VTI-CoT: Visual-Textual Interleaved Chain of Thought for Video Reasoning

• URL: http://arxiv.org/abs/2606.05736v1
• Date: Thu, 04 Jun 2026 05:55:15 GMT
• Title: VTI-CoT: Visual-Textual Interleaved Chain of Thought for Video Reasoning
• Abstract summary: Video reasoning aims to understand complex temporal events and causal relationships within videos.<n>Existing Chain-of-Thought (CoT) video reasoning methods rely on text-only information for logical deduction.<n>We propose Visual-Textual Interleaved CoT framework to integrate textual reasoning steps with corresponding visual frames.
• Score: 10.735514435202662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video reasoning aims to understand complex temporal events and causal relationships within videos. Recently, Chain-of-Thought (CoT) has been introduced to this field to enhance reasoning accuracy. However, existing CoT-based video reasoning methods primarily rely on text-only information for logical deduction, overlooking critical visual information during the inference process. Inspired by the human cognitive mechanism of reviewing visual segments during inference, we propose VTI-CoT, a Visual-Textual Interleaved CoT framework. VTI-CoT integrates textual reasoning steps with corresponding visual frames. Given the scarcity of visual-textual interleaved CoT in existing datasets, we develop an automated annotation pipeline to construct high-quality multimodal CoT data. Further, reasoning over long-form videos entails increasingly long CoT token sequences, which severely hinders training convergence and efficiency. To address this, we employ Optical Character Recognition (OCR)-based compression techniques to compress CoT supervision signals into a single canvas. Experimental results demonstrate that VTI-CoT achieves state-of-the-art performance among models of the same parameter scale while significantly improving training efficiency.

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