PreScience: A Benchmark for Forecasting Scientific Contributions

• URL: http://arxiv.org/abs/2602.20459v1
• Date: Tue, 24 Feb 2026 01:37:53 GMT
• Title: PreScience: A Benchmark for Forecasting Scientific Contributions
• Authors: Anirudh Ajith, Amanpreet Singh, Jay DeYoung, Nadav Kunievsky, Austin C. Kozlowski, Oyvind Tafjord, James Evans, Daniel S. Weld, Tom Hope, Doug Downey,
• Abstract summary: PreScience is a scientific forecasting benchmark that decomposes the research process into four interdependent generative tasks.<n>We develop baselines and evaluations for each task, including LACERScore, a novel measure of contribution similarity.<n>The resulting synthetic corpus is systematically less diverse and less novel than human-authored research from the same period.
• Score: 32.63164451901248
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Can AI systems trained on the scientific record up to a fixed point in time forecast the scientific advances that follow? Such a capability could help researchers identify collaborators and impactful research directions, and anticipate which problems and methods will become central next. We introduce PreScience -- a scientific forecasting benchmark that decomposes the research process into four interdependent generative tasks: collaborator prediction, prior work selection, contribution generation, and impact prediction. PreScience is a carefully curated dataset of 98K recent AI-related research papers, featuring disambiguated author identities, temporally aligned scholarly metadata, and a structured graph of companion author publication histories and citations spanning 502K total papers. We develop baselines and evaluations for each task, including LACERScore, a novel LLM-based measure of contribution similarity that outperforms previous metrics and approximates inter-annotator agreement. We find substantial headroom remains in each task -- e.g. in contribution generation, frontier LLMs achieve only moderate similarity to the ground-truth (GPT-5, averages 5.6 on a 1-10 scale). When composed into a 12-month end-to-end simulation of scientific production, the resulting synthetic corpus is systematically less diverse and less novel than human-authored research from the same period.

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