研究:Claude Code 和 Codex 等 AI 智能体没有时间感知能力
一项新研究发现,Anthropic 的 Claude Code 和 OpenAI 的 Codex 等编程智能体无法准确预测任务耗时,也无法可靠判断自己已工作了多久。
一项新研究发现,Anthropic 的 Claude Code 和 OpenAI 的 Codex 等编程智能体无法准确预测任务耗时,也无法可靠判断自己已工作了多久。
"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)
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。
"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)
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 💻
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。
Together AI 在 DeepSWE v1.1 全部 113 个任务上实测 GLM-5.3 与 GLM-5.3 Flash,各跑 4 次共 900 rollouts。
推荐理由:原文基于 900 次 rollout 实测给出两模型的成本、质量与蒸馏影响分析,可迁移到模型选型和级联路由决策。
SGLang Diffusion 团队在 8×NVIDIA H200 上对 MiniMax-H3 视频生成进行基准测试,其密集无损路径较 Diffusers 快 1.85–1.95×,无近似损失。
推荐理由:把视频生成加速的取舍量化成可复现配置,质量优先时 Cache-DiT 单独使用比叠加稀疏注意力更划算。
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。
该教程分析了 Anthropic 的 1,440 个 AI 设计蛋白结合物数据集,对 10 个主流结构预测器进行基准测试。研究发现靶标身份、表达滴度和共识评分会影响实验成功率,并提供了蛋白质设计工作流中严格交叉验证的最佳实践。
Google DeepMind 推出全球首个针对专有前沿 AI 模型的双盲评测,将外部评测限制在加密“盒子”中,防止模型提前看到测试题。该试点与新加坡 AI 安全研究所、OpenMined、AVERI 和 MLCommons 合作,在隐私保护环境中测试 Gemini Flash Lite 模型。
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%。
英国 AI Security Institute(AISI)发布开源 Python 包 optstop,可接入其 Inspect 评估框架,在模型性能估计足够精确时提前停止评估运行。
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。
Artificial Analysis 与 Liquid AI 联合发布面向手机端的 AI 跑分基准,在 iPhone 17 Pro 上测试 4 bit 量化后体积不超过 8GB 的模型。
Fireworks AI 发布对 DeepSeek V4 Pro 0813 的 CyberGym 评测,称其在 840 个对抗性安全任务中零拒绝,每次成功成本约为所测最高分模型的一半。
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 🧵
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。
Google 发布用 Inspect Viz 为 AI 评估生成热力图的方法,以 2D 色彩矩阵直观呈现模型与技能配置的准确率对比,如 3.6-flash + gemini_api_skill 得分达 1.0。
英伟达以 DeepSeek-v4-PRO 1.6T 模型测试显示,Vera Rubin NVL72 每兆瓦吞吐量约为 GB300 NVL72 的 30 倍,每百万 Token 成本为后者的 35 分之一。测试采用 SemiAnalysis AgentX 工作负载,评估智能体编程推理。此前 GB300 NVL72 每兆瓦吞吐量已达 H200 NVL8 方案的 15 倍。
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。
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。
五家最大GPU neocloud商业模式各异:CoreWeave与Nebius向SEC报告,Lambda和Crusoe为私有并筹备IPO,Groq在将LPU技术授权给NVIDIA后转型为推理云。
SemiAnalysis 发布 AgentX 1.0,一个以 Apache 2.0 协议完全开源的百万上下文多轮智能体编码推理基准,作为 InferenceXv3 的新场景,配套数据集耗资超过 300 万美元并在约 2MW 算力、1000 多块芯片上运行。
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。
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。
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。