SemFlowRAG: Directed Semantic Flow from Abstraction to Evidence for Complex Reasoning
Retrieval-Augmented Generation (RAG) enhanced by Knowledge Graphs has shown promise in complex multi-hop reasoning tasks. However, existing graph-based retrieval methods typically rely on flat, undirected topologies. During the retrieval process, the probability flow often gets trapped in high-degree abstract concept nodes which we define as ``probability black holes'', leading to semantic drift and noise accumulation. To address this, we propose SemFlowRAG, a framework that reconstructs the flat retrieval space into a corpus-adaptive semantic gradient graph. This data-driven self-organization enables a hierarchical structure to emerge naturally from the data distribution, capturing the i...
| Authors | Houyuan Qin, Rong Wu, Qinyuan Qin et al. |
|---|---|
| Primary category | cs.IR |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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