A survey that categorizes RIR benchmarks by domain and modality, proposes a taxonomy for integrating reasoning into retrieval pipelines, and outlines key challenges.
LATTICE : LLM -guided hierarchical retrieval
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
RGAO combines retrieval-based complexity assessment with a formal budget algebra to enable dynamic topology selection in multi-agent code generation with provable conservation.
AgenticRAG equips an LLM with iterative retrieval and navigation tools, delivering 49.6% recall@1 on BRIGHT, 0.96 factuality on WixQA, and 92% correctness on FinanceBench.
Ψ-RAG improves cross-document multi-hop QA performance using an adaptive hierarchical abstract tree and agent-powered hybrid retrieval, outperforming RAPTOR by 25.9% and HippoRAG 2 by 7.4% in average F1.
citing papers explorer
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A Survey of Reasoning-Intensive Retrieval: Progress and Challenges
A survey that categorizes RIR benchmarks by domain and modality, proposes a taxonomy for integrating reasoning into retrieval pipelines, and outlines key challenges.
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Retrieval-Conditioned Topology Selection with Provable Budget Conservation for Multi-Agent Code Generation
RGAO combines retrieval-based complexity assessment with a formal budget algebra to enable dynamic topology selection in multi-agent code generation with provable conservation.
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AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases
AgenticRAG equips an LLM with iterative retrieval and navigation tools, delivering 49.6% recall@1 on BRIGHT, 0.96 factuality on WixQA, and 92% correctness on FinanceBench.
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Hierarchical Abstract Tree for Cross-Document Retrieval-Augmented Generation
Ψ-RAG improves cross-document multi-hop QA performance using an adaptive hierarchical abstract tree and agent-powered hybrid retrieval, outperforming RAPTOR by 25.9% and HippoRAG 2 by 7.4% in average F1.