arXiv:2510.24870v2 Announce Type: replace Abstract: We introduce MiRAGE, an evaluation framework for retrieval-augmented generation (RAG) from multimodal sources. As audiovisual media becomes a prevalent source of information online, it is essential for RAG systems to integrate information from these sources into generation. However, existing evaluations for RAG are text-centric, limiting their applicability to multimodal settings. MiRAGE is a claim-centric approach to multimodal RAG evaluation, consisting of InfoF1, which assesses factuality and information coverage, and CiteF1, which assesse
As AI models advance, the need to integrate and accurately evaluate their performance on multimodal data, especially retrieval-augmented generation, is becoming critical for effective deployment.
Evaluating multimodal RAG is crucial for ensuring factuality, information coverage, and proper citation in AI systems that handle diverse data types like audiovisual media, moving beyond current text-centric limitations.
The introduction of MiRAGE provides a standardized framework for assessing multimodal RAG, enabling more rigorous development and deployment of advanced AI applications.
- · AI developers
- · multimodal AI platforms
- · content creation platforms
- · research institutions
- · AI systems with poor multimodal integration
- · platforms relying solely on text-based RAG
Improved multimodal RAG systems will lead to more accurate and reliable AI outputs across various applications.
Enhanced evaluation frameworks will accelerate the development of AI models capable of handling complex, real-world data effectively.
The widespread adoption of multimodal RAG could enable entirely new forms of intelligent automation and content generation, significantly impacting knowledge work and media industries.
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