This paper proposes a new approach to handling streaming omni-modal models, called Omni-Streaming Thinking (OST), which helps prevent models from prematurely committing to interpretations based on incomplete audio or visual information. A practitioner might care about this because it can lead to more accurate and reliable responses in real-time applications.
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