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10月16日周五
  1. Rohan Paul61

    Google 量子 AI 团队首次在物理硬件上演示魔力态培育,在 105 量子比特 Willow 处理器上错误率较以往方法降低约 40 倍,保留下来的态达到 99.99% 的逻辑保真度,成果发表于 Science。该方法会对每个态进行筛查并丢弃不合格尝试,仅约 8% 的尝试通过筛选,且仍需在更大规模上验证;作者称这是通往纠错量子计算机的一步。

    引用Sundar Pichai@sundarpichai

    Some magical progress towards "magic state" in quantum! Our @GoogleQuantumAI team has demonstrated "magic state cultivation" on physical hardware for the first time. The result is a 40x error reduction on our Willow processor vs. previous methods, achieving a state-of-the-art 99.99% logical fidelity. It's another step towards useful, error-corrected quantum computers to help solve the hardest problems. The result is published in Science today, watch the video below to see how it works

  2. Apple Machine Learning Research43

    Apple 提出 SSR:用选择式结构化推理打造高效多模态搜索智能体

    Apple 研究团队提出 Selection-based Structured Reasoning(SSR),将推理从开放式生成改为从预设可复用自然语言候选中选择,无需辅助任务头。在 2B 和 4B 模型、七个多模态搜索基准上,SSR 平均成功率与同规模领先搜索智能体相当,每轮推理延迟降低超 90%,单题模型总推理延迟降低 28–54%。

  3. LlamaIndex:产品、工程与评测47

    LlamaIndex 发布 ExtractBench Pro,聚焦文档抽取的长尾难题

    LlamaIndex 发布 ExtractBench Pro,这是基于真实长尾文档构建的 ExtractBench 扩展评测集,包含 100 份合成文档、六大类新挑战。该数据集由确定性流水线从种子和配置文件生成,覆盖图表取值、跨页规则、内部术语、图示关系等场景,刚性流水线表现吃力,而配备合适工具的智能体系统更能适应。

  4. OpenAI:部署安全与系统卡27

    OpenAI 发布 GPT-Live-1.1 系统卡

    OpenAI 公布 GPT-Live-1.1 与 GPT-Live-1.1 Quip 的系统卡,说明两者训练数据来自公开互联网信息、第三方合作数据以及用户和人类训练师提供或生成的数据。数据处理流程包含严格过滤以保障质量并降低风险,同时用安全分类器减少有害或敏感内容的使用。

  5. Microsoft Research 博客59

    Microsoft Research 发布 zkVM 系统 Magnetar,比现有最快系统平均快 10 到 39 倍

    Microsoft Research 发布零知识虚拟机 Magnetar,可直接证明用 Rust、Go 或 C 编写的 RISC-V 程序,在 15 个基准程序上平均比现有最快 zkVM 快 10 到 39 倍。在笔记本电脑上每秒可证明 720 万至 870 万条处理器指令,证明约 12 毫秒内可验证,安全性只依赖 SHA-256 或 SHA-3 等标准哈希函数,无需受信任设置或椭圆曲线。

10月15日周四
  1. Rohan Paul55

    ETH Zurich 等机构团队提出 LLM-to-Flow Distillation(LFD),让科学生成模型直接追随自然语言发现目标,如设计具抗癌活性的多肽。

    引用Riccardo De Santi@r_de_santi

    We’re bringing natural-language control to any scientific generator. We are very excited to introduce LLM-to-Flow Distillation (LFD), a method to adapt scientific generators to high-level natural-language discovery goals. Starting from textual goals like “design peptides with promising anticancer activity” or “design small molecules binding to the D2 dopamine receptor”, LFD distills high-level goal-related knowledge from LLMs into scientific generators through iterative DPO on LLM-generated sub-goals. 🔥 LFD does not need task-specific proxy rewards, works with any frontier LLM via API-access, and outperforms state-of-the-art reward-guided methods such as FlowGRPO and DiffusionNFT on held-out computational evaluations. (1/6)

  2. 公众号:小红书技术48

    小红书 AllSpark 提出 CompoWorld:用组合式环境扩展通用 Agent 训练

    小红书 AllSpark 团队提出 CompoWorld,通过组合可复用服务来扩展通用 Agent 的训练环境与任务空间,构建了 448 个服务、10,130 个工具。团队用 3K 条 SFT 轨迹和 1K 个 RL 任务训练 Qwen3.6-35B-A3B,在八项 Agent 基准上平均提升 9.17 分,AutomationBench 成功率从 10.33% 升至 32.33%。

  3. IT之家·人工智能72

    上海人工智能实验室团队用书生 S2 大模型完成三维 Ghys 猜想证明

    上海人工智能实验室发文宣布,许地生团队依托书生科学多模态大模型 S2,通过人机协同完成困扰数学界 30 余年的三维 Ghys 猜想证明。北京大学董彬、刘若川院士团队用 SHEAF 框架完成形式化验证,论文已发布于 arXiv,形式化代码开源在 GitHub。上海人工智能实验室表示该范式可推广至其他搜索空间大而专家稀缺的基础研究领域。

  4. meng shao53

    Kombai 发布 Design Creativity Bench,用 80 个开放式 UI 任务评估模型设计创造力,最佳模型 GPT-6 Astra 得 65.5,人类参考为 92.3。

    引用Dipanjan Dey@Dipanjan_Dey

    ⚠️ Frontier AI models still fall significantly short of human designers on design creativity. ⚠️ Introducing Design Creativity Bench, a new benchmark evaluating AI models’ creativity on UI design tasks. Despite rapid advances in their ability to generate functional websites and product UIs, leading AI models still struggle to make creative design decisions. We evaluated 16 frontier models across 80 open-ended UI tasks, each adapted to two distinct product domains alongside human designer references. Existing UI benchmarks often measure a single output quality metric. Instead, we measure three dimensions of design creativity: - Originality: How distinct a model's designs are from other models' outputs for the same brief. - Creative Range: How much a model adapts its design decisions when the product domain changes. - Appropriateness: How well the generated UI satisfies the functional requirements of the brief. The results reveal a substantial gap in overall creativity: The best model, @OpenAI GPT-6 Astra, scores 65.5 against a human reference of 92.3. We're publishing the full leaderboard, methodology, and reproducible evaluation framework to make design creativity a measurable dimension of frontier model capability. 📊 Benchmark Leaderboard: https://kombai.com/benchmark/ 📄 Paper: https://arxiv.org/abs/2610.11539 🤗 HuggingFace Datasets: https://huggingface.co/datasets/Kombai/design-creativity-bench 🔗 Github: https://github.com/kombai-io/design-creativity-bench/

  5. Rohan Paul55

    White Circle 开源了 RISE,一个自动化红队测试算法,用于检测 AI 图像生成器能否被诱导生成其规则禁止的图片。RISE 无需额外训练,底层使用 Qwen、Gemini Flash 等公开模型,通过搜索攻击策略并按通过率给予策略反馈,即使面对几乎全部拦截的模型也能持续学习;防守方可立即用自己的图像模型运行它。

    引用White Circle@whitecircle

    We are open-sourcing RISE, our red-teaming algorithm that breaks the safeguards of the latest image models from OpenAI, DeepMind, and others. RISE uses readily available models like Qwen and Gemini Flash under the hood, with no additional training required. Read more: https://whitecircle.com/research/breaking-frontier-image-models

  6. Meta AI:Research Blog54

    Meta 发布 Muse 模型辅助健康科研探索及十篇研究论文

    Meta 介绍其人类主导的 AI 科研系统探索,使用 Muse Spark 1.3 与 Muse Wonder 构建编码脚手架,访问五百万份去标识电子健康记录,医学研究者四周内完成十篇覆盖传染病、肿瘤、内分泌等领域的研究手稿。每篇论文经过医学同行反馈、生物统计学家审查和第三方独立专家评审;作者称最快七天内从首个提示词完成手稿,最佳情况下效率提升至传统流程的十倍。

  7. Dongxi 东锡 NLP30

    推荐阅读! Hybrid Reasoning Agents

    引用Jiaru "Rubin" Zou@Jiaru_Zou

    Your LLM agent has to think out loud, token by token, before it can search, run code, or act in the world. We humans don’t. We ponder in silence and speak when it matters. Human language was never meant to be the limit of machine thought. Introducing 🌗Hybrid Reasoning Agents - Think in latent space, act through text - Two modes, one unified multi-turn flow - No black box, fully readable latent thoughts - Any LLM can become a HybridAgent

  8. dex56

    ianlivingstone 等社区成员发布 Agent Baseline Software Factory 参考架构首版草案,用于保护日益自主的软件工厂,已在 agentbaseline.org 开放反馈与贡献。

    引用Ian Livingstone@ianlivingstone

    Today we're publishing the first draft of the Agent Baseline Software Factory Reference Architecture! An opinionated blueprint for securing increasingly autonomous software factories, built with folks across the community. It's open for feedback and contributions! https://agentbaseline.org/reference-architecture

  9. Hacker News:AI 热帖44

    为什么 LLM 生成的鹈鹕长这样?

    一项新基准 Design Creativity Bench 用 80 个开放式 UI 设计任务评测 16 个前沿模型,从原创性、创意跨度和合理性三个维度衡量设计创造力。表现最好的 OpenAI GPT-6 Astra 得分 65.5,人类设计师参考分为 92.3,差距明显。该基准的排行榜、方法论与可复现评测框架已公开。

  10. Dongxi 东锡 NLP59

    White Circle 团队开源红队算法 RISE,可对 OpenAI、DeepMind 等最新图像模型进行 jailbreak。文中称 GPT-Image-2.5-Flare 被成功攻击的概率约为上一代的 3 倍,Nano Banana 2.1 约为上一代的 2 倍。

    引用White Circle@whitecircle

    We are open-sourcing RISE, our red-teaming algorithm that breaks the safeguards of the latest image models from OpenAI, DeepMind, and others. RISE uses readily available models like Qwen and Gemini Flash under the hood, with no additional training required. Read more: https://whitecircle.com/research/breaking-frontier-image-models