PreSciencePreScience/PreScience

PreScience是一个用于科学预测的数据集和基准，基于约98K篇最近的AI研究论文（目标论文），以及相关的作者发表历史和引用链接，总计约502K篇论文。这些论文记录包括标题、摘要、消歧的作者身份、关键参考文献、主题标签、引用轨迹和元数据快照，并遵循时间截断原则。数据集实例化了七个示例任务：五个论文锚定任务（贡献生成、合作者预测、先前工作选择、引用计数预测和未来组合预测）以及两个聚合主题趋势预测变体。数据来源为2023年10月至2025年9月期间在arXiv上发布的六个AI相关类别（cs.CL、cs.LG、cs.AI、cs.CV、cs.IR、cs.NE）的研究论文。数据集还包括训练和测试分割、作者消歧映射、文件结构（如Parquet和JSONL文件），并支持使用HuggingFace或代码库加载。

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 to be systematically less diverse and less novel than human-authored research from the same period.