用海量论文生成可验证的科学理论，比传统方法更准更可靠
Generating Literature-Driven Scientific Theories at Scale
- 基于13.7万篇论文构建理论，而非仅依赖模型记忆
- 生成理论在4600篇新论文中预测准确率显著提升
- 适合需要可解释性与证据支持的科学发现场景
当前自动化科学发现多聚焦于实验生成，而更高层次的理论构建仍缺乏探索。本文提出从大规模科学文献中合成包含定性与定量规律的理论。我们利用13.7k篇源论文生成2.9k个理论，对比了基于文献支撑与基于参数化知识的生成方式，以及以准确性或新颖性为目标的差异。实验表明，相较于使用参数化LLM记忆生成，文献驱动的方法在匹配已有证据和预测后续4600篇论文结果方面均显著更优。
原文摘要 · Abstract (English)
Contemporary automated scientific discovery has focused on agents for generating scientific experiments, while systems that perform higher-level scientific activities such as theory building remain underexplored. In this work, we formulate the problem of synthesizing theories consisting of qualitative and quantitative laws from large corpora of scientific literature. We study theory generation at scale, using 13.7k source papers to synthesize 2.9k theories, examining how generation using literature-grounding versus parametric knowledge, and accuracy-focused versus novelty-focused generation objectives change theory properties. Our experiments show that, compared to using parametric LLM memory for generation, our literature-supported method creates theories that are significantly better at both matching existing evidence and at predicting future results from 4.6k subsequently-written papers
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