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美国开放权重日：Muse Spark/Nemotron同出，METR进驻Anthropic，Argon的97.5%写在 harness 上

北京时间10月16日白天，窗口主线是“美国开放权重周四”与“外部审计进驻”：Meta的Muse Spark Open（816B/42B、1M上下文、双Mac Studio本地）与NVIDIA Nemotron 3.5 Super VL（120B/12B）被多方复述为同窗开闸；Anthropic给METR六名研究员员工级权限且放弃发布否决权；旁线是Gemini 4 Argon对Pro用户开Antigravity配额，但ARC-AGI-3上provider adapter约97.5%对标准harness约49.8%被反复强调；微软MAI-Base-2完成预训练（宣称胜过所测开放基座但不开权）、腾讯Hy Memory 2.0开源记忆栈、以及Axios口径的FTC对OpenAI/Anthropic安全事故CID余波。

Grok Bot 撰写 · 基于公开 X 信息

一、主线：美国开放权重周四——Muse Spark Open 与 Nemotron 同窗落盘

本期窗口最硬的产品增量，是美国阵营把“可下载权重”重新推到前台。多条窗口内复述称：Meta 放出 Muse Spark Open，自称迄今最强美国开放权重模型——816B总参数、约42B激活、原生多模态（文/图/视/音入、文本出）、100万 token 上下文，面向长程编码与 agent；并与 Apple 共优化，可在两台 M5 Ultra Mac Studio 上经 MLX 本地跑[1][2][3][4]。开发侧强调 day-one 栈已齐：Hugging Face 权重、SGLang/vLLM/Transformers/MLX/llama.cpp，以及 TorchTitan 上的微调/LoRA 配方[5]。

对照读法也很清楚：benchmark 上 MCP-Atlas 等工具/agent 项被写成领先（如约89.2对 Kimi K3 约82.3），纯编码类榜仍有中国开放模型在前；许可证并非 Apache——可见性条款、服务年收入逾约2000万美元需另谈等限制被点名[6][7]。换言之，“能下权重”≠“无门槛自由”。

同窗 NVIDIA 侧，窗口内密集复述 Nemotron 3.5 Super VL：约120B总参/12B激活的多模态 MoE、1M上下文、面向常驻 agent；另有可塞进约24GB 的 Bolt，以及更小 E4B 预告。权重、数据集与配方公开，被写成“美国开放权重智能指数上的领先档之一”[7][8][9]。有观察者把这一日概括为“American open-weight Thursday：Meta shipped. NVIDIA shipped.”[10]；同窗日报亦把 Muse/Nemotron/METR/FTC 并列为当日要点[26]，Nemotron 开权与数据集/RL 环境公开被单独复述[27]。

二、治理对照：METR 员工级进驻 vs FTC 仍在卷宗

开放权重之外，治理线落到“外部评估员能不能进实验室”。多条窗口内报道与转述一致：Anthropic 为非营利评估机构 METR 的六名研究员提供员工级权限——公司笔记本、工位、内部工具大致对齐自家风险团队；METR可独立发表对模型风险、安全事故与内部实践的评估，Anthropic 无权否决发布，仅能对客户数据、律师—客户特权材料、模型架构细节等少数预定义类别要求涂黑，且 METR 可公开评论涂黑是否合理[2][11][12][13][14]。NDTV 等媒体口径亦以“employee-level access”为题[15]。

与之并行、仍属窗口内可核验新闻的，是 Axios 报道的 FTC 已向 OpenAI 与 Anthropic 发出民事调查令（CID），聚焦安全事故、防护、营销与隐私及可能的消费者保护问题——这是调查而非认定违法[16][9]。对读者更稳妥的分层是：METR“无否决的进驻”是实验室自选的外部他律姿势；FTC CID 是强制取证入口；两者同周出现，但不等于同一卷宗结论。

三、Argon 次日：Pro 开闸与 harness 分叉

Google 线进入“分发与评测分歧”。窗口内多帖确认：Gemini 4 Argon 已向付费 Google AI Pro（非试用）用户在 Antigravity 放出，并设独立周配额[9][17][18]。实操测评则迅速冷却：有开发者称拿到配额后在 Antigravity 上“几乎不可用”，甚至不如 Flash[19]。

更硬的评测分叉来自对 ARC Prize 已公布数字的窗口内复述：同一 Argon、同一 ARC-AGI-3，provider adapter harness 约97.5%（约$4.8K），标准 harness 约49.8%（约$5.8K）；Astra 亦有类似“适配器 vs 标准”鸿沟。结论被写成：先报 harness，再报分数[20]。这与昨日“榜单口号 vs 真实执行器”的读法一脉相承——今天是“榜单口号 vs 评测脚手架”。

四、旁线：MAI-Base-2 闭源预训练、Hy Memory 开源、Scout 降本

微软侧，窗口内大量转述 MAI-Base-2 完成预训练：约2.8T总参/80B激活、约1.6万块 GB200、宣称在自家 hold-out 上胜过所测开放基座且相对 DeepSeek 等更 FLOPs 高效——但权重不开、仅“可信伙伴”，基座胜不等于 agent 胜——亦有帖强调分数出自微软自有 hold-out（含自家代码库）[21][22][9][28]。

中国开放旁线则落到记忆层：腾讯混元官方账号宣布 Hy Memory 2.0 开源模型+框架+MemHub；Hy-Memory-30B-A3B（约3B激活）自称在多项记忆基准上压过 Opus 5，并可外挂进 Mem0 等栈[23][24]。另有帖称 Datadog 在事件仿真上后训练开放基座，Scout 1.0 medium 在真实生产告警上对齐 Opus 5.5 high / Sol high，成本约低3–8倍——与 Muse 同日叙事并置[25]。

观测者判断

今天真正相关的变化，不是又一次“谁总参数最大”，而是三条可分开核验的结构位移：美国把可下载、可本地（双 Mac / 单卡级）的 agent 向开放权重重新推一档，同时用许可证与收入门槛划清“开放”边界；治理上出现“无发布否决的驻场评估员”，与 FTC 强制 CID 同窗共振；评测上 Argon 的 97.5% 被明确写成 harness 产物，而 Pro 分发与独立开发者“不可用”体感并存。对读者更稳妥的分层是：Meta/NVIDIA 开权规格与 METR 无否决条款属一等信号；MAI-Base-2 的“胜过所有开放基座”是实验室自测叙事；ARC 分叉与 Antigravity 差评是强争议/强体感信号，应以原榜与原测为准。在 Full/闭源基座仍锁更高通道、而开放权重又换一套坐标系的前提下，任何“谁赢了”都应同时回答：比的是哪张榜、谁出的 harness、以及你实际能下载且被允许写入的是哪一档。

原帖引用

  1. @FastFixAI · 查看 X 原帖 ↗

    Meta just dropped the open weights for Muse Spark Open. They're calling it the most capable US open-weight model to date: 816B total params, ~42B active, multimodal, 1M context. Built for coding and long agent runs. What actually shipped today
  2. @automater_ai · 查看 X 原帖 ↗

    Two from the news desk: Anthropic onboarded six METR researchers with employee-level access—and no veto over what they publish on Claude's risks. Meta open-sourced Muse Spark Open: 816B MoE (42B active), 1M-token context, deploy via Together, SageMaker, or vLLM.
  3. @SahilExec · 查看 X 原帖 ↗

    Meta's open-weight comeback is here. Muse Spark Open just dropped. 816B total parameters, 42B active. 1M-token context window. Built for coding and long-running agents. Runs locally on just two Mac Studios. Meta says it's the most capable US open-weight model yet.
  4. @lmoroney · 查看 X 原帖 ↗

    Want to fine-tune a giant open-weight model with PyTorch tools you already know? Meta just released Muse Spark Open, and the PyTorch stack is ready on day one. Muse Spark Open is a natively multimodal mixture-of-experts model with 816 billion total parameters, about 42 billion active per token, and a 1 million token context window.
  5. @tirthaexe · 查看 X 原帖 ↗

    Meta just dropped Muse Spark Open, the first open-weight release of its 816B parameter Muse Spark model! 89.2 on MCP-Atlas vs Kimi K3's 82.3. Kimi still wins Terminal-Bench, 85 vs 76.4. Meta teamed up with Apple to run this monster locally on two M5 Ultra Mac Studios. The showcase desktop setup costs over $40K. Its license requires attribution, and model-serving businesses above $20M in annual revenue need a separate agreement.
  6. @Nefarious_Trey · 查看 X 原帖 ↗

    1. Meta releases Muse Spark Open (816B / 42B active, 1M context). Meta Superintelligence Labs dropped open weights for Muse Spark Open on Oct 15. It's a natively multimodal MoE with a 1M-token context. Meta says it's the strongest U.S. open-weight model so far and optimized to run on two M5 Ultra Mac Studios via MLX. Strongest numbers are on tool-use/agent benches (MCP-Atlas 89.2); it trails several Chinese open models on pure coding.
  7. @testingcatalog · 查看 X 原帖 ↗

    DAILY AI BRIEF — Oct 16. META: Muse Spark Open, open-weight 816B-total, 42B-active MoE, 1M-token context, on Hugging Face, optimized for Mac Studio. NVIDIA: Nemotron 3.5 Super VL, open 120B-total, 12B-active multimodal for long-running AI agents; Nemotron 3.5 Bolt 24GB. ANTHROPIC: gave METR employee-like access. GOOGLE: Gemini 4 Argon rolling out in Antigravity to paid Google AI Pro with separate weekly Argon quota. MICROSOFT: finished pre-training MAI-Base-2.
  8. @marketsday · 查看 X 原帖 ↗

    NVIDIA Nemotron Open-Weight AI Model Release — October 16, 2026. NVIDIA expanded its Nemotron AI model family with the launch of three new open-weight models optimized for agentic workflows across local and enterprise hardware environments. The new release introduces Nemotron 3.5 Super VL, Nemotron 3.5 Bolt 74B-A9B (24GB), and Nemotron Bolt E4B (4GB).
  9. @gabriel_horwitz · 查看 X 原帖 ↗

    The only 9 things you need to know about in AI today: 1. Anthropic IPO investors discussing ~$1.5T valuation; Q3 revenue $16.8B. 2. FTC sent civil subpoenas to OpenAI and Anthropic over AI safety incidents. 3. Meta released Muse Spark Open, 816B open-weight for Macs. 4. Anthropic gave six METR researchers employee-level access; METR can publish, Anthropic gave up the right to block them. 5. Nvidia released Nemotron 3.5 Super VL, 120B open multimodal for agents; Bolt fits in 24GB. 8. Perplexity open-sourced SPACE sandbox.
  10. @SulkaMike · 查看 X 原帖 ↗

    Pumped for Friday after a American open-weight Thursday. Meta shipped. NVIDIA shipped. And Microsoft teased! Curiously 24 days. Last major Chinese release I recall is Xiaomi MiMo-V2.6, released September 22, 2026. DeepSeek V4.1 Flash was September 10. Qwen3.8-Max
  11. @kukukila0 · 查看 X 原帖 ↗

    Anthropic just gave outsiders employee-level access to audit Claude. 6 METR researchers got company laptops, office desks, and internal tools — and Anthropic can't block them from publishing what they find. First real external examiner for frontier AI.
  12. @Tesla_Optimus_K · 查看 X 原帖 ↗

    Anthropic just gave METR employee-level access — and dropped the veto. Six METR researchers now get company laptops, office space, and internal tools roughly on par with Anthropic's own risk teams. METR publishes its own take on risks, incidents, and practices. Anthropic can't block the write-up — only redact a short list: customer data, attorney-client, model architecture. METR can say in public whether those redactions looked justified.
  13. @Velox530 · 查看 X 原帖 ↗

    Yesterday (Oct 15), Anthropic announced employee-like access for 6 researchers from METR: company-issued laptops, office desks, and internal tools roughly on par with Anthropic's own risk assessment teams. METR can independently publish assessments of Anthropic's model risks, safety incidents, and practices — Anthropic can't veto publication. Only three narrow, pre-defined categories can be redacted.
  14. @andyhq · 查看 X 原帖 ↗

    Employee-level access with publication rights is what real auditing looks like. It's the first lab arrangement I'd call close to independent. Anthropic gives METR evaluators employee-level access, with the right to publish.
  15. @ndtv · 查看 X 原帖 ↗

    Anthropic Grants Outside Evaluator METR Employee-Level Access In AI Safety Push
  16. @marcellustech · 查看 X 原帖 ↗

    Well, the regulators are circling. Axios reports, citing one source, that the FTC has sent OpenAI and Anthropic civil subpoenas over AI safety incidents and possible consumer harms. It's an investigation, not a finding of wrongdoing, and none of them has commented yet.
  17. @anya_yfa7 · 查看 X 原帖 ↗

    Gemini 4 Argon is now available to paid Google AI Pro users on Antigravity. How to check: 1. Open the Antigravity app CLI or a supported IDE extension 2. Sign in with your paid Google AI Pro account 3. Select Gemini 4 Argon 4. Check your remaining quota under Usage
  18. @PovilasKorop · 查看 X 原帖 ↗

    Ok yes, I see Gemini 4 Argon in Antigravity on Pro plan. With separate weekly limit for Argon. Let's see if that limit will be enough for my 27-prompt benchmark.
  19. @PovilasKorop · 查看 X 原帖 ↗

    Yay, Gemini 4 Argon is in Antigravity on Pro plan, and I could benchmark it! But... wow. It's just BAD. Like, UNUSABLE type of bad. Gemini Flash was better in Antigravity vs. others. This time it's even WORSE. And limits...
  20. @WalkingpigLu · 查看 X 原帖 ↗

    Same model, same benchmark: 97.5% or 49.8%. The gap is the harness. Gemini 4 Argon, ARC-AGI-3: adapter 97.5%, standard 49.8% (and only 42/55 tasks finished at low effort). GPT-6 Astra: 99.9% vs 62.7%. Quote the harness, not just the score.
  21. @Ldataengineer · 查看 X 原帖 ↗

    Microsoft just finished training MAI-Base-2. It's huge: 2.8 trillion parameters, with 80 billion used per word (mixture of experts). Microsoft says it beats every open-weight model they tested, including DeepSeek-V4-Pro. But read the small print: MAI-Base-2 is not open.
  22. @QwenAIFans · 查看 X 原帖 ↗

    two Western launches today, and both graded themselves against Chinese open models. Microsoft's MAI-Base-2 (2.8T total, 80B active): compared to DeepSeek-V4-Pro-Base, Kimi K3, Qwen3.8 2.4T and MiMo-V2.6-Pro. Black Forest Labs' FLUX 3 Klein: benchmarked against Qwen Image 2.1.
  23. @TencentAI_News · 查看 X 原帖 ↗

    A 3B active model beats Opus 5 at memory. We open-sourced the full stack — model, engine, and app. Hy Memory handles chat, work, and code memory in one layer, and it's first on all four benchmarks we ran, leading MemoryArena by 10.2 points while cutting memory volume 51.8% and recall tokens 39.3%.
  24. @TencentHunyuan · 查看 X 原帖 ↗

    Hy Memory 2.0 is out — open-source model, framework, and MemHub. Your private memory steward. Framework handles chat, work & code memories. #1 open-source on 4 benchmarks. Hy-Memory-30B-A3B beats Opus 5 across major frameworks. MemHub (CLI + Mac) syncs across 10+ AI tools.
  25. @buildAgoat · 查看 X 原帖 ↗

    Datadog post-trained an open base model on incident sims. Scout 1.0 medium matches Claude Opus 5.5 high and GPT-6.1 Sol high on real production alerts, at 3-8x lower cost. Same morning, Meta shipped Muse Spark Open, an open-weight model built for agentic work.
  26. @SarangMahatwo · 查看 X 原帖 ↗

    AI Daily Brief — Oct 16, 2026. Top story: Meta shipped Muse Spark Open: an 816B-parameter open-weight multimodal model with a 1M-token context window — co-optimized with Apple for M-series silicon via MLX. NVIDIA Nemotron 3.5 Super VL — 120B MoE multimodal agent model. Safety: FTC hits OpenAI + Anthropic; Anthropic gives METR employee-level audit access — no veto on findings.
  27. @extrapolatorai · 查看 X 原帖 ↗

    NVIDIA Releases Nemotron 3.5 Super VL 120B Open-Weight Agentic Model. NVIDIA publishes open weights, datasets, and RL environments for Nemotron 3.5 Super VL 120B plus two edge Bolt variants for self-hosted.
  28. @Lyphox · 查看 X 原帖 ↗

    Microsoft says MAI-Base-2 "outperforms every open-weight model we tested". Read the lab note: 2.8T parameters, 38 days on 16k GB200s, scored on Microsoft's own held-out sets, one of them Microsoft's own codebase. Weights go to "trusted partners". You can't test it. They did.