ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models 事件

ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models arXiv:2510.09711v2 Announce Type: replace Abstract: Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and generalization capabilities beyond traditional embedding-based approaches. However, existing LLM-based methods often struggle to fully exploit structured semantic representations, as the continuous embedding space