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HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation
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Retrieval-Augmented Generation (RAG) systems often struggle with imperfect retrieval, as traditional retrievers focus on lexical or semantic similarity rather than logical relevance. To address this, we propose \textbf{HopRAG}, a novel RAG framework that augments retrieval with logical reasoning through graph-structured knowledge exploration. During indexing, HopRAG constructs a passage graph, with text chunks as vertices and logical connections established via LLM-generated pseudo-queries as edges. During retrieval, it employs a \textit{retrieve-reason-prune} mechanism: starting with lexically or semantically similar passages, the system explores multi-hop neighbors guided by pseudo-queries and LLM reasoning to identify truly relevant ones. Experiments on multiple multi-hop benchmarks demonstrate that HopRAG's \textit{retrieve-reason-prune} mechanism can expand the retrieval scope based on logical connections and improve final answer quality.
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Cited by 4 Pith papers
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TRIAGE: Trustworthy Retrieval Instrumentation And Graph Evaluation
TRIAGE instruments Graph-RAG with gold-free stage metrics and a usage-time diagnostic chain that localizes failures to extraction, graph/schema, or retrieval levers.
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Harmonia: End-to-End RAG Serving Optimization
An end-to-end RAG serving framework that uses component-level batching, resource allocation, and runtime prioritization to improve throughput and reduce SLO violations.
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Evaluating Multi-Hop Reasoning in Large Language Models: A Chemistry-Centric Case Study
A new 971-question chemistry benchmark shows that even the best large language models, given full context, still fail on many multi-step reasoning questions.
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MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval
MoLoRAG boosts multi-page document QA by traversing a page graph and scoring pages with both semantic and VLM-judged logical relevance, improving retrieval and answer accuracy on four benchmarks.
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