Chenrun Wang

Large language models can generate research ideas that appear novel, but novelty alone does not determine whether an idea is worth following up. In practice, researchers also need ideas that are feasible, experimentally testable, and consistent with the evidence they build on. We therefore assess early-stage proposal quality through four dimensions: novelty, feasibility, experimental design, and evidence consistency. We formulate scientific ideation as graph-grounded hierarchical search over a structured scientific evidence graph. Constructed at the method level, the graph encodes core methods, components, limitations, future work, baselines, datasets, and their relations. On top of this representation, we propose a two-stage framework: the first stage explores candidate mechanism directions over evidence-backed topic and problem frames, and the second stage refines promising candidates through evidence-grounded closure, binding ideas to concrete baselines, datasets, and experimental protocols. In controlled evaluation on 40 matched problem frames from 20 computer science topics, we compare against ResearchAgent, Chain-of-Ideas, direct LLM generation, and Text-RAG under the same four-dimensional protocol. The proposed framework achieves the highest mean overall score in both the dual-LLM and expert evaluations, with its clearest gains in feasibility and evidence consistency. ResearchAgent performs better in novelty and experimental design in both evaluations and remains close overall in the dual-LLM evaluation. Ablations further support complementary contributions from structured evidence, hierarchical search, and proposal closure.