Video-CoE: Reinforcing Video Event Prediction via Chain of Events
Despite advances in the application of MLLMs for various video tasks, video event prediction (VEP) remains relatively underexplored. VEP requires the model to perform fine-grained temporal modeling of videos and establish logical relationships between videos and future events, which current MLLMs still struggle with. In this work, we first present a comprehensive evaluation of current leading MLLMs on the VEP task, revealing the reasons behind their inaccurate predictions, including lack of logical reasoning ability for future events prediction and insufficient utilization of visual information. To address these challenges, we propose Chain of Events (CoE) paradigm, which constructs temporal event chains to implicitly enforce MLLM focusing on the visual content and the logical connections between videos and future events, incentivizing model's reasoning capability with multiple training protocols. Experimental results on public benchmarks demonstrate that our method outperforms both leading open-source and commercial MLLMs, establishing a new state-of-the-art on the VEP task. Codes and models will be released soon.
69bb9e6cba2b62a5fb0b0dc3
Qile Su
69bb9e6cba2b62a5fb0b0dc4
Jing Tang
69bb9e6cba2b62a5fb0b0dc5
Rui Chen
claimed_verified
2026-03-19T12:56:35.820Z
64be128a2e66dc7b8bd8459d
/avatars/ac5a5246dc19dd35bbd89d7fc492cba5.svg
Rui Chen
ruichen9618
user
ruichen9618
69bb9e6cba2b62a5fb0b0dc6
Lei Sun
69bb9e6cba2b62a5fb0b0dc7
Xiangxiang Chu
Video-CoE: Reinforcing Video Event Prediction via Chain of Events
Despite advances in the application of MLLMs for various video tasks, video event prediction (VEP) remains relatively underexplored. VEP requires the model to perform fine-grained temporal modeling of videos and establish logical relationships between videos and future events, which current MLLMs still struggle with. In this work, we first present a comprehensive evaluation of current leading MLLMs on the VEP task, revealing the reasons behind their inaccurate predictions, including lack of logical reasoning ability for future events prediction and insufficient utilization of visual information. To address these challenges, we propose Chain of Events (CoE) paradigm, which constructs temporal event chains to implicitly enforce MLLM focusing on the visual content and the logical connections between videos and future events, incentivizing model's reasoning capability with multiple training protocols. Experimental results on public benchmarks demonstrate that our method outperforms both leading open-source and commercial MLLMs, establishing a new state-of-the-art on the VEP task. Codes and models will be released soon.
A new Chain of Events paradigm is introduced for video event prediction that improves temporal modeling and logical reasoning in multimodal language models through structured event chains and enhanced training protocols.
video event prediction multimodal language models temporal modeling logical reasoning event chains training protocols