SciPaths: Forecasting Pathways to Scientific Discovery
Scientific progress depends on sequences of enabling contributions, yet existing AI4Science benchmarks largely focus on citation prediction, literature retrieval, or idea generation rather than the dependencies that make progress possible. In this paper, we introduce discovery pathway forecasting: given a target scientific contribution and the prior literature available at a specified time, the task is to (1) identify the enabling contributions required to realize it and (2) ground each in prior work when such prior work exists. We present SciPaths, a benchmark of 262 expert-annotated gold pathways and 2,444 silver pathways constructed from machine learning and natural language processing...
| Authors | Eric Chamoun, Yizhou Chi, Yulong Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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