VideoDeepResearch: Long Video Understanding With Agentic Tool Using
Long video understanding (LVU) presents a significant challenge for current
multi-modal large language models (MLLMs) due to the task's inherent complexity
and context window constraint. It is widely assumed that addressing LVU tasks
requires foundation MLLMs with extended context windows, strong visual
perception capabilities, and proficient domain expertise. In this work, we
challenge this common belief by introducing VideoDeepResearch, a novel agentic
framework for long video understanding. Our approach relies solely on a
text-only large reasoning model (LRM) combined with a modular multi-modal
toolkit, including multimodal retrievers and visual perceivers, all of which
are readily available in practice. For each LVU task, the system formulates a
problem-solving strategy through reasoning, while selectively accessing and
utilizing essential video content via tool using. We conduct extensive
experiments on popular LVU benchmarks, including MLVU, Video-MME, and LVBench.
Our results demonstrate that VideoDeepResearch achieves substantial
improvements over existing MLLM baselines, surpassing the previous
state-of-the-art by 9.6%, 6.6%, and 3.9% on MLVU (test), LVBench, and
LongVideoBench, respectively. These findings highlight the promise of agentic
systems in overcoming key challenges in LVU problems.
684b91c73b733ba333687033
Huaying Yuan
684b91c73b733ba333687034
Zheng Liu
684b91c73b733ba333687035
Junjie Zhou
claimed_verified
2025-06-24T08:10:49.711Z
6564a2ceedae9c33b7654a1f
/avatars/42f09356a1282896573ccb44830cd327.svg
JUNJIE ZHOU
user
JUNJIE99
684b91c73b733ba333687036
Ji-Rong Wen
684b91c73b733ba333687037
Zhicheng Dou
VideoDeepResearch: Long Video Understanding With Agentic Tool Using
Long video understanding (LVU) presents a significant challenge for current
multi-modal large language models (MLLMs) due to the task's inherent complexity
and context window constraint. It is widely assumed that addressing LVU tasks
requires foundation MLLMs with extended context windows, strong visual
perception capabilities, and proficient domain expertise. In this work, we
challenge this common belief by introducing VideoDeepResearch, a novel agentic
framework for long video understanding. Our approach relies solely on a
text-only large reasoning model (LRM) combined with a modular multi-modal
toolkit, including multimodal retrievers and visual perceivers, all of which
are readily available in practice. For each LVU task, the system formulates a
problem-solving strategy through reasoning, while selectively accessing and
utilizing essential video content via tool using. We conduct extensive
experiments on popular LVU benchmarks, including MLVU, Video-MME, and LVBench.
Our results demonstrate that VideoDeepResearch achieves substantial
improvements over existing MLLM baselines, surpassing the previous
state-of-the-art by 9.6%, 6.6%, and 3.9% on MLVU (test), LVBench, and
LongVideoBench, respectively. These findings highlight the promise of agentic
systems in overcoming key challenges in LVU problems.
VideoDeepResearch, a text-only reasoning model with modular tools, surpasses existing baselines in long video understanding tasks without extending context windows or enhancing visual perception capabilities.
long video understanding multi-modal large language models VideoDeepResearch text-only large reasoning model multimodal retrievers visual perceivers MLVU Video-MME LVBench LongVideoBench agentic systems