多跳知识图谱问答一直难在中间节点没语义关联，RSF-GLLM用循环软流解决了这个瓶颈，比那些全靠LLM暴力推理的更快更准。
RSF-GLLM框架将可微图推理与答案生成解耦，采用GRU引导的查询更新器和动态门控机制遍历语义不相似的桥接节点。引入流稀疏正则化保证从软概率收敛到离散推理路径。在WebQSP和CWQ数据集上，RSF-GLLM取得了与基于LLM的方法竞争的性能，且推理效率更高。
RSF-GLLM: Bridging the Semantic Gap in Multi-Hop Knowledge Graph QA via Recurrent Soft-Flow and Decoupled LLM Generation
Multi-hop Question Answering over Knowledge Graphs faces a critical challenge: traditional retrieve-then-read pipelines break differentiability, preventing the retriever from learning to bridge the semantic gap where intermediate nodes lack lexical overlap with the query. To address this, we propose RSF-GLLM, a framework decoupling differentiable graph reasoning from answer generation. Our Recurrent Soft-Flow (RSF) module employs a GRU-guided query updater to propagate continuous relevance scores, utilizing a dynamic gating mechanism to traverse semantically dissimilar bridge nodes via structural cues. We introduce flow sparsity regularization to theoretically guarantee convergence from soft probabilities to discrete reasoning paths. These paths are extracted and textualized to fine-tune a Large Language Model (LLM), ensuring generation is grounded in factual topology. Experiments on WebQSP and CWQ demonstrate that RSF-GLLM achieves competitive performance with superior inference efficiency compared to LLM based computationally expensive approaches.