CWQ

RSF-GLLM: Differentiable graph reasoning framework for multi-hop knowledge graph QA

RSF-GLLM is a new framework for multi-hop question answering over knowledge graphs that decouples differentiable graph reasoning from LLM-based answer generation. The core Recurrent Soft-Flow (RSF) module uses a GRU-guided query updater with dynamic gating to traverse semantically dissimilar bridge nodes, with flow sparsity regularization guaranteeing convergence to discrete reasoning paths. Extracted paths are textualized to fine-tune an LLM, grounding generation in factual graph topology. Experiments on WebQSP and CWQ benchmarks show competitive accuracy with improved inference efficiency over LLM-heavy baselines.