Beyond Visual CoT: Internalized Visual Thinking for Proactive Video Reasoning

Analysis

Why This Paper Matters

This paper addresses a critical bottleneck in multimodal large language models (MLLMs) for video reasoning: the high inference overhead of visual chain-of-thought (Visual CoT). While Visual CoT improves reasoning by generating intermediate images, it is computationally expensive, making it impractical for real-time proactive video reasoning. The authors propose Internalized Visual Thinking (IVT), a post-training framework that shifts the visual thinking process from inference to training time, enabling the model to reason directly without generating future frames. This is significant because it offers a path to more efficient and practical video reasoning systems, which are essential for applications like autonomous driving, surveillance, and interactive agents.

The paper's findings challenge the prevailing assumption that explicit pixel-space generation is necessary for effective visual reasoning. By demonstrating that latent future-frame prediction during training can internalize visual thinking, the authors open new avenues for designing MLLMs that are both accurate and efficient. This could influence future research on world modeling and reasoning in multimodal systems, potentially leading to more scalable and deployable AI solutions.

Technical Contributions

• IVT Framework: Introduces a post-training approach that jointly optimizes textual prediction and next-embedding prediction on unlabeled videos, encouraging the model to capture motion, object transitions, interactions, and latent intent.
• Inference Efficiency: At inference, IVT generates answers directly without synthesizing or re-encoding future frames, maintaining the same inference pathway as direct-answer fine-tuning.
• Controlled Studies: Systematically evaluates target representations, decoder designs, prediction horizons, data mixtures, training curricula, and predictive objectives, providing insights into effective design choices.
• Latent Prediction: Uses latent representations of future frames as prediction targets, which is more efficient than pixel-space prediction and still captures essential visual dynamics.

Results

IVT improves over direct-answer fine-tuning on all six evaluation settings, demonstrating consistent gains across diverse tasks. Compared with explicit Visual CoT, IVT achieves comparable or better performance while reducing average end-to-end latency by more than 5x. This latency reduction is substantial, making IVT a compelling choice for real-time video reasoning applications. The controlled studies likely reveal optimal configurations, though specific metrics are not detailed in the abstract.

Significance

The broader impact of this work lies in its potential to shift the paradigm of visual reasoning in MLLMs. By showing that predictive world modeling can be internalized during training, the paper suggests that explicit pixel-space generation at inference may be unnecessary. This could lead to more efficient and scalable multimodal reasoners, enabling deployment in resource-constrained environments. Furthermore, the idea of internalized thinking could extend beyond video reasoning to other domains, such as embodied AI and robotics, where real-time decision-making is critical. The paper's findings encourage researchers to explore training-time internalization as a general principle for improving efficiency without sacrificing accuracy.