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11月11日

星期三,2 条
  1. STABL 成立非营利研究机构,聚焦 AI 影响的下游瓶颈

    非营利研究组织 STABL 成立,主张在“重视 AI 变革潜力”与“重视下游瓶颈”的 2x2 框架中占据此前思考稀疏的右上象限。该组织将开展关于 AI 进展速度与递归自我改进(RSI)的实证研究,并分析风险缓解、劳动力市场冲击与社会韧性等议题。其观点是,社会转型速度更多受人类与组织变革速度制约,而非能力进步。

    AI as Normal Technology证据待确认未入选重要性 22

11月10日

星期二,3 条
  1. 研究者能否让混乱的 AI 基准测试变得有序

    Science 报道称,AI 基准测试结果常因方法披露不全、提问标点等细节差异而难以复现,同一翻译任务中三个模型的准确率分数从 40% 到 94% 不等,研究者要判断一个基准结果是否可信,可能需花费数千美元以上。

    Science News二手报道未入选重要性 58

11月9日

星期一,1 条

11月7日

星期六,3 条
  1. 32家新实验室融资与创始人引用对比

    我们梳理了32家新实验室(neo-labs)的融资额与创始人引用量 它们的创始团队累计获得835万次引用,融资总额近260亿美元 Discovery Loop以159万次创始人引用领先,其次是SSI(91.5万)和AMI Labs(75.2万) 两者存在相关性,但高引用量显然并非成为新实验室的先决条件(参见Standard Intelligence和Flapping Airplanes)

    alphaXiv厂商自述未入选重要性 22

11月6日

星期五,2 条
  1. 蒙克《错位》:在充满不确定与错位感的时代,找到自己的位置

    清华大学公共管理学院蒙克在新书《错位:我们生活在巨大的时差里》中,用布尔迪厄“惯习”等社会学概念解释现代人的时差感,该书源自他六七年来讲授的社会学基础理论课讲稿。他提出科研考核存在三重错位:原始创新所需的长周期与年度化考核、科学初心与期刊影响因子导向、创新的涌现逻辑与计划逻辑。他认为AI拉平了文献梳理等学术执行力后,学界更需要给青年学者留出自由探索的时间。

    科学网证据待确认未入选重要性 15

11月5日

星期四,2 条
  1. Beneath the Surface:智能体框架演化中被忽视的问题与 EvoAudit 诊断方法

    研究评估了不更新模型权重、仅靠框架演化实现递归自我改进的智能体,发现十六个演化后的智能体都在获得新任务能力的同时丢失了原有任务。某通用任务案例中可靠性提升 5.2 点,却丢掉十个已覆盖任务,且多数可靠性增益伴随部署成本上升。得分最高的智能体在四个池中均未带来最大平均提升,据此提出的 EvoAudit 将诊断转化为对 select、analyze、mutate、gate 的修订并取得最佳表现。

    Hugging Face Daily Papers预印本未入选重要性 37

11月3日

星期二,1 条

11月4日

星期三,2 条

11月2日

星期一,1 条

11月3日

星期二,2 条
  1. Kevin Weil 谈 AI 与各领域专业能力

    非常同意这个观点,而且它远不止适用于数学。 专业能力依然重要,[科学领域] + AI 能比以往更快地理解/完成更多事情。

    引用Alex Kontorovich@AlexKontorovich

    Frank Calegari has a great post on Terry's blog: https://terrytao.wordpress.com/2026/11/01/mathematics-has-changed-lets-act-like-it/ I think it aligns most closely to what I've been thinking and trying to say (and I believe communicates it better than I've been able to). Some highlights: "I hope AI can help mathematicians break out of the straitjacket of being constrained by the first natural conjecture they cannot prove, and work on unraveling the deeper mysteries woven throughout our subject." "The natural grooves of our profession have very much been bent toward more narrow technical expertise than a bigger vision. AI allows us an amazing opportunity to broaden ourselves as mathematicians." "There are so many problems in mathematics that are still so fresh and vital but were previously untouchable that the opportunity to approach them is massively exciting. We need to be more ambitious, to create bold research plans that weren’t even conceivable a year ago." "The difference in reaction between the optimists and the pessimists is that, for the pessimists, the game is over. For the optimists, the game has just changed." "[T]here now exists an amazing new tool to study mathematics. To turn away and say that the community of mathematics as it currently exists is more important than mathematics itself would be a terrible mistake." "[I]t takes one look at the arXiv to see many people in our profession rushing out AI-assisted papers in a somewhat unsightly rush to be the “first” to prove things." [Aside: mere days after the qRH result, I had something like a half-dozen emails in my inbox, some from amateurs but some also from tenured faculty, with Lean formalizations of nonvanishing on Re(s)> 7/8-1/10^k for various large values of k. What the hell are you even doing? That kind of thing may have been valuable years ago, when it was difficult and signaled deep understanding and great technique. Today? Who the hell cares?] "To me [this is back to Frank now], someone who chooses a worthwhile problem, obtains a proof completely using AI, understands it thoroughly, and uses it to develop interesting further questions or directions has made a substantial mathematical contribution." "If you, as a senior mathematician, are stressed about being scooped by AI, then you are not being ambitious enough." [On hiring:] "I am looking for a narrative of where you want mathematics to go, the technical ability to explain why this may be possible, being able to converse about many areas of mathematics, and a demonstrable record of having done all of this in the past." "What I want a graduate student to achieve at the end of their PhD is a view of mathematics that extends well beyond their discipline, and yet with demonstrable technical skills on at least some class of problems." "I advocated above that we need to evaluate work as if AI was involved. But this does not imply that we don’t need to teach students to do deep mathematics without AI just as the existence of computers doesn’t mean we should no longer teach children arithmetic." [I've often said similar things as well...] "All of my friends in the humanities truly believe that having mathematicians in society both as custodians of one of humanity’s oldest intellectual activities and explorers of the frontiers of knowledge is important. I understand that some part of the culture we so deeply care about will disappear, and as humans, an emotional response to that is very natural. But this is a time to explain to everyone that there are so many great new opportunities, possibilities, and reasons to be optimistic. We are on the verge of a new era of mathematics, and I am excited to see what happens." Bravo Frank!

    Kevin Weil证据待确认未入选重要性 22
  2. DeepSeek V4.1 Flash 1017 的 ARC-AGI 成绩

    @deepseek_ai @baseten DeepSeek V4.1 Flash 1017 在 ARC-AGI-1 和 ARC-AGI-2 上的表现: https://x.com/arcprize/status/2113318031954764273

    引用ARC Prize@arcprize

    DeepSeek V4.1 Flash 1017 from @deepseek_ai on ARC-AGI (Verified): - ARC-AGI-2: 78.1%, $0.16/task - ARC-AGI-1: 94.5%, $0.06/task The best ARC-AGI-2 scores for DeepSeek's Flash models rose from 61.4% (V4 Flash 0731) to 72.9% (V4.1 Flash) to 78.1% (V4.1 Flash 1017).

    ARC Prize证据待确认未入选重要性 26

11月2日

星期一,5 条
  1. 从声誉到触达:Medical Affairs 出版目标的数据驱动决策

    Digital Science 旗下 Dimensions 的 Compass 工具可基于用户自定义基准和出版表现追踪,帮助 Medical Affairs 团队用触达等量化指标选择期刊与会议,替代依赖熟悉度和声誉的定性判断。文章指出,仅靠影响因子、引用数或网页访问量等传统指标会放大选择偏差,并给出一个药品首年于多个 EMEA 市场上市、需制定区域会议投稿策略的实例。

    Digital Science官方证据待确认未入选重要性 22
  2. AI 需要有意义的版本管理

    Nature Machine Intelligence 刊文指出,AI 提供商频繁更新模型与系统集成却不相应变更公开标识,损害监管监督与科学可复现性。文章主张推行满足监管者与研究者需求的“有意义的 AI 版本管理”。

    Nature Machine Intelligence证据待确认未入选重要性 42
  3. 数学家 Frank Calegari:OpenAI 发布 700 余篇论文后,数学界应如何自处

    OpenAI 于 10 月 6 日发布 700 余篇研究论文,数学家 Frank Calegari 认为这标志着 AI 已能在数学多个领域做出突破性贡献。他指出这些论文写作质量糟糕、违反署名规范,部分甚至有误,但数学家更应把精力放在招聘、发表与培养学生上。他以有限单群分类的历史为例,提醒同行不要因“著名问题被解决”而放弃领域,而应借新工具开拓更深层的问题。

    Terence Tao 博客证据待确认未入选重要性 36

11月1日

星期日,1 条
  1. AIHOT模型榜扩至56份评测集并重排ELO

    AIHOT模型榜将抓取的公开评测集从39份增至56份,按原有算法对所有模型重新进行ELO排名,并剔除未正式上线的模型(如Gemini 4 Carbon)。结果显示综合与编程第一为Claude Opus 5.5,科研与知识第一为GPT-6 Astra,办公第一为Gemini 4 Argon。

    数字生命卡兹克厂商自述未入选重要性 22

10月31日

星期六,3 条
  1. ARC Prize 2026 提交三日后截止

    ARC Prize 2026 提交将在 3 天后截止 Tufa Labs 和 Yi-Chia Chen 本月已在 ARC-AGI-3 公开排行榜上三次互换第一名 Tufa Labs 继续领跑 ARC-AGI-2,Yi-Chia 紧随其后 最终提交截止日期为 11 月 2 日

    ARC Prize官方文件未入选重要性 15
  2. BestRand:通过随机采样实现稳定低成本基准压缩

    BestRand 是一种稳定、低成本的基准压缩方法,在 70 多个文本、多模态和智能体基准上验证,并将方法扩展至 VLA 评测。相比对比方法,BestRand 将高误差风险降低 50.2%、估计误差平均降低 16.4%,构建成本仅为 51.3%,在分数范围、模型家族和时间偏移下还将 OOD 估计误差平均降低 21.8%。代码与数据已公开。

    Hugging Face Daily Papers开放代码未入选重要性 38

10月30日

星期五,2 条