October 2, 2025Open Access

ViTCoT: Video-Text Interleaved Chain-of-Thought for Boosting Video Understanding in Large Language Models

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ViTCoT enhances video reasoning performance compared to text-only methods with significant neuron activation.
The novel approach integrates visual modality into reasoning, addressing limitations of current large language models.
Key-video selection for the study utilized MLLMs in constructing the Video-Text Interleaved Benchmark.
The study demonstrates promising results in bridging low-level visual signals and high-level reasoning.

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Introduction of ViTCoT improves video reasoning in large language models, indicating better cognitive alignment with visual perception.

Zhang et al. (2025) studied this question.

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