Multimodal Retrieval-Augmented Generation for Knowledge-Grounded Visual Question Answering
Keywords:
multimodal retrieval-augmented generation; visual question answering; knowledge grounding; vision-language models; system architecture; governance; robustness; fairness; sustainabilityAbstract
Knowledge-grounded visual question answering asks a system to answer natural-language questions about images by combining visual perception, linguistic reasoning, and external evidence that is not contained in the image alone. Conventional vision-language models often rely on parametric memory and therefore struggle with rare entities, time-sensitive facts, specialized domains, and questions that require verifiable provenance. Multimodal retrieval-augmented generation addresses this limitation by coupling cross-modal retrieval with generative reasoning, allowing the system to fetch relevant text, images, tables, diagrams, or structured records before producing an answer. This paper examines the architectural, infrastructural, and governance implications of multimodal retrieval-augmented generation for knowledge-grounded visual question answering. It analyzes retrieval architectures, unified semantic spaces, fusion mechanisms, and deployment trade-offs involving latency, recall, freshness, robustness, fairness, privacy, and sustainability. The discussion emphasizes that multimodal retrieval-augmented systems are not merely larger models; they are socio-technical infrastructures whose behavior depends on index construction, retrieval policies, content provenance, human oversight, and jurisdictional constraints. The paper compares deployment scenarios across medicine, education, industry, and public services, showing that different domains impose distinct requirements for auditability, abstention, and update cycles. It concludes that future progress depends on evaluation regimes that measure groundedness, citation quality, abstention, and long-term operational resilience rather than only answer accuracy.
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