arXiv:2606.13141v1 Announce Type: new Abstract: Retrieval-augmented generation is moving beyond text into long, egocentric video, where systems must select query-relevant chunks across multiple modalities and temporal granularities. Yet progress in VideoRAG is limited by two gaps: existing benchmarks allow queries to be answered without the video, obscuring retrieval errors, and prior methods apply a single modality-granularity configuration per query, ignoring chunk-level variability. We address both by introducing V-RAGBench, a benchmark of $\langle$query, evidence chunk, answer$\rangle$ tri
The rapid advancement of AI beyond text, particularly into multimodal intelligence involving video, necessitates more sophisticated retrieval and generation techniques to handle complex, real-world data like long egocentric video.
This development pushes the frontier of AI's ability to understand and interact with unstructured, multi-modal data, critical for robust AI systems in applications ranging from robotics to intelligent assistants.
The introduction of V-RAGBench provides a superior tool for evaluating and developing VideoRAG systems, enabling the precise identification and utilization of relevant segments within vast video datasets, moving beyond text-centric RAG limitations.
- · AI research labs
- · Video analytics companies
- · Robotics companies
- · Generative AI platforms
- · Companies relying on simplistic video analysis
- · Legacy retrieval systems
- · Static AI models
Improved VideoRAG systems will lead to more accurate and contextually aware AI responses from video data.
Enhanced video understanding will accelerate the development of sophisticated multimodal AI agents capable of operating in complex physical environments.
The ability to efficiently extract and utilize information from long videos could democratize access to vast amounts of visual data for training and deployment across various industries.
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Read at arXiv cs.AI