VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG explores VideoStir is a structured, intent-aware RAG framework for understanding long videos by leveraging spatio-temporal graphs and an MLLM-backed relevance scorer.. Commercial viability score: 8/10 in Long Video Understanding.
Use This Via API or MCP
This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Page Freshness
Canonical route: /paper/videostir-understanding-long-videos-via-spatio-temporally-structured-and-intent-aware-rag
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Agent Handoff
Canonical ID videostir-understanding-long-videos-via-spatio-temporally-structured-and-intent-aware-rag | Route /paper/videostir-understanding-long-videos-via-spatio-temporally-structured-and-intent-aware-rag
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/videostir-understanding-long-videos-via-spatio-temporally-structured-and-intent-aware-ragMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.05418"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG",
"normalized_query": "2604.05418",
"route": "/paper/videostir-understanding-long-videos-via-spatio-temporally-structured-and-intent-aware-rag",
"paper_ref": "videostir-understanding-long-videos-via-spatio-temporally-structured-and-intent-aware-rag",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
Research neighborhood
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Preparing verified analysis
Dimensions overall score 8.0
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