用历史数据验证，从文献矛盾中自动发现值得研究的科学问题。
Evidence-Based Scientific Question Discovery: A Framework with Historical Backtesting
- 通过分析论文间证据矛盾生成可验证的研究问题
- 2021年前的证据生成的问题在2021-2026年被实际研究，两个被解答
- 适合对科学发现机制感兴趣的科研人员和AI辅助研究者
当前AI系统擅长回答问题，但科学探索的瓶颈在于发现值得研究的问题。本文提出一个框架，将可追溯、可复现、范围可控的研究语料转化为排序后的可证伪研究问题：证据以携带来源的主张形式表示；跨论文矛盾被检测、分类并经人工裁定；剩余信号被提炼为问题，并通过两阶段协议分别评估科学优先级与执行优先级。该框架在系外行星大气领域进行实例化，该领域兼具文献、结构化目录与空间望远镜档案。历史回测显示，所有基于2021年前可用证据生成的问题，在2021至2026年间均被未见文献实质性回应：其中两个问题被解决，包括一个被学界后来明确驳斥的前提，而排名第一的问题也被独立提出且至今未解。结果表明，从证据矛盾中系统性发现的问题，正是科学家后续投入研究的课题。
原文摘要 · Abstract (English)
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 archives. In a historical backtest, all questions generated from evidence available before 2021 were substantively engaged by the 2021 to 2026 literature the sys?tem never saw: two were answered, including one whose premise the community later explicitly refuted and the top ranked question is independently posed and still open. These results sug?gest that systematic question discovery from evidence tensions surfaces the questions working scientists subsequently invest in.
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