[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-1ecbb79a-d843-43ad-b533-c01ae396275f":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"1ecbb79a-d843-43ad-b533-c01ae396275f","Qwen-Audio-3.0-Realtime 发布：On-Policy Distillation 把实时语音的智商与延迟一起拉满","实时语音模型长期面临一道单选题：要把首响延迟压到毫秒级，往往得砍掉推理深度。多数产品只能选一边——做客服就放弃共情，做陪伴就别指望工具调用。今年 5 月，阿里 Qwen-Audio Preview 在 Artificial Analysis 语音推理榜以 97.6% 登顶，但「快」与「聪明」在大规模实时场景里如何兼得，官方一直没有端到端方案。\n\n7 月 15 日发布的 Qwen-Audio-3.0-Realtime 补上了这一环。核心是 On-Policy Distillation（在线策略蒸馏）：语音模型自回归生成时，由更大文本大模型实时打分并纠正输出，把「会思考的大脑」和「会说话的嘴」在同一次前向里解耦训练。配合口语偏好、通用推理、Agentic、音频理解四位教师，模型在智商、共情、Agent 调用、双工流畅度四条线同时升级，拆出推理更强的 Plus 与速度更快的 Flash 两版。\n\n更值得玩味的是 Agent 维度。Qwen-Audio-3.0-Realtime 不再需要明确指令才触发工具，调用结果自动沉淀到对话记忆——这意味着语音端首次具备与文本 LLM 同等的 FunctionCall 体验，并原生支持 MCP 协议与外部 API、知识库对接。共情和双工部分，引入「多模态感知双工控制」子模型，用音频信号、语义、声纹共同决定是否打断、说话人切换。\n\nWAIC 前后各家都在卷语音 Agent。比起 TTS+ASR+LLM 三段式拼装，端到端语音大模型才是真正可复用的语音 Agent 基座。阿里这一步把实时性、推理深度、Agent 能力用一套蒸馏框架捏在一起，并直接挂上 MCP 生态——节奏不慢。","https:\u002F\u002F36kr.com\u002Fnewsflashes\u002F3896642705245828","c36a21ac-2a77-421b-9519-1e150695732a",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release",{"id":18,"name":19,"slug":19,"description":13,"color":13},"499f4b56-819d-49a3-9609-33e775143b86","multimodal",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b1853a5a-d940-42b7-94f9-0488ee3f2cf7","new-model","2026-07-15T10:00:00Z","2026-07-15T10:08:09.445167Z","2026-07-15T10:08:09.445176Z",true,"agent",4]