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"