Ground, Iterate, Trace: Decomposing AI Research Ideation

Large language models have been widely used to generate plausible research ideas, yet it remains unclear what makes automated ideation effective and how generated novel proposals arise from prior scientific work. We introduce the Creative AI Researcher (CAR), an agentic framework combining literature grounding, iterative development and explicit conceptual operators. CAR constructs a semantic knowledge graph from retrieved literature and equips an ideation agent with graph-retrieval tools and four operators: bisociation, analogy, exploration and elaboration. Each operator call is checked against structural acceptance conditions and recorded, producing a trace of intermediate transformations and declared literature sources. We evaluate CAR with two language-model backbones across 40 LiveIdeaBench research topics and two execution benchmarks. CAR achieves significantly higher methodological novelty (MN) than single-shot prompting and The AI Scientist. Its methodological novelty does not differ significantly from the Research Ideation Agent, while its feasibility scores are higher on both backbones. With a shared experiment executor, CAR achieves the highest pairwise win rate among the evaluated AI methods on FML-bench-Lite and BioXArena. In a ten-condition ablation study, MN point estimates favor CAR over one-step proposal on both backbones, but no MN contrast survives correction for multiple comparisons. The results do not establish a consistent quality benefit from explicit operators beyond free-form iteration. CAR makes the sequence of operations and declared literature sources inspectable, separating judged idea quality from procedural traceability.