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Foundation（AAIF）。该格式由 Anthropic 在开发 Claude Code 时设计，2025 年 10 月推出，12 月开放为跨平台标准，目前已有 ChatGPT、Cursor、GitHub Copilot 等 70 多个 Agent 平台接入。Anthropic 将 Skill 描述为给 AI Agent 准备的可复用工作手册，开发者可将任务步骤写入 SKILL.md，并附上脚本和参考资料，供不同 Agent 按需读取。MCP 负责连接外部工具和数据，Skills 负责说明 Agent 如何完成特定工作。此次捐出的是标准名称、标识、GitHub 组织和官网域名。AAIF 于 2025 年由 Anthropic、OpenAI 和 Block 共同发起，此前已接收 MCP。今后 Anthropic、OpenAI 和 Google 的工程师将以个人身份共同维护 Skills 标准，不再由 Anthropic 单独主导。Vercel 统计，skills.sh 目录已收录约 100 万条技能，累计近 2.8 亿次安装。Snyk 曾在 ClawHub 的技能样本中确认 76 个恶意技能，Anthropic 已提议建立版本管理、正式提案流程和安全指南。",1792725720000,"MarsBit",null,"Fri Oct 23 2026 03:22:00 GMT+0000 (Coordinated Universal Time)",{"id":22,"title":23,"content":24,"publishTime":25,"important":26,"sourceName":18,"language":19,"dateString":27},225379,"企业AI年化支出暴增5.4倍至1770亿美元，Anthropic拿走55%模型API预算","动察 Beating AI 快讯，Menlo Ventures 发布 2026 年企业 AI 报告。按其估算，截至今年 9 月，全球企业 AI 部署市场的年化支出规模达到 1768 亿美元，是去年 330 亿美元的 5.4 倍。 这轮增长主要靠老客户增加投入。调查显示，付费企业占比从 55% 升至 65%，但受访企业的平均 AI 支出已经涨到去年的 4.7 倍。年初制定预算时，它们原本只计划增加到 1.9 倍。 AI Agent 是推动支出上涨的重要原因。过去主要由员工主动发起模型调用，现在 Agent 可以连续执行任务、并行调用多个模型，即使没人操作，也可能持续消耗 Token。OpenRouter 的数据显示，Agent 已贡献平台 80% 的 Token 消耗。 模型厂商之间的差距也在拉大。Anthropic、OpenAI 和 Google 合计占据企业 AI 部署支出的 69%，高于去年的 50%。单看大模型 API 支出，Anthropic 的份额从 40% 升至 55%，OpenAI 从 27% 升至 31%，Google 则从 21% 降至 9%。在 AI 编程工具市场，Claude Code 和 Codex 合计拿下 70% 的支出。",1793786040000,true,"Wed Nov 04 2026 09:54:00 GMT+0000 (Coordinated Universal Time)",{"id":29,"title":30,"content":31,"publishTime":32,"important":9,"sourceName":18,"language":19,"dateString":33},225125,"OpenAI与Anthropic将首次设立美国中期选举「作战室」，应对虚假信息传播与平台滥用","火星财经消息，11 月 4 日，OpenAI 和 Anthropic 将首次为美国中期选举设立专门的「作战室」，以确保各自平台提供准确的选举信息。Anthropic 发言人表示，公司各团队全年都在加强选举相关防护并执行平台政策，选举前设立的专门团队将监测平台滥用情况，并准备快速应对突发事件。 科技平台设立选举「作战室」的做法可追溯至 2018 年，当时 Meta 为应对选举不实信息扩散首次设立此类团队。如今，平台还要面对深度伪造视频和 AI 搜索传播错误信息的风险。报道举例称，谷歌 AI Mode 上周没有回答「谁赢得了 2020 年美国总统选举」的问题，谷歌表示正在修复。 AI 输出难以预测、近期网络安全问题，以及 AI 搜索工具倾向于迎合用户预期，都会增加监测和响应的难度。Meta 前公共政策主管 Katie Harbath 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Time)",{"id":48,"title":49,"content":50,"publishTime":51,"important":26,"sourceName":18,"language":19,"dateString":52},219966,"Kimi训练AI老板，General Reasoning训练整家AI公司","动察 Beating AI 快讯，今年 1 月，Kimi K2.5 推出 Agent Swarm，让一个主 Agent 招募、指挥多个子 Agent，随后的 K2.6 将规模扩大到最多 300 个子 Agent。 但 Kimi 的强化学习方法 PARL 只训练负责指挥的「老板」，冻结子 Agent。原因是多人完成一项任务，很难分清谁立了功；如果所有人同时调整策略，训练也容易变得不稳定。 General Reasoning 偏要试试另一条路。团队推出 120B 参数模型 Avalon-M1-Mini，并提出 RLCR（集体奖励强化学习）。它让一个 Agent 担任 CEO，最多招募 8 名员工，通过留言板和共享工作区协作。所有成员使用同一套模型，任务完成后不再逐个计算贡献，而是根据团队总成绩一起接受强化学习。 这套方法在 147 个训练环境、超过 200 万项不同任务中取得了持续改进。软件任务中，主管工程师在招募岗位中的占比从 0.4% 升至约 13%，至少开展一次互审的团队占比从 42% 提高到 64%。分工也发生变化：从每人各写一部分代码，逐渐转向一人主写、其他人负责测试和挑错。原本需要人工设计的管理层和质检流程，在训练中自行形成。 但组队成绩更好，有一部分只是因为用了更多 Token。研究人员给单个 Agent 同样多的输出 Token，就能追回团队约六成的得分优势。团队的主要好处是可以同时干活：在 12 项评测中，达到相近成绩的估算耗时平均只有单 Agent 的约四分之一。 研究人员还模拟了今年 OpenAI 智能体攻击 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 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