AI 新闻 · V2

根据《数字服务法》，我们已将ChatGPT指定为超大型在线搜索引擎，并将Reddit与Roblox指定为超大型在线平台。 它们现有四个月时间履行额外DSA义务。 详情见：https://ec.europa.eu/commission/presscorner/detail/en/ip_26_1772 #DSA

我深挖了一圈：Anthropic正因为这件事被起诉。而且200美元套餐的每周用量，实际上大约只是100美元套餐的2倍。 混淆从这里开始：官网页写“比Pro多5倍或20倍用量”，让人以为200美元的Max 20x是100美元Max 5x的四倍。 更下面才写明：倍数只针对每5小时会话，并不适用于单独的每周上限，且每周上限具体数字未公开。 6月提起的集体诉讼称：Max 5x每周大约只有Pro的3.5倍，Max 20x大约6–8倍；因此200美元套餐每周用量大约只是100美元套餐的2倍。 “20x”在每个5小时窗口内成立，但每周上限让整体观感完全不同。 （感谢@Hesamation率先找到原始诉状）

等一下……你是说Claude Max的x20只适用于5小时会话额度，而不是每周额度？

下周我们该发什么？

澄清一下：虽然两边都叫20X，但在Codex里，这个倍数专门作用于每周用量上限；而且我们的两档Pro套餐都没有5小时限制。 Pro 20X基本就是Plus订阅用量的精确20倍，名副其实。

昨天其实想说的是：我们已达到2500万活跃用户。为了庆祝，现已重置所有ChatGPT Work与Codex付费订阅的用量额度。 更多来自“重置公司”的消息，稍后再见。

是的。而且那些还在发 AI 生成邮件的人真是彻底疯了。

出色的长帖。读一读它（以及我上一篇 Substack 文章），可以给 Dwarkesh 的叙事提供亟需的平衡。

很好的指南。而且 AI 实验室不该再让 Simon（以及在较小程度上的我）来解释他们发布的各种模式该怎么用。 拜托实验室的人，你们有能写文档的 AI！能做解说！能当导师！你们完全做得到！

到明年年底，AI 将能以超人类水平完成任何数字化工作（不需要塑造原子的那种）。

TechCrunch梳理美国近期将外国先进机器人纳入限制范围并对进口无人机及零部件加征关税的影响；Counterpoint数据显示，2026年上半年全球出货量前五的人形机器人厂商均来自中国，合计占86%。美国限制措施及中美机器人出货差距亦获AP等独立来源支持。

攻击者利用Vidar、Lumma、StealC、RedLine、Acreed及AMOS等通用信息窃取程序取得Claude活动会话；Anthropic已强制退出受影响会话、移除支付方式并处理未授权费用。BleepingComputer此前获得的用户通知与SecurityWeek报道一致；现有证据显示感染源位于用户设备，并非Claude平台自身遭入侵。

SLB将支付约34亿美元现金并承接约7亿美元债务，收购数据中心热管理和换热设备商Kelvion；双方2026年数据中心业务预计合计收入超过20亿美元。交易信息已由MarketScreener及SLB提交的监管文件交叉确认。

HUMAIN已在沙特上线基于AMD Instinct MI355X GPU、EPYC CPU和Cisco Silicon One网络的生产级AI基础设施；下一阶段拟自2027年起部署最多250兆瓦，合资项目目标是在2030年前达到1吉瓦。MarketScreener、Arab News等独立报道与官方公告相互印证。

Forced alignment evaluation typically requires manually annotated timestamps, limiting large-scale and multilingual analysis. We introduce two corpus-level metrics based on self-supervised (SSL) speech representations for reference-free forced alignment evaluation: Phoneme-Cluster Mutual Information (PCMI) and Word Acoustic Consistency Score (WACS). PCMI measures agreement between aligned phoneme

When training Mixture-of-Experts (MoE) language models with expert parallelism, all-to-all token dispatch and combine collectives can consume a substantial fraction of end-to-end training time. In this work, we study communication-efficient MoE models (CE-MoE), in which we adopt a heterogeneous layer pattern that decouples token-mixing and channel-mixing depth. Compared to conventional models whic

Neural operators provide fast surrogate models for approximating operators between function spaces, but their predictions often lack uncertainty quantification. We develop a split conformal framework to guarantee that a calibrated pointwise band around the neural operator output contains the true solution on at least a $1-γ$ fraction of the evaluation domain, with probability at least $1-α$ over t

We investigate whether automatic speech recognition (ASR) errors in user input can lead to unsafe outputs from Embodied AI (EAI) models. We find that ASR errors can lead to harmful instructions being accepted and executed by EAI models, thereby reducing safety. We simulate ASR errors and combine them with existing safety benchmarks (SafeAgentBench and POEX) to evaluate how different errors affect

Texture image classification plays a significant role in computer vision applications, including industrial inspection, medical image analysis, remote sensing, and object recognition. Handcrafted features can capture local texture characteristics but may have limited capability to represent complex visual patterns. In contrast, deep learning models automatically learn discriminative representation

Recent advances in generative AI allow users to create 3D models from text or images. However, these models prioritize visual plausibility over geometric accuracy, often generating results with flaws that compromise their intended use post-fabrication. We present InstructMesh, an interactive post-generation refinement tool that enables selective repair of generative 3D models through region select

A code world model accepted by a sampling gate can be exactly right on everything the gate can see and arbitrarily wrong beyond it. We characterize what a certified model can know, and what its errors can cost, when the omission is an annular freeze mode enclosing an unreachable interior. The gate quotient makes the question precise: acceptance-with-certainty determines the model exactly on the re

Model merging combines multiple task-specific fine-tuned LLMs into a single multi-task model without additional training. However, merged models are known to suffer from representation bias: systematic drift between the merged model's hidden states and those of each individual source model. Prior work (Yang et al., 2024a) study and mitigate this bias for encoder-based vision models using a lightwe

Recent generative approaches to geometry estimation adapt pretrained image diffusion models and treat the task as image-conditioned generation. Leveraging off-the-shelf image diffusion models, they either (i) train task-specific geometry models (for depth and surface normal estimation) independently, losing the opportunity of exploring the intrinsic correlation of these geometric targets, or (ii)

As a precursor to high-dimensional biomedical data modeling, reliable feature selection can reduce computational expense, improve modeling performance, and yield simpler, more interpretable models. However, most filter-based feature selection methods struggle to detect feature interactions, while wrapper or embedded feature selection methods are computationally expensive. Relief-based algorithms (

Modern agent systems assemble capabilities at runtime, and this dynamic composition has recently received a complete formal treat ment in the spatiotemporal-composability calculus, in which a capability is a component carrying a tracked inverse, and agents are assembled as plugins. This plugin form is carried by a single process sharing one context, a carrier that places all components in one phys

Neural-network optimization in 2025-2026 is no longer well described as a succession of new Adam variants. The design space has expanded from coordinates to matrices and layers, from fixed training horizons to policies over time, and from mathematical update rules to state representations that must survive sharding and low-precision computation. This survey organizes recent optimizers and training

Can a listener recover what a speaker means from the form of an utterance alone? We answer this question information-theoretically, and for a listener given by any featurizer of text, including the hidden states of contemporary large language models. Modeling language use as a joint distribution over meanings, contexts, and utterances, we derive upper bounds on the probability that a decoder recov

We study kernel ridge regression under anisotropic Gaussian data, where the input covariance decays as a power law with exponent $α\geq 0$ for polynomial inner-product kernels. We derive asymptotically sharp expressions for the kernel spectrum and the generalization error in the polynomial high-dimensional regime $n=Θ(d^κ)$, revealing how anisotropy reshapes the learning curves. For weak anisotrop

We study the mixing time of weighted Dikin walks for sampling from exponential distributions on polytopes and truncated positive-semidefinite (PSD) cones. Our first result gives a general total-variation mixing bound under strong self-concordance, $\barν$-symmetry, and mixed-trace regularity on the local metric. The key idea is to control the Metropolis--Hastings acceptance probability on a high-p

Synthetic data can improve statistical inference when real data are scarce, but naively treating synthetic samples as real data can introduce bias and lead to unreliable inference. We develop a general framework for synthetic-augmented inference across a population of related tasks. It characterizes synthetic augmentation by the number of synthetic observations and their weight. Central to our fra

Tendon-driven hands are anthropomorphic, and moving the actuators off the joints is what makes a hand of this capability affordable to build. Two effects produce that saving. Routing force through a cable removes the requirement that a motor fit inside the joint it drives, so smaller and cheaper motors suffice, and one motor can drive several joints through a single cable, so fewer motors are need

We propose QGPINNs, a physics-informed neural network framework developed in PyTorch for the numerical solution of nonlocal differential equations on quantum graphs. The framework is designed as a general computational implementation in which the solution on each edge of the graph is approximated by a neural network, while a unified graph-based loss function enforces the governing equations togeth

临界交汇：精神渴求与人工智能的药/毒两面性 - Church Life Journal

金融稳定理事会主席警告前沿人工智能（AI）模型带来的风险 - Financial Stability Board

监管机构称，人工智能驱动的网络风险是全球金融稳定的首要担忧 - Reuters

财政部向查默斯颁发AI生产力奖——但附带条件 - Politico

AI专家分析比尔·盖茨关于人工智能危险的文章 - News4JAX