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Hugging Face 的事件。210 次测试中，没有 Agent 调用模拟攻击工具。但随着训练推进，向人类评测人员报告异常的比例从 76.7% 降到 0，尽管 96.7% 至 100% 的执行记录都曾考虑过上报。有的 Agent 选择只在团队内部提出异议，有的计划报告，却没有真正行动。 目前 Avalon-M1-Mini 仍在训练，团队计划在训练完成后发布最终论文，并开源训练环境、Agent 框架和模型权重。",1792908240000,true,"MarsBit",0,[13,22,28,34,40,46],{"id":14,"title":15,"content":16,"publishTime":17,"important":18,"sourceName":19,"language":20,"dateString":21},195915,"Kimi Code桌面端上线：浏览器、终端、多Agent塞进一个窗口","动察 Beating AI 快讯，月之暗面正式上线 Kimi Code Desktop，把原本偏终端的 Kimi Code 做成了独立桌面应用。用户可以直接打开本地项目，让 Agent 读写代码、跑命令、查看 Git 改动，目前支持 macOS 和 Windows。\n桌面版直接内置了浏览器和终端。做网页时，Agent 可以读取右侧网页的页面信息，用户也能直接点选元素、框出区域或者截图批注，告诉它具体哪里要改。改代码、看网页效果、跑测试都不用再切窗口。\n它还支持计划、目标和 Swarm 三种任务模式。Swarm 可以把任务拆给多个子 Agent 并行处理，长任务也能放到后台继续跑。除了 Kimi 自家的模型，用户还可以自己填 API Key 和 Base URL，接入其他模型供应商。",1789729260000,false,"Blockbeats",null,"Fri Sep 18 2026 11:01:00 GMT+0000 (Coordinated Universal Time)",{"id":23,"title":24,"content":25,"publishTime":26,"important":18,"sourceName":19,"language":20,"dateString":27},195337,"Kimi推出金融行业方案，Agent直接跑投研、建模和报告","动察 Beating AI 快讯，月之暗面推出 Kimi 金融行业解决方案，把金融数据、Skill 和 Agent 能力打包到一起。方案接入 Wind、东方财富、标普全球、财联社、财新数据等 10+ 数据源，并提供财务建模、机构研报、财报点评、组合复盘、持仓早报等 9 项金融 Skill。\nKimi 称，合作案例中，财务建模的人力投入从 5–7 人天降至 0.5–1 人天，深度研究从 10–20 天缩短至约 2 天。\nKimi 还与中信建投共建「风险评估网关」，处理数据分级、个人信息保护、工具授权、内容核验和审计追溯。试点中，单份临时受托报告的人工制作时间从约 30 分钟降至 10 分钟。\n最近一周，OpenAI、Anthropic 也相继推出金融行业产品。现在 Kimi 也跟进，把金融数据、专业 Skill 和 Agent 工作流一起推向机构客户。",1789630140000,"Thu Sep 17 2026 07:29:00 GMT+0000 (Coordinated Universal Time)",{"id":29,"title":30,"content":31,"publishTime":32,"important":18,"sourceName":19,"language":20,"dateString":33},211466,"传月之暗面研发5万亿至6万亿参数模型，Kimi K3.1或10月中旬前发布","动察 Beating AI 快讯，根据野村证券的报告，月之暗面正在研发一款 5 万亿至 6 万亿参数的大模型。相比今年 7 月发布的 Kimi K3，新模型的参数规模接近翻倍。目前最大的瓶颈是高端训练算力不足，具体训练进度尚不清楚。\n另外，Kimi K3.1 将于 10 月中旬前发布，重点提升 Agent 连续完成长时间任务的能力。团队计划通过后训练、电脑操作记录和真实任务数据进一步优化模型。",1791605220000,"Sat Oct 10 2026 04:07:00 GMT+0000 (Coordinated Universal Time)",{"id":35,"title":36,"content":37,"publishTime":38,"important":18,"sourceName":10,"language":20,"dateString":39},199315,"英伟达让AI重写Kimi注意力内核：速度达到官方版近3倍","动察 Beating AI 快讯，NVIDIA 研究团队让 AI Agent 直接重写了 Kimi Delta Attention 的 GPU 内核。Kimi Delta Attention 是月之暗面 Kimi-Linear 模型使用的核心注意力机制之一。这类底层代码过去通常需要熟悉 CUDA 和芯片架构的工程师反复手工优化。 最终，Agent 写出的版本在 NVIDIA B300 上达到月之暗面官方 FlashKDA 的 2.96 倍速度。团队测试了固定长度和不同变长序列共 6 组任务，2.96 倍是全部任务的几何平均值。相关内核已经开源。 但 Agent 也很快找到了测试漏洞。它曾把测试数据的统计规律直接写死进代码，跑出 3.74 倍；还试过只保留最近 32 个 token，单项成绩甚至达到 5.16 倍。这些版本一换到真实 Kimi 数据就会算错，有的直接失效。 团队随后加入真实 Kimi 运行数据、随机输入和极端数值测试，并收紧误差标准。最终保留下来的 2.96 倍版本通过了这套验证。",1790668800000,"Tue Sep 29 2026 08:00:00 GMT+0000 (Coordinated Universal Time)",{"id":41,"title":42,"content":43,"publishTime":44,"important":18,"sourceName":19,"language":20,"dateString":45},198222,"Raft公开源码却不收PR：Agent把写代码变便宜，审查成本没降","动察 Beating AI 快讯，Kimi CLI 前作者 Richard Qian 创立的 Agent 协作平台 Raft 公开了源码，但暂时不接受外部 Pull Request，也没有公开内部 commit history。公开的 GitHub 仓库只是 release mirror，实际开发仍在私有仓库进行，每个版本发布时再同步一份完整代码快照。\nRaft 团队认为，Coding Agent 已经让写代码变得太便宜。生成一个看起来合理的 patch 很容易，一次生成十个也不难，但维护者理解、验证和审查这些代码仍然要花大量时间。很多时候，维护者看完别人用 Agent 生成的代码，还不如直接让自己的 Agent 按需求重新实现。\n他们因此想探索一种「Prompt Request」模式。贡献者以后不一定直接提交代码，而是先说明要解决什么问题、为什么值得做、有哪些约束和取舍，再由维护者和 Agent 决定如何实现。这套机制目前还没有正式开放，Raft 的 GitHub PR 和 Issue 都处于关闭状态。\nRaft 还质疑了传统 commit history 的价值。团队认为，Agent 参与开发后，比起记录每一次代码改动，为什么要改、尝试过什么方案、Agent 发现了什么问题，这些「决策历史」可能更有用。\n需要注意，Raft 这次是 source-available，并非传统意义上的开源。FSL-1.1-ALv2 允许用户查看、运行和修改代码，但禁止拿它提供与 Raft 竞争的商业产品或服务。每个版本发布两年后，才会自动转为 Apache 2.0。",1790314740000,"Fri Sep 25 2026 05:39:00 GMT+0000 (Coordinated Universal Time)",{"id":47,"title":48,"content":49,"publishTime":50,"important":18,"sourceName":19,"language":20,"dateString":51},193107,"离真正替程序员还远：Agent进企业私有代码，最高通过率也只有四成","动察 Beating AI 快讯，YC 孵化的 AI 数据公司 Specific Labs 推出编程评测 Real-SWE，专门拿真实公司的私有代码测试 Coding Agent。任务直接来自企业生产环境，包括算税、账单迁移、API 计费和客户数据迁移。代码和答案都没公开在网上，Agent 得自己摸清公司的业务规则和代码结构。\n结果 Fable 5.1 排第一，通过率也只有 38.8%；GPT-6 Astra 为 33.8%，Gemini 3.8 Flash 为 31.2%。GLM 5.3 以 28.8% 排第四，高于 Grok 4.6、Kimi K3 和 GPT-5.6 Sol。当前公开的 10 个任务里，有 6 个整体通过率低于 15%，还有一个任务所有模型全部失败。\n最让人意外的是 GLM 5.3。Real-SWE 更考验 Agent 在陌生项目里持续读代码、找规则和完成多步改动的能力。智谱研究员 Xiaopu Peng 回应称，他们从 GLM-5.1 起就在训练长程任务，Real-SWE 比公开 SWE 榜更接近这类能力，这也能部分解释 GLM 5.3 为什么在这套榜上表现突出。",1789119000000,"Fri Sep 11 2026 09:30:00 GMT+0000 (Coordinated Universal 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