AI RESEARCH
GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion
arXiv CS.CL
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ArXi:2604.21649v1 Announce Type: cross Large Language Models (LLMs) have shown immense potential in Knowledge Graph Completion (KGC), yet bridging the modality gap between continuous graph embeddings and discrete LLM tokens remains a critical challenge. While recent quantization-based approaches attempt to align these modalities, they typically treat quantization as flat numerical compression, resulting in semantically entangled codes that fail to mirror the hierarchical nature of human reasoning.