title: MR. Video: "MapReduce" is the Principle for Long Video Understanding
summary: We propose MR. Video, an agentic long video understanding framework that
  demonstrates the simple yet effective MapReduce principle for processing long
  videos: (1) Map: independently and densely perceiving short video clips, and
  (2) Reduce: jointly aggregating information from all clips. Compared with
  sequence-to-sequence vision-language models (VLMs), MR. Video performs detailed
  short video perception without being limited by context length. Compared with
  existing video agents that typically rely on sequential key segment selection,
  the Map operation enables simpler and more scalable sequence parallel
  perception of short video segments. Its Reduce step allows for more
  comprehensive context aggregation and reasoning, surpassing explicit key
  segment retrieval. This MapReduce principle is applicable to both VLMs and
  video agents, and we use LLM agents to validate its effectiveness.
    In practice, MR. Video employs two MapReduce stages: (A) Captioning:
  generating captions for short video clips (map), then standardizing repeated
  characters and objects into shared names (reduce); (B) Analysis: for each user
  question, analyzing relevant information from individual short videos (map),
  and integrating them into a final answer (reduce). MR. Video achieves over 10%
  accuracy improvement on the challenging LVBench compared to state-of-the-art
  VLMs and video agents.
    Code is available at: https://github.com/ziqipang/MR-Video
paper_authors._id: 6808486f043aa415b647ca77
paper_authors.name: Ziqi Pang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-23T13:56:06.272Z
paper_authors._id: 642a33ea5673845d9854f458
paper_authors.avatarUrl: /avatars/ea8ed29a0b4ad629a2ba6e7d26cbd923.svg
paper_authors.fullname: Ziqi Pang
paper_authors.type: user
paper_authors.user: ziqipang
paper_authors._id: 6808486f043aa415b647ca78
paper_authors.name: Yu-Xiong Wang
paper_title: MR. Video: "MapReduce" is the Principle for Long Video Understanding
paper_summary: We propose MR. Video, an agentic long video understanding framework that
  demonstrates the simple yet effective MapReduce principle for processing long
  videos: (1) Map: independently and densely perceiving short video clips, and
  (2) Reduce: jointly aggregating information from all clips. Compared with
  sequence-to-sequence vision-language models (VLMs), MR. Video performs detailed
  short video perception without being limited by context length. Compared with
  existing video agents that typically rely on sequential key segment selection,
  the Map operation enables simpler and more scalable sequence parallel
  perception of short video segments. Its Reduce step allows for more
  comprehensive context aggregation and reasoning, surpassing explicit key
  segment retrieval. This MapReduce principle is applicable to both VLMs and
  video agents, and we use LLM agents to validate its effectiveness.
    In practice, MR. Video employs two MapReduce stages: (A) Captioning:
  generating captions for short video clips (map), then standardizing repeated
  characters and objects into shared names (reduce); (B) Analysis: for each user
  question, analyzing relevant information from individual short videos (map),
  and integrating them into a final answer (reduce). MR. Video achieves over 10%
  accuracy improvement on the challenging LVBench compared to state-of-the-art
  VLMs and video agents.
    Code is available at: https://github.com/ziqipang/MR-Video
paper_ai_summary: MR. Video, a MapReduce-based framework, enhances long video understanding by independently processing short video clips and aggregating information, outperforming VLMs and video agents on LVBench.
paper_ai_keywords: MapReduce long video understanding short video clips sequence-to-sequence vision-language models video agents sequence parallel perception context aggregation LVBench LLM agents captioning standardizing characters objects