CoI‑Pro: Scientific Idea Generation via Retrieval over Research Trajectories

Scientific idea generation is crucial for automated research and scientific progress. Existing methods typically follow a three-step pipeline: first constructing idea chains or graphs for a given topic, then selecting an idea generation direction, and finally producing concrete ideas. However, these methods fail to explicitly incorporate historical research trajectories from other topics, which often leads to low-quality or incremental ideas. In contrast, human researchers frequently propose innovative ideas by drawing on research trajectories from other topics, rather than confining themselves to their own topic. For instance, suppose Topic A has a research trajectory of “supervised learning → unsupervised learning → semi-supervised learning.” If a new Topic B is currently at the “unsupervised learning” stage, an automated idea generation model without referencing Topic A’s trajectory would likely remain confined to proposing ideas within unsupervised learning. Conversely, if provided with Topic A’s trajectory as a reference, the model is far more likely to generate a novel idea extending to semi-supervised learning, thereby substantially enhancing both the quality and innovativeness of the generated idea. Thus, inspired by the above intuition, we propose a novel method, called CoI-Pro, by retrieving relevant historical research trajectories in other topics to guide idea generation for a target topic. Extensive experiments show that CoI-Pro achieves average preference scores of 57.7%, 74.9%, and 58.9% against AI-Researcher, AI-Scientist-v2, and the CoI Agent, respectively, under three LLM judges on 50 temporally separated computer-science topics.