovery: A Framework with Historical Backtesting
Current AI systems are optimized for answering questions; the scientific enterprise is bottlenecked earlier, at discovering the questions worth investigating. We present a framework that turns a traceable, reproducible, scope controlled research corpus into ranked, falsifiable research questions: evidence is represented as provenance carrying claims; cross paper tensions are detected, typed, and human adjudicated; surviving signals are refined into questions and ranked by a two stage protocol separating scientific priority from execution priority. We instantiate the framework on exoplanet atmospheres, a domain that uniquely combines literature, structured catalogs, and space telescope arc...
| Authors | Hui Mao |
|---|---|
| Primary category | cs.DL |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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