2026-09-10

Show-Harness: Just a VLM Agent Can Play Robots

👍 127

09/08 20:00

Foundation vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains challenging. We present Show-Harness, an Embodied Harness that enables VLMs to "play" robots through a compact semantic interface linking intent to action

中文介绍 提出了 Show-Harness，一个具身的控制界面，使得视觉语言模型（VLMs）能够通过简洁的语义接口实现对机器人控制的能力。该方法通过将意图与动作相连接，促进了 VLMs 在机器人操作中的应用。重要性在于，它推动了智能体领域中 VLMs 的实际应用。

AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems

👍 86

09/07 20:00

Large language model (LLM)-based multi-agent systems (MAS) achieve strong performance by employing specialized multiple agents, yet their performance depends on the prompt design of each agent. For MAS prompt optimization, textual gradient methods that guide prompt updates using natural-language fee

中文介绍 针对多代理系统（MAS）中的提示优化问题，提出 AgentGrad 方法，通过文本梯度方法指导提示更新。该方法通过干预指导提升多个代理在特定任务上的表现，验证了其有效性。此研究丰富了多代理系统领域的优化手段，具有广泛的实际应用潜力。

Programmable World Model

👍 81

09/08 20:00

Recent video world models generate increasingly realistic and interactive visual experiences, yet lack reliable mechanisms for maintaining persistent world state and enforcing programmable rules over extended interactions. We introduce Programmable World Model, a framework that decouples world-state

中文介绍 引入了 Programmable World Model，一个框架，旨在解决视频世界模型在持久状态管理和可编程规则执行方面的不足。该框架将世界状态与交互解耦，提供了灵活性。其重要性在于，它促进了长期交互下智能体的稳定表现，将推动机器人技术与仿真领域的发展。

OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining

👍 61

09/06 20:00

World-Action Models inherit world knowledge from video-generative priors, and channel it into executable control signals through embodied experience. Existing systems, however, are monolithic: the generative backbone, visual representation, architecture, information flow, inference procedure, and tr

中文介绍 提出 OpenWAM，一个模块化的探究框架，用于系统化的世界-动作模型预训练。通过将生成骨干、视觉表示等组件解耦，提升了模型在控制信号执行中的效果。此研究为开发灵活、可扩展的世界-动作模型铺平了道路，具有广泛的应用前景。

VDiff-Bench: A Challenging Benchmark for Fine-Grained Image Difference Identification

👍 30

09/04 20:00

Multimodal Large Language Models (MLLMs) perform strongly on general visual understanding tasks such as visual question answering, yet they often struggle with a basic comparative skill: identifying what has changed between two similar images. We introduce VDiff-Bench, a challenging multiple-choice

中文介绍 引入 VDiff-Bench，作为挑战性的基准，用于细粒度的图像差异识别。此基准填补了多模态大语言模型在相似图像变更识别方面的性能差距，为视觉理解任务提供新的评估工具。研究结果对图像处理和计算机视觉领域具有重要意义。

WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data

👍 28

09/03 20:00

Recent advances in wearable sensing enable continuous monitoring of physiological and behavioral signals, yet existing benchmarks rarely evaluate whether AI systems can reason over a real user's longitudinal wearable record. We introduce WearableQA, a benchmark comprising 4,084 10-option multiple-ch

中文介绍 WearableQA 基准的提出旨在评估 AI 系统是否能够对真实用户的可穿戴设备数据进行推理。包含 4084 个选项的多项选择题测试，填补了现有评估标准的空白，为健康监测和个人化医疗应用提供了新的研究方向。

SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agents

👍 19

09/07 20:00

SWE-Bench Pro has emerged as a standard benchmark for evaluating software engineering agents on challenging repository-level tasks. However, our analysis work show that its evaluation is undermined by two sources of unreliability: reward hacking, enabled by leakage of gold solutions or hidden evalua

中文介绍 分析了 SWE-Bench Pro 的评估可靠性问题，揭示了奖励篡改和隐含评估的潜在风险。该研究为软件工程代理的评估提供了新的视角，提高了基准可靠性和有效性，推动了软件工程自动化工具的发展。

SAEScientist-Bench: Can AI Agents Conduct Autonomous SAE Interpretability Research?

👍 16

09/07 20:00

While research on recursive self-improvement (RSI) has predominantly automated model training pipelines, reliable autonomous development demands a missing pillar: post-hoc monitoring and auditing to understand what models learn and ensure safe alignment. Mechanistic interpretability tools are essent

中文介绍 提出了一种机制可解释性工具，以回应自我改进研究中缺乏后期监测的问题。该研究强调在 AI 代理的鲁棒性与调优中，需对模型学习过程进行管理与审计，对确保安全对齐至关重要，进而推动智能系统的理论与实践发展。

Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model

👍 16

09/04 20:00

We present Cadence, an error-bounded lossy compressor for numeric time series pairing a 330M-parameter time-series foundation model (Google TimesFM-3) with an adaptive arithmetic coder, guaranteeing |x_t-x_t|leτ on every sample. One negative result constrains the design space: for lossless coding a

中文介绍 Cadence 作为一种有界误差的损耗压缩技术，将 330M 参数的时间序列基础模型与自适应算术编码器结合，确保样本误差限制在特定范围内。此研究对时间序列数据压缩和处理有重要的意义，尤其在需要实时性能的应用中。

Scores Alone Do Not Prove Discovery: The Discovery Certification Protocol for Auditing AI Research Agents

👍 15

09/06 20:00

AI research agents combine prior knowledge, public sources, and experimental feedback to produce useful results. The Discovery Certification Protocol (DCP) turns claims about these results into executable recovery and feedback tests. Gate 1 validates useful improvement on sealed evaluation. Gate 2 g

中文介绍 Discovery Certification Protocol (DCP) 将 AI 研究代理的能力带入监督框架，验证结果的有用改进。这一协议通过执行可回收测试，确保了研究成果的可信度，对 AI 研究的审计标准化有积极推动作用。

SyncWorld: Visual Calibration Enables World Models as Zero-Shot Simulators

👍 11

09/07 20:00

World models are increasingly used as policy-in-the-loop imagination environments, where reliable rollouts require fine-grained controllability with respect to low-level robot actions. A key obstacle to scaling such models in robotics is that actions are not a universal language in pixel space: chan

中文介绍 SyncWorld 通过视觉校准使世界模型作为零-shot 仿真器得以推广。该方法提升了低级机器人动作与模型之间的可控性，为机器人自动化的决策和执行提供了新的思路，增强了智能体在复杂环境中的适应能力。

A*-Thought-V2: Efficient Latent Reasoning via Geometric Dynamics of LLM

👍 11

09/07 13:56

Chain-of-Thought (CoT) improves the reasoning ability of Large Language Models (LLMs) but incurs substantial computation and context costs. Existing methods either lose intermediate information through hard pruning or lack a principled criterion for continuous compression. We present A*-Thought-V2,

中文介绍 A*-Thought-V2 提出了利用几何动态优化大型语言模型 (LLMs) 的推理能力，降低计算和上下文成本。该方法在保留信息和提升推理效率方面具有明显改进，对 LLM 的推理能力提升具有重要意义。

DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents

👍 9

09/05 20:00

High-quality structured organic reaction data are essential for developing artificial intelligence for chemistry (AI4Chem), yet much of this knowledge remains dispersed across patent text, images, and reaction schemes. We present DianShi-RxnDB, a large-scale, fine-grained organic reaction data platf

中文介绍 DianShi-RxnDB 是一个高质量精细的有机反应数据平台，提升了 AI 在化学领域的应用可能性。该平台通过全自动化流程建立，解决了当前反应数据散布的问题，为化学智能体的研究提供了基础数据支持。

Φ-Bench: Can Large Language Models Engineer the Infrastructure That Powers Them?

👍 9

09/08 20:00

Large language models (LLMs) have demonstrated remarkable capabilities in reasoning and code generation, raising the prospect that they could assist in developing and optimizing the very infrastructure that powers them. However, existing benchmarks mainly focus on isolated kernels, predefined operat

中文介绍 Φ-Bench 评估大型语言模型在基础设施开发和优化中的能力，强调了当前基准对隔离核心的局限性。该研究推动了 LLMs 在基础设施领域的应用潜力，有助于推动智能体的自主开发。

Revisiting Complete Reasoning Traces for Post-Training

👍 8

09/06 20:00

Large language models (LLMs) are often post-trained on pre-collected reasoning trajectories to improve their reasoning capability. Such trajectories tend to be long due to complex, interwoven paths, which often include detours on the path toward the answer. However, it has been underexplored whether

中文介绍 重新审视了 LLMs 的后训练，通过整合完整推理轨迹提升推理能力。研究表明，在路径复杂时优化路径仍需探索，为强化 LLMs 的逻辑推理、提升推理效率提供新方向。

RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting

👍 8

09/06 20:00

Image relighting is traditionally tackled via complex inverse rendering pipelines, which suffer from ill-posed optimization, or single-image generative models that ignore crucial multi-view cues necessary for understanding 3D geometry and material interactions. To address these limitations, we intro

中文介绍 引入 RelightFormer，通过更简单的前馈生成网络，实现针对多视角物体重照明。该方法解决了传统重照明模型的局限，推动了计算机视觉和图像处理技术的进步，对 3D 物体理解至关重要。

SchemeArena: Factorized Stress Testing of Scheming in LLM Agents

👍 7

09/07 20:00

We study scheming in LLM agents, in which agents covertly pursue misaligned goals. Our focus is to understand how scheming arises from the interaction of key factors, such as instrumental goals, environmental affordances, oversight conditions, and perceived consequences. Prior work examines only a s

中文介绍 SchemeArena 探索 LLM 代理中的策划行为，关注多种因素的交互、环境支持及监督条件。该研究揭示了策划风险的复杂性，为安全的 LLMs 开发和监管提供了新思路，促进了智能系统的安全性研究。

AgenticGen: Reward-Guided Agentic Video Generation for Advertising

👍 7

08/30 20:00

Advertising video generation is not only a video synthesis task, but also a product-conditioned reasoning problem whose success is measured by online business metrics. Recent video foundation models can generate realistic clips from multimodal conditions, yet they do not optimize how a product shoul

中文介绍 AgenticGen 通过奖励导向的方式实现广告视频生成，结合多模态条件，优化产品展示效果。该研究提升了业务指标的有效性，为人工智能在营销应用中的发展提供了新的视角。

StochBench: A Domain-Specific Benchmark for Stochastic Processes in Lean

👍 6

09/07 20:00

Leading benchmarks for formal theorem proving with large language models are small collections drawn from competition math, such as the IMO and Putnam, that poorly represent field-specific applications. We introduce StochBench, a Lean 4 benchmark of 450 graduate stochastic-processes problems at vary

中文介绍 StochBench 引入一个领域特定的基准，评测大语言模型在随机过程中的表现。通过针对 450 个研究生随机过程问题的挑战性评估，促进了形式化定理证明在具体领域的应用，推动了数学与计算智能的交叉研究。

A Three-Layer Caching Architecture for Low-Latency LLM Web Search on Commodity CPU Hardware

👍 5

08/11 20:00

AI-powered search products such as ChatGPT search, Google's AI Overviews, and Perplexity provide LLM-synthesized answers grounded in live web results. We developed OreoLook (formerly lixSearch), an open-source answer engine using automated browser agents and provider-routed LLM inference. Its local

中文介绍 提出了一种三层缓存架构，用于在普通 CPU 硬件上实现低延迟 LLM 网络搜索。OreoLook 作为开源答复引擎，提升了基于最新网页结果的实时生成能力，对搜索引擎开发具有重要现实意义。

10 Ways People Are Making Money With GPT-6 Astra

@sairahul1 · 143.5K 粉丝 · 1.5M 阅 · 561 赞 · 53 转

09/09 06:08

Someone gave GPT-6 Astra $52. 13 hours later: $11,345.95. Someone else gave it $43.47 and one instruction. It turned that into $41,495 overnight. Someone went to sleep. Woke up to $200 in their

中文介绍 博主分享了利用 GPT-6 Astra 进行盈利的案例，展示了几位用户使用不同金额启动后的盈利情况，如 $52 变为 $11,345.95，甚至 $43.47 变成 $41,495。这种现象反映出借助 AI 进行投资的潜力和风险，同时也引发对收益的热议。

What Happens When AI Starts Improving AI?

@apexin_ai · 395 粉丝 · 281.6K 阅 · 500 赞 · 84 转

09/08 00:23

Astra shows how far AI has come. What takes it further? The continued progress of today’s most capable models depends on smarter decisions before training, better strategies during training, and more

中文介绍 博主探讨了 AI 发展中的一个重要问题：AI 如何提升 AI。随着技术进步，训练前的决策和训练过程中的策略将直接影响模型的性能。这表明在 AI 领域，更智能化的决策正在成为提升效率的关键因素。

How to use ChatGPT Astra for UGC so well it feels illegal

@lucaspatiri_ · 5.8K 粉丝 · 275.5K 阅 · 506 赞 · 41 转

09/08 12:12

openai shipped its most capable model on september 3. i wasn't going to write this. we've generated 2.6 billion organic views across 62 campaigns, managed 590 creators, and produced 22,504 tracked

中文介绍 博主分享了如何使用 ChatGPT Astra 创造用户生成内容（UGC）的方法，指出其在62个活动中获得了26亿的自然浏览量。这一经验展示了 AI 在内容创作中的强大能力，尤其是在市场推广和合作方面。

From GPT-5.6 Sol to GPT-6 Astra: Why Am I Repeating Myself?

@BradGroux · 6.6K 粉丝 · 229.0K 阅 · 561 赞 · 118 转

09/09 18:47

I've been using GPT-6 Astra with the workflow I developed around GPT-5.6: documented task scope, repository context, standard operating procedures, and verification before delivery. In my testing,

中文介绍 博主介绍使用 GPT-6 Astra 的工作流程，重点讲述了文档任务范围、上下文存储和标准操作程序的设定。这反映出在新工具发布后，如何保持效率并整合旧有经验的重要性。

How to design brand mascots with AI (krea). No experience needed

@semochkin_alex · 1.5K 粉丝 · 191.5K 阅 · 507 赞 · 44 转

09/09 08:34

Hi, I'm Alex, cofounder at @relate_studio. In this tutorial, I'll walk you through how we design brand mascots with AI: finding first concepts in Krea, refining the best direction in Nano Banana 2,

中文介绍 博主通过教程分享了如何使用 AI 设计品牌吉祥物的方法，包括在 Krea 中找概念和使用 Nano Banana 2 精炼设计。这种新兴技能反映了 AI 在创意行业中的应用潜力，吸引了许多对设计感兴趣的人士。

Pushing Local AI Forward

@MiaAI_lab · 27.4K 粉丝 · 180.6K 阅 · 505 赞 · 58 转

09/09 09:45

For the past three months I’ve been building and shipping local AI recipes, mostly for NVIDIA DGX Spark systems. What started as a personal project has grown far beyond what I expected. My GitHub

中文介绍 博主分享了过去三个月在构建和发布本地 AI 配方方面的经验，主要应用于 NVIDIA DGX Spark 系统。这种项目的扩展性揭示了本地 AI 开发和创新的增长趋势，尤其是在硬件关联的计算任务中。

Reducing cost and improving performance with Claude Platform

@ClaudeDevs · 683.4K 粉丝 · 106.4K 阅 · 1.3K 赞 · 70 转

09/08 13:01

Tuning prompt caching, instructions, and effort can reduce Claude's cost without sacrificing application performance. Performance and cost are often viewed as a trade-off: to spend less, you accept

中文介绍 博主探讨了如何通过调整提示缓存和指令来降低 Claude 的使用成本，而不影响应用性能。这一策略突显出成本控制在 AI 应用开发中的重要性，主要是行业内共识的反映。

How to Use GPT-6 Astra to Find Profitable Trading Strategies 24/7

@RohOnChain · 68.8K 粉丝 · 102.3K 阅 · 524 赞 · 48 转

09/08 08:39

I will break down exactly how to build an AI agent like hedge funds that finds profitable trading strategies from the markets 24/7 with GPT-6 Astra. Let's get straight to it. Bookmark This - I'm

中文介绍 博主介绍了利用 GPT-6 Astra 建立一个 AI 代理进行全天候市场交易策略开发的方法，意在模仿对冲基金。这反映了 AI 在金融交易中的应用潜力和对技术掌控的要求。

Personal statement on joining the OpenAI board

@paulfchristiano · 5.3K 粉丝 · 70.7K 阅 · 654 赞 · 96 转

09/09 13:05

I am excited to be joining the OpenAI nonprofit board, serving on the Safety and Security Committee to support safety oversight. Based on the recent trajectory of capabilities and the continued

中文介绍 博主宣布加入 OpenAI 非营利组织董事会，参与安全监督委员会工作。此举突显了在快速发展的 AI 能力背景下，保障技术安全的重要性及行业内对于伦理的关注。

LLM-as-Judge Architectures: Putting Evals Into Your Agent Runtime

@JoshARosen · 1.3K 粉丝 · 47.0K 阅 · 510 赞 · 49 转

09/08 10:00

LLM-as-judge is already a common part of building AI applications. Run your application against a dataset, have another model grade the results, and use the scores to figure out whether the latest

中文介绍 博主讨论了“LLM 作为评判”的架构在 AI 应用中的应用，即通过模型打分验证结果。这种方法表明了使用 AI 自我评估的重要性，反映了行业内对多模型协作的关注和探讨。

Software Engineer to AI Engineer: Best Move 2026

@TechWithTimm · 16.2K 粉丝 · 40.2K 阅 · 506 赞 · 79 转

09/09 10:57

A lot of software engineers see AI engineering as a completely different career. But it’s not. I have a friend who works as an AI engineer and makes around $200,000 to $300,000 a year. When I asked

中文介绍 博主分析了软件工程师转向 AI 工程师的职业路径，强调两者之间的关联性及其经济回报。这表明 AI 技能的市场需求正在增长，吸引越来越多的技术人才关注这一转型。

AlphaGenome Atlas: A High-Resolution Map of Human DNA Accelerating Biological Discovery

@GoogleAI · 2.5M 粉丝 · 37.4K 阅 · 643 赞 · 90 转

09/08 10:41

Today, we’re introducing AlphaGenome Atlas 🧬 an interactive platform for genomics discovery. In 2025, @GoogleDeepMind released AlphaGenome to help decode the complex language of our DNA — but

中文介绍 博主介绍了 AlphaGenome Atlas，一个用于生物学发现的高分辨率人类DNA图谱平台。这一项目展示了 AI 在基因组学研究中的应用潜力，反映了科学界对于精准医学的日益重视。

My Son Had Six Weeks to Learn Two Years of Saxon Math. We Used AI.

@DataRepublican · 957.2K 粉丝 · 37.3K 阅 · 846 赞 · 155 转

09/09 22:21

NYC and LA are restricting student AI. We used it to expose what school missed—and repair two years of gaps in six weeks. Part 1 of 3 in a series. Between my husband and I, three of our parents were

中文介绍 博主分享了使用 AI 帮助子女弥补学业缺口的经历，成功在六周内完成两年的数学课程。这一实例突出 AI 在教育补救和学习支持中的应用，强调了技术在教育领域的影响。

The Complete Guide to pstack Pt. 2

@poteto · 92.6K 粉丝 · 23.3K 阅 · 822 赞 · 60 转

09/09 13:02

In my last post, I showed you why verification is the foundation of everything I do with agents, and how to get started creating your own verification skills. The key lesson was that if an agent can't

中文介绍 博主在分享中继续探讨验证在 AI 代理中的基础重要性，以及如何开始创建自己的验证技能。这一内容强调了验证过程在实现 AI 可靠性和有效性时的重要性。

The Physical World Does Not Hand You a State Vector

@jhMITgunship · 1.5K 粉丝 · 18.3K 阅 · 1.4K 赞 · 1 转

09/08 13:23

What superintelligence needs to operate in the physical world For much of the work we give models from OpenAI, Anthropic and the other frontier labs, the world has already been turned into context.

中文介绍 博主讨论了超智能体在物理世界操作所需的条件，强调现实世界的复杂性对于 AI 模型性能的挑战。这指出了技术开发过程中需要克服的物理与虚拟间的障碍。

Alaya AI: Mastering Multimodal Sensor Synchronization for Next-Gen Autonomous Systems

@Alaya_AI · 321.1K 粉丝 · 10.6K 阅 · 504 赞 · 450 转

09/10 06:15

As autonomous vehicles, robotics, and spatial computing platforms evolve, AI model training is shifting away from isolated 2D image annotation toward complex multimodal sensor fusion. Today’s frontier

中文介绍 博主介绍了为新一代自主系统掌握多模态传感器同步的重要性，展示了 AI 模型训练的演变方向。这一趋势反映了 AI 在复杂应用场景中的潜力和未来发展方向。

How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules

OpenAI News · 09/10 12:00

César de la Fuente’s lab uses Codex and ChatGPT to search living and extinct genomes for antimicrobial candidates to fight drug-resistant infections.

中文介绍 塞萨尔·德拉·丰特的实验室利用Codex和ChatGPT在已知和灭绝基因组中寻找抗微生物候选物，以对抗耐药性感染。

Now everyone can put data to work

OpenAI News · 09/10 11:00

Meet the Data agent in ChatGPT Work. Connect company data, uncover insights, and build interactive dashboards with AI using natural language.

中文介绍 ChatGPT Work中的数据代理可以连接公司数据，挖掘洞察，并使用自然语言构建互动仪表盘，帮助用户更好地利用数据。

Powering AI is an architecture problem

MIT Tech Review AI · 09/10 07:00

On July 22, 2026, a transmission line fault in Ashburn, Virginia—the heart of the world’s largest data center cluster—knocked more than 3 gigawatts of load off the grid in seconds. And it wasn’t the first time. Two years earlier, a single failed surge arrester dropped roughly 60 Virginia facilities

中文介绍 2026年7月22日，位于弗吉尼亚州阿什本的全球最大数据中心集群发生输电线路故障，影响超过3吉瓦的负载。这并非首次，前两年一台冲击保护器故障导致约60吉瓦的负载下线。

Introducing ChatGPT for Financial Services

OpenAI News · 09/10 03:00

Introducing ChatGPT for Financial Services, combining built-in financial data and GPT-6 Astra for research, modeling, and client-ready materials.

中文介绍 OpenAI推出ChatGPT金融服务，结合内置金融数据和GPT-6 Astra，用于研究、建模和客户材料的制作。

Expanding AI access and cyber defense for federal, state, local, and tribal governments

OpenAI News · 09/10 03:00

OpenAI and GSA will offer eligible federal, state, local, and tribal governments $0 license fees, 50% off usage, and expanded cyber defense support.

中文介绍 OpenAI与GSA将向符合条件的联邦、州、地方和部落政府提供0美元的许可费，使用费用降低50%，并扩展网络安全支持。

[AINews] not much happened today

Latent Space · 09/09 23:33

a quiet day

中文介绍 今天发生的事情不多，整体保持安静。

Introducing the Agents API

OpenAI News · 09/09 20:00

Build and launch cloud agents with the Agents API, a managed service powered by the Codex harness for orchestration, long-running sessions, and tool use.

中文介绍 OpenAI推出Agents API，提供构建和启动云代理的托管服务，支持长时间会话和工具使用。

Build more natural voice experiences with GPT‑Live‑1 in the API

OpenAI News · 09/09 20:00

GPT‑Live‑1 brings natural, full-duplex voice conversations to the API, with stronger instruction following, custom voices, and telephony support.

中文介绍 GPT-Live-1为API带来自然的双向语音对话，支持更强的指令执行、自定义语音和电话通信。

Rebuilding AUTOMATIC1111 with Gradio Workflow

Hugging Face Blog · 09/09 20:00

中文介绍 关于使用Gradio Workflow重建AUTOMATIC1111的内容未披露具体信息。

DeepSeek Flash v4.1 🤖, Siri AI 📱, Anthropic AI economics 📈

TLDR AI · 09/09 20:00

中文介绍 有关DeepSeek Flash v4.1、Siri AI和Anthropic AI经济学的信息尚未披露具体内容。

Paul Christiano joins OpenAI Foundation Board

OpenAI News · 09/09 13:00

Paul Christiano joins the OpenAI Foundation Board and its Safety and Security Committee, bringing experience in AI alignment, safety, and standards.

中文介绍 保罗·克里斯蒂亚诺加入OpenAI基金会董事会及其安全与保障委员会，带来在AI对齐、安全和标准方面的经验。

IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license

Hugging Face Blog · 09/09 11:36

中文介绍 IBM发布了SOTA Granite时间序列模型PatchTST-FM-r2，并提供商业友好的许可，但具体内容未详细说明。

The AI policy window is open. We need to act.

OpenAI News · 09/09 09:00

Chris Lehane argues that stronger AI capabilities require stronger safety evidence, shared standards, and durable policy action while the policy window remains open.

中文介绍 克里斯·莱哈恩指出，强大的AI能力需要更强的安全证据、共享标准和持续的政策行动，以抓住政策窗口机会。

GPT-6 Astra: The next generation in intelligence for work

OpenAI News · 09/09 07:00

Meet GPT-6 Astra, OpenAI’s most capable model for business, with advanced reasoning, computer use, and stronger writing and design judgment.

中文介绍 GPT-6 Astra是OpenAI商业领域最先进的模型，具备卓越的推理、计算机使用和更强的写作与设计判断能力。

[AINews] OpenAI reports Navier-Stokes singularity find in 88 hours using Astra-next, roughly 10,000 agents and 130B tokens (>$40M), a contender for second ever Millennium Prize awarded

Latent Space · 09/09 01:04

Overshadowing Cognition's $48B Series E, Mistral's $24B Series D, Meta's Muse agent, and GPT Image 2.5. The most jam packed, feel the AGI day in the history of AI.

中文介绍 OpenAI通过Astra-next技术在88小时内发现Navier-Stokes奇点，动用了约10,000个代理及1300亿个代币，预计成本超过4000万美元。