Videos, with their unique temporal dimension, demand precise grounded understanding, where answers are directly linked to visual, interpretable evidence. Despite significant breakthroughs in text-based reasoning with large language models, multi-modal reasoning -- especially for videos -- remains limited.
In this work, we fill this gap by introducing VideoMind, a novel video-language agent for temporal-grounded video reasoning. Our method involves two key innovations: (1) We identify four essential capabilities for grounded video reasoning and propose a role-based agentic workflow, comprising a planner to coordinate roles, a grounder for temporal event localization, a verifier to assess event candidates, and an answerer for question answering. (2) To efficiently integrate these roles during inference, we propose a novel Chain-of-LoRA mechanism, where a unified base model with multiple LoRA adapters is leveraged to enable seamless role switching, balancing efficiency and flexibility.
Extensive experiments on 14 benchmarks across 3 tasks, including Grounded VideoQA, Video Temporal Grounding, and General VideoQA, demonstrate the effectiveness of the proposed scheme in advancing video agent, test-time scaling, and long-form video reasoning.
@article{liu2025videomind,
title={VideoMind: A Chain-of-LoRA Agent for Long Video Reasoning},
author={Liu, Ye and Lin, Kevin Qinghong and Chen, Chang Wen and Shou, Mike Zheng},
journal={arXiv preprint arXiv:2503.13444},
year={2025}
}