# PreScience: A Dataset and Benchmark for Scientific Forecasting

arXiv: https://arxiv.org/abs/2602.20459 · HTML: https://mlsanity.com/paper/2602.20459/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2026.

## Built on by (most cited first, 3 of 3)

- [Scientific reasoning does not reliably translate into scientific forecasting in frontier AI](https://mlsanity.com/paper/2605.22681/) (2026)
- [Can Large Language Models Forecast What Researchers Study Next?](https://mlsanity.com/paper/2609.00747/) (2026)
- [Inspire: Benchmarking Scientific Literature Search for Open Research Problems](https://mlsanity.com/paper/2609.33233/) (2026)

## Abstract (arXiv)

Can AI systems trained on the existing scientific record forecast the advances that will follow? We introduce PreScience, a dataset and benchmark for scientific forecasting built around 98K recent AI research papers, together with companion papers covering author publication histories and citation links, yielding 502K papers in total. The resulting paper records include titles, abstracts, disambiguated author identities, influential references, topic labels, citation trajectories, and metadata snapshotted to respect temporal cutoffs. We instantiate seven exemplar tasks: five paper-anchored tasks -- contribution generation, collaborator prediction, prior work selection, citation count prediction, and future combination prediction -- and two aggregate topic trend forecasting variants. We develop baselines ranging from simple heuristics and embedding methods to frontier language models and agentic systems, and introduce LACER, an LLM-based metric for evaluating similarity of generated contribution descriptions that agrees better with human judgments than existing metrics. Finally, we compose task models to generate a 12-month synthetic corpus and find that the resulting papers are systematically less diverse and less novel than human-authored research from the same period. We release the PreScience dataset (https://huggingface.co/datasets/allenai/prescience) and code (https://github.com/allenai/prescience).