AI 新闻 · V2

之所以「没有关于实际发生情况的地面真相信息」，是因为你们这家「开放」公司不愿公开自己在做什么；但我们同意需要监管来确保透明度。

3.8 并不是世界上最强的模型，但相当强，而且表现超出其体量！

Fable 5.1：「根据《伊利亚特》的船舶目录创建地图等。我要能以 3D 探索每艘准确的船；要有真实图像与考古数据可供调用；用智能体测试以确保准确与美观，然后修订。」https://homer-catalogue-of-ships.netlify.app/

所有 AI 最终都会收敛到符号学习（即通过寻找能解释数据的最短符号程序来建模），因为那是最优高效的 AI 形式。但这并不意味着只有一条通向该终点的进化路径。

非常高兴分享：我们打算与 NVIDIA 联手，以 12,930,300,000 美元完成收购。Hugging Face 创立十年后，开源 AI 正处在拐点。感谢社区，我们已经证明开源可以成为闭源 API 的补充甚至替代；但要更大规模落地，需要更多算力、支持、协作与可见度。因此我们去找了黄仁勋，他正提出与我们一起这样做。除了加码 NVIDIA 对开源 AI 的巨大贡献（我今年早些时候称他们为「美国开源 AI 之王」），他们承诺大力支持 Hugging Face 与我们的使命，同时保持平台开放、独立且算力无关。创始人与团队都会留下，继续推进这一使命。我们共同认为可以把开源变成构建 AI 的默认方式，目标是赋能 1 亿 AI 开发者拥有自己的智能，而不是租用智能。对未来十年充满期待！

奥尔特曼称审查过程富有成效，并表示随着模型能力增强，实验室与各国安全机构的接触将更重要；Axios称Astra达到OpenAI所定义的关键网络能力等级，但此次审查确认暂未获得第二家独立信源直接证实。

法案拟要求建立可持续验证智能体行为、防篡改操作日志和机器可读智能体清单等标准，并推动联邦承包商遵循；目前只找到Axios独家报道，尚未检索到议员官网或国会文本。

NVIDIA正式宣布收购Hugging Face，称平台仍将支持各家模型、框架、云服务和计算平台，不强制使用NVIDIA算力；AP、Reuters和Axios均在时间窗口内独立报道。

Generative models have been studied experimentally and theoretically as priors for inverse problems such as compressed sensing. Recent work by Gunn et al. studied the use of generative priors with tunable complexity, where a family of generative priors with varying complexity is maintained and a specific complexity can be selected at inversion time. They demonstrated that lower reconstruction erro

Sign language processing systems have traditionally operated at the sentence level, ignoring critical discourse phenomena fundamental to sign language comprehension. We introduce DiscoSign, a computational approach for discourse-aware text to sign language gloss translation grounded in linguistic research. We address three key phenomena within our modular Large Language Model (LLM)-based translati

People increasingly use language models to support life decisions. Many such decisions involve a probabilistic forecast: How likely is a major life event, a natural disaster, or an economic outcome? Users of language models may implicitly trust that these forecasts fall out of a coherent world model. In this paper, we evaluate the coherence of language model probabilistic forecasts through a proce

For more than 20 years, the Model-RB benchmark frb100-40 remained an open challenge; since 2014, its public record had stood at 99 of 100 variables. We give a directly checkable 100-vertex independent set for its 4,000-vertex graph. Together with a verified partition into 100 cliques of size 40, the witness proves that the maximum independent-set size is 100 and the minimum vertex-cover size is 3,

Root cause analysis (RCA) is a critical task in telecom network operations, but diagnosing performance degradations in modern 5G and emerging 6G networks remains challenging due to complex cross-layer dependencies. While large language models (LLMs) offer promising capabilities for reasoning and knowledge integration, directly applying vanilla LLMs to telecom RCA often leads to hallucination, unst

Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for large language model (LLM) post-training, but its reliance on coarse outcome rewards leads to limited guidance on intermediate reasoning processes. Existing approaches such as process reward modeling and on-policy distillation introduce additional constraints, such as reliance on a specialized reward model

Researchers increasingly use artificial intelligence to construct measures of social, organizational, and occupational characteristics that are absent from conventional surveys. We propose AICOME, AI COntextual MEasurement, a framework for evaluating whether AI-derived respondent-level measures can recover individual and group-level effects in contextual models. The key idea is that an AI measure

Meshfree methods such as smoothed particle hydrodynamics (SPH) with kernel corrections, radial basis function-generated finite differences (RBF-FD), and the local anisotropic basis function method (LABFM) construct discrete differential operators by imposing polynomial consistency on a local stencil. For stencils containing more nodes than there are consistency constraints, the resulting linear sy

Stable 4-bit floating-point (FP4) pretraining is difficult because the E2M1 payload represents only a narrow range of magnitudes. NVIDIA's Transformer Engine \nv{} recipe addresses this with current-tensor scaling, a randomized Hadamard transform (RHT), and bfloat16 (BF16) final layers, adding work outside the FP4 matrix multiplications. We instead pair E2M1 payloads with unsigned E5M3 (\ue{}) blo

Competitive programming has become a key test of large language model reasoning, with international competitions such as IOI and ICPC representing its most challenging settings. We present an end-to-end specialization pipeline combining large-scale problem curation, synthetic reasoning traces, supervised fine-tuning (SFT), and reinforcement learning (RL). Using 22,000 curated problems, we train Ne

LLMs are trained to generate natural language. However, various strands of evidence indicate that an LLM's externalized linguistic outputs and mechanistically-extracted linguistic features can be an unreliable lens for understanding internal model computation. We introduce the term ``linguistic illegibility'' to broadly refer to scenarios in which an LLM's externalized or mechanistically-probed la

We study how far gradient descent (GD) can be accelerated by predetermined stepsizes in smooth convex optimization. Going beyond the classical $Ω(n^{-2})$ first-order oracle lower bound of Nemirovsky and Yudin, we prove an $Ω(n^{-1.6342})$ non-anytime lower bound and an $Ω(n^{-1.2408})$ anytime lower bound. These improve the recent $Ω(n^{-1.932})$ non-anytime lower bound of Ma and Chen and the $Ω(

Harnessing naturally occurring feedback from user interactions offers a promising learning signal for Large Language Models (LLMs). However, recent studies suggest this feedback is inherently noisy and difficult to leverage effectively. We challenge this conception by demonstrating that user feedback is a highly actionable signal for improvement, and that its perceived ineffectiveness stems from a

Autonomous robots powered by deep learning face a fundamental auditability challenge: when incidents occur, investigators cannot reconstruct why the system made specific decisions. This paper presents TRACE (Transparent Reasoning Architecture for Credible Execution), a decision framework that ensures every autonomous action can be traced back to sensor evidence through documented causal chains. Th

Ordinary differential equations (ODEs) underlie models in science and engineering, and many applications need derivatives of their solutions with respect to parameters. Ensembles of independent trajectories suit graphics processing units (GPUs), but current GPU software forces a trade-off: the fastest ensemble solvers cannot be differentiated in reverse mode at the speed they solve, and the solver

We introduce the Graph Machine (GM), an architecture that maintains an $O(n)$-sized state and accesses it through sparse, dynamic routing. Unlike methods with fixed-size states or sparse but static routing, GM preserves $O(n)$ complexity in its sparse layers without restricting the potentially accessible state size to $O(1)$. Instead, GM uses edges - pointer-like objects updated differentiably by

Recent web agents use world models for test-time action selection by sampling candidate actions, predicting the resulting web states, and ranking them with a ranker model or a Process Reward Model (PRM). These world models are typically trained via supervised next-state prediction to generate fixed representations like HTML or AXTree snapshots. However, this objective is misaligned with the downst

Speech brain-computer interfaces (speech BCIs) translate neural activity into language, offering a path towards restoring speech for people with paralysis and, more broadly, enabling new forms of natural human-computer interaction. Despite this promise, the field lacks a common measure of progress because systems use different datasets, recording methods, types of speech, and vocabularies, so thei

New approach to enterprise LLM governance: unified intranet gateway control to eliminate exposed API keys and data leakage finance.biggo.com

Your LLM Can Return Perfect JSON and Still Be Wrong Towards Data Science

Agentic AI UW brings next step in artificial intelligence to UW-Madison The Daily Cardinal

Call for papers Museum International: ‘Artificial Intelligence in Museums’ International Council of Museums

AI company scores world’s 1st approval for breast triage tool that skips radiologist review Radiology Business

Child sexual abuse survivor alleges Elon Musk’s AI chatbot used photos of her to generate new illegal images The Guardian

Nvidia Buys Hugging Face in $12.9 Billion Deal The New York Times