Abstract
Scientific research is a complex, multi-stage workflow rather than a single act of text generation. The ideation process typically emerges through literature search, paper reading, tool use, claim checking, cross-paper synthesis, brainstorming, rejection of weak directions, and iterative writing. Existing resources capture individual components of this process, but datasets that jointly record tool use, evidence acquisition, intermediate artifact evolution, and idea- or proposal-level endpoints remain limited. This report introduces \method, a multi-turn process-trajectory dataset for scientific ideation and proposal generation. Each instance records a research process from evidence gathering to either idea selection or proposal construction. Rather than freely fabricating trajectories, \method starts from human-selected high-quality research papers and proposal artifacts and uses a Generator--Advisor synthesis loop. The Generator produces the visible trajectory through actions, observations, and artifact edits, while the Advisor has access to the full generation context and checks grounding, causal order, naturalness, and leakage from hidden targets. This reverse-to-forward procedure produces multi-turn research data that remains aligned with real scientific artifacts while approximating the uncertainty, evidence use, and staged convergence of research practice. \method provides both a dataset and a general recipe for synthesizing process-supervision data for scientific research agents.
Abstract (translated)
URL
https://arxiv.org/abs/2607.10144
