EvoGraph-R1: Self-Evolving Multimodal Knowledge Hypergraphs for Agentic Retrieval
Retrieval-augmented generation (RAG) has emerged as a critical paradigm for grounding Multimodal Large Language Models (MLLMs) in external knowledge. Recent GraphRAG methods introduce structured entity-relation graphs to improve retrieval and reasoning. However, they remain limited by treating knowledge graphs as static data structures built offline and queried in a single pass. This static paradigm misaligns with the interactive, iterative nature of knowledge-intensive reasoning, creating three bottlenecks: (i) text-centric fragmentation that impedes cross-modal reasoning, (ii) frozen structu
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 59%douglasjordan2/c0 →
- PossiblePossibly related (embedding) · 59%Atomic-man007/Awesome_Multimodel_LLM →
- PossiblePossibly related (embedding) · 55%abhisadineni/ChatBot →
- PossiblePossibly related (embedding) · 53%llmsresearch/llm-flashcards →
- PossiblePossibly related (embedding) · 52%bibinprathap/VeritasGraph →
- LinkedLinked via arxiv author · 85%Jiashi Lin →
“EvoGraph-R1: Self-Evolving Multimodal Knowledge Hypergraphs for Agentic Retrieval”
- LinkedLinked via arxiv author · 85%Changhong Jiang →
“EvoGraph-R1: Self-Evolving Multimodal Knowledge Hypergraphs for Agentic Retrieval”
- LinkedLinked via arxiv author · 85%Xiangru Lin →
“EvoGraph-R1: Self-Evolving Multimodal Knowledge Hypergraphs for Agentic Retrieval”