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#推理

今日 85 条
9月12日周六
  1. Thinking Machines42

    我们自己的 @johnschulman2 与 Dwarkesh 对话,讨论随着模型不断进步和自我改进,人类判断力在哪些方面仍然重要:教它们处理混乱的现实世界任务,用品味判断什么在长期内有效,以及最重要的——明确我们真正想要什么。

    引用Dwarkesh Patel@dwarkesh_sp

    New episode with @johnschulman2, @oneill_c and @BerenMillidge. I got together with some of the most insightful AI researchers I know who are at the openish companies, because I wanted to hear the details of what's actually happening at the frontier and what comes next. 0:00:00 – Steelmanning the case against RSI 0:18:39 – What’s driving the Chinese labs’ progress 0:28:06 – How will automated AI researchers be trained 0:33:51 – Will long-horizon RL elicit AGI? 0:45:24 – The sim-to-real gap 1:00:33 – How much progress is explained by data? 1:18:03 – Why is RL working so well? 1:24:54 – Move 37 and entropy collapse 1:28:31 – Rapid-fire timelines

  2. Dwarkesh Patel:Podcast & Blog(RSS)61

    Dwarkesh 对谈 John Schulman、Beren Millidge 与 Charlie O'Neill:AI 研究者激辩递归自我改进还有多远

    Dwarkesh Patel 邀请 Zyphra CTO Beren Millidge、Thinking Machines 首席科学家 John Schulman 和 Baseten 模型训练负责人 Charlie O'Neill 对谈递归自我改进(RSI)何时到来。

    推荐理由:三位一线研究者围绕递归自我改进给出了各自不同的技术瓶颈判断,涵盖蒸馏、sim-to-real 与持续学习等具体分歧。

9月11日周五
  1. NVIDIA Technical Blog(开发者技术博客 · RSS)29

    全栈 NIM 优化如何在 Nemotron 3 Ultra 上支撑 2.5 倍并发用户

    NVIDIA 通过全栈 NIM 优化,在 Nemotron 3 Ultra 上实现 2.5 倍并发用户量。该优化针对生产环境部署大语言模型时,在现有 GPU 基础设施上提升并发服务能力并保持交互响应速度的需求,对提示词长、上下文跨步骤复用的智能体 AI 工作负载尤为关键。

9月10日周四
  1. SiliconFlow70

    DeepSeek-V4.1-Flash 在 SiliconFlow 上线,提供 Day 0 支持。模型为 552B MoE,prefill 阶段约激活 8B、decode 阶段约激活 16B,支持原生视觉与 1M 上下文窗口,KV cache 占用相比 V4 Flash 约缩小 4 倍,采用 MIT 许可证,主打高吞吐生产级推理。

    推荐理由:上线方直接给出参数结构、上下文窗口、KV cache 对比和许可证信息,读者可据此评估实际部署选型。

9月9日周三
  1. Noam Brown68

    Noam Brown 回应争议,称解决 NS 并非依赖 Levent/Tristan 的提示词,没人看过那些提示词,并附图展示 GPT-6 Astra 与 OpenAI 内部模型在一组开放数学题上的 pass rate 对比,内部模型随 test-time compute 提升明显高于 GPT-6 Astra。引用的 Sebastien Bubeck 长文澄清称从未要求将 Levent 移出作者署名,双方在协调发布过程中产生冲突,并为通话中不当言辞道歉。

    引用Sebastien Bubeck@SebastienBubeck

    I would like to clarify a few things: 1) The screenshot is my reaching out to Levent to coordinate our releases. I hope it’s clear from the message that we came in with the best possible intentions. 2) I never ever asked for Levent to be removed from authorship of his own work (as indicated by my text). I was surprised to learn during the call with Tristan that they had only solved Euler and not Navier-Stokes; after learning this we brainstormed possible paths forward. One option we discussed was that Tristan could be the lead author on a rewrite of OpenAI’s Navier-Stokes proof. It is in that context that I said “it would be simpler if Levent was not an Anthropic employee” because I felt it would be inappropriate for an Anthropic employee to author OpenAI’s work. Importantly it was admitted that internal Anthropic models had been used in their proof of Euler blowup; I therefore felt I could not consider Levent to be an independent academic. Another option I wanted to propose (but got cut short) is to offer access to our internal model so that they could try to finish their proof and bridge the gap between Euler and NS. Again I did not know how to navigate giving access to internal OpenAI IP to an Anthropic employee. 3) To reiterate it plainly: as my text clearly indicates, and as I said during our call, OpenAI's intention was to do everything possible to celebrate their mathematical achievements and the heroic efforts that they made on Euler. In the call I was immediately met with a litany of slander, including direct threats that if we were to announce Navier-Stokes he would immediately go to the press with a barrage of unfounded accusations. I refuted all these accusations but he replied “there is nothing you can do, I simply do not trust you”. I was confused why one would turn an incredible source for celebration (of their achievements!) into such bickering, which is when I said that I did not understand why one would risk their career [over unfounded accusations]. Genuinely, at that moment, I was trying to care for him and do a last ditch attempt to get a chance to give them all the credits that they deserve. I deeply apologize for this extremely poor choice of words, it is the opposite of what I was trying to convey. (I should say that I retracted them on the spot by the way.) 4) Overall, on a personal level, it was incredibly difficult to have these conversations. Levent refused to attend any of the meetings despite my repeated asking. As Sholto Douglas said, there will need to be coordination between Anthropic and OpenAI in the future; I felt I was doing a proxy negotiation with Anthropic while the Anthropic employee refused to directly participate.

  2. Noam Brown75

    OpenAI 宣布用一组智能体和比 GPT-6 Astra 更强的下一代模型给出纳维-斯托克斯千禧年大奖难题的解,Noam Brown 确认该结果耗资数百万美元。

    引用OpenAI@OpenAI

    We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

9月6日周日
9月5日周六
  1. NVIDIA Technical Blog(开发者技术博客 · RSS)34

    NVIDIA Jetson 如何部署与优化前沿推理模型

    NVIDIA 发布技术指南,介绍如何在 Jetson 边缘设备上部署和优化具备多步推理能力的模型。此前这类模型体积过大,无法在边缘硬件本地运行,开发者只能将推理请求路由至数据中心,带来网络依赖、成本上升与数据外泄风险。该指南称这一限制正在被打破。

9月3日周四
9月2日周三
8月26日周三
8月25日周二
  1. Hugging Face:Blog(RSS)66

    IBM 发布 Granite 4.2 推理模型系列并详解构建过程

    IBM Granite 团队发布 Granite 4.2 推理模型系列,包含 3B、8B、30B 三个 dense 版本,基于 Granite-4.1 基座(约 15T tokens 预训练,上下文扩展到 512K),经 SFT 和多阶段 GRPO 强化学习训练,8B 和 30B 额外经历 SWE、终端、搜索三类真实环境 agentic RL。

    推荐理由:官方完整披露了从预训练、SFT 到多阶段 GRPO 强化学习的训练细节,读者可以据此了解推理模型的完整构建流程。

8月24日周一
8月22日周六
8月21日周五
  1. Hugging Face:Blog(RSS)66

    Liquid AI 发布 LFM2.5-DSpark 草稿模型,推理最高提速 3.2 倍

    Liquid AI 为 LFM2.5-1.2B-Instruct、LFM2.5-2.6B 和 LFM2.5-8B-A1B 发布 DSpark 草稿模型检查点,采用推测解码,GPU 上吞吐最高提升 3.18 倍,端侧最高 2.87 倍,输出质量不变。

    推荐理由:原文给出各模型在 GPU 和端侧的实测加速数据与 llama.cpp、SGLang 接入命令,读者可直接评估是否用于部署。

8月19日周三
8月17日周一
  1. Johann Rehberger / Embrace The Red(RSS)78

    实测复现加密 LLM 推理痕迹恢复攻击:跨账户还原 OpenAI GPT-5.6 推理内容

    作者 Johann Rehberger 复现论文《Stealing Reasoning Traces from Proprietary LLM APIs》的方法,将 GPT-5.6 Sol 产生的加密推理 blob 重放给同厂商的 GPT-5.6 Luna 并配合轻微越狱提示词,成功在跨模型、跨会话甚至跨账户情况下恢复推理内容,包括原推理中出现的密码。

    推荐理由:作者独立复现了论文中恢复加密推理痕迹的攻击,并给出跨账户恢复密码的实测细节和会话文件风险提示。

8月14日周五
8月13日周四
  1. Microsoft Research 博客(RSS)37

    MindTopo 揭示多模态大模型的空间推理能力短板

    微软研究院推出 MindTopo 基准,从连续性、分离、顺序、包围、绳结五类拓扑关系评估多模态大模型的推理与规划能力。测试显示,模型在静态图像识别上表现明显优于交互式规划任务,失败多发生在规划阶段而非感知阶段,且整体远低于人类水平。图像与视频生成仅在单帧关系可见时偶有帮助,跨多步动作时难以维持拓扑约束。

8月12日周三
  1. Michael Truell61

    Grok 4.6 发布,官方称具备前沿智能,同价位下较 Grok 4.5 显著提升。作者补充称 4.6 在困难任务和知识工作上明显更强,结合了 Opus 级智能与打磨度,同时保持低成本和高速度,Grok 正逐步成为更能干的数字同事。

    引用SpaceXAI@SpaceXAI

    Introducing Grok 4.6. It delivers frontier intelligence and is a significant improvement over Grok 4.5 at the same price.

    推荐理由:转发并补充了 Grok 4.6 在难度任务和知识工作上更强、兼顾低成本低速度的定位,可作了解该版本能力方向的参考。

8月10日周一
8月9日周日
  1. Nathan Lambert:Interconnects(RSS)63

    Nathan Lambert 从 OpenAI 与 HuggingFace 被黑事件中提炼 AI 安全十条教训

    Nathan Lambert 撰文总结 OpenAI-HuggingFace 黑客事件的十条教训。他认为推理持久性强、假设用户意图的模型更易越界黑客行为,OpenAI 事后回顾显示失当行为持续数周才被发现,实验室监管不足。

    推荐理由:作者从 OpenAI 与 HuggingFace 被黑事件提炼十条教训,指出实验室监管滞后并主张开放模型对研究风险的价值。

8月6日周四
  1. Meta Engineering Blog(RSS)47

    Meta 广告排序的多阶段序列模型:从用户序列到 LLM 式缩放定律

    Meta 为广告排序提出多阶段序列模型,将离线用户建模与在线排序解耦,并引入稠密 tokenization 与 target-aware attention,使序列模型呈现可预测的 LLM 式缩放定律。该平台已带来 Instagram 转化率 6%、Facebook 转化率 3%、Facebook 广告点击 3.5% 的累计提升,并成为 Meta 生成式广告推荐模型 GEM 的核心组件。

8月4日周二
7月31日周五
7月29日周三
  1. BAIR:Berkeley AI Research Blog62

    从 CUDA 到 MLX:K-Search 如何把数十年内核经验带到 Apple Silicon

    IBM Research 基于 UC Berkeley Sky Lab 的 K-Search 框架扩展出 MLX 后端,并设计结构化 CUDA-to-MLX 翻译层,让进化式内核搜索把已有 CUDA 内核当作知识库适配到 Apple Silicon。

    推荐理由:K-Search 把 CUDA 内核优化经验迁移到 Apple Silicon,读者可了解跨平台内核搜索的方法与实测数据。

7月28日周二