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This paper proposes a categorical approach to generate falsifiable research ideas by modeling papers as small categories and using a functor-preservation gate to filter cross-domain analogies, improving over traditional LLM-based methods.
Proposes Graphs of Research (GoR), a supervised fine-tuning method that uses citation evolution graphs as supervision for LLM-based research idea generation, achieving state-of-the-art results against gpt-4o-driven baselines.