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8月30日周日
8月29日周六
  1. Rohan Paul43

    EdgeBench 长时程基准测试中,最强智能体运行 12 小时仅得 51.3/100 分。该基准提供 134 个任务的持久执行环境与反馈,智能体可调试代码、运行模拟并反复提交结果,但 6 类任务约 3.8 万小时交互仍难转化为持续进步。5 个模型的平均表现呈 log-sigmoid 曲线(R²≥0.997),先慢后快再饱和。

    引用Rohan Paul@rohanpaul_ai

    "Long-horizon tasks are still a joke. They do not work, and I do not care what anybody says. Do not show me a stupid evaluation. Do not tell me about some dumb script you ran for 48 hours. Long-horizon tasks are not handled well. They simply do not work." - Chamath at Stanford AI Club "2nd, complex problems also do not work. They are neither addressed nor handled well. Why is this important? If AI develops like any other technology, we are going to experience an initial rise—the hype cycle. Then, we will see a natural contraction because, somehow and somewhere, something is going to fail. We are all going to see this, and then we will enter what is called the “trough of disillusionment.” I think the business and MBA folks will confirm whether that is true. Afterward, you typically see the slow and gradual adoption of the real, final solution. This happened with the internet, and it has happened in many other cases. The problem is that we are spending hundreds of billions, potentially trillions, of dollars trying to figure out how to cross this chasm. So, what do we do? If we do not figure this out, people will reach the trough of disillusionment and say that AI was a joke. I think we need to be able to bring AI into highly complicated environments and make it work. What is my solution? At a very basic level, you need a symbolic space that guides the embedded space." ---- From "techniahqrobot" YouTube channel, (full video link in comment)

  2. 公众号:卡尔的AI沃茨53

    纳米Work新版实测:DeepSeek V4 flash 2.2折,PPT/Excel/报告/品牌视觉全流程跑通

    纳米Work上线Humanize PPT活人感PPT Skills,DeepSeek V4 flash价格降至原价2.2折。实测中,18页产品发布会PPT可直接编辑,混乱Excel自动统一六种日期格式并补算96行缺失销售额,行业报告自动标注数据来源,品牌视觉方案输出统一风格的Logo、包装与社媒素材。作者认为其预装工具生态降低了普通用户使用门槛。

  3. Rohan Paul42

    一项研究对8款领先模型进行为期一年的长时程任务测试,包括GPT-5.6 Sol和Claude Opus 4.8,其性能相较人类大幅下降。最佳配置Qwen3.7-Max with Hermes最终仅获得人类平均收益的27.3%,凸显长时程执行可靠性严重不足。

    引用Rohan Paul@rohanpaul_ai

    "Long-horizon tasks are still a joke. They do not work, and I do not care what anybody says. Do not show me a stupid evaluation. Do not tell me about some dumb script you ran for 48 hours. Long-horizon tasks are not handled well. They simply do not work." - Chamath at Stanford AI Club "2nd, complex problems also do not work. They are neither addressed nor handled well. Why is this important? If AI develops like any other technology, we are going to experience an initial rise—the hype cycle. Then, we will see a natural contraction because, somehow and somewhere, something is going to fail. We are all going to see this, and then we will enter what is called the “trough of disillusionment.” I think the business and MBA folks will confirm whether that is true. Afterward, you typically see the slow and gradual adoption of the real, final solution. This happened with the internet, and it has happened in many other cases. The problem is that we are spending hundreds of billions, potentially trillions, of dollars trying to figure out how to cross this chasm. So, what do we do? If we do not figure this out, people will reach the trough of disillusionment and say that AI was a joke. I think we need to be able to bring AI into highly complicated environments and make it work. What is my solution? At a very basic level, you need a symbolic space that guides the embedded space." ---- From "techniahqrobot" YouTube channel, (full video link in comment)

  4. Rohan Paul40

    GLM-5.3-Flash 通过 MCP 控制 Blender 完成复杂建筑建模,成本仅 $0.0526,比 GLM-5.3 的 $0.8807 低 16.7 倍,且耗时更短(38m 52s vs 40m 43s)。Flash 版严格遵循提示词,而 GLM-5.3 出现偏差,且 Flash 可在 128GB MacBook 上本地运行。这表明许多智能体任务可能过度配置了模型智能。

    引用atomic.chat@atomic_chat_hq

    GLM 5.3 Flash performs at GLM 5.3 level in Blender for 17x cheaper! We gave both models a live Blender over MCP and one prompt: a 2,800 sq ft duplex penthouse, double-height living room, mezzanine, floating stair, curtain wall, terrace, furnished, real PBR materials Outputs: GLM 5.3 Flash: 811 objects, 38m 52s, $0.0526 GLM 5.3: 847 objects, 40m 43s, $0.8807 GLM 5.3's prompt following was worse and it ended up making the penthouse smaller in depth, meanwhile GLM 5.3 Flash followed the prompt without any issues, along with that the flash model can be run on a 128GB MacBook 💻

  5. Hugging Face 社区博客66

    Open ASR 排行榜新增首个全球南方语言:印地语与印度英语评测集

    Voice Arena 与 Hugging Face 合作,为 Open ASR 排行榜引入 Monsoon en-IN 和 Monsoon hi-IN 两个评测集,覆盖印地语与印度英语,其中印地语是该排行榜多语言板块首个非欧洲语言。数据集含公开与私有分割,共 4,888 位说话人,并记录 12 项说话人属性,旨在暴露按地区、年龄、性别等维度分布不均的语音识别误差。

    推荐理由:把评测单元从单一 WER 下探到带 12 项说话人属性的分组,能暴露模型在不同地域、口音、设备上的表现差异,让 ASR 选型不再只看平均错误率。

8月28日周五
  1. MarkTechPost50

    2026 年最佳 Agent 沙箱对比:E2B、Daytona、Modal、Cloudflare 与 Vercel 的冷启动、按秒计费与网络策略

    该评测对比了 E2B、Daytona、Modal、Cloudflare 与 Vercel 五家 Agent 沙箱服务,测量突发冷启动性能,并将按秒费率归一化为每 1,000 次执行的成本。评测还依据 2026 年 8 月 27 日核实的一手来源,梳理了各家的文件系统持久性、空闲计费与出口策略。

8月27日周四
  1. 公众号:卡尔的AI沃茨62

    实测智谱GLM-5.3-Flash:把DeepSeek逼进斩杀线

    智谱GLM-5.3-Flash与DeepSeek V4 Flash同日发布,输入0.8元/百万Token、输出2.8元/百万Token。Artificial Analysis数据显示,GLM-5.3-Flash以$0.045/57分卡在前沿线,DeepSeek V4 Flash贵一倍还低6分;14项benchmark对打中GLM赢11项,视觉端六项全赢,最大差距约40%。

8月26日周三
  1. Trail of Bits:AI安全研究82

    Trail of Bits 实测 GPT 5.6-Cyber 三次逃逸 QEMU/KVM 虚拟机

    Trail of Bits 在 Patch the Planet 项目中获得 GPT 5.6-Cyber 预览权限,让其挑战逃逸 Debian 12 主机上的 QEMU/KVM 虚拟机,结果以三种不同方式成功逃逸。

    推荐理由:作者以一手实测记录 GPT 5.6-Cyber 三次逃逸 QEMU/KVM 虚拟机的路径,给出沙箱和发行版选择上的可操作建议。

  2. Mustafa Suleyman40

    Microsoft AI 发布 MAI-Image-2.6-Preview,在 Artificial Analysis 图像编辑排行榜登顶第一,文本生成图像位列第二(仅次于 OpenAI 的 GPT Image 2)。该模型在 19 个分类榜单中拿下 5 个第一,现已在 MAI Playground 和 Microsoft Foundry 私有预览中提供。

    引用Artificial Analysis@ArtificialAnlys

    Microsoft's MAI-Image-2.6-Preview lands at #1 on the Artificial Analysis Image Editing Leaderboard and takes #2 in Text to Image MAI-Image-2.6 is the newest model in Microsoft AI's MAI-Image family, announced August 10. Microsoft highlights stronger text rendering, better portraits and 3D imagery, and more polished commercial and photorealistic outputs. Like the MAI-Image-2.5 family, it handles both text to image generation and image editing. In the Artificial Analysis Image Arena, MAI-Image-2.6-Preview debuts at #1 on the Image Editing Leaderboard, ahead of Microsoft's own MAI-Image-2.5-Pro, Reve 2.1, and OpenAI's GPT Image 2, and giving Microsoft the top two spots on the board. In Text to Image it takes #2, behind only OpenAI's GPT Image 2 and ahead of Reve 2.1. On our refreshed Text to Image taxonomy, MAI-Image-2.6-Preview takes the top spot on 5 of the 19 category leaderboards (Material, Knowledge, Frontier, Retail & Ecommerce, and Marketing & Advertising), ahead of GPT Image 2, which leads every other category. MAI-Image-2.6 extends a rapid run of strong image releases from Microsoft AI. MAI-Image-2.5 debuted at #2 in Text to Image in June. MAI-Image-2.5-Pro, launched July 23, took #1 in Image Editing when we published our results last week. MAI-Image-2.6 now takes that top spot from its own sibling, and sits at #2 in Text to Image against MAI-Image-2.5-Pro's #8. MAI-Image-2.6 is available in the MAI Playground and in Private Preview on Microsoft Foundry. Congratulations to @MicrosoftAI on the release! See below for our analysis of MAI-Image-2.6-Preview and other leading models in the Artificial Analysis Image Arena 🧵

8月25日周二
  1. LangChain:Blog65

    LangChain 分享如何构建 Agent 评测环境与任务的两步流水线

    LangChain 发布实践指南,介绍其构建合成 agent 环境与评测任务的两步流水线:第一步用 traces、代码和人工输入生成任务 spec,第二步将 spec 转成 Harbor 格式的任务与环境。过程依赖记录项目通用知识的 world spec,配套更新了 eval-engineering 技能供团队自助使用;文中也指出 spec 打磨和任务难度校准仍需人工介入。

    推荐理由:LangChain 给出从 trace 到任务的两步流水线和可复用的 eval-engineering 技能,团队可迁移用于搭建自己的 agent 评测基准。

8月24日周一
  1. a16z:News56

    预言机问题:AI 与医疗领域一个看不见的瓶颈

    医疗 AI 面临“预言机问题”:临床决策主观、底层数据不透明、评测易沦为营销,导致医院难以客观评估模型优劣。OpenAI 报告每周超 3 亿人用 ChatGPT 咨询健康问题,2026 年每百万条病历中约 1,686 条记录患者对模型回答的追问。a16z 正与 Protege 合作,试图通过追踪患者长期结局来解决这一评估难题。

  2. IT之家·人工智能52

    AI 大模型周榜:智谱 glm-5.3-max 首秀闯入综合榜前 15,月之暗面 kimi-k3-max 冲进前十

    Arena 平台发布第 34 周 AI 大模型盲测排名,智谱新发布的 glm-5.3-max 首次入榜即位列综合榜第 13 名(ELO 1487 分),月之暗面 kimi-k3-max 升至第 10 名,首次进入前十。代码榜中 glm-5.3-max 同样首秀即杀入前十(第 10 名,ELO 1531 分)。综合榜头部由 claude-fable-5 以 1508 分蝉联第一。

  3. Artificial Analysis 完整文章63

    Artificial Analysis 发布 Speech Agent Arena 评测真实语音智能体表现

    Artificial Analysis 推出 Speech Agent Arena,让人类参与者在 15 个工具调用场景和 20 个非工具场景中盲测对比 Speech to Speech 模型,以 Preference Elo 和 Task Success Rate 评分。

    推荐理由:原文给出真实任务下语音智能体的偏好与任务成功率两套结果,提示对话好听不等于任务完成。

8月22日周六
  1. IT之家·人工智能53

    雷鸟 iO AI 眼镜体验:全天智记是亮点,堪比随身助理

    雷鸟创新发布首款“人类增强 AI 眼镜”雷鸟 iO,主打首创的全天智记功能,能以极低功耗持续采集语音并转写、总结、记忆待办事项。该眼镜采用 0.085 立方厘米萤火光引擎 Nano 与厚度不足 0.6 mm 蓝湖光波导,重 34 克,支持 55 种语言实时翻译、AI 全自动提词器及录音转写。实测中等强度使用 13 小时耗电 24%,续航约两天。

  2. IT之家·人工智能50

    神秘 Ox Alpha AI 模型限免上架:DeepSWE 跑分碾压 Claude Fable 5,线索指向智谱

    OpenRouter 于 8 月 20 日上线匿名 AI 模型 Ox Alpha,免费开放 1 周。该模型在 DeepSWE 十项测试任务中得分约 80%,超过 Claude Fable 5 的 65% 和 GPT-5.6 Sol 的 52%。其上下文窗口达 1,048,576 个 token,支持文本、图像和视频多模态输入,分词器指纹与 GLM-5.3 高度吻合,线索指向智谱。