Modality Relevance is not Modality Utility: Post-hoc Selective Modality Escalation for Cost-Aware Multimodal RAG
arXiv:2607.05438v1 Announce Type: cross Abstract: Multimodal retrieval-augmented generation (RAG) grounds a generator in evidence drawn from heterogeneous modalities -- text, tables, and images. The dominant deployment choice is binary and made before the model has tried to answer: either run a cheap text(+table) pipeline, or pay for an expensive vision-language model (VLM) over every image. Recent adaptive systems improve on this by selecting the modality or fidelity pre-retrieval, from a question-conditioned predictor of which modality will be needed. We show that this is the wrong decision
The proliferation of multimodal RAG systems and the increasing computational cost associated with large vision-language models necessitate more efficient resource allocation techniques.
This research provides a method for optimizing the cost-efficiency of multimodal AI systems, which is crucial for their widespread deployment and economic viability in real-world applications.
The decision-making process for utilizing expensive modalities in RAG systems shifts from pre-retrieval prediction to a post-hoc, relevance-based selective escalation, potentially reducing operational costs significantly.
- · AI developers
- · Cloud providers (cost-conscious clients)
- · Enterprises deploying multimodal RAG
- · Inefficient multimodal RAG systems
- · Developers relying solely on pre-retrieval modality prediction
Multimodal RAG applications become more economically feasible to deploy at scale.
Increased adoption of multimodal RAG leads to new product categories and capabilities across various industries.
The reduced cost barrier accelerates the integration of complex multimodal understanding into pervasive AI agents, enhancing their capabilities in diverse environments.
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Read at arXiv cs.AI