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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,false,"Blockbeats",0,[13,20,26,33,39,45],{"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},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,"Sat Oct 10 2026 08:16:00 GMT+0000 (Coordinated Universal Time)",{"id":27,"title":28,"content":29,"publishTime":30,"important":9,"sourceName":31,"language":18,"dateString":32},220583,"Prophetlab 获数千万元种子轮融资 预测引擎登顶 FutureX 榜首","火星财经消息，据雷峰网·鲸犀报道，AI 预测引擎公司 Prophetlab 已完成数千万元种子轮融资，由常春藤资本等机构联合投资。公司成立不到四个月，其预测引擎 Prophet Engine 已登顶字节 Seed 发布的 FutureX 总榜榜首，并获得数百万商业化订单。Prophetlab 早期项目利用 AI 大模型预测世界杯赛果，成功预测决赛结果及四强队伍，部分模型准确率约 75%。团队由国内外高校 AI PhD 与 FinTech Master 组成，创始成员曾参与阿里千问、腾讯混元等千亿量级模型训练，并与上海交通大学严骏驰教授团队开展产学研合作。公司即将发布专注商业预测的 Investor's Last Exam 榜单。",1793084340000,"MarsBit","Tue Oct 27 2026 06:59:00 GMT+0000 (Coordinated Universal Time)",{"id":34,"title":35,"content":36,"publishTime":37,"important":9,"sourceName":10,"language":18,"dateString":38},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":40,"title":41,"content":42,"publishTime":43,"important":9,"sourceName":10,"language":18,"dateString":44},206639,"AI订阅大摸底：Claude补贴比国产Coding Plan还狠","动察 Beating AI 快讯，SemiAnalysis 把 OpenAI、Anthropic、Meta、SpaceXAI、MiniMax、月之暗面、智谱、Cursor 和 Cognition 的 AI 订阅套餐逐个测了一遍。它分别测试输入、缓存写入、缓存读取和输出会消耗多少订阅额度，再按自己 9 月的 Agent 使用结构计算，其中约 96.6% 是缓存读取。最后再套用各家的 API 标价，看同样一批 token 如果直接走 API，需要花多少钱。SemiAnalysis 将这个数字称为「API 等价价值」。\nClaude 的 Sonnet 和 Opus 补贴最狠。同样每月 200 美元的 Max 20x，使用 Sonnet 5.5 最多可跑出约 12,529 美元的 API 用量，相当于月费的 62.6 倍；Opus 5.5 约为 11,726 美元，相当于 58.6 倍。换成更贵的旗舰模型 Fable 5.1，却只剩 2,485 美元，也就是 12.4 倍。同一个套餐能差近 5 倍，因为不同模型和不同类型的 token，扣掉后台额度的速度并不一样。Anthropic 给 Sonnet 和 Opus 留了大量额度，对 Fable 则明显收紧。\n国产 Coding Plan 同样大量补贴重度用户，但力度仍追不上 Claude。SemiAnalysis 测得，132 美元的 MiniMax Token Ultra 可折算约 3,088 美元 MiniMax-M3 API 用量，相当于月费的 23.4 倍；168 美元的智谱 GLM Coding Max 约为 11.6 倍；199 美元的 Kimi Code Ultra 约为 6.7 倍。即使是其中最高的 MiniMax，每美元折算价值也只有 Claude Sonnet、Opus 的四成左右。\nOpenAI 最近反而开始主动收紧补贴。新购 Pro 200 的各档模型 token 额度被直接减半，此前购买的套餐可暂时保留旧额度到 10 月 29 日。现在 200 美元 ChatGPT Pro 使用 GPT-6.1 Sol，折算价值约 2,084 美元，相当于月费的 10.4 倍；同价 Claude Opus 是它的 5.6 倍。即使撇开两款模型 API 单价的差异，只算实际 token，两者也是约 286 亿对 102 亿，Claude 仍多 2.8 倍。\n但到",1791290460000,"Tue Oct 06 2026 12:41:00 GMT+0000 (Coordinated Universal Time)",{"id":46,"title":47,"content":48,"publishTime":49,"important":9,"sourceName":10,"language":18,"dateString":50},199380,"ElevenLabs v4登顶TTS榜，超过Gemini和Qwen","动察 Beating AI 快讯，ElevenLabs 发布新一代语音模型 Eleven v4 和实时版 Eleven v4 Turbo。v4 主打更自然的情绪和声音控制，Turbo 面向实时语音 Agent，中位推理延迟约 100ms。\n在 Artificial Analysis 最新 TTS 盲测榜中，Eleven v4 目前以 1315 Elo 排第一。Cartesia Sonic 3.6 为 1275，Gemini 3.8 Flash TTS 为 1267，Qwen-Audio-3.0-TTS-Plus 为 1258。上一代 Eleven v3 目前只有 1169，排第 17。\nv3 已经支持笑声、耳语等音频标签，v4 主要把这套控制做得更准。用户可以直接指定语气、节奏、情绪和音效，也可以用自然语言描述一句话该怎么说。模型还把语言覆盖从 70 多种扩到 90 多种，Instant Voice Clone 只需要约 10 秒音频，并增强了长文本和多人对话中的声音一致性。\nTurbo 则专门解决实时对话的速度问题。官方文档显示，v3 Conversational 的中位推理延迟约为 280ms，v4 Turbo 降到约 100ms。",1790678220000,"Tue Sep 29 2026 10:37:00 GMT+0000 (Coordinated Universal 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