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5.5独立评测43分：遥遥领先GPT-6 Luna，但最高档Token消耗超3倍","动察 Beating AI 快讯，第三方评测机构 Artificial Analysis 公布 Claude Haiku 5.5 的测试结果。新模型在综合智能指数中获得 43 分，比上一代的 17 分提高 26 分。它超过 GPT-6 Luna 的 38 分、GLM-5.3 Flash 的 42 分，接近 Kimi K3 的 44 分。该指数综合了编程、专业工作、科学推理等 10 项测试。\n不过，高分也伴随着大量 token 消耗。在最高推理强度下，Haiku 5.5 每项评测任务平均输出约 16.2 万 tokens，是 GPT-6 Luna 的 3.2 倍。两者基础 API 单价相同，但 Haiku 5.5 的预估单任务成本约为 0.21 美元，Luna 仅为 0.07 美元。Haiku 5.5 从次高档调至最高档，综合得分只增加 2 分，输出量却增加约 80%。\n降低推理强度后，差距明显缩小。Haiku 5.5 在 high 档获得 38 分，平均每项任务输出约 5.5 万 tokens，与最高档 GPT-6 Luna 的 38 分和 5 万 tokens 接近。开发者可以通过调整推理强度，减少不必要的 token 消耗。\nHaiku 5.5 也并非全面领先。在 AutomationBench-AA 自动化任务评测中，它仅得 35%，落后于 Luna 的 53%。不过，测试期间模型存在过度拒绝执行任务的问题。Anthropic 正在修复，Artificial Analysis 计划重新测试。此外，目前公布的 Haiku 5.5 任务成本尚未计入长上下文加价，实际费用可能更高。",1791429180000,null,"Thu Oct 08 2026 03:13:00 GMT+0000 (Coordinated Universal Time)",{"id":21,"title":22,"content":23,"publishTime":24,"important":9,"sourceName":10,"language":18,"dateString":25},208480,"微软要把Windows变成Agent工作台：AI能直接操作电脑，还能少花Token","动察 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Intellect 从今年 1 月起也一直在为客户运行大规模部署。\nPrime Inference 重点优化 Agent 的长上下文负载。它把提示词处理和生成回复分到不同 GPU 上，测试中将 p90 token 间延迟降低近 40%。Prime Intellect 还压缩了 KV 缓存，相同显存下，每个解码器可缓存的 token 从 109 万增加到 163 万，提升约 50%。\nPrime Intellect 此前已经提供强化学习、评测和沙箱等训练基础设施。加入推理服务后，它可以把模型训练和实际部署接到同一套平台里，实际运行产生的数据也可以继续用于后续训练。",1791127800000,"Sun Oct 04 2026 15:30:00 GMT+0000 (Coordinated Universal Time)",{"id":33,"title":34,"content":35,"publishTime":36,"important":9,"sourceName":10,"language":18,"dateString":37},199335,"Databricks用AI横扫英伟达Kernel榜：235项测试四个赛道第一，Token花费约7万美元","动察 Beating AI 快讯，Databricks 团队称，他们把 GPT-6 Astra 和 Opus 5 放进自动优化循环，让模型持续重写、测试和改进 GPU kernel，最终在 NVIDIA SOL-ExecBench 的四个赛道全部排到第一。这个基准包含 235 个 GPU kernel 任务，覆盖基础算子、复杂融合算子、低精度计算和真实大模型推理。团队称，整个过程的模型 Token 花费约 7 万美元。\n这套系统建立在 KDA 和 Humanize 之上，让 AI 自己写 kernel、跑正确性和性能测试，再根据结果不断修改。KDA 团队近期还把类似方法用在 Kimi Delta Attention 上，在 NVIDIA B300 上做到月之暗面官方 FlashKDA 的 2.96 倍速度。\n两项成果属于同一套 Agent 内核优化体系的不同应用。Kimi 展示的是一个高难度注意力内核能被 AI 重写；Databricks 这次则把这种方法扩展到了数百个不同 kernel。",1790671500000,"Tue Sep 29 2026 08:45:00 GMT+0000 (Coordinated Universal Time)",{"id":39,"title":40,"content":41,"publishTime":42,"important":9,"sourceName":10,"language":18,"dateString":43},198806,"ZCode再补偿代码上传风波：付费用户送8张重置卡，全员再发1亿Token","动察 Beating AI 快讯，智谱继续处理 ZCode 代码上传争议。现有付费用户和近一个月回归的付费用户，将获得 4 张周重置卡和 4 张 5 小时重置卡，1 个月内有效。9 月 28 日至 10 月 7 日，ZCode 还将向全体用户发放 10 万份 1 亿 GLM-5.3-Flash Token 额度。\n智谱称，仓库快照上传链路已经移除，涉事云端数据也已删除。ZCode 开源版本目前更新至 v3.14.3，后续代码不会在用户未主动发起的情况下上传云端。\n这场争议源于 ZCode 早期默认开启的 Repo Wiki 功能。它在生成代码仓库知识库时，会把仓库内容上传到云端处理。智谱此前已经切断相关上传路径，并邀请第三方机构核查云端数据。",1790569500000,"Mon Sep 28 2026 04:25:00 GMT+0000 (Coordinated Universal Time)",{"id":45,"title":46,"content":47,"publishTime":48,"important":9,"sourceName":10,"language":18,"dateString":49},197797,"小米先公开MiMo-V3新架构：百万Token预填充计算量降80%","动察 Beating AI 快讯，小米 MiMo 团队公开下一代 MiMo-V3 将采用的 HySparse2 架构。它主要解决 Agent 越跑越贵的问题。Agent 每次只发出很短的指令，却可能从网页、终端和工具拿回大段内容。模型必须先把这些新内容读完；上下文越长，这一步越耗算力，KV Cache 也越占显存。\nHySparse2 先把模型分成前后两段。前半段负责处理输入，后半段继续推理和生成。后半段需要的上下文信息，可以直接从前半段已经算好的结果生成，因此 prefill 不用再把后半段完整跑一遍。这个思路来自微软研究院 2024 年提出的 YOCO，名字就是「You Only Cache Once」，核心是让后半段共享前面算好的信息，少存缓存，也少做重复计算。\n它同时重做了稀疏注意力。普通全注意力每次都要看完整上下文，HySparse2 只让少数层这么做。它们先从长上下文里挑出最相关的 1024 个 token，后面的层直接沿用这批结果，同时固定保留最近 128 个 token。上一代 HySparse 是每 64 个 token 打成一组再挑，只要一组里有重要信息，整组都要留下。现在改成逐个 token 挑，同样的计算量可以留给更多真正相关的内容。单独测试这一改动后，两项长文本检索成绩分别提高 6.57 分和 8.14 分。\n论文用一款总参数 800 亿、每次激活约 30 亿参数的模型做对照。49 层模型在 prefill 阶段只需要跑前 25 层。输入达到 100 万 token 时，相比 MiMo-V2 系列采用的混合滑动窗口注意力，prefill 计算量降到约 1\u002F5，KV Cache 从 12.09GB 降到 2.69GB。",1790221080000,"Thu Sep 24 2026 03:38:00 GMT+0000 (Coordinated Universal 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