构建1170条科研构思全流程轨迹，还原真实研究决策过程。
IdeaTrail: Full-Process Agent Trajectories for Scientific Ideation
- 通过生成-指导循环模拟科研全流程动作与推理
- 每条轨迹包含工具使用、证据获取与中间成果记录
- 适合训练具象化科研能力的智能体，助研究自动化
科学构思涉及文献检索、论文阅读、工具使用、论断验证、跨论文整合、头脑风暴、弱方向剔除及迭代写作等多个阶段。然而现有资源多只捕捉孤立环节或最终成果，缺乏过程连接。我们提出IdeaTrail，一个包含1,170条多轮科研构思与提案生成轨迹的数据集。每条轨迹从证据收集开始，到想法选定或提案构建结束，联合记录工具使用、获取证据、中间产物与推理过程。该数据集通过人类精选论文与提案，经生成器-指导者循环合成：生成器输出可见动作、观察与产物修改序列，指导者则基于完整上下文检查事实依据、因果顺序、自然性及隐藏目标泄露。反向到正向的设计使轨迹既贴近真实科研产出，又保留研究实践中的不确定性、证据依赖与分阶段收敛特征。IdeaTrail不仅提供可复用的过程监督信号，还给出构建科研智能体数据的一般方法。
原文摘要 · Abstract (English)
Scientific ideation unfolds over multiple stages, including literature search, paper reading, tool use, claim checking, cross-paper synthesis, brainstorming, rejection of weak directions, and iterative writing. Yet most existing resources capture isolated components or final artifacts rather than the process connecting them. We introduce IdeaTrail, a dataset of 1,170 multi-turn trajectories for scientific ideation and proposal generation. Each trajectory follows a research process from evidence gathering to either idea selection or proposal construction, jointly recording tool use, acquired evidence, intermediate artifacts, and reasoning. IdeaTrail is synthesized from human-selected research papers and proposal artifacts through a Generator--Advisor loop. The Generator produces the visible sequence of actions, observations, and artifact edits, while the Advisor uses the full generation context to check grounding, causal order, naturalness, and leakage from hidden targets. This reverse-to-forward design keeps trajectories aligned with real scientific artifacts while retaining the uncertainty, evidence use, and staged convergence characteristic of research practice. IdeaTrail provides both reusable process supervision and a general recipe for constructing scientific-research-agent data.
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