[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$frswfTz336wzgeQKe_B0pf32tkZx7PkcMVMgpQ7DH674":3,"$ftj59r1gjLoEyNw2AuKVdH0zJuD4wfIxEl0X0_jyB7SU":53},{"detail":4,"recommendList":12},{"id":5,"title":6,"content":7,"publishTime":8,"important":9,"sourceName":10,"language":11},196297,"Jev到底有没有门槛？OpenJEV已经分成4派：不吐字容易，概率靠谱很难","动察 Beating AI 快讯，Jev 发布还不到一周，开源社区已经开始复刻这种「不写答案、直接给概率」的模型。\n它们都想做同一件事：省掉逐 token 生成，直接给出选项和概率。但具体怎么做，已经分成了几条完全不同的路线。\n1. SemIf 最简单，连模型都不用重新训练。它直接拿现成的 Qwen，在模型准备回答 A、B、C 时截住，不让它继续往下写，直接读取每个选项的分数，再换成概率。\n2. Simple Jev 也用现成大模型，但进一步省掉重复计算。同一段材料只读一次，后面的多个问题共用这部分结果，再分别判断答案。作者也明确说，它复刻的是 Jev 的使用方式，准确率、速度和概率校准都没有做到等价。\n3. Laya 干脆不用会生成文字的大模型，换成更像传统分类器的编码模型。好处是小而快，短板也很明显。一次从 77 个选项里做选择时，Laya 准确率只有 42.5%，Jev 是 87.0%。\n4. Von 走的也是专用决策模型路线。它只有约 4 亿参数，不生成文字，一次前向直接完成 Choice、Noul 和 Score，更像一个能读自然语言的新一代分类器。\n5. Verdict 更小，只有约 1.5 亿参数。作者重点处理了两个问题：答错了还报很高的置信度，以及把选项顺序换一下，结果就跟着变。它更在意如何让概率和判断都稳定下来。\n6. Kev 选择保留大模型。它拿 Qwen 做底座，再加专门的决策结构，让同一份输入只读一次，多个问题各自算概率。第三方此前逆向 Jev 时，也推测它可能用了类似设计。\nKev 自测中，拿训练时没见过的新题测试，8B 版准确率约 78%，Jev 约 86%。更明显的差距在置信度，Kev 还有一些答错的题会给出 90% 以上把握。\n7. Nimble 重点放在训练上。它用 Qwen3.5-9B，再准备一批「只改一个事实，正确答案就翻转」的数据做后训练。在 324 个没参与训练的测试样本里，Nimble 有 90.1% 选中了参考答案，Jev 是 93.2%。不过作者也提醒，目前还不能保证模型「报 90% 就真的有 90% 正确」。\n8. OpenJev 走得最远，直接换成 DiffusionGemma。它先把几个答案位置留空，再像扩散模型补图一样一次填出来。在 RTX PRO 6000 上，单请求中位延迟约 94ms。\n几天下来，OpenJEV 已经分成四路：直接读取现成模型的答案分",1789882440000,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},187368,"Qwen27B改成扩散Agent：速度拉到500+Token\u002Fs，厂商自测单项超Sol","动察 Beating AI 快讯，专攻超高速大模型的 Celeris 发布一款基于 Qwen3.8-27B 改出来的混合扩散 Agent 模型 Celeris-1 Magnus，官方称在不少场景能跑到 500+ Token\u002Fs。\n在 τ³-Banking 的 97 个银行客服任务上，Magnus 开启 low reasoning 后完成率达到 41.2%，中位耗时 55 秒。同场的 GPT-5.6 Sol 是 38.1%、79 秒。\n传统模型基本一个 Token 一个 Token 往后写，Magnus 能一次生成多个 Token，再反复修正。需要严格按顺序生成的内容再串行处理，因此一次计算可以往前推进更多 Token。",1788256260000,null,"Tue Sep 01 2026 09:51:00 GMT+0000 (Coordinated Universal Time)",{"id":21,"title":22,"content":23,"publishTime":24,"important":25,"sourceName":10,"language":18,"dateString":26},199854,"SGLang给普通大模型加上Jev式决策模式，不微调也能直接做选择","动察 Beating AI 快讯，大模型推理\u002F部署框架 SGLang 新增原生决策 API，让 Qwen3.8-27B 这类现成 LLM 和多模态模型不用改权重、也不用重新微调，就能直接做选择、判断和打分。开发者给出当前情况和几个候选答案，模型直接返回每个选项的概率，不再先生成一段文字再解析。\n它利用的是大模型本来就会计算的「下一个 token 概率」。比如候选答案是 A、B、C，普通聊天模型会继续生成文字；SGLang 则直接读取模型对 A、B、C 的概率，看看它更倾向哪个答案。模型本身没变，只是换了一种读取结果的方式。\nSGLang 用 Qwen3.8-27B 做了一个《宝可梦 FireRed》演示。模型根据实时游戏状态判断攻击、换精灵还是治疗，单次决策低于 100ms，并一次打通四天王和冠军。Qwen3.8-27B 本身支持图像输入，所以这套方法也能处理多模态决策。\nSGLang 还新增 \u002Fv1\u002Fsystemone 接口，兼容 Jev 使用的 TypeSafe SDK。已经使用这套 SDK 的应用，可以把服务地址改成自己的 SGLang 实例继续调用。新功能已经进入 nightly 版本，计划随 v0.5.21 正式发布。",1790764320000,true,"Wed Sep 30 2026 10:32:00 GMT+0000 (Coordinated Universal Time)",{"id":28,"title":29,"content":30,"publishTime":31,"important":9,"sourceName":32,"language":18,"dateString":33},197577,"TypeSafe AI 决策模型 Jev 正式登陆 B.AI API，输入低至 $0.042\u002F百万 Token","ChainCatcher 消息，B.AI 平台宣布 TypeSafe AI 推出的首款 System One 模型 Jev 正式上线 API。 Jev 专为软件内的高速结构化决策打造，不生成任何文本：开发者输入应用状态与类型化问题后，模型直接返回带概率与置信度的决策结果，无需 JSON Prompt，也无需解析输出。模型支持 Choice \u002F Score \u002F Noul 三类原语并行评估，端到端延迟约 70–500ms，输入仅 $0.042\u002F百万 Token，输出免费，适用于工单路由、内容审核、风险评分及 Agent 分支判断等场景。 目前 Jev 已在 B.AI API 的“官方”→“全量接入”列表中开放，可通过 Jev-1.13.0 或 Jev-Latest 调用。",1790169300000,"ChainCatcher","Wed Sep 23 2026 13:15:00 GMT+0000 (Coordinated Universal Time)",{"id":35,"title":36,"content":37,"publishTime":38,"important":9,"sourceName":39,"language":18,"dateString":40},226610,"西方AI圈政治正确到魔怔了：给阿里Qwen灌输「西方价值观」","动察 Beating AI 快讯，Hermes Agent 开发商 Nous Research 发布 Arete-27B，直接拿阿里的 Qwen3.8-27B 当底座，专门进行「西方价值观」训练。 Nous 自己也承认，中国开源模型目前实力很强，只是价值取向不符合他们的标准。于是又用约 5300 万 Token 进行微调，重点涉及言论自由、民主、隐私等议题。 为了检验改造效果，Nous 还专门出了 803 道政治价值观测试题，请来 xAI 的 Grok 4.7 当裁判。按照自家的评测标准，模型的「宣传率」从 22% 降至约 1%，通用能力也没有明显下降。",1794024300000,"MarsBit","Sat Nov 07 2026 04:05:00 GMT+0000 (Coordinated Universal Time)",{"id":42,"title":43,"content":44,"publishTime":45,"important":9,"sourceName":10,"language":18,"dateString":46},208744,"Jev登顶Ramp软件黑马榜：一个月内企业采用率增长1个百分点","动察 Beating AI 快讯，美国企业支付平台 Ramp 发布最新软件采购榜单，数据来自平台上超过 7 万家企业的真实交易。TypeSafe AI 凭借 Jev 登上「爆发增长」榜首。在市场份额增长榜上，它也排到第二，仅次于 Anthropic，OpenAI 则排第四。\nJev 上线不到一个月，企业采用率就增长了约 1 个百分点。它是一种专门帮助软件做判断的 AI 模型，不生成聊天回复，只负责分类、选择和评分。每百万输入 tokens 收费 0.042 美元，输出免费。DeepSeek、Featherless AI 和 OpenRouter 等低成本模型或模型接入平台也出现在榜单中。\n但企业对昂贵 AI 工具的需求同样在增长。视频生成平台 Runway 和 Higgsfield 都进入市场份额增长前十，目前主要用于广告和社交媒体营销。\nRamp 首席经济学家 Ara Kharazian 认为，廉价模型可能压低企业的 AI 调用成本，但图像和视频生成需求增加，又可能推高整体 AI 支出。",1791461640000,"Thu Oct 08 2026 12:14:00 GMT+0000 (Coordinated Universal Time)",{"id":48,"title":49,"content":50,"publishTime":51,"important":9,"sourceName":10,"language":18,"dateString":52},201575,"AWS、Cloudflare、Databricks都跟进Jev，决策模型快成标配了","动察 Beating AI 快讯，Jev 发布后，AWS、Cloudflare 和 Databricks 最近都跟进了决策模型。\nAWS 推出开源的 Strands Decider 2B。它基于 Qwen3.5-2B，去掉文本生成模块，换成专门负责选答案的决策模块，再用 LoRA 微调。官方测试中，它在 RTX 3090 上单次决策中位延迟约 115ms，完整训练约 11 小时。\nCloudflare 一口气发布 Clef 和 Clef-flash，分别基于 Qwen3.8-27B 和 Qwen3.5-9B 改造，并全部开源。在 Cloudflare 对 Jev 官方评测集的测试中，Clef 系列在 4 类任务中的 3 类超过 Jev；Clef-flash 的中位延迟为 38.8ms，Jev 为 524.1ms。\nDatabricks 则把这套能力直接做进了平台。新上线的 ai_decide 可以在 SQL 或 REST API 中调用，对数据做分类、打分和 Agent 路由，接口还直接兼容 TypeSafe AI API。官方称一次决策可以在不到一秒内返回。\n从开发者自己复刻，到 AWS、Cloudflare、Databricks 跟进，决策模型正在迅速从一个新概念变成 Agent 基础设施里的常见组件。",1790996040000,"Sat Oct 03 2026 02:54:00 GMT+0000 (Coordinated Universal 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