VideoExplorer: Think With Videos For Agentic Long-Video Understanding

TL;DR

VideoExplorer introduces an iterative, question-driven framework for long-video understanding that enhances reasoning accuracy, interpretability, and efficiency by integrating planning, temporal grounding, and perception.

Contribution

It proposes a novel reasoning framework that formulates sub-questions and locates relevant video segments iteratively, along with a new dataset and training pipeline for long-video understanding.

Findings

Outperforms existing methods on long-video reasoning benchmarks

Demonstrates robustness and adaptability across tasks

Achieves more faithful and interpretable reasoning processes

Abstract

Long-video understanding~(LVU) is a challenging problem in computer vision. Existing methods either downsample frames for single-pass reasoning, sacrificing fine-grained details, or depend on textual reasoning over task-agnostic representations, hindering task-specific perception and exploration. In this paper, we propose VideoExplorer, a framework grounded in the principle of ``thinking with video'', which naturally intertwines planning, temporal grounding, and scalable perception into a coherent reasoning process. Rather than reasoning over a static context, VideoExplorer iteratively formulates sub-questions, locates relevant moments, and performs task-oriented, temporally scalable video understanding until reaching the final answer, enabling faithful, efficient, and interpretable reasoning. To address the lack of LVU training resources, we construct a long-video reasoning dataset using difficulty-adaptive sampling to ensure high-quality trajectories on complex tasks. Building on this dataset, we design a two-stage training pipeline: supervised trajectory initialization followed by trajectory-level preference optimization, encouraging adaptive temporal grounding and iterative information integration guided by downstream rewards. Extensive evaluations on popular long-video understanding and reasoning benchmarks demonstrate VideoExplorer's significant advantage over existing baselines, highlighting its robustness, adaptability, and efficiency. Our code is made publicly available in this repository(https://github.com/yhy-2000/VideoDeepResearch).