Core Viewpoint - The article introduces Mini-Omni-Reasoner, a new real-time reasoning paradigm designed for dialogue scenarios, which allows models to think and express simultaneously, enhancing interaction quality while maintaining logical depth [4][11][25]. Group 1: Introduction to Mini-Omni-Reasoner - Mini-Omni-Reasoner is inspired by human cognitive processes, where individuals often think and speak simultaneously rather than waiting to complete their thoughts before speaking [7][25]. - The model employs a "Thinking-in-Speaking" paradigm, contrasting with traditional models that follow a "thinking-before-speaking" approach, which can lead to delays in interaction [11][25]. Group 2: Model Architecture and Mechanism - The architecture of Mini-Omni-Reasoner consists of two components: Thinker, responsible for logic and reasoning, and Talker, focused on dialogue, allowing for efficient task execution [12][15]. - The model alternates between generating response tokens and reasoning tokens in a 2:8 ratio, balancing reasoning depth with real-time speech synthesis [13][15]. Group 3: Data and Training Process - A comprehensive data pipeline, including the Spoken-Math-Problems-3M dataset, was developed to address the "Anticipation Drift" issue, ensuring the model does not prematurely reveal conclusions [17][19]. - The training process is divided into five stages, progressively aligning text reasoning capabilities with speech modalities to ensure effective performance [19][20]. Group 4: Experimental Validation - Mini-Omni-Reasoner was tested against various models, demonstrating significant performance improvements over the baseline model Qwen2.5-Omni-3B [21][24]. - The model's ability to maintain natural and concise responses while ensuring high-quality reasoning was validated through comparative analysis [24]. Group 5: Future Directions - The article emphasizes that Mini-Omni-Reasoner is a starting point for further exploration into reasoning capabilities in dialogue systems, encouraging ongoing research in this area [26][28].