CORE: Coarse-to-Fine Search with Context Management for Deep Research
- Published
- Source
- arXiv
- Paper number
- 1408
- Field
- RAG / Agents
- arXiv ID
- 2611.07859
Key points
- CORE is a training-free deep research harness that combines coarse-to-fine search with context management.
- The harness first retrieves broad passages with a dense retriever, then reuses the token-level similarities from a late-interaction reranker to return only the most relevant snippet from each passage.
- Context management lets the agent expand the context around sources it has already retrieved and replaces repeated snippets with compact references, reducing context waste.
- Across three agent models and three benchmarks, CORE achieved the highest answer accuracy and evidence recall, even on corpora containing up to 553M documents.
- On the largest ClimbMix corpus, CORE with GPT-5.4 reached 68.42% accuracy, higher than 56.14% for OpenResearcher and 50.88% for Tongyi DeepResearch.
- In the component analysis, accuracy rose from 62.61% with coarse retrieval only to 70.84% with coarse-to-fine search and 80.12% for the full CORE with context management.
Paper links
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