RATIO: A Benchmark for Retrieval Across Typed Ideation Operations in Scientific Literature

Abstract:Retrieved scientific literature can serve as inspiration for both human and AI scientists. Inspiration can take different forms: prior work may directly suggest how to address a problem, or surface directions at different levels of abstraction - zooming out to a more general view or zooming in to a concrete realization. We introduce RATIO (Retrieval Across Typed Ideation Operations), a large-scale benchmark in which relevance is defined by three operations which we name ideation moves: Address retrieves potential approaches for stated problems, Broaden retrieves more general formulations, and Specify retrieves concrete instantiations. RATIO is constructed from millions of full-text scientific papers across CS literature via a general recipe that extends discourse-marker distant supervision - previously used only for classification - to corpus-scale retrieval, combined with extensive LLM and human vetting. Experiments show that operation-specific fine-tuning substantially boosts retrievers but leaves much room for further improvements. RATIO provides a scalable training and evaluation framework for retrieval components that support literature-grounded ideation, opening up new research avenues on scientific inspiration retrieval.

Computation and Language (cs.CL); Information Retrieval (cs.IR)

arXiv:2608.27394 [cs.CL]

(or arXiv:2608.27394v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.27394 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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