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This paper introduces RSF-GLLM, a framework that decouples differentiable graph reasoning from LLM generation to address the semantic gap in multi-hop knowledge graph question answering, achieving competitive performance with superior inference efficiency.
Practitioner Rory Sawyer reflects on a decade of applying program analysis to bridge the gap between code and human intent, emphasizing static analysis as a communication tool for correctness beyond execution.