[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fSnkJnbtKE_IYEq-KJPZmj71HmO2MPJV9qDE0gCpQS98":3,"$ftj59r1gjLoEyNw2AuKVdH0zJuD4wfIxEl0X0_jyB7SU":51},{"detail":4,"recommendList":12},{"id":5,"title":6,"content":7,"publishTime":8,"important":9,"sourceName":10,"language":11},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,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},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,null,"Sat Oct 10 2026 08:16:00 GMT+0000 (Coordinated Universal Time)",{"id":21,"title":22,"content":23,"publishTime":24,"important":9,"sourceName":10,"language":18,"dateString":25},196483,"Instinct长期记忆被逆向：Git+Markdown，和Supermemory几乎同一路线","动察 Beating AI 快讯，Supermemory 的创始人 Dhravya Shah 逆向分析了个人 Agent 助手 Instinct 的长期记忆系统。他发现，Instinct 没有把全部聊天原样塞回上下文，而是把长期信息整理成一套 Git 管理的 Markdown 文件，再持续维护用户当前的状态。\nSupermemory 本身就是专门给 Agent 做长期记忆的基础设施。它会从聊天、文件和网页里提取事实，维护人物关系、时间变化和用户画像，再通过 API、MCP 等方式把相关记忆交给 Agent。Shah 称，拆完 Instinct 后发现，两者对「Agent 应该怎么记东西」的思路非常接近。\n按他的黑盒测试，Instinct 每次对话都会先拿到一份约 4250 Token 的用户状态摘要，包括生活背景、操作偏好和沟通习惯。更细的人物、偏好、决定和任务则留在 Markdown 文件中，需要时再用类似 grep 的关键词搜索。Shah 没有发现向量索引或 BM25 检索。\n这些记忆还会被后台持续整理。系统会把临时信息归进具体任务，把经历概括成长期偏好，用新事实修正旧事实，再用 Git 留下版本历史。主 Agent 主要负责读取，真正的整理和合并则由后台流程完成。Shah 推测这轮整理大约每天运行一次。\nShah 认为这套设计已经可以用 Supermemory 基本复刻。他给出的示例只用了约 60 行代码，把用户画像、记忆搜索、历史版本和持续更新重新接进 Agent。\n此前 Reflexion 共同作者 Ashwin Gopinath 猜测，Instinct 的优势可能是持续维护一份「当前状态」，知道什么仍然有效、什么已经过期、什么事情还没做完。现在看来，这份状态背后可能没有什么神秘数据库，而是一套被持续整理的文件系统。",1789959780000,"Mon Sep 21 2026 03:03:00 GMT+0000 (Coordinated Universal Time)",{"id":27,"title":28,"content":29,"publishTime":30,"important":9,"sourceName":10,"language":18,"dateString":31},195369,"GLM-5.3开始优化自己的系统，智谱唐杰：RSI最小闭环已经出现","动察 Beating AI 快讯，智谱披露，GLM-5.3 驱动的 Infra Agent 已经参与优化 GLM-5.3-Flash 的推理系统。从首次跑通到承接全部生产流量不到两周，端到端吞吐提升到最初的 3.2 倍。\n联合创始人兼首席科学家唐杰在复盘中提到，Agent 现在最容易卡住的地方，往往不是不会写代码，而是不知道问题出在哪。比如一次修改让吞吐下降 20%，它知道自己改坏了，却不知道是哪一层出了问题，下一步该查什么。\n于是智谱把资深工程师平时的排查方法做成一套 Agent 可以直接使用的工具。Agent 改完代码后，可以自己检查结果对不对、时间耗在哪里、哪种方案更快，再根据结果继续修改。\n靠这套方法，Agent 找出了长上下文计算误差、KV 传输阻塞和解码 kernel 重复计算等问题。其中一个 kernel 重写后提速 1.71 倍。\n唐杰判断，工程师的角色会越来越像「设计反馈的人」。人负责定目标、设边界和审核高风险修改，Agent 自己提假设、改代码、跑实验。离完整的 RSI 还很远，但「模型优化系统，系统再服务模型」这个最小闭环已经出现。",1789635480000,"Thu Sep 17 2026 08:58:00 GMT+0000 (Coordinated Universal Time)",{"id":33,"title":34,"content":35,"publishTime":36,"important":9,"sourceName":10,"language":18,"dateString":37},195259,"谷歌让Agent学会「做梦」：复盘过去试错，下一轮自己少走弯路","动察 Beating AI 快讯，谷歌研究团队发布 Dream-RSI，让 AI Agent 做完一轮任务后，把过去的成功和失败拿来「做梦」。系统会保存每次尝试和结果，再利用这些旧记录推演不同做法，比如先试哪条路、什么时候放弃、要不要同时多试几个方案。因为结果以前已经跑过，这些推演不用重新执行任务。\n系统会从中选出表现更好的做法，直接用到下一轮。新一轮又会留下更多成功和失败，再拿来「做梦」、继续改策略。这样一轮接一轮，就是论文所谓的「递归自我改进」。目前底层模型本身不会变化，真正被改进的是 Agent「下一步怎么做」的方法。\n论文在算法、数学优化和 GPU kernel 等 8 个任务上测试了这套方法。以 Gemini 3.1 Pro 的一个算法任务为例，相比固定做法，Agent 调用从 550 次降到 317 次，同时最终找到的程序运行得更快。",1789615620000,"Thu Sep 17 2026 03:27:00 GMT+0000 (Coordinated Universal Time)",{"id":39,"title":40,"content":41,"publishTime":42,"important":9,"sourceName":10,"language":18,"dateString":43},193723,"会改自己代码也不算真正自我进化：上海交大等给RSI划了5级","动察 Beating AI 快讯，上海交大联合清华、字节跳动、小红书、上海 AI Lab 等团队发布综述《The Last AI Built by Humans》，梳理 491 篇相关研究，试图给越来越泛的「AI 自我进化」划一条更严格的线。\n论文把递归自我改进（RSI）分成 5 级。L1 只是执行人类规定好的改进流程；L2 可以自己决定怎么改；L3 连下一轮该学什么也能自己决定；L4 会根据实际运行中的反馈，持续修改记忆、技能、代码或 harness，让这些改动影响之后的任务；到了 L5，连负责产生下一轮改进的搜索方法、评估器和研究策略本身，也可以被修改，并交给下一轮继续使用。\n所以单纯「会改自己代码」还不够。比如一个 Coding Agent 能重写自己的源码，但如果下一代怎么选、按什么标准打分、什么修改能留下来，仍由人写死，它只能证明自己会自我修改，还没有掌握完整的递归自我改进。\n论文还把最高的 L5 拆成两层。第一层是「形式上递归」：AI 已经能修改负责后续改进的机制，并让下一轮继续沿用。第二层是「真的越改越强」：修改后的机制还得在相近资源和独立评测下，确实产生更强的下一代。\n491 篇论文里，43.8% 被归为 L1，31.6% 在 L2，L5 只有 29 篇，占 5.9%。大量研究其实还集中在执行人类设计好的改进、自动寻找改法；真正把「如何改进」这套机制本身交给 AI 的工作很少。即使摸到 L5，长期稳定累积优势也还没有证明。\n这篇论文主要是在给 RSI 领域立一套分类框架。L1 到 L5 是作者提出的标准，也还不是行业公认的统一分级。论文同时纳入了学术论文、技术报告、官方博客和开源系统，因为不少前沿 RSI 实践还没有进入正式论文。",1789355940000,"Mon Sep 14 2026 03:19:00 GMT+0000 (Coordinated Universal Time)",{"id":45,"title":46,"content":47,"publishTime":48,"important":9,"sourceName":49,"language":18,"dateString":50},218331,"去中心化存储与 AI Agent 记忆平台 Lighthouse 完成战略融资","Foresight News 消息，去中心化存储与 AI Agent 记忆平台 Lighthouse 完成战略融资，总融资额达到 158 万美元，参投方包括 Walrus 基金会、Protocol Labs、Smape Capital、CSP DAO、Balaji Srinivasan、Big Brain Holdings、Fenbushi Capital、HASH CIB、LongHash Ventures、Mask Network、NGC Ventures、2Link 和 Formless Capital 等。\nLighthouse 表示，融资将用于加速 Memory Infrastructure（记忆基础设施）、MCP 集成，以及基于 IPFS + Filecoin + Walrus 的去中心化存储层建设，让 AI Agent 能够实现跨会话、跨模型的共享可验证记忆，无需每次从零开始。",1792488660000,"Foresight News","Tue Oct 20 2026 09:31:00 GMT+0000 (Coordinated Universal 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