M$^3$KG-RAG: Multi-hop Multimodal Knowledge Graph-enhanced Retrieval-Augmented Generation

Researchers introduce M³KG-RAG, a novel multimodal retrieval-augmented generation system that enhances large language models by integrating multi-hop knowledge graphs with audio-visual data. The approach improves reasoning depth and answer accuracy by filtering irrelevant information through a new grounding and pruning mechanism called GRASP.

M³KG-RAG addresses a critical limitation in current multimodal AI systems: the disconnect between retrieval-augmented generation and real-world knowledge graph coverage. Traditional multimodal RAG systems rely on raw similarity matching in embedding spaces, which frequently retrieves contextually irrelevant information that confuses language models and degrades output quality. This research tackles the problem through architectural innovation rather than brute-force scaling, introducing a multi-agent pipeline that constructs richer, context-aware knowledge graphs spanning audio and visual modalities.

The significance lies in how the system handles the multi-hop reasoning problem—the ability to follow chains of logical connections across multiple entities and relationships. Previous MMKG systems suffer from sparse connectivity and modality gaps, forcing models to work with incomplete information. By enriching triplets with contextual metadata and implementing selective pruning, M³KG-RAG enables more faithful reasoning that aligns retrieved knowledge with user queries.

For the AI industry, this represents progress toward more reliable multimodal systems that can handle complex queries requiring reasoning across video, audio, and text domains. Applications in video understanding, embodied AI, and multimodal search engines stand to benefit from improved grounding and reduced hallucination. The lightweight multi-agent architecture also suggests scalability without prohibitive computational overhead.

The research trajectory points toward hybrid systems combining structured knowledge with neural retrieval—a pattern gaining momentum as practitioners recognize embedding-only approaches plateau. Future developments will likely focus on extending these techniques to truly long-horizon reasoning tasks and expanding modality coverage beyond audio-visual domains.

• →M³KG-RAG constructs multi-hop knowledge graphs that maintain audio-visual modality coverage while enabling more precise entity-query alignment.
• →GRASP mechanism filters redundant context by grounding entities to queries and evaluating answer-supporting relevance, reducing hallucination in model outputs.
• →System demonstrates significant improvements over existing multimodal RAG approaches across diverse benchmarks through architectural innovation rather than parameter scaling.
• →Multi-agent pipeline design enables lightweight construction of context-enriched knowledge graph triplets without excessive computational requirements.
• →Approach addresses fundamental limitations in similarity-based retrieval by implementing domain-aware knowledge pruning for improved reasoning faithfulness.