Rohan Paul@rohanpaul_ai · X一项对4,760个科学事件的研究发现，AI模型在“解释”科学方面优于“预测”科学。模型在识别可能的研究路径（尤其是选择题形式）时表现较好，但在预测科学发现是否会实际发生、何时发生以及何种方法有效等更难任务上表现薄弱，准确率接近随机猜测。即使提供额外历史信息，模型改善有限。这表明，模型内嵌大量科学知识并不等同于具备可靠的科学预见能力。研究论文发表于arXiv（2605.22681），标题为《Forecasting Scientific Progress with AI》。
AI can explain science better than it can forecast science.
Across 4，760 scientific events， the models were much better at recognizing possible research paths than forecasting actual outcomes.
Models often recognize a plausible research idea when the answer is already nearby， especially in multiple-choice form.
But they are much weaker at the harder thing： predicting whether a discovery will actually happen， when it will happen， and what method will make it work.
That means the models are still much better at hindsight than foresight.
When asked whether a scientific claim will actually be realized， the models hover near chance， and when asked when progress will arrive， they systematically push it too far into the future.
Even when the authors gave models extra older information， the models improved a bit but still did not become reliable at predicting future scientific progress.
So having lots of scientific knowledge inside a model does not automatically make it a good scientific forecaster.
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Paper Link - arxiv. org/abs/2605.22681
Paper Title： "Forecasting Scientific Progress with AI"