Forming Feasible Research Ideas by Analogical Transfer over Rationale-Bearing Graphs

Large language models are increasingly used to generate research ideas, yet human studies find their ideas weakest on feasibility. Existing work scores or tests feasibility once an idea exists; how the idea takes shape has received little attention. We present IdeaStrata, a harness that addresses feasibility while the idea is formed. Cognitive theories of analogy ask of a sound transfer whether a relation, with the conditions under which it holds, remains valid in a new setting, the question on which feasibility turns; IdeaStrata makes this check the representation the model operates on and the steps it carries out. Each input paper becomes a Hierarchical Layered Graph whose edges carry a rationale, the mechanism and conditions under which a relation holds; rationale transfer maps a relation onto concepts of another paper only when those conditions still hold, and structural verification removes transferred relations that cannot hold together. An idea is written only from relations that pass both checks. On IdeaBench with two backbones, IdeaStrata generates the most feasible ideas, with a mean feasibility 1.8x (GPT-4o) and 2.4x (Qwen3.8-Flash) that of the strongest baseline, and shifts the whole feasibility distribution upward, including target papers for which all three generated ideas are judged more feasible than the target itself. Its ideas are least often criticized for the concerns the design targets, such as a relation applied where its conditions do not hold, and removing either the rationale or structural verification lowers feasibility and raises these concerns.