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9月23日周三
  1. The Decoder:AI News68

    阿里 Qwen 发布 Qwen-Audio-3.1 五款语音模型,语音价格最高下调 95%

    阿里 Qwen 团队发布 Qwen-Audio-3.1,包含五款覆盖语音识别(ASR)、语音合成(TTS)和实时交互的模型。ASR 改进多语言和方言识别并自动清理填充词,ASR-Next 支持多说话人识别、时间戳及情绪和环境声检测;TTS-Next 将语言模型与扩散方法结合,可一次性生成语音、音效和背景音频。同步降价:TTS 约 70%,Realtime 约 85%,ASR 最高 95%。

  2. IT之家·人工智能55

    特斯拉北美车机上线 Grok Bot,可语音下单购物、管理日程

    特斯拉在 X 平台官宣 Grok Bot 于北美车机上线,驾驶员可免手动查看邮件、管理日程、线上购物和梳理待办。功能通过唤醒口令、方向盘按键或应用启动器触发,访问 Gmail、Google Calendar 等第三方服务需配置连接器,目前仅对 SuperGrok Heavy 订阅用户开放,其他订阅档位后续获得资格,连接器则所有用户均可配置。

  3. OpenAI Developers:Blog47

    用 GPT-Live-1 和 Codex 把 LED 显示屏变成语音助手 Jack

    OpenAI 开发者用 GPT-Live-1 全双工语音模型和 Codex,把一块 128 × 64 HUB75 LED 面板改造成语音助手 Jack,整套系统跑在 Raspberry Pi 上。GPT-Live-1 负责持续对话与打断,需要外部信息时委派给树莓派,由 GPT-5.6 Luna 以低推理强度发起 Responses API 请求,查询天气、日历和 MTA 地铁到站信息。

  4. Meta AI:Research Blog70

    Meta 发布 Muse Realtime Avatar,将实时语音转化为可交互的动态化身

    Meta 发布 Muse Realtime Avatar,将 Muse Realtime Voice 的语音流实时转化为带表情、手势和全身动作的交互化身,可基于参考图像驱动人像、插画、动物或日常物品。

    推荐理由:原文给出了蒸馏方案、延迟数字和与商业系统的对比结果,读者可以据此评估其实时化身生成的技术路线和落地水平。

9月22日周二
9月21日周一
9月20日周日
  1. Rohan Paul52

    Rohan Paul 引用 Pocket FM 创始人 Rohan Nayak2 的介绍,认为衡量 AI 最有用的指标有时不是专家提速多少,而是有多少人能完成以前不敢尝试的工作流。

    引用Rohan Nayak@RohanNayak2

    Introducing Sherpa: the most advanced fiction writing AI We accelerated from $250M in ARR to $500M because Sherpa helped increase content production by 1200% in 1 year Sherpa was trained on 5.5B hours of playtime with minute by minute dynamic retention data. 550K+ creators have produced 2.6M hours of content annualised using it Pocket FM is like Netflix for audio-only dramas, with our own pool of one-person studios. 10% of eligible writers on Pocket FM make >$200K One blockbuster produced >$100M in revenue 3 writers have become millionaires in <2 yrs We built Sherpa to enable anyone to make >$1M by writing world-class fiction stories: 1. The Idea: Drop a 1-2 sentence concept. Sherpa interrogates it like a veteran editor on tension, stakes, and psychology 2. World & Characters: It builds out the complete lore, tone, and character psychologies 3. Sub-Plot planning: Breaks the premise into arcs, arcs into episodes, and episodes into scenes 4. Scene-by-Scene Generation: Outlines and drafts entire episodes, with you able to steer, rewrite, or override anytime 5. Editorial Review: Stress-tests every draft for pacing, engagement drop-offs, prose, and coherence before it locks 6. One-Tap Production: Pick a voice, convert to audio drama, and publish directly to Pocket FM’s millions of listeners 7. Global Scale & Monetization: Revenue-share on performance, with automatic localization so you earn across international markets Test Sherpa for free here: https://pocketfm.com/sherpa _____________________________________________ Generic LLMs fail at serialized fiction because they lack a long-horizon narrative reward function. Sherpa solves this through three core technical leaps: 1. Narrative World Model (State Tracking & Retrieval): Context windows degrade over long runs. Sherpa constructs an evolving semantic knowledge graph tracking character states, secrets, and plot dependencies. High-speed retrieval surfaces exact context on demand, maintaining zero continuity decay across hundreds of episodes 2. Hierarchical Story Planner: When writing a 500-episode story like Naruto, you need to plan 100s of sub plots. Rather than generating linearly, Sherpa decomposes narrative across discrete levels: season -> arc -> sequence -> episode -> scene. Rather than generating everything upfront, like a generic LLM, Sherpa uses progressive planning and dynamic replanning. As the story evolves, it identifies what changed, traces the downstream impact, and replans only the affected parts. 3. Prose Engine (Trained on series' retention data): LLMs write robotically, but serial fiction needs emotion, tension, pacing, and dialogue that sounds like real people. Sherpa's Prose Engine was designed specifically for storytelling. It was built on 1B+ tokens of Pocket's own stories, trained by learning from what listeners engage with, where they drop off, and what keeps them hooked. Feedback is taken from specialized evaluator models that measure every scene against a 40-item checklist. (Evaluator models were benchmarked against human reviewers and matched them 80–90% of the time.) _______________________________________________ Owning distribution and creation puts us in a very unique spot. More shows -> More data -> Sherpa becomes better -> more creator success -> more creators -> more shows Pocket FM has already seen one $100M IP. I believe Sherpa will soon lead to dozens of single-person studios creating billion-dollar shows. Most people are scared of AI but I think it'll unlock more human creativity, help creators earn more, and bring the next great IPs to life. This will create millions of jobs and new income streams.

9月19日周六
  1. IT之家·人工智能60

    SpaceXAI 发布 Grok Voice Transcribe 2.0 语音转文本模型,错误率降低约一半且价格不变

    当地时间 9 月 18 日,SpaceXAI 发布 Grok Voice Transcribe 2.0 语音转文本模型,在价格不变的前提下错误率降低约一半。该模型基于 Grok Voice 底层音频基础模型构建,在 Artificial Analysis 全部 32 款流式模型中准确率居首;官方四个内部数据集评测均超越 1.0 版本。

  2. xAI:News64

    xAI 发布 Grok Voice Transcribe 2.0 语音转写模型

    xAI 发布语音转写模型 Grok Voice Transcribe 2.0,称在真实场景评测中准确率是 1.0 的两倍,价格不变,在 Artificial Analysis 榜单 32 个流式模型中准确率排名第一。

    推荐理由:官方给出内部评测与公开榜单数据,并用同价翻倍准确率和定价细节说明升级幅度,适合评估语音转写方案时参考。

9月18日周五
  1. Qwen:Blog Retrieval69

    Qwen 发布 Qwen3.8-LiveTranslate 实时同传模型,LAAL 降至 2.3 秒

    Qwen 发布 Qwen3.8-LiveTranslate,采用 Interleave 架构与 Hybrid-MoE Thinker–Talker 设计重构实时同声传译,平均滞后(LAAL)从上一代的 2.8 秒降至 2.3 秒。

    推荐理由:官方给出了架构设计、LAAL 从 2.8 秒到 2.3 秒的数字和多语言评测结果,读者可以据此评估其实时翻译能力的实际变化。

  2. Qwen:Blog Retrieval78

    Qwen 发布原生全模态模型 Qwen3.8-Omni-Flash,主打音视频智能体任务交付

    Qwen 发布下一代原生全模态模型 Qwen3.8-Omni-Flash,支持文本、图像、音频和视频输入及 1M token 上下文窗口,29 项评测平均分较 Qwen3.5-Omni-Plus 提升超过 25%,音频输入每小时价格下降超过 98%,音视频输入每小时价格下降超过 93%。

    推荐理由:官方发布同时开放 API 和开源插件与运行时,读者可以据此评估音视频智能体在剪辑、会议、实时交互等工作流中的落地方式。

9月17日周四
  1. OpenBMB53

    面壁智能(OpenBMB)转发 Dubedo 的测试版发布,称这是 VoxCPM2 希望促成的产品形态。Dubedo 支持 30 种语言配音、按说话人克隆声音,并在同一编辑器内微调台词和时间轴,beta 入口为 https://dev.dubedo.com。

    引用Dubedo@dubedostudio

    Your content. More languages. Your voices. 🎙️ Meet Dubedo: dub into 30 languages, clone each speaker’s voice, and fine-tune the words and timing in one editor. Our beta is open! Try the interactive demo or bring your own clip. 👉 https://dev.dubedo.com #aidubbing #voicecloning

9月16日周三
  1. 蚂蚁 inclusionAI:GitHub 新仓库63

    蚂蚁 inclusionAI 开源 Realtime-Venus 全双工交互系统

    蚂蚁集团 Venus 团队与清华大学在 GitHub 开源 Realtime-Venus,一个支持全双工对话与异步任务委托的实时交互系统。系统包含三个组件:Realtime-Venus-Omni(9B,流式音视频对话与主动交互)、Realtime-Venus-Audio(9B,语音交互与音频理解)以及负责后台任务执行并将结果返回同一对话的 Harness 运行时。

    推荐理由:蚂蚁和清华开源了全双工对话加异步委托任务的系统,含 9B 的 Omni 与 Audio 两个模型和 Harness 运行时,可直接下载推理或跑浏览器 demo。

  2. IT之家·人工智能54

    努比亚 NaviX Ultra 发布:主打 AI 智能体能力,5999 元起

    努比亚发布 NaviX Ultra 手机,宣称是首款 AI 智能体手机,售价 5999 元起,国补后 5499 元起。该机由豆包手机助手主导 AI 体验,搭载 Seed 全双工语音大模型和第五代骁龙 8 至尊版,支持一句话跨应用执行任务,端到端任务成功率超 80%,AI 唤醒率优于竞品 48%,并完成智能体大模型备案。

  3. IT之家·人工智能51

    vivo 发布四款蓝心大模型,推出系统级蓝心 Harness

    vivo 在 2026 开发者大会上发布四款蓝心大模型,包括端到端 MoE 架构的 BlueLM-RealTime、30 亿参数的 BlueLM-Nano 和搭载自研 Agentic 引擎的 BlueLM-Flash / Pro。同时推出系统级蓝心 Harness,深入原系统内核层,已增加 6000 多项原子技能,支持超万种任务,并预研蓝心 30B MoE 端侧大模型。