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Qwen-Audio-Agent is presented, a harness that combines full-duplex voice interaction with asynchronous task execution through a foreground-background architecture that supports the complementary use of direct tool calls for immediate operations and backend delegation for multi-step tasks.
Qwen-Audio-3.1-Realtime brings these requirements together through Think, Act, and Speak and Coordinate, evaluating audio reasoning, multilingual understanding, tool use, conversational behavior, full-duplex interaction, and safety.
This work presents a multimodal conversational-context framework for LLM-based ASR that integrates a scenario-controlled data pipeline, scalable multimodal context training, and systematic evaluation and introduces MM-ContextASR Bench, which evaluates contextual understanding and entity error correction across five scenarios.