AI RESEARCH
Think-as-You-See: Streaming Chain-of-Thought Reasoning for Large Vision-Language Models
arXiv CS.CV
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ArXi:2603.02872v2 Announce Type: replace 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.