Reasoning over Coupled Receptive Fields: Eliminating Subgraph Redundancy at Scale

Conditional message passing (CMP) currently represents a leading paradigm for knowledge graph reasoning. Since vanilla CMP performs full-graph propagation per query, existing methods adopt subgraph-wise sampling to improve computational efficiency. However, these extracted subgraphs still exhibit high structural overlap. Consequently, the same local structures are repeatedly accessed and processed, leading to substantial redundancy that remains a major bottleneck for scaling reasoning to large knowledge graphs. To address this issue, we propose reasoning over \textbf{Co}upled \textbf{R}eceptive \textbf{F}ields (CoRF) to eliminate subgraph redundancy at scale. Rather than sampling a separate subgraph for each query, CoRF constructs a set of receptive supports that allow multiple queries to share receptive fields, thereby reducing subgraph redundancy. Meanwhile, we introduce boundary padding and residual compensation mechanisms to ensure topological integrity and query coverage. We further incorporate cost-aware load balancing to improve distributed execution efficiency. Comprehensive experiments on state-of-the-art CMP models demonstrate that CoRF achieves up to a 5.9$\times$ speedup and a 10.0$\times$ reduction in memory usage, without compromising reasoning accuracy.