Multimodal Retrieval-Augmented Generation via Semantic-Aware Deep Hashing and Approximate Nearest Neighbor Search
Keywords:
retrieval-augmented generation, multimodal systems, deep hashing, approximate nearest neighbor search, semantic indexing, system architecture, sustainability, fairnessAbstract
Retrieval-augmented generation (RAG) architectures have transformed the landscape of large-scale natural language processing by grounding generative outputs in external knowledge repositories, thereby reducing factual hallucination and improving response quality. As real-world applications increasingly demand the integration of textual, visual, and other modalities, extending RAG to multimodal settings introduces profound systems challenges related to indexing latency, storage efficiency, semantic alignment, and retrieval quality. This paper presents a comprehensive system-level investigation of multimodal RAG frameworks underpinned by semantic-aware deep hashing and approximate nearest neighbor (ANN) search. We examine the design space where high-dimensional multimodal embeddings are compressed into compact binary hash codes that preserve cross-modal semantic similarity while enabling sub-linear retrieval over billion-scale repositories. The discussion encompasses architectural trade-offs in joint and modality-specific hashing, the interplay between hashing objectives and ANN index structures, and the systemic implications for deployment, scalability, energy consumption, and fairness. We analyze governance and policy considerations arising from large-scale multimodal retrieval, including provenance attribution, bias amplification across modalities, and the sustainability of indexing infrastructure. By synthesizing cross-domain perspectives, the paper provides forward-looking insights into building robust, efficient, and ethically grounded multimodal RAG systems that can serve as knowledge-intensive backbones in high-stakes environments.
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