发表机构
ShanghaiTech University（上海科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
介绍用于科学构思和提案生成的多轮过程轨迹数据集IdeaTrail，从高质量论文和工件出发，经生成器-顾问合成循环，产生与真实工件一致且近似研究实践的多轮数据，为科研智能体提供数据及合成过程监督数据的方法。
AI 中文摘要
科学研究是一个复杂的多阶段工作流程，而非单一的文本生成行为。构思过程通常通过文献搜索、论文阅读、工具使用、主张核查、跨论文综合、头脑风暴、摒弃薄弱方向以及迭代写作等方式呈现。现有资源仅捕捉该过程的个别组件，而联合记录工具使用、证据获取、中间工件演变以及想法或提案级端点的数据集仍很有限。本报告介绍了IdeaTrail，一个用于科学构思和提案生成的多轮过程轨迹数据集。每个实例记录从证据收集到想法选择或提案构建的研究过程。IdeaTrail并非随意编造轨迹，而是从人工挑选的高质量研究论文和提案工件出发，采用生成器-顾问合成循环。生成器通过动作、观察和工件编辑生成可见轨迹，顾问则可访问完整的生成上下文并检查基础、因果顺序、自然性以及来自隐藏目标的泄漏。这种从后向前的过程产生了多轮研究数据，既与真实科学工件保持一致，又近似研究实践中的不确定性、证据使用和分阶段收敛。IdeaTrail为科学研究智能体提供了一个数据集以及合成过程监督数据的通用方法。
英文摘要
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.
