RATIO: A Benchmark for Retrieval Across Typed Ideation Operations in Scientific Literature
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
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 53%AI for Scientific Literature Mining: PubMed, Semantic Scholar, and What Actually Works - Technology Networks →
- PossiblePossibly related (embedding) · 51%We’ve got a workshop on production retrieval-augmented generation with open models, benchmarked end to end, thought it’d be relevant here [D] →
- FuzzySimilar title/name (fuzzy) · 59%jeinlee1991/chinese-llm-benchmark →
“Fuzzy title match (0.73): “RATIO: A Benchmark for Retrieval Across Typed Ideation Opera” ≈ “jeinlee1991/chinese-llm-benchmark””
- LinkedLinked via arxiv author · 85%Maayan Sharon →
“RATIO: A Benchmark for Retrieval Across Typed Ideation Operations in Scientific Literature”
- LinkedLinked via arxiv author · 85%Tom Hope →
“RATIO: A Benchmark for Retrieval Across Typed Ideation Operations in Scientific Literature”