The field of video understanding and reasoning is rapidly advancing, with a focus on developing models that can effectively process and analyze visual information. Recent developments have highlighted the importance of reinforcement learning, self-supervised learning, and multimodal fusion in improving video reasoning capabilities. Notably, the use of semantic segmentation, chain-of-thought reasoning, and process-aware modeling has shown promising results in enhancing video understanding. Furthermore, the development of efficient and lightweight models has enabled on-device video concept segmentation and tracking, making these technologies more accessible and practical for real-world applications.

Some noteworthy papers in this area include: VideoP2R, which proposes a novel process-aware video RFT framework that achieves state-of-the-art performance on six out of seven video reasoning and understanding benchmarks. ViSS-R1, which introduces a self-supervised reinforcement learning GRPO algorithm and a framework that streamlines and integrates pretext-task-based self-supervised learning into the MLLM's R1 post-training paradigm, demonstrating effectiveness and superiority on six widely-used video reasoning and understanding benchmarks. VideoSeg-R1, which adopts a decoupled architecture that formulates the task as joint referring image segmentation and video mask propagation, achieving state-of-the-art performance in complex video reasoning and segmentation tasks. VANS, which leverages reinforcement learning to align a Vision-Language Model with a Video Diffusion Model for Video-Next-Event Prediction, achieving state-of-the-art performance in both video event prediction and visualization.