D$^2$F-ReAG: Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation

2026-08-05

AI Summary

This work addresses the limitations of existing retrieval-augmented generation methods in multi-hop question answering, which often struggle to balance efficiency and accuracy due to the absence of dynamic question decomposition and effective reasoning filtering. The authors propose a novel reasoning-enhanced generation paradigm that dynamically decides whether to decompose a question based on the reliability of root-level reasoning, and subsequently refines the final answer using verification results from sub-questions. By integrating a dynamic reasoning depth control mechanism that combines reliability assessment, logical decomposition, and retrieval-augmented generation, the method achieves significant performance gains over state-of-the-art approaches on three established benchmarks—HotpotQA, 2WikiMultihopQA, and MuSiQue—demonstrating marked improvements in accuracy for complex multi-hop reasoning tasks.

Abstract

Large language models (LLMs) often generate inaccurate answers due to their reliance on static internal knowledge. Retrieval-augmented generation (RAG) addresses this limitation by integrating external knowledge and excelling at single-hop queries. However, it struggles with multi-hop questions that require cross-document reasoning. Existing methods, such as graph structured RAG or question decomposition, often lack dynamic decomposition and effective filtering, which leads to lower efficiency and accuracy. To overcome these limitations, we propose Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation (D2F-ReAG), a novel paradigm that adaptively controls reasoning depth by judging the reliability of the root-level reasoning. If the root reasoning is reliable, the model directly generates the answer. Otherwise, the question is logically decomposed into sub-questions, and the verified reasoning derived from these sub-questions is used to refine the root reasoning. Experiments on three multi-hop benchmarks demonstrate the effectiveness of our method in handling complex multi-hop questions.

Problem

• multi-hop reasoning
• retrieval-augmented generation
• dynamic decomposition
• reasoning filtering
• cross-document reasoning

Innovation

• Dynamic Decomposition
• Reasoning Filtering
• Multi-Hop Reasoning
• Retrieval-Augmented Generation
• Adaptive Reasoning Depth