VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG

• URL: http://arxiv.org/abs/2604.05418v3
• Date: Thu, 16 Apr 2026 09:51:53 GMT
• Title: VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG
• Abstract summary: VideoStir is a structured and multimodal intent-aware long-video RAG framework.<n>It structures a video as atemporal graph at clip level, and then performs multi-hop retrieval to aggregate evidence.<n>It also introduces an MLLM-backed intent-relevance scorer that retrieves frames based on their alignment with the query's reasoning intent.
• Score: 33.938909878793815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scaling multimodal large language models (MLLMs) to long videos is constrained by limited context windows. While retrieval-augmented generation (RAG) is a promising remedy by organizing query-relevant visual evidence into a compact context, most existing methods (i) flatten videos into independent segments, breaking their inherent spatio-temporal structure, and (ii) depend on explicit semantic matching, which can miss cues that are implicitly relevant to the query's intent. To overcome these limitations, we propose VideoStir, a structured and intent-aware long-video RAG framework. It firstly structures a video as a spatio-temporal graph at clip level, and then performs multi-hop retrieval to aggregate evidence across distant yet contextually related events. Furthermore, it introduces an MLLM-backed intent-relevance scorer that retrieves frames based on their alignment with the query's reasoning intent. To support this capability, we curate IR-600K, a large-scale dataset tailored for learning frame-query intent alignment. Experiments show that VideoStir is competitive with state-of-the-art baselines without relying on auxiliary information, highlighting the promise of shifting long-video RAG from flattened semantic matching to structured, intent-aware reasoning. Codes and checkpoints are available at https://github.com/RomGai/VideoStir.

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