MSR-MEL synthesizes instance-centric, group-level, lexical, and statistical evidence with LLMs and asymmetric teacher-student GNNs to outperform prior unsupervised methods on multimodal entity linking benchmarks.
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cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
CodaRAG improves RAG by using a CLS-inspired three-stage pipeline of knowledge consolidation, multi-dimensional associative navigation, and interference elimination, delivering 7-11% gains on GraphRAG-Bench for factual and reasoning tasks.
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Multi-Perspective Evidence Synthesis and Reasoning for Unsupervised Multimodal Entity Linking
MSR-MEL synthesizes instance-centric, group-level, lexical, and statistical evidence with LLMs and asymmetric teacher-student GNNs to outperform prior unsupervised methods on multimodal entity linking benchmarks.
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CodaRAG: Connecting the Dots with Associativity Inspired by Complementary Learning
CodaRAG improves RAG by using a CLS-inspired three-stage pipeline of knowledge consolidation, multi-dimensional associative navigation, and interference elimination, delivering 7-11% gains on GraphRAG-Bench for factual and reasoning tasks.