results of LLaVA-Video-7B (Zhang et al., 2024e) with EC-RAG on two representative examples from Video-MME (Fu et al., 2025) in Figures 6 and 7. Both cases involve temporal reasoning questions from long documentary videos, where the baseline model fails due to the inherent ambiguity of sparsely sampled frames. As illustrated, by constructing temporal event chains from multimodal evidence—particularly ASR transcripts that capture the chronological progression of topics and events—EC-RAG enables the model to accurately resolve temporal ordering and causal relationships that are indistinguishable from visual information alone. The results demonstrate that event-level retrieval effectively bridges the gap between sparse visual sampling and the dense temporal reasoning required for long video understanding.
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