[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fLRUe0W2e5S_gvDv0q1jvN_k-7Y4M9oYwZju7hzzr8K8":3,"$ftj59r1gjLoEyNw2AuKVdH0zJuD4wfIxEl0X0_jyB7SU":54},{"detail":4,"recommendList":12},{"id":5,"title":6,"content":7,"publishTime":8,"important":9,"sourceName":10,"language":11},188967,"GPT-6 Astra拿下Perplexity研究Agent榜首：比Fable 5.1高13.5%，还便宜6.1%","动察 Beating AI 快讯，Perplexity 用自家的 Agent 基准 WANDR 测了 GPT-6 Astra，得分 0.682，是目前测过的模型里最高。平均每个任务成本 11.98 美元。相比 Claude Fable 5.1，分数高 13.5%，成本反而低 6.1%；相比 Opus 5，分数高 27%，成本只高 3.3%。\nWANDR 专门测「又广又深」的研究任务。它有 500 个真实研究任务，要求 Agent 不只是找到几个答案，而是尽量找全符合条件的对象，再逐个查资料、核身份，并给每条结果配上可验证的来源。典型任务包括竞品研究、尽调、文献检索、市场分析和人才搜寻。\n此前 Fable 5.1 以 0.601、每任务 12.76 美元排第一，Opus 5 为 0.537、11.60 美元。Astra 这次把分数明显往上推了一截，而且不是用更高成本硬堆出来的。",1788507360000,false,"Blockbeats",0,[13,20,27,34,41,47],{"id":14,"title":15,"content":16,"publishTime":17,"important":9,"sourceName":10,"language":18,"dateString":19},199326,"AI网络安全榜爆冷：GPT-6、Claude旗舰因安全拒答掉队，小米MiMo追平Grok登顶","动察 Beating AI 快讯，Artificial Analysis 推出独立的 Cyber Index，专门评测 AI Agent 做企业网络防御的能力。它不是从原来的通用 Intelligence Index 里抽几项测试，而是另外采用 CWE-Bench-AA、DeepsecBench-AA 和 CyberGym-E2E-AA 三项网络安全评测，分别测试漏洞审计与修补、漏洞发现，以及从发现漏洞到复现并修补的完整流程，三项各占三分之一。\n首批榜单中，Grok 4.7（xhigh）和小米 MiMo-V2.6-Pro 同为 56 分，GPT-6 Luna（max）53 分，GLM-5.3-Flash 50 分。MiMo 平均每个任务成本只有 0.18 美元，Grok 4.7 要 11.67 美元；Luna 更低，只要 0.12 美元。\n不过，这张榜不能简单理解成模型本身的网络安全能力排名。GPT-6 Sol、GPT-6 Astra 和 Claude Opus 5.5 都参加了测试，但在 CyberGym-E2E-AA 上几乎全部拒答，其中 Sol 和 Astra 拒绝全部任务，Opus 5.5 拒绝 98%。拒答直接记 0 分，Luna 反而愿意继续完成这些任务，所以综合成绩更高。Artificial Analysis 也表示，大量拒答让这些前沿模型的实际能力很难判断。\nGLM-5.3 也出现了明显反常。完整版在 Artificial Analysis 的通用能力榜上高于 Flash，但这次 Cyber Index 只有 36 分，Flash 达到 50 分。主要差距来自 CyberGym-E2E-AA：Flash 得到 74%，完整版只有 29%。Artificial Analysis 暂未解释这一差距的原因。",1790669700000,null,"Tue Sep 29 2026 08:15:00 GMT+0000 (Coordinated Universal Time)",{"id":21,"title":22,"content":23,"publishTime":24,"important":9,"sourceName":25,"language":18,"dateString":26},198372,"GPT-6 Astra考过「科目二」？5分22秒驶入锥桶车位","动察 Beating AI 快讯，三名中国台湾地区研究人员搭建 DrivingBench，将 GPT-6 Astra、GPT-5.6 Sol、Grok 4.6 和 Claude Fable 5.1 等通用大模型接入一辆真实的 2022 款丰田卡罗拉，进行类似「科目二」的真实车辆驾驶测试。测试赛道全长约 130 米，由彩色锥桶组成 8 米宽通道，车辆需连续通过多个弯道，最终驶入一个 7×5 米的锥桶车位。 此前从未接受专门驾驶训练的 GPT-6 Astra，在首次测试中因判断失误偏离赛道，但第二次调整策略后，以 5 分 22 秒完成约 134.7 米赛程并成功驶入车位，成为四个模型中唯一「通过科目二」的模型。 测试中，Astra 将弯道及障碍物附近车速控制在 0.5 至 0.8 米\u002F秒，并根据实时摄像头画面持续调整方向。相比之下，Sol、Grok 和 Fable 均未完成测试，其中 Sol 和 Grok 连续三次失败，Fable 第三次行驶至约 45% 位置后停止。 此次测试显示，未经专门驾驶数据训练的通用大模型已能够在低速真实环境中完成基础驾驶任务，但距离实际道路自动驾驶仍存在明显差距。",1790348460000,"MarsBit","Fri Sep 25 2026 15:01:00 GMT+0000 (Coordinated Universal Time)",{"id":28,"title":29,"content":30,"publishTime":31,"important":9,"sourceName":32,"language":18,"dateString":33},220690,"PSI 借助 GPT 与 Claude 公布 5 项量子研究成果","ChainCatcher 消息，AI 物理实验室 Physical Superintelligence（PSI）宣布，借助 GPT-6 Astra、Claude 等模型，在 5 道长期悬而未决的量子信息难题上取得新进展，并公开论文、证明代码和验证材料。研究过程中，物理学家负责指导和核验，相关模型参与推导，团队还让 Codex、Claude Code 中的 AI Agent 编写 Lean 形式化证明，由计算机检查推理是否成立。其中一项研究证明，在特定量子场中，即使两个区域相隔很远，量子纠缠依然存在，但距离超过一定范围后，就无法再通过 CHSH 贝尔不等式检验出非局域关联。另一项研究证明，某类量子操作连续进行足够多次后，一定会破坏纠缠。这 5 项成果并非全部彻底解决了原来的难题，部分结论只适用于特定条件，仍有问题尚未解决。",1793098740000,"ChainCatcher","Tue Oct 27 2026 10:59:00 GMT+0000 (Coordinated Universal Time)",{"id":35,"title":36,"content":37,"publishTime":38,"important":39,"sourceName":10,"language":18,"dateString":40},197392,"一夜两条路线：Opus5.5冲到第一，GPT-6把同档成本砍半","动察 Beating AI 快讯，Artificial Analysis 完成对昨夜几款新模型的第三方实测。Claude Opus 5.5 max 在 Intelligence Index v4.3 拿到 58 分，目前排名第一。GPT-6 Astra 和 Fable 5.1 都是 53 分，Opus 5 为 51 分。Opus 5.5 在 10 项评测里拿下 6 项最高分。\n但这个最高分也用了更多 token。Opus 5.5 max 每项任务平均输出约 11.9 万 tokens，比 Opus 5 的 7.3 万多约 60%。靠 API 降价 20% 和缓存读取降价 60%，单任务成本最终为 5.98 美元，和 Opus 5 的 5.86 美元基本持平。更实用的 medium 档拿到 51 分，已经追平 Opus 5 max，单任务成本只有 1.34 美元。\nOpenAI 走的则是另一条路。GPT-6 Sol 和 Luna 的 Intelligence Index 表现与 GPT-5.6 前代接近，但价格明显下降。Sol max 每项任务成本从 1.99 美元降到 1.06 美元，Luna 从 0.18 美元降到 0.07 美元。编码 Agent 评测里，Sol 从 55 分升到 57 分，成本也下降约一半；Luna 从 43 分降到 41 分，但单任务成本低约 60%。\n两家的路线已经开始出现分化：Opus 5.5 用更多推理把能力上限推到新高，GPT-6 Sol 和 Luna 则主要把原有能力做得更便宜。 在 Artificial Analysis 的性能\u002F成本图上，两家的新模型都占据了新的 Pareto 前沿。",1790144340000,true,"Wed Sep 23 2026 06:19:00 GMT+0000 (Coordinated Universal Time)",{"id":42,"title":43,"content":44,"publishTime":45,"important":9,"sourceName":25,"language":18,"dateString":46},218712,"智谱新模型被曝最高 3.5 万亿参数，有望达 Claude Fable 级别","火星财经消息，据动察 Beating AI 快讯，一份在投资社区流传的专家访谈纪要称，人工智能公司智谱下一代基座模型并非此前传闻的 1.6 万亿参数，而是 2.5 万亿至 3.5 万亿参数。智谱今年 8 月发布的 GLM-5.3 仍沿用上一代基座，总参数约 7440 亿；若消息属实，新基座规模约为现款的 3.4 至 4.7 倍。纪要还称，训练并不顺利，智谱采用了更激进的模型架构，预训练期间一度出现 Loss 不降，即持续投入算力但训练指标无法继续改善。受访者认为问题最近应已解决，预计很快完成训练，并预测新模型能达到 Claude Fable 级别的旗舰水平。纪要另称，新模型从架构设计阶段就把推理速度和成本作为硬约束，目标是发布首日即可大规模提供服务。同时，已有多家云厂商洽谈收入分成，以应对大模型发得出、供不上的问题。",1792567440000,"Wed Oct 21 2026 07:24:00 GMT+0000 (Coordinated Universal Time)",{"id":48,"title":49,"content":50,"publishTime":51,"important":9,"sourceName":52,"language":18,"dateString":53},216172,"FLock「这那模型」获 Qwen 官方支持，联合阿里云实现性能突破，决策测试超越 GPT-5.6、Claude Opus 5","Foresight News 消息，去中心化 AI 平台 FLock.io 旗下开源决策模型 THIS \u002F THAT「这那模型」获得阿里巴巴 Qwen 官方认可与宣发支持。该模型基于 Qwen3.5 2B 构建，利用 Qwen Max 生成训练数据，并结合 FLock Research Team 在自然语言处理领域国际学术会议 ACL 2026 上发表的 GraphSynth 和 CircuitSynth 两项研究成果，在阿里云平台完成训练。此前，FLock 已与 Qwen、阿里云建立长期技术与生态合作关系，并曾联合 Qwen 在韩国高校举办 AI Hackathon，持续推动 AI 技术创新与开发者生态建设。\n据团队公布的测试结果，THIS \u002F THAT Model 1.2 在 1710 道复杂决策测试中成功率达到 87.8%，较 1.0 版本的 40.6% 大幅提升，并超过 GPT-5.6（81.6%）和 Claude Opus 5（83.4%）。模型仅有 18.8 亿参数，无需生成输出 Token，单次决策延迟约 31.4 毫秒，可在本地设备运行，适用于 AI Agent、企业 AI 及自动化工作流等场景。\nFLock.io 表示，此次模型成果进一步展示了 FLock 在原创 AI 研究、数据合成及高性能专用模型训练方面的技术实力，也是 FLock 与 Qwen、阿里云长期技术及生态合作的又一重要成果。未来，双方将继续探索高效、隐私保护型 AI 模型在更多实际场景中的应用。",1791891360000,"Foresight News","Tue Oct 13 2026 11:36:00 GMT+0000 (Coordinated Universal 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