Abstract
Large Vision Language Models (LVLMs) exhibit strong Chain-of-Thought (CoT) capabilities, yet most existing paradigms assume full-video availability before inference, a batch-style process misaligned with real-world video streams where information arrives sequentially. Motivated by the streaming nature of video data, we investigate two streaming reasoning paradigms for LVLMs. The first, an interleaved paradigm, alternates between receiving frames and producing partial reasoning but remains constrained by strictly ordered cache updates. To better match streaming inputs, we propose \textbf{Think-as-You-See (TaYS)}, a unified framework enabling true concurrent reasoning. TaYS integrates parallelized CoT generation, stream-constrained training, and stream-parallel inference. It further employs temporally aligned reasoning units, streaming attention masks and positional encodings, and a dual KV-cache that decouples visual encoding from textual reasoning. We evaluate all paradigms on the Qwen2.5-VL family across representative video CoT tasks, including event dynamics analysis, causal reasoning, and thematic understanding. Experiments show that TaYS consistently outperforms both batch and interleaved baselines, improving reasoning performance while substantially reducing time-to-first-token (TTFT) and overall reasoning delay. These results demonstrate the effectiveness of data-aligned streaming reasoning in enabling efficient and responsive video understanding for LVLMs. We release our code at \href{this https URL}{this repository.}
Abstract (translated)
大型视觉语言模型（LVLM）表现出强大的链式思维（CoT）能力，但大多数现有方法假设在推理之前视频是完全可用的，这与实际视频流中信息依次到达的情况不一致。鉴于视频数据的数据流特性，我们研究了两种适用于LVLM的流式推理范式。第一个是交错型范式，在接收帧和生成部分推理之间交替进行，但仍受到严格顺序缓存更新的限制。为了更好地匹配流式输入，我们提出了一种名为“所见即思考（TaYS）”的统一框架，该框架实现了真正的并发推理能力。TaYS集成了并行化的CoT生成、受流限训练和流平行推断，并进一步采用了时间对齐的推理单元、流式的注意力掩码与位置编码以及双KV缓存机制，后者将视觉编码从文本推理中解耦出来。 我们在Qwen2.5-VL家族上评估了所有范式在代表性视频CoT任务上的表现，包括事件动态分析、因果推理和主题理解。实验结果表明，TaYS始终优于批量处理和交错型基准模型，在提高推理性能的同时大幅减少了首次生成令牌所需时间（TTFT）及整体推理延迟。这些结果显示，与数据一致的流式推理方法对于LVLM实现高效的视频理解和响应性具有有效性。我们将在该网址发布我们的代码：\[此链接\]
URL
https://arxiv.org/abs/2603.02872
