发表机构
Center for Brain-Inspired Computing Research, Tsinghua University（清华大学类脑计算研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SCoR层级框架，将研究方向发现建模为科学概念间的首次共现、关系形成及类型预测，构建大规模基准并引入HiSCoR模型，在2025-2026窗口显著提升预测性能。
AI 中文摘要
预见新兴研究方向是人工智能辅助科学的一个关键目标。现有方法主要预测哪些概念将在未来的论文中共同出现，但共现捕捉的是共同关注，而非连接的科学意义，例如一种方法是否使用、结合、替换或反驳另一种方法。我们将研究方向发现表述为在共享候选对空间上的层级式科学关系预测，包含三个时间对齐的任务：首次共现、首次科学关系形成以及形成时的关系类型。我们基于2017年至2026年间发表的187,848篇cs.CV论文构建了SCoR-Graph，产生了270,687个整合概念、745万条共现边以及615,036条带类型的有向关系边。从特定截止时间的图快照中，我们推导出SCoR-Bench，一个针对这三种能力、经过泄漏审计的基准，其整个关系类型测试集具有专家验证的金标准标签。我们进一步引入了HiSCoR，一个任务自适应模型家族，通过编码截止前事件历史并将关系形成条件化为未来的共现，将关系涌现建模为一个随时间演化、层级约束的过程。在保留的2025-2026年时间窗口上，HiSCoR取得了0.9515的AUROC，比最强的时间图基线相对提升了2.4%，并将群体AUPRC提升了14.0%；其关系类型变体达到了0.7795的Macro-AUROC。消融研究表明，语义、共现和类型化关系视图提供了互补的预测证据。SCoR将研究方向预测从预测哪些概念将共现，推进到预测有证据支持的科学关系是否以及如何出现。
英文摘要
Anticipating emerging research directions is a critical goal of AI-assisted science. Existing methods mainly predict which concepts will co-occur in future papers, but co-occurrence captures shared attention rather than the scientific meaning of a connection, such as whether one method uses, combines, replaces, or contradicts another. We formulate research-direction discovery as hierarchical scientific-relation forecasting over a shared candidate-pair space, comprising three temporally aligned tasks: first co-occurrence, first scientific-relation formation, and relation type at formation. We construct SCoR-Graph from 187,848 cs.CV papers published between 2017 and 2026, yielding 270,687 consolidated concepts, 7.45 million co-occurrence edges, and 615,036 typed, directed relation edges. From cutoff-specific graph snapshots, we derive SCoR-Bench, a leakage-audited benchmark for these three capabilities, with expert-verified gold labels for the entire relation-type test set. We further introduce HiSCoR, a task-adapted model family that models relation emergence as a temporally evolving, hierarchically constrained process by encoding pre-cutoff event histories and conditioning relation formation on future co-occurrence. On the held-out 2025-2026 window, HiSCoR achieves an AUROC of 0.9515, a 2.4% relative improvement over the strongest temporal-graph baseline, and improves population-AUPRC by 14.0%; its relation-type variant achieves a Macro-AUROC of 0.7795. Ablations show that semantic, co-occurrence, and typed-relation views provide complementary predictive evidence. SCoR advances research-direction forecasting from predicting which concepts will co-occur to anticipating whether and how evidence-backed scientific relations will emerge.
Comments15 pages, 7 figures, 6 tables; includes supplementary material. Jingze Wang and Fred Sun contributed equally; Shangqi Guo is the corresponding author
