Decoupling Semantics and Logic: A Training-Free Coarse-to-Fine Pipeline for Video Retrieval-Augmented Generation
arXiv:2606.07924v1 Announce Type: cross Abstract: This paper presents our system description for the 2nd Workshop on Multimodal Augmented Generation via MultimodAl Retrieval (MAGMaR). Addressing the critical challenges of cross-lingual long-video comprehension, strict persona adherence, and zero-hallucination temporal grounding, we propose a fully training-free, two-stage cascaded Video RAG pipeline. Our architecture strategically decouples semantic retrieval from cognitive logical reasoning through a modality-aware division of labor. In the first stage, a high-recall semantic pre-fetching mod
This paper addresses real-world limitations of current video retrieval-augmented generation systems, coinciding with the rapid evolution and deployment of multimodal AI.
Improving video understanding, especially for long and cross-lingual content, with strict adherence and zero-hallucination, is critical for robust AI applications across many industries.
The development of training-free, cascaded RAG pipelines that decouple semantics from logic could significantly reduce computational costs and improve reliability for complex multimodal tasks.
- · AI developers
- · Content platforms
- · Multimodal AI research
- · Enterprises using video AI
- · Systems with high hallucination rates
- · Inefficient video processing models
More accurate and reliable AI systems for video comprehension will emerge.
This could accelerate the adoption of AI-powered video analysis in sensitive applications like education, defense, and legal review.
The reduced training burden could democratize access to advanced video RAG capabilities, fostering innovation in smaller developer communities.
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