SciEvoBench: Forecasting Scientific Method Transitions from Historical Evidence

Scientific progress is shaped by how new methods build on and change existing ones. Recent benchmarks evaluate large language models (LLMs) on forecasting future scientific contributions, but predicting a contribution alone does not make explicit how an existing method develops into a successor. We introduce SciEvoBench, a benchmark for method transition forecasting built on SciEvoEnv, a two-layer graph environment linking papers, extracted methods, and their evolution relations. Given a source method and the scientific record available at a specified time, the Event task predicts whether a successor will first be observed within three months. For realized events, the Content task predicts the affected source aspect (Where) and successor mechanism (What). Both tasks support Static evaluation from fixed evidence and Dynamic evaluation through interactive exploration. Evaluations of open-weight and proprietary models show that Dynamic access improves Event discrimination for some models, while identifying potential improvement directions remains easier than predicting successor mechanisms. Structural baselines reveal predictive signal in relations and recency beyond activity alone. Controlled analyses with Qwen3.5-9B show that Dynamic forecasts match more recorded mechanisms without a reliable increase in target-window matches. Early reading mainly improves valid-answer coverage; further reading, re-answering, and evidence replacement yield no reliable gain in mechanism prediction. These findings distinguish access to predictive information and related knowledge from anticipating a specific technical change at the right time.