title: VideoMind: A Chain-of-LoRA Agent for Long Video Reasoning
summary: Videos, with their unique temporal dimension, demand precise grounded
  understanding, where answers are directly linked to visual, interpretable
  evidence. Despite significant breakthroughs in reasoning capabilities within
  Large Language Models, multi-modal reasoning - especially for videos - remains
  unexplored. In this work, we introduce VideoMind, a novel video-language agent
  designed for temporal-grounded video understanding. VideoMind incorporates two
  key innovations: (i) We identify essential capabilities for video temporal
  reasoning and develop a role-based agentic workflow, including a planner for
  coordinating different roles, a grounder for temporal localization, a verifier
  to assess temporal interval accuracy, and an answerer for question-answering.
  (ii) To efficiently integrate these diverse roles, we propose a novel
  Chain-of-LoRA strategy, enabling seamless role-switching via lightweight LoRA
  adaptors while avoiding the overhead of multiple models, thus balancing
  efficiency and flexibility. Extensive experiments on 14 public benchmarks
  demonstrate that our agent achieves state-of-the-art performance on diverse
  video understanding tasks, including 3 on grounded video question-answering, 6
  on video temporal grounding, and 5 on general video question-answering,
  underscoring its effectiveness in advancing video agent and long-form temporal
  reasoning.
paper_authors._id: 67d8eeb17e184aa2954d19f4
paper_authors.name: Ye Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-21T15:01:38.181Z
paper_authors._id: 6250eb5c1fd03f78d0ae550f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1649470297585-noauth.jpeg
paper_authors.fullname: Ye Liu
paper_authors.type: user
paper_authors.user: yeliudev
paper_authors._id: 67d8eeb17e184aa2954d19f5
paper_authors.name: Kevin Qinghong Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-18T09:28:47.081Z
paper_authors._id: 64440be5af034cdfd69ca3a7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64440be5af034cdfd69ca3a7/qmx24QiDFT29vleCxL9TX.jpeg
paper_authors.fullname: Qinghong (Kevin) Lin
paper_authors.type: user
paper_authors.user: KevinQHLin
paper_authors._id: 67d8eeb17e184aa2954d19f6
paper_authors.name: Chang Wen Chen
paper_authors._id: 67d8eeb17e184aa2954d19f7
paper_authors.name: Mike Zheng Shou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-18T09:29:07.309Z
paper_authors._id: 661ab3da2b14565c7acccf5c
paper_authors.avatarUrl: /avatars/fa4fc03664803e02aede4d4c3d50b393.svg
paper_authors.fullname: Mike Zheng Shou
paper_authors.type: user
paper_authors.user: AnalMom
paper_title: VideoMind: A Chain-of-LoRA Agent for Long Video Reasoning
paper_summary: Videos, with their unique temporal dimension, demand precise grounded
  understanding, where answers are directly linked to visual, interpretable
  evidence. Despite significant breakthroughs in reasoning capabilities within
  Large Language Models, multi-modal reasoning - especially for videos - remains
  unexplored. In this work, we introduce VideoMind, a novel video-language agent
  designed for temporal-grounded video understanding. VideoMind incorporates two
  key innovations: (i) We identify essential capabilities for video temporal
  reasoning and develop a role-based agentic workflow, including a planner for
  coordinating different roles, a grounder for temporal localization, a verifier
  to assess temporal interval accuracy, and an answerer for question-answering.
  (ii) To efficiently integrate these diverse roles, we propose a novel
  Chain-of-LoRA strategy, enabling seamless role-switching via lightweight LoRA
  adaptors while avoiding the overhead of multiple models, thus balancing
  efficiency and flexibility. Extensive experiments on 14 public benchmarks
  demonstrate that our agent achieves state-of-the-art performance on diverse
  video understanding tasks, including 3 on grounded video question-answering, 6
  on video temporal grounding, and 5 on general video question-answering,
  underscoring its effectiveness in advancing video agent and long-form temporal
  reasoning.
paper_ai_summary: VideoMind, a novel video-language agent, achieves state-of-the-art performance in various video understanding tasks using a role-based agentic workflow and a Chain-of-LoRA strategy.
paper_ai_keywords: temporal reasoning role-based agentic workflow planner grounder verifier answerer Chain-of-LoRA LoRA adaptors grounded video question-answering video temporal grounding general video question-answering