EvoGraph-R1: Self-Evolving Multimodal Knowledge Hypergraphs for Agentic Retrieval

2026-07-14

AI Summary

This work addresses the limitations of existing graph-augmented retrieval methods, which treat knowledge graphs as static structures and thus struggle to support cross-modal, iterative, and revisable reasoning. The paper proposes a self-evolving graph retrieval framework that unifies knowledge graph construction and retrieval within an agent-driven, closed-loop evolutionary mechanism. By modeling multimodal knowledge as a dynamic hypergraph environment, the framework enables an intelligent agent to perform adaptive multi-hop reasoning through actions such as querying, expanding, editing, and answering. The agent’s behavior is governed by a Markov decision process, allowing it to learn optimal reasoning strategies. Evaluated on multimodal visual and textual question-answering benchmarks, the approach significantly outperforms current retrieval-augmented generation (RAG) methods, achieving notable advances in accuracy, knowledge coverage, and traceable reasoning.

Abstract

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 structures unable to incorporate new evidence or correct errors, and (iii) rigid single-pass retrieval without adaptive refinement. To overcome these limitations, we introduce EvoGraph-R1, a self-evolving GraphRAG framework that reconceptualizes knowledge graphs as dynamic environments shaped through agent interactions. We formulate retrieval as a Markov Decision Process (MDP) where the agent observes the graph state and executes actions to query (GraphRetrieve), expand (WebSearch), refine (GraphEdit), or terminate (Answer) the reasoning. These actions reshape the hypergraph structure and generate feedback signals that guide subsequent evolution. Through this closed loop, the hypergraph evolves by integrating new evidence, correcting errors, and refining structure to support multi-hop reasoning. Experiments on multimodal VQA and text QA benchmarks demonstrate substantial improvements over existing RAG baselines in accuracy, coverage, and traceability, establishing self-evolving knowledge graphs as a fundamental paradigm across modalities.

Problem

• Retrieval-augmented generation
• Multimodal reasoning
• Static knowledge graphs
• Interactive retrieval
• Knowledge graph evolution

Innovation

• Self-Evolving Knowledge Graph
• Multimodal Hypergraph
• Agentic Retrieval
• Markov Decision Process
• GraphRAG