This work proposes 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.
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.
Legal consultation questions exhibit multi-level complexity. A single retrieval strategy often leads to over-reasoning for simple questions and poor interpretability for complex ones, making it difficult to meet the requirements for both answer quality and efficiency in high-risk scenarios. To address this issue, this paper proposes CoAL-RAG, a complexity-aware legal retrieval-augmented generation method, which constructs a multi-dimensional evaluation mechanism based on ``question essence''and ``retrieval consistency''to enable adaptive routing of retrieval strategies. First, the reasoning demand is quantified according to the logical structure of the question. Then, the discrepancy between semantic retrieval and keyword retrieval is utilized to indirectly reflect problem complexity, thereby selecting the most appropriate retrieval strategy and dynamically filtering contextual information. Experimental results demonstrate that the proposed method significantly outperforms baseline models not only on Chinese legal benchmarks (SocialLawQA, LawBench) but also demonstrates strong cross-jurisdictional generalization on English datasets (LexGLUE, CaseHold). Specifically, on Chinese datasets, the BLEU score improves by 42.5\% and ROUGE-L reaches 3.6 times that of knowledge graph-based methods. On English benchmarks, CoAL-RAG maintains highly competitive accuracy, achieving an optimal balance between generation quality, deep logical reasoning, and system efficiency across different legal systems.
Jinyue Su, Zhuofeng Zhao, Huanhuan Wang et al.· 0 citations
Retrieval-Augmented Generation (RAG) has improved the factual grounding of large language models; however, conventional retrieval strategies remain limited for long-document question answering, as relevant information is often distributed across multiple document sections and may be inferential rather than lexically similar to the query. This paper proposes a reasoning-aware hierarchical traversal mechanism that interleaves chain-of-thought generation at each depth-first search node evaluation step, incorporating the resulting reasoning embedding into a combined node scoring function alongside query similarity. The hierarchical tree is constructed through iterative chunking, embedding, clustering, and summarization; retrieval is then guided by this combined score under a dual-threshold pruning mechanism that adaptively controls traversal depth and breadth. The proposed approach is evaluated on the NarrativeQA and QuALITY benchmarks against a semantic similarity-based traversal baseline. On QuALITY, the method achieves marginal gains in overall accuracy (+0.7%) and F1 (+0.1%), while substantially reducing the abstain rate from 4.1% to 1.8%, with improvement concentrated on normal-difficulty questions; on hard questions, the baseline outperforms the proposed method. On NarrativeQA, ROUGE-L F1, BLEU-1, and BLEU-4 improve slightly, though METEOR decreases, reflecting inconsistent metric-level effects. These results suggest that step-wise CoT reasoning can improve node selection quality in hierarchical traversal, with gains most evident on standard-difficulty questions. Performance on hard questions and metric-level consistency across NarrativeQA remain open challenges, indicating that the quality of the generated reasoning signal is a key bottleneck for further improvement.
A scale-aware comparative study of reasoning enhancement for SLMs across three major families of methods: prompting-based reasoning, retrieval-based augmentation, and knowledge graph guided scaffolding shows that reasoning-enhancement strategies are not universally transferable across model scales under the evaluated settings.
Zhen-Zhen Gu, Jie Liu, Xian Liu· Journal of King Saud Univers...· 0 citations
This work proposes Step-wise Training for In-context Reasoning (STIR), a model to dynamically decide when to retrieve a single logically consistent next step, just using the current problem and its intermediate state as the query.
Cheng Yang, Zhenya Huang, Liyang He et al.· Proceedings of the 32nd ACM...· 0 citations
Experimental results demonstrate that the VDGR-RAG method significantly outperforms a variety of RAG baselines in terms of both knowledge retrieval recall and QA accuracy.
Wenqi Chen, Haofei Yang, Rui Yang et al.· 0 citations