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  <title>AI Radar · 研究 · 评测</title>
  <link>https://ai-radar-ik42.pages.dev/</link>
  <description>AI Radar 自动聚合 AI 官方博客、GitHub Release、可信媒体与社区讨论，将同一事件的多个来源聚类，标注验证状态（已确认 / 多源佐证 / 有限来源 / 传闻），并持续追踪后续进展。</description>
  <language>zh-cn</language>
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    <title>[有限来源] Optimizing Qwen3.6 / Qwen3.8-27B on 16GB VRAM: Complete Benchmark Results and Setup Guide (~30-50tps at 32k to 72k context)</title>
    <link>https://ai-radar-ik42.pages.dev/event/ev-88117478c6ef/index.html</link>
    <guid isPermaLink="false">ev-88117478c6ef-zh</guid>
    <pubDate>Tue, 18 Aug 2026 02:28:24 GMT</pubDate>
    <description>有限来源 · 1 src · This post was made with AI. I tried to remove as much slop as possible and keep it straight to the point to save your time as I know how annoying AI slop posts can be, but I still wanted to retain all the details so it can be used as a resource for comparison with other future q…</description>
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  <item>
    <title>[有限来源] 我们在评测根本没人真正跑的模型</title>
    <link>https://ai-radar-ik42.pages.dev/event/ev-1aa471d129fc/index.html</link>
    <guid isPermaLink="false">ev-1aa471d129fc-zh</guid>
    <pubDate>Mon, 17 Aug 2026 21:53:54 GMT</pubDate>
    <description>有限来源 · 1 src · qwen3.8-27b looks genuinely impressive on the benchmark tables - beating models many times its size on some of them. but those numbers come from bf16 weights, and nobody here is running a 27b at bf16. we&#39;re running the 4-bit at ~17gb because that&#39;s what fits on a 4090 or a 24gb…</description>
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  <item>
    <title>[有限来源] 在 4 张 RTX 3090 上实测 Qwen3.8-27B</title>
    <link>https://ai-radar-ik42.pages.dev/event/ev-4f8f243ce02f/index.html</link>
    <guid isPermaLink="false">ev-4f8f243ce02f-zh</guid>
    <pubDate>Mon, 17 Aug 2026 21:17:41 GMT</pubDate>
    <description>有限来源 · 1 src · A while back I made a post about my 4x3090 rig in a Silverstone RV-02 . Check it out if you&#39;re a conoissuer of OG PC cases. With the incredible Qwen 3.8 27B release I ran benchmarks. So in case you are rocking 3090s you might be interested in this. Everything below is entirely L…</description>
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    <title>[有限来源] 本地 Agentic 编程评测：Qwen 3.8 27B（多种权重量化/缓存量化/引擎/推理档位）对比其他模型</title>
    <link>https://ai-radar-ik42.pages.dev/event/ev-f32b2a2af121/index.html</link>
    <guid isPermaLink="false">ev-f32b2a2af121-zh</guid>
    <pubDate>Mon, 17 Aug 2026 20:45:23 GMT</pubDate>
    <description>有限来源 · 1 src · In medium reasoning mode, it both scores higher than the 3.6 version, AND is very much more efficient (almost half requests needed, and a third less tokens generated) - at DeepSeek v4 Flash 3107 MXFP4 level
The xhigh mode is advertised to be the best one for hard tasks. In this…</description>
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  <item>
    <title>[有限来源] Artificial Analysis 评测：Qwen3.8-27B 与 DeepSeek V4、GPT-5.6 Luna Max 并驾齐驱</title>
    <link>https://ai-radar-ik42.pages.dev/event/ev-4de3452cd1db/index.html</link>
    <guid isPermaLink="false">ev-4de3452cd1db-zh</guid>
    <pubDate>Mon, 17 Aug 2026 17:26:46 GMT</pubDate>
    <description>有限来源 · 1 src · submitted by /u/anderspitman
[link] [comments]</description>
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  <item>
    <title>[有限来源] [论文] Intern-S2-Mobius：知识与推理解耦的基础模型</title>
    <link>https://ai-radar-ik42.pages.dev/event/ev-a30357b6312b/index.html</link>
    <guid isPermaLink="false">ev-a30357b6312b-zh</guid>
    <pubDate>Mon, 17 Aug 2026 12:49:03 GMT</pubDate>
    <description>有限来源 · 1 src · We introduce Mobius-v0, an architecture that comprises a globally shared Memory (FFN) that stores knowledge vectors and multiple Reasoners (Self-Attn) that iteratively achieve compositional reasoning. Using hidden states as cache and carrier, reasoners repeatedly query memory fo…</description>
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  <item>
    <title>[有限来源] LLM 不会「跳跃」——DeepMind 论文指出 LLM 无法产生新颖的解释性假说</title>
    <link>https://ai-radar-ik42.pages.dev/event/ev-d666afc16f4e/index.html</link>
    <guid isPermaLink="false">ev-d666afc16f4e-zh</guid>
    <pubDate>Mon, 17 Aug 2026 09:58:16 GMT</pubDate>
    <description>有限来源 · 1 src · submitted by /u/juanviera23
[link] [comments]</description>
  </item>
  <item>
    <title>[已确认] 复现 2200 篇 ICML 论文教会我们的事</title>
    <link>https://ai-radar-ik42.pages.dev/event/ev-80d2b1019426/index.html</link>
    <guid isPermaLink="false">ev-80d2b1019426-zh</guid>
    <pubDate>Thu, 13 Aug 2026 00:00:00 GMT</pubDate>
    <description>已确认 · 1 src · </description>
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  <item>
    <title>[已确认] 医疗研究 AI 系统 AMIE 在首创研究中展示实时临床视频问诊能力</title>
    <link>https://ai-radar-ik42.pages.dev/event/ev-f3e7c9ebce6a/index.html</link>
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    <pubDate>Tue, 11 Aug 2026 17:00:00 GMT</pubDate>
    <description>已确认 · 1 src · AMIE promotional video</description>
  </item>
  <item>
    <title>[已确认] 涉及 OpenAI 模型的第三方网络安全评估</title>
    <link>https://ai-radar-ik42.pages.dev/event/ev-37aa7ebe2f1d/index.html</link>
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    <pubDate>Tue, 04 Aug 2026 19:00:00 GMT</pubDate>
    <description>已确认 · 1 src · OpenAI explains recent third-party cybersecurity evaluation incidents and outlines new safeguards to strengthen AI model testing and evaluation.</description>
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  <item>
    <title>[已确认] 开启两个设置让我们的 ARC-AGI-3 得分翻三倍</title>
    <link>https://ai-radar-ik42.pages.dev/event/ev-b29d9984715a/index.html</link>
    <guid isPermaLink="false">ev-b29d9984715a-zh</guid>
    <pubDate>Wed, 29 Jul 2026 15:00:00 GMT</pubDate>
    <description>已确认 · 1 src · How two API settings improved GPT-5.6 performance on ARC-AGI-3, boosting scores and efficiency by retaining reasoning and enabling compaction.</description>
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  <item>
    <title>[已确认] 用 ChatGPT 学术研究者版加速科学发现</title>
    <link>https://ai-radar-ik42.pages.dev/event/ev-7094112115d8/index.html</link>
    <guid isPermaLink="false">ev-7094112115d8-zh</guid>
    <pubDate>Wed, 29 Jul 2026 10:00:00 GMT</pubDate>
    <description>已确认 · 1 src · OpenAI is giving 100,000 academic researchers free access to ChatGPT&#39;s most advanced AI models to accelerate scientific research, collaboration, and discovery.</description>
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  <item>
    <title>[已确认] OpenAI 与 Hugging Face 联手处置模型评估期间的安全事件</title>
    <link>https://ai-radar-ik42.pages.dev/event/ev-95209b786bce/index.html</link>
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    <pubDate>Tue, 21 Jul 2026 07:00:00 GMT</pubDate>
    <description>已确认 · 1 src · OpenAI and Hugging Face share early findings from a security incident during AI model evaluation, highlighting advanced cyber capabilities and lessons for defenders.</description>
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