[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fZIJuxbXYxiY3ZjNx7w_Mn9qSykghTILn2D-RdHaEtls":3,"$ftj59r1gjLoEyNw2AuKVdH0zJuD4wfIxEl0X0_jyB7SU":50},{"detail":4,"recommendList":12},{"id":5,"title":6,"content":7,"publishTime":8,"important":9,"sourceName":10,"language":11},211765,"RSIGym开源：让AI研究如何改进AI，瞄准递归自我改进","动察 Beating AI 快讯，做 AI 训练和评测工具的 Evolvent AI 开源了 RSIGym，目标是研究 AI 的递归自我改进（RSI）。简单说，就是让 AI 自己寻找系统弱点、提出修改方案、测试效果，再尝试用改进后的系统开展下一轮研究。\nRSIGym 将训练、推理、评测和沙箱做成现成服务。研究 Agent 可以生成训练数据、调整微调参数、训练模型，再根据评测结果修改方案。它还能修改 Harness，也就是控制模型如何调用工具、执行任务的程序。研究者可以让 Agent 只改训练数据、只改 Harness，或者同时优化两者。平台统一管理实验预算、权限和执行日志。\n团队还推出 RSI-Index，测试 AI Agent 改进另一套 AI 系统的能力。6 款研究 Agent 分别从相同的 Qwen3.5 基础模型和简单 Harness 出发，在编程、数学等 5 类任务上进行优化。Opus 5 综合排名第一。它改造后的系统在 SWE-bench Verified 的 100 题子集上，成绩从 17.67% 提高到 50.33%；数学测试 AIME 则从 31.67% 提高到 97.78%。\n另一组实验只改 Harness，不动模型权重。Opus 5 根据 DeepSeek Harness 的执行日志，修复了输出截断后停止工作、提前宣布任务完成等问题。在 Qwen3.6 模型权重完全不变的情况下，Terminal-Bench 2.0 成绩从 30.34% 提高到 40.82%。\n团队还尝试让 Qwen3.8-27B 改进自身，由它同时担任研究 Agent 和被改进的模型。它在 6 道测试题上找到了一套看起来有效的微调方案，但在完整的 37 道题中，成绩反而从 56.66% 略降至 56.26%。此外，每组实验只进行一次研究运行，Agent 在开发阶段也能接触评测题目。目前还没有证据表明，改进后的 AI 可以继续提升自身的研发能力，实现多轮递归自我改进。",1791620160000,false,"Blockbeats",0,[13,20,26,32,38,44],{"id":14,"title":15,"content":16,"publishTime":17,"important":9,"sourceName":10,"language":18,"dateString":19},194435,"又一条RSI路线出现：模型参数不动，Agent靠Memory持续进化","动察 Beating AI 快讯，UCSD 助理教授黄碧薇创办的因果世界模型公司 Aether AI 开源 RSIAgent。\n这是一套不训练模型的递归自我改进框架。底层模型参数全程固定，Agent 会自己寻找值得练习的任务、实际操作、检查结果，再把成功方法和失败教训写进长期 Memory。下一轮继续利用这些经验，逐步补上能力短板。\n系统由三个 Agent 配合。Curriculum Agent 决定接下来练什么，Actor Agent 真正操作软件，Verifier Agent 独立检查结果。探索分成两步：先广泛尝试不同任务，再针对失败、隐藏限制和边界情况继续深挖。最后 Memory 会被冻结，直接拿去执行正式任务。\n在 OSWorld 2.0 上，加入这套 RSI 后，平均部分得分从 71.97% 提升到 78.98%；Agents’ Last Exam 从 83.75% 提升到 84.82%。不过这不是整套测试的严格 A\u002FB 对比。OSWorld 只有一半任务实际用了 RSI 后的新结果，其余任务继续沿用原成绩。\n它和 Prime Agent 这类 Harness 自我改进思路属于同一个大方向：模型权重不变，持续更新模型外面的东西。 RSIAgent 的特点是把改进重点放在 Memory，再用自主出题和独立验证，让这份外部经验库不断积累。",1789470180000,null,"Tue Sep 15 2026 11:03:00 GMT+0000 (Coordinated Universal Time)",{"id":21,"title":22,"content":23,"publishTime":24,"important":9,"sourceName":10,"language":18,"dateString":25},207552,"DeepSeek Harness底座Cordis有了Rust实现：DimAgent准备用它重构","动察 Beating AI 快讯，DimAgent 创始人 Han Cheng 开源插件运行时 Rutis，并表示接下来会用它支撑 DimAgent 的新架构。Rutis 用 Rust 重新实现了 Cordis 的核心设计，后者正是 DeepSeek Harness 使用的底层插件框架。\nDeepSeek Harness 用 Cordis 管理模型、工具、会话、Agent Loop 等组件。每个组件都可以做成插件，只声明自己需要和提供哪些服务，Cordis 负责启动、卸载、依赖变化和资源清理。\nRutis 把这套机制搬到了 Rust，同时支持 TypeScript、JavaScript 和 Python 插件。不同语言的插件可以互相调用，也可以分布在不同进程和机器上运行。现有 Cordis 插件也可以直接接入，不需要全部重写。",1791349620000,"Wed Oct 07 2026 05:07:00 GMT+0000 (Coordinated Universal Time)",{"id":27,"title":28,"content":29,"publishTime":30,"important":9,"sourceName":10,"language":18,"dateString":31},192308,"DeepSeek开始模型和Agent一起训：V4.1 Flash为Harness专项优化","动察 Beating AI 快讯，DeepSeek Harness v0.1.5 发布。V4.1 Flash 在训练阶段就针对 Harness 做了专项训练和优化，覆盖标准、PTC 和极简三种模式。Harness 已经直接进入模型训练环节，不再只是模型发布后再接一个 Agent 外壳。\n新版还加入实验性的 Agent Teams。父子 Agent 可以双向通信，主 Agent 能给不同子 Agent 指定模型和推理强度；多个 Agent 还能共享任务、互相发消息，最后由主 Agent 汇总。\nV4.1 Flash 还有一个很特别的长任务优化：Agent 跑到一半修改系统提示词，也可以尽量保留已有 KV Cache。长任务不用因为前面的规则改了一点，就把后面大段上下文重新计算。\nWeb 端也加入文件上传和预览，支持 PDF、图片、代码等格式。插件可以往左右 Sidebar 和内容区接入自己的 UI，长会话的加载、恢复和内存占用也一起做了优化。",1789022580000,"Thu Sep 10 2026 06:43:00 GMT+0000 (Coordinated Universal Time)",{"id":33,"title":34,"content":35,"publishTime":36,"important":9,"sourceName":10,"language":18,"dateString":37},187368,"Qwen27B改成扩散Agent：速度拉到500+Token\u002Fs，厂商自测单项超Sol","动察 Beating AI 快讯，专攻超高速大模型的 Celeris 发布一款基于 Qwen3.8-27B 改出来的混合扩散 Agent 模型 Celeris-1 Magnus，官方称在不少场景能跑到 500+ Token\u002Fs。\n在 τ³-Banking 的 97 个银行客服任务上，Magnus 开启 low reasoning 后完成率达到 41.2%，中位耗时 55 秒。同场的 GPT-5.6 Sol 是 38.1%、79 秒。\n传统模型基本一个 Token 一个 Token 往后写，Magnus 能一次生成多个 Token，再反复修正。需要严格按顺序生成的内容再串行处理，因此一次计算可以往前推进更多 Token。",1788256260000,"Tue Sep 01 2026 09:51:00 GMT+0000 (Coordinated Universal Time)",{"id":39,"title":40,"content":41,"publishTime":42,"important":9,"sourceName":10,"language":18,"dateString":43},186453,"CREAO再融千万美元：让Agent从每次失败里自己改Harness","动察 Beating AI 快讯，企业 Agent 平台 CREAO 完成新一轮千万级美元战略融资，累计融资超过 3000 万美元。CREAO 现在重点做的是 Harness 自我迭代，也就是让 Agent 根据真实任务里的错误和反馈，持续调整自己的执行系统。\nHarness 是模型外面控制 Agent 怎么干活的一层，包括任务规划、工具调用、记忆、模型选择和出错后的处理方式。CREAO 已经跑通其中一部分自动纠错：Agent 完成任务后，系统会自动检查结果；发现问题后生成工单，AI 再调查原因、写修复、验证结果。新版本先用少量真实流量测试，效果变差就自动回滚。CREAO 官方此前已经公开过这套系统。\n下一步，CREAO 准备把这种改进扩大到更多 Harness 环节。例如，同一类 Agent 反复在某个工具或任务流程上出错，平台可以根据大量真实任务的数据调整整套执行方式，新创建的同类 Agent 也能直接用上这些改进。\n再往后，CREAO 还准备把一部分经验训进模型。它计划基于开源模型做垂直场景的后训练，让常见任务更多由成本较低的专用模型完成，复杂任务再调用前沿模型。",1788165180000,"Mon Aug 31 2026 08:33:00 GMT+0000 (Coordinated Universal Time)",{"id":45,"title":46,"content":47,"publishTime":48,"important":9,"sourceName":10,"language":18,"dateString":49},195749,"Qwen3.8-Omni-Flash上线：自己看视频、调工具、交成片","动察 Beating AI 快讯，阿里千问上线原生全模态模型 Qwen3.8-Omni-Flash，支持文本、图片、音频、视频和 1M 上下文。\n这代明显加强了音视频 Agent 能力。模型可以自己从视频里找信息、规划任务，再调用外部工具完成剪辑、配音和渲染。官方展示了长会议处理、短剧翻译、电影解说、MV 制作和视频转图文笔记等场景。\n面对几小时的长视频，它也不用从头到尾全部处理。模型会先判断答案可能在哪，再逐步定位关键片段。OmniVideoBench 上，准确率从 63.4 提到 67.8，Token 消耗从 145,736 降到 79,117，少了 45.7%。\n千问称，Qwen3.8-Omni-Flash 在 30 项评测上的平均成绩比 Qwen3.5-Omni-Plus 提升超过 26%，音视频能力接近 Gemini 3.8 Flash，音频能力整体超过后者。音频输入成本下降超过 98%，音视频输入下降超过 93%。\n目前 Qwen3.8-Omni-Flash 模型权重尚未公开，只开放了 API。千问同时开源了 Qwen-MM-Plugins，可以装进 Claude Code、Codex、Gemini CLI、Qwen Code 等 Agent，给它们补上图片、音频、长视频处理和视频剪辑能力。\n另一套 Qwen-Live Harness 更像实时调度层。用户可以直接和实时全模态模型说话，再把复杂任务交给 Gemini CLI、Claude Code、Codex 等后台 Agent 持续执行。它会跟踪任务进度，做完后再主动回来告诉你结果。",1789704180000,"Fri Sep 18 2026 04:03:00 GMT+0000 (Coordinated Universal 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