NaviRAG organizes documents hierarchically and uses an LLM agent for active navigation across granularity levels to raise retrieval recall and answer quality on long-document QA tasks.
InThe Twelfth International Conference on Learning Representations
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A comprehensive survey that organizes query expansion methods in the PLM/LLM era along four design dimensions, synthesizes application patterns, and outlines future directions.
R²-Searcher introduces fine-grained evidence modeling, retrieval reflection, and R²PO RL to calibrate retrieval-reasoning boundaries and improve multi-hop QA performance.
citing papers explorer
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NaviRAG: Towards Active Knowledge Navigation for Retrieval-Augmented Generation
NaviRAG organizes documents hierarchically and uses an LLM agent for active navigation across granularity levels to raise retrieval recall and answer quality on long-document QA tasks.
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Query Expansion in the Age of Pre-trained and Large Language Models: A Comprehensive Survey
A comprehensive survey that organizes query expansion methods in the PLM/LLM era along four design dimensions, synthesizes application patterns, and outlines future directions.
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R$^2$-Searcher: Calibrating Retrieval and Reasoning Boundaries for Agentic Search
R²-Searcher introduces fine-grained evidence modeling, retrieval reflection, and R²PO RL to calibrate retrieval-reasoning boundaries and improve multi-hop QA performance.