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Research questionHow can structured RAG synthesize evidence scattered across many documents while limiting answer-generation cost?Relevant evidence may be distributed across many documents and connected through dependencies that a rigid reasoning structure cannot anticipate. Gathering and combining that evidence can also require costly answer generation.
Information Retrieval
Natural Language Processing
Reasoning
Retrieval-Augmented Generation
Latest papersRecent research connected to this question, newest first.A Tree-based RAG Framework for Evidence-Intensive QA via Adaptive Planning and Topology-Aware Evidence GatheringThe source presents APT-RAG for structured RAG and reports results on evidence-intensive QA benchmarks. Its evidence is limited to the described framework and benchmark experiments; broader deployment behavior is not established.research paper · Sep 4, 2026
Related questions
How can long-form RAG answer multi-hop questions across changing temporal, spatial, and relational contexts?How can LLM agents answer recurring questions over unstructured documents without repeatedly rereading them?How can retrieval-augmented generation reason causally over dynamic external information without losing context?How can long-context RAG preserve global document structure during retrieval?
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