Rethinking Chain-of-Thought Reasoning for Videos
Yiwu Zhong, Zi-Yuan Hu, Yin Li, Liwei Wang
2025-12-11
Summary
This paper investigates whether complex, lengthy reasoning is truly necessary for artificial intelligence to understand videos, building on recent advances in models that can process both images and text.
What's the problem?
Current AI models that 'think through' problems with videos, similar to how a person would explain their reasoning step-by-step, often require a lot of processing power because they analyze many parts of the video and generate very long explanations. The researchers questioned if all this detail is actually needed for good results.
What's the solution?
The researchers developed a new method to make video-understanding AI more efficient. They focused on compressing the visual information the AI looks at and encouraging it to generate shorter, more focused explanations before making a decision. This new system doesn't need a lot of pre-existing examples of good reasoning to work well.
Why it matters?
This work suggests that AI doesn't necessarily need to mimic human-like, detailed reasoning to effectively understand videos. By using concise reasoning and focusing on the most important visual information, we can create AI systems that are faster, more efficient, and still accurate, which is a big step forward for practical applications of video AI.
Abstract
Chain-of-thought (CoT) reasoning has been highly successful in solving complex tasks in natural language processing, and recent multimodal large language models (MLLMs) have extended this paradigm to video reasoning. However, these models typically build on lengthy reasoning chains and large numbers of input visual tokens. Motivated by empirical observations from our benchmark study, we hypothesize that concise reasoning combined with a reduced set of visual tokens can be sufficient for effective video reasoning. To evaluate this hypothesis, we design and validate an efficient post-training and inference framework that enhances a video MLLM's reasoning capability. Our framework enables models to operate on compressed visual tokens and generate brief reasoning traces prior to answering. The resulting models achieve substantially improved inference efficiency, deliver competitive performance across diverse benchmarks, and avoid reliance on manual CoT annotations or supervised fine-tuning. Collectively, our results suggest that long, human-like CoT reasoning may not be necessary for general video reasoning, and that concise reasoning can be both effective and efficient. Our code will be released at https://github.com/LaVi-Lab/Rethink_CoT_Video.