2026-09-10

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

👍 59

09/07 08:00

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 提出开放式模块化World-Action Model预训练框架，将生成主干、视觉表示、架构、信息流、推理与训练流程解耦为独立组件，便于系统性探索世界知识向可控执行信号的迁移。

Show-Harness: Just a VLM Agent Can Play Robots

👍 54

09/09 08:00

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Embodied Harness框架，通过紧凑的语义接口将VLM的世界智能转化为机器人控制信号，无需大量定制即可让基础视觉语言模型直接操控机器人。

Programmable World Model

👍 40

09/09 08:00

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 提出可编程世界模型框架，将世界状态管理与视频生成解耦，支持在长交互中维持持久世界状态并执行可编程规则，弥补现有视频世界模型缺乏状态一致性控制的缺陷。

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

👍 30

09/05 08:00

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 针对多模态大模型难以识别相似图像间细微差异的弱点，构建VDiff-Bench基准，以多选题形式评测模型对精细图像差异的识别能力，揭示现有MLLM在比较类任务上的明显短板。

AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems

👍 28

09/08 08:00

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LLM多智能体系统性能受prompt设计制约的问题，提出AgentGrad干预引导式prompt优化方法，通过自然语言反馈指导各智能体prompt更新，系统性提升多智能体协作表现。

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

👍 26

09/04 08:00

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 构建包含4,084道十选项多选题的WearableQA基准，基于真实用户纵向可穿戴设备数据评测AI系统的健康推理能力，填补现有基准缺乏长时程生理与行为信号推理评测的空白。

RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?

👍 21

09/04 08:00

Vision-Language-Action (VLA) models have shown promising progress in language-conditioned robotic manipulation. However, existing datasets and benchmarks mainly evaluate task completion under predefined settings, offering limited insight into model reasoning under increasing spatial and procedural c

中文介绍 评估VLA模型在复杂场景与长时程任务中的表现，突破现有数据集仅在前定义设置下评测任务完成率的局限，探索模型在空间与流程复杂度递增条件下的推理能力边界。

CoVeR: Coverage-Based Token Pruning for Multi-View 3D Reasoning in VLMs

👍 20

09/08 08:00

Representing a 3D scene as multi-view images allows 2D VLMs to reason in 3D by reusing priors from pre-training, sidestepping the scarcity of annotated 3D data. However, it produces thousands of redundant visual tokens whose cost grows with every view. Existing visual token pruners fall into two fam

中文介绍 针对多视角3D推理中VLM产生大量冗余视觉token的问题，提出CoVeR基于覆盖度的token剪枝方法，在保留3D场景关键信息的同时显著降低多视角表示的计算开销。

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

👍 17

09/08 08:00

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 针对SWE-Bench Pro中gold solution泄露与hidden evaluation导致的reward hacking问题，提出SWE-Bench Pro Verified改进版，提升软件工程智能体评测的可靠性。

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

👍 16

09/05 08:00

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 提出Cadence误差有界有损时序压缩方法，结合330M参数的TimesFM-3时序基础模型与自适应算术编码，逐样本保证压缩误差|x_t-x̂_t|≤τ，并给出无损编码不可行的负结论约束设计空间。

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

👍 15

09/08 08:00

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SAEScientist-Bench基准，评测AI智能体能否自主开展SAE可解释性研究，聚焦递归自我改进中缺失的事后模型审计与理解环节，验证智能体在机制可解释性任务上的能力。

ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding

👍 15

09/07 08:00

In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods rely on offline inference with global temporal sampling, which violates causality and prevents deployment in live surveillance streams. Conversely, gene

中文介绍 提出ReactVAU慢-快解耦框架用于实时流式视频异常理解，摒弃离线全局时序采样导致的因果性违背，实现可部署于实时监控场景的在线推理。

Recognition-Refusal Misalignment in LLMs: Why Models Answer Structurally Unanswerable Questions

👍 15

08/29 08:00

Large language models often answer structurally unanswerable questions, such as computing cot(-540°) or evaluating (1).startswith("1"), instead of abstaining. We ask whether this failure reflects missing recognition or failed routing from recognition to abstention. Across instruction-tuned models fr

中文介绍 研究LLM回答结构性不可回答问题（如cot(-540°)）而非拒答的根源，区分是识别能力缺失还是从识别到拒答的路由失败，跨多个指令微调模型进行系统验证。

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

👍 14

09/07 08:00

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 提出发现认证协议DCP，将AI研究智能体的成果声明转化为可执行的恢复与反馈测试，Gate 1验证密封评测上的有效改进，Gate 2验证新场景泛化能力。

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

👍 11

09/08 01:56

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 提出A*-Thought-V2高效隐式推理方法，基于LLM的几何动力学原理实现连续压缩，避免CoT硬剪枝导致的信息丢失，在降低计算与上下文开销的同时保持推理质量。

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

👍 8

09/06 08:00

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 提出DianShi-RxnDB大规模精细有机反应数据平台，通过全自动管线从专利文本、图像与反应方案中构建结构化数据，为AI4Chem研究和智能体应用提供数据基础设施。

RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting

👍 8

09/07 08:00

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 提出RelightFormer前馈生成Transformer用于多视角物体重光照，利用多视角线索理解3D几何与材质交互，克服传统逆渲染的优化不稳定性与单图生成模型忽略3D信息的局限。

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

👍 7

09/08 08:00

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 提出SyncWorld通过视觉校准实现世界模型的零样本模拟能力，解决像素空间中机器人动作缺乏通用语言的问题，使世界模型可作为策略环想象环境支持细粒度可控rollout。

Revisiting Complete Reasoning Traces for Post-Training

👍 7

09/07 08:00

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 研究后训练中完整推理轨迹的必要性，指出预收集的推理轨迹因复杂交织路径常含绕路，探讨是否可采用更精简的推理路径作为后训练数据以降低成本。

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

👍 6

09/09 08:00

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 提出Φ-Bench基准，评测LLM能否设计并优化驱动自身的系统基础设施，超越现有仅关注孤立kernel和预定义算子的评测，探索模型在基础设施工程层面的能力边界。

10 Ways People Are Making Money With GPT-6 Astra

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

09/09 18:08

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 汇总 GPT-6 Astra 自动化赚钱案例：$52 起步获 $11,345，$43 一夜变 $41,495，展示 AI 代理自主经营的收入潜力与代理经济模式。

What Happens When AI Starts Improving AI?

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

09/08 12:23

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 探讨 AI 自我改进路径：训练前决策优化、训练策略改进、推理效率提升三个层面协同驱动，分析前沿模型持续进化的飞轮效应。

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

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

09/09 00:12

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 UGC 团队 Astra 实操分享：62 个 campaign 累计 26 亿自然播放，管理 590 位创作者，产出 22,504 条追踪内容，方法感似作弊。

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

09/10 06:47

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 GPT-5.6 Sol 升级到 GPT-6 Astra 工作流体验：沿用任务范围文档化、SOP、交付前验证流程，能力提升明显但重复劳动仍存。

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 20:34

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 品牌吉祥物 AI 设计教程：先用 Krea 探索初始概念方向，再用 Nano Banana 2 精修细节，零基础也能上手的品牌视觉设计工作流。

Pushing Local AI Forward

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

09/09 21:45

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 持续三个月为 NVIDIA DGX Spark 构建本地 AI 食谱项目，从个人实验发展为规模化开源仓库，推动本地化 AI 部署的社区实践。

Reducing cost and improving performance with Claude Platform

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

09/09 01:01

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 Anthropic 官方教程：优化 prompt 缓存、指令和推理力度（effort），可同时降低 Claude 成本并提升性能，打破两者必取其一的取舍。

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 20:39

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 拆解用 GPT-6 Astra 搭建 24/7 交易代理：自动扫描市场数据、发现盈利策略，复刻对冲基金的量化 AI 工作流全流程。

Using Grok Bot: Figma Photomosaics

@mattyp · 60.1K 粉丝 · 82.8K 阅 · 515 赞 · 56 转

09/08 06:03

You may have seen some three-panel mosaics on your timeline. Here's how you can create them with @bot in @figma. Any designer knows how they'd make one, it's just a bit of a pain in the ass. So we

中文介绍 用 Grok Bot 在 Figma 中自动生成三格拼贴画（Photomosaics），将设计师原本繁琐的手动拼接流程转为 AI 一键出图，省时省力。

Personal statement on joining the OpenAI board

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

09/10 01:05

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 AI 安全研究员 Paul Christiano 宣布加入 OpenAI 非营利董事会，进入安全与监督委员会，基于对前沿模型能力趋势的判断参与治理。

Exactly how I edit videos with Astra

@ForwardEditor · 10.0K 粉丝 · 61.4K 阅 · 512 赞 · 36 转

09/08 08:54

This weekend, I watched a movie with my daughter while my computer worked on a video I would have normally spent most of a day editing. The video was a favor for someone in my personal life. I'm a

中文介绍 视频创作者分享 Astra 替代全天手动剪辑体验：陪女儿看电影时，电脑自动完成原本需半天的视频制作，效率实现质变提升。

The Math Needed for AI Inference Engineering (Complete Roadmap)

@TheVixhal · 23.0K 粉丝 · 59.7K 阅 · 541 赞 · 58 转

09/08 01:07

In this article, I'm going to break down the essential math you need for AI inference engineering. I'll also share the exact roadmap and resources that helped me personally. Let's get straight to it.

中文介绍 AI 推理工程必备数学知识完整路线图：梳理线性代数、概率等核心基础，附学习资源，面向想转型推理优化领域的开发者。

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

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

09/08 22:00

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 将 LLM-as-Judge 模式嵌入 Agent 运行时：用另一模型实时评分应用输出，动态驱动决策与迭代，超越仅做离线评估的传统模式。

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 22:41

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 Google DeepMind 发布 AlphaGenome Atlas：交互式基因组发现平台，提供高分辨率人类 DNA 图谱，让研究者可视化探索基因功能。

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

09/10 10:21

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 家长用 AI 帮孩子六周追赶两年 Saxon 数学课程，系列文章探讨 NYC 和 LA 限制学生使用 AI 的政策，主张 AI 暴露学校遗漏知识。

The Complete Guide to pstack Pt. 2

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

09/10 01:02

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 pstack 完整指南第二部分，聚焦验证机制深入实践，延续第一部分核心理念：Agent 无法验证执行结果就无法可靠完成任务。

The Physical World Does Not Hand You a State Vector

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

09/09 01:23

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 探讨超级智能操作物理世界的前提：当前模型收到的都是已结构化上下文，但物理世界不会自动提供状态向量，这是关键瓶颈。

Powering AI is an architecture problem

MIT Tech Review AI · 09/10 19:00

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，全球最大数据中心集群所在地弗吉尼亚州阿什伯恩发生输电线路故障，秒级切断超3吉瓦负载。此前两年前一次故障导致约60吉瓦下网。文章指出AI算力供电本质是电网架构问题。

[AINews] not much happened today

Latent Space · 09/10 11:33

a quiet day

中文介绍 Latent Space当日AI新闻总结：当天无重大事件发生，整体平静。

Rebuilding AUTOMATIC1111 with Gradio Workflow

Hugging Face Blog · 09/10 08:00

中文介绍 Hugging Face博客介绍如何使用Gradio Workflow框架重建AUTOMATIC1111图像生成工作流，提升易用性与可扩展性。

Paul Christiano joins OpenAI Foundation Board

OpenAI News · 09/10 01:00

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 Paul Christiano加入OpenAI基金会董事会及安全与安保委员会，其在AI对齐、安全及标准领域的专业经验将为OpenAI治理提供支撑。

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

Hugging Face Blog · 09/09 23:36

中文介绍 IBM在Hugging Face发布Granite Time Series PatchTST-FM-r2时序模型，采用商业友好许可证，面向时序分析场景提供开源方案。

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

OpenAI News · 09/09 21:00

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 Chris Lehane撰文称AI能力增强需配套更强的安全证据、共享行业标准与持久政策行动，当前政策窗口期仍存但不会长期开放。

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

OpenAI News · 09/09 19:00

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 OpenAI推出GPT-6 Astra，面向企业场景，具备高级推理、计算机使用能力及更强的写作与设计判断力，为公司最强大模型。

[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 13:04

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用Astra-next、约1万个智能体、1300亿token（成本超4000万美元）在88小时求解Navier-Stokes方程，有望获第二个千禧年数学奖。同日Cognition完成480亿美元E轮、Mistral获240亿美元D轮。

What OpenAI’s latest controversy tells us about the future of math

MIT Tech Review AI · 09/09 11:10

OpenAI’s latest mathematical milestone has quickly become mired in controversy. Today, the company announced that its agents have solved one of the Millennium Prize Problems, some of the most important open problems in mathematics. Under normal circumstances, that solution would be a huge feather in

中文介绍 OpenAI宣称智能体已解决千禧年数学难题之一，但随即引发争议。MIT科技评论探讨该事件对数学研究未来走向的深层启示。

Images 2.5 🎨, OpenAI solves Navier-Stokes 🧮, AlphaGenome Atlas 🧬

TLDR AI · 09/09 08:00

中文介绍 TLDR AI当日要点：OpenAI发布Images 2.5、求解Navier-Stokes方程，Google DeepMind发布AlphaGenome Atlas基因组预测图谱。

How GPT-5.6 Sol helps run quantum computing experiments

OpenAI News · 09/09 01:00

See how an MIT researcher uses GPT-5.6 Sol with Codex to autonomously run quantum computing experiments, analyze results, and calibrate qubits.

中文介绍 一位MIT研究者利用GPT-5.6 Sol配合Codex自主运行量子计算实验、分析结果并校准量子比特，展示了AI在量子领域的实用能力。

Safety for Whom? Refusing the Right Subset of a Topic, Not the Whole Topic

Hugging Face Blog · 09/08 22:23

中文介绍 Hugging Face博客讨论AI安全边界问题，主张应针对话题内的不当子集进行精准拒绝，而非对整类话题一律屏蔽。

AlphaGenome Atlas: A predictive map of every possible DNA letter change in the human genome

DeepMind Blog · 09/08 22:00

AlphaGenome Atlas maps the molecular effects of 9 billion single-letter DNA variants across the human genome.

中文介绍 Google DeepMind发布AlphaGenome Atlas，可预测人类基因组中90亿个单字母DNA变异的分子效应，提供全基因组变异预测图谱。

The Work Now Within Reach

OpenAI News · 09/08 21:00

Explore how more capable, affordable AI can expand the work people and businesses can accomplish—and make growth more economical.

中文介绍 OpenAI发文探讨更强大、更经济的AI如何扩展个人与企业可完成的工作范围，推动经济增长模式更加普惠和经济高效。

Introducing ChatGPT Images 2.5

OpenAI News · 09/08 19:30

ChatGPT Images 2.5 helps turn your ideas, sketches, and reference photos into more personalized, polished images that better reflect your ideas.

中文介绍 OpenAI推出ChatGPT Images 2.5，可将用户的想法、草图及参考照片转化为更个性化、精细度更高的生成图像。