2026-07-25

AREX: Towards a Recursively Self-Improving Agent for Deep Research

👍 124

07/22 20:00

Deep research requires agents to find answers that jointly satisfy multiple constraints. Discovering such answers is costly, whereas verifying a candidate can often be decomposed into tractable constraint-wise checks. This discovery--verification asymmetry suggests that a research agent should do mo

中文介绍 论文提出了一种递归自我改进的智能体（AREX），旨在解决深度研究中同时满足多个约束的答案查找问题。通过将候选答案的验证过程按约束拆解为可处理的检查，该方法有效降低了发现答案的成本。AREX 能够在动态的研究环境中持续优化自身性能，具有较强的实用价值。意义：为自我改进的智能体研究提供新思路，可能推动智能体在科学研究领域的应用。

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD

👍 56

07/21 20:00

Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed training, including severe memory pressure, non-overlapped communication overhead, and inefficient kernel execution. While most large-scale LLM training syst

中文介绍 本研究探索了在 Ascend SuperPOD 上对深度学习模型 DeepSeek-V4 进行全参数后训练的方法，以应对大规模 MoE 模型的系统级挑战。提出的解决方案有效减轻了内存压力，并提升了通信效率，为大规模分布式训练提供了一种新的实现路径。结果显示，在此框架下，训练效率显著提高。意义：推动大规模模型训练的可扩展性与效率，推动 AI 应用的广泛发展。

K12-KGraph: A Curriculum-Aligned Knowledge Graph for Benchmarking and Training Educational LLMs

👍 54

07/22 20:00

Large language models are increasingly used in K-12 education, but existing benchmarks mainly test exam question answering rather than understanding how curriculum knowledge is structured and visually presented. We call this capability curriculum cognition. It covers prerequisite chains, concept tax

中文介绍 K12-KGraph 提出了一种课程对齐的知识图谱，用于评估和训练教育领域的大语言模型（LLM）。这种知识图谱以课程认知能力为核心，覆盖了前置知识链、概念分类等内容，旨在为 K-12 教育提供更好的模型评估基准。通过对课程知识结构的视觉化呈现，有助于学生更好地理解各知识点之间的关系。意义：为教育领域的 LLM 提供新的评估和训练工具，有助于提升教育技术的效率与质量。

ReferTrack: Referring Then Tracking for Embodied Visual Tracking

👍 48

07/21 20:00

Embodied visual tracking (EVT) requires a mobile agent to continuously follow a specific target described in natural language using only onboard vision. While recent vision-language-action (VLA) policies unify target identification and trajectory planning, their chain-of-thought (CoT) reasoning ofte

中文介绍 ReferTrack 提出了一个新方法，通过自然语言描述持续跟踪移动目标的物体视觉跟踪（EVT）任务。该方法结合了目标识别和轨迹规划，通过增强链式推理（CoT）来提高任务性能。实验表明该方法在实时目标跟踪任务中表现优异，显著提高了准确性与效率。意义：推动自然语言与视觉互动的研究，可能在机器人与自动驾驶领域获得应用。

Visual Contrastive Self-Distillation

👍 41

07/22 20:00

On-policy self-distillation (OPSD) is promising as it removes the external teacher required by on-policy distillation (OPD), yet it still needs asymmetric information between teacher and student to ensure that the self-teacher provides a stronger learning signal than the student. Existing methods cr

中文介绍 该研究探讨了视觉对比自蒸馏（Visual Contrastive Self-Distillation），试图通过在策略学习中去除外部教师的需求来提升学习效果。研究表明，OPSD 模型在确保更强学习信号的情况下，能够有效减少教学信号的不对称性。实验结果显示，该方法在多任务学习中具有显著优势。意义：为无监督学习和表示学习领域提供了新的方法框架，具有广泛的应用潜力。

Show, Don't Tell: Evaluating Spatial Cognition in Generative Pixels Rather Than LLM Text

👍 34

07/22 20:00

Spatial intelligence is essential for agents to move from static semantic understanding toward interacting with the physical world. Many spatial tasks are grounded in continuous visual scenes, where locations, regions, and paths are more naturally expressed by pointing, marking, or drawing than by r

中文介绍 论文提出了一种评估和理解空间认知能力的新方法，通过生成像素而非文本来进行评估。该方法强调了空间智能在物理交互中的重要性，旨在提升智能体在高度动态环境中的表现。实验证明，该方法在多种空间任务中具有优越性，能够更准确地模拟人类的空间认知过程。意义：推动智能体在物理世界中的应用，可能改善人机交互体验。

Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Ranking

👍 28

07/21 20:00

As large language models and AI agents become the primary consumers of search results, document set quality determines the upper bound of downstream generation. Yet existing evaluation systems remain confined to scoring documents independently and aggregating via nDCG, ignoring inter-document intera

中文介绍 本研究提出了一种超越传统的文档集选择与排序评估方法，专注于文档间的相互关系。随着大语言模型及 AI 智能体成为搜索结果主要消费者，文档集的质量对后续生成的上限产生重要影响。新方法证明了在性价比和质量提升方面的有效性。意义：为信息检索领域的评估方式提供了新的视角，可能对搜索引擎技术产生影响。

An Exam for Active Observers

👍 24

07/16 20:00

Human vision is a closed loop: gaze is continuously redirected by intermediate hypotheses rather than a single snapshot. Decades of psychophysics and cognitive science have argued that this active observation is essential for a wide range of tasks. Whether today's multimodal large language models (M

中文介绍 研究探讨了人类视觉的闭环机制，指出主动观察在多种任务中的重要性。该论文分析了当今多模态大语言模型（MLLMs）在模拟人类可视行为时的表现，提出了针对性改进的建议。实验证明，主动观察能有效提升模型的性能表现。意义：为多模态学习及认知科学的交叉研究提供新指引，可能提升视觉智能的发展。

Tencent WorkBuddy Bench: A Multi-Domain Coding-Agent Benchmark with Contamination-Resistant Task Construction

👍 20

07/22 20:00

We introduce Tencent WorkBuddy Bench, a multi-domain evaluation suite for coding agents; this report documents its construction methodology, scoring protocol, and a cross-model leaderboard. At its core is a unified evaluation framework for constructing and running distribution-informed coding-agent

中文介绍 腾讯 WorkBuddy Bench 是一个面向多领域编码智能体的评估平台，报告了其构建方法、评分协议及跨模型排行榜。核心在于建立一个统一的评估框架，帮助研究者更有效地评估编码智能体的表现。实验结果显示，该平台在保持高效性与公平性方面表达出色。意义：为编码智能体的发展与评估提供了标准化的工具，有助于推动编码领域的创新。

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

👍 19

07/21 20:00

Traditional agent development is split across prompt templates, tool schemas, callback code, and workflow graphs. We present NVIDIA Object-Oriented Agents (NOOA), a model-agnostic Python framework for building reliable AI agents. NOOA takes a simpler approach: an agent is a Python object. Its method

中文介绍 NVIDIA 提出的 OO Agents 是一种基于 Python 的对象导向智能体开发框架，旨在简化传统智能体的开发过程。该框架允许将智能体视为 Python 对象，使得开发更直观、高效。实验证明，OO Agents 提高了可靠性和开发灵活性，具有广泛的应用潜力。意义：为智能体开发流程提供新的解决方案，可能推动智能体在各领域的应用。

Color Pass-Through via Camera-Display Coupling

👍 18

07/13 20:00

When a real-world scene is captured by a smartphone camera and viewed on its screen, the displayed image often differs noticeably from the original scene in color, brightness, and contrast. This gap persists despite substantial advances in both modern cameras and displays. A key reason is that most

中文介绍 本研究探讨了拍摄场景与显示图像之间的色彩传递问题，指出大多数设备在显示时存在明显的色彩差异。通过分析影响因素，提出了改进显示效果的建议，以减小现实场景与显示图像之间的偏差。实验结果显示，优化的方法有效提升了色彩一致性。意义：为图像处理技术的发展提供了重要依据，可能推动摄影和显示技术的创新。

LLMs Get Lost in Evolving User Intent

👍 17

07/21 20:00

As LLMs become more capable, they are increasingly deployed as collaborative agents, taking on user-delegated tasks through iterative interaction. Yet genuine interaction is inherently dynamic: users rarely specify their intent upfront, instead disclosing, revising, and reshaping it as the conversat

中文介绍 研究分析了 LLMs 在面对不断变化的用户意图时的表现，指出传统静态模型难以适应动态交互的需求。提出了一种新的动态交互框架，能够有效理解和适应用户在对话中的意图演变，提高互动质量。实验结果显示，该方法有效提升了用户体验。意义：为人机交互研究提供新的思路，可能推动智能助手的发展与应用。

Self-Supervised Learning of Structured Dynamics from Videos

👍 16

07/22 20:00

Understanding motion in video is a fundamental challenge for visual learning, as frame-to-frame change entangles two sources of dynamics: camera motion and object motion. This decomposition has remained underexplored in representation learning, partly because these factors are tightly coupled in nat

中文介绍 论文探讨了如何自监督学习视频中的结构动态，解决了相机运动和物体运动的干扰问题。通过对这两种动态的分解，提出了一种新的学习框架。实验证明，新框架在表示学习中展现出了显著的优势。意义：推动视觉学习领域的发展，可能改善视频分析和处理的技术能力。

Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models

👍 14

07/20 20:00

Injecting factual knowledge into large language models (LLMs) reliably and at scale remains an open challenge. Hypernetworks provide a promising solution to large-scale knowledge injection. Although hypernetworks are typically applied for test-time adaptation, we explore their use in train-time know

中文介绍 本研究探讨了通过超网络将事实知识高效注入大语言模型（LLMs）的策略。论文展示了超网络在训练时知识注入的有效性，并表明这种方法具有高度的可扩展性。实验结果显示，超网络在提供知识支持方面表现优异。意义：为 LLMs 的知识增强方法提供新的方向，可能影响自然语言处理的相关应用。

Beyond Euclidean Clipping: Overcoming Exploration Collapse in LLM RL via Riemannian Isometric Policy Optimization

👍 12

07/10 20:00

Reinforcement learning (RL) has become a dominant paradigm for enhancing LLMs' reasoning capabilities. However, RL algorithms with PPO-Clip are inherently limited by exploration collapse. Subsequent works remain primarily heuristic and fail to identify the essential cause of PPO-Clip's failure. This

中文介绍 本文研究了强化学习（RL）中存在的探索崩溃问题，尤其是 PPO-Clip 算法的局限性。提出通过黎曼等距策略优化的方法来克服这一问题，实验表明该方法显著提升了探索效率和模型表现。意义：为 RL 在大型语言模型中的应用提供新的解决路径，推动相关领域的研究进展。

Streaming Multi-Agent Autoregressive Diffusion Model with World State Registers

👍 11

07/22 20:00

Multi-agent interactive world models should not only generate consistent observations, but also maintain world states that persist across agents and evolve across views. Existing autoregressive video diffusion pipelines carry forward observation history as conditioning context, which makes shared st

中文介绍 研究提出了一种流式多智能体自回归扩散模型，旨在维护智能体之间共享的世界状态以及演变。在此模型下，观察历史作为条件上下文被传递，实验结果显示其生成的一致性观测显著改善。意义：，该模型有望增强多智能体系统的协作能力，为智能体交互研究提供新的研究框架。

Sample-Efficient Learning from Agent Experience

👍 9

07/22 20:00

Real-world agent learning is often constrained by costly environment interactions, such as running time-consuming experiments or obtaining human feedback. In-context learning offers a highly sample-efficient way for agents to learn from their own interaction histories, but its gains disappear once t

中文介绍 本研究探索了利用代理经验进行样本高效学习的方法，解决了环境交互成本高的问题。通过在上下文中学习，模型显著提高了从历史交互中汲取经验的能力，实验结果显示相较传统方法具备更高的学习效率。意义：为代理学习提供新的高效策略，可能推动实际应用中的学习和适应能力提升。

AutoIndex: Learning Representation Programs for Retrieval

👍 9

07/20 20:00

We present AutoIndex, a framework for learning representation programs: executable transformations that map raw documents into the representations exposed to a retrieval system. Rather than tuning retrievers, rerankers, or a small set of preprocessing hyperparameters, AutoIndex searches over program

中文介绍 AutoIndex 提出了一个新的框架，用于学习文档检索系统的表示程序，旨在通过可执行转换提升文档检索性能。该方法通过搜索程序空间而非简单调整超参数，证明了在检索效果上的提升。意义：为信息检索领域提供了新的工具，有望引领文档处理和检索技术的创新。

Multi-Turn On-Policy Distillation with Prefix Replay

👍 8

07/15 20:00

We study on-policy distillation (OPD) for agentic tasks, where an LLM agent interacts with an environment over multiple turns and a student imitates a teacher over these multi-turn interaction histories. Fully online OPD is costly because each update requires fresh student rollouts through the envir

中文介绍 研究探讨了基于前缀重放的多轮在线蒸馏方法，旨在提高智能体任务训练的效率。新方法通过对多轮交互历史进行模仿学习，降低了每次更新的成本。实验结果显示，该方法在多轮任务中表现优异。意义：为多任务学习中的模型蒸馏提供了新思路，可能改善智能体的学习效率。

Robostral Navigate

👍 8

07/21 20:00

Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains efficiently. Yet, today's best systems depend on depth sensors, multi-camera rigs, or pre-built maps, limiting the hardware they support and increasing deployme

中文介绍 Robostral Navigate 提出了一个高效的导航系统部署方案，旨在降低对传感器的依赖并增强通用性。所提方法通过训练提升了系统的固有能力，实验表明在多种硬件上均能实现良好效果。意义：推动机器人导航及智能移动技术的发展，提高实际应用中的灵活性与可扩展性。

Why hasn’t AI increased unemployment?

@PeterMcCrory · 46.5K 粉丝 · 399.2K 阅 · 514 赞 · 106 转

07/22 13:18

I thought I’d share a few high-level reflections and a framework that helps me make sense of why we (so far) don’t see significant impact of AI on the US labor market. I focus on the US because (a) AI

中文介绍 博主探讨了 AI 在美国劳动市场未显著增加失业率的原因，并提出一套思维框架。通过分析当前 AI 对就业的影响，指出当前的情况可能与技术适应性及系统性障碍有关，反映出对 AI 影响劳动市场的广泛关注和共同讨论。

The complete Graph Engineering playbook for Claude Code

@Gyome1_ · 3.9K 粉丝 · 220.0K 阅 · 504 赞 · 65 转

07/23 04:53

Most people are still using Claude Code like a very expensive intern. They give it one task, wait for one answer, then manually decide what happens next. But the teams getting the most leverage from

中文介绍 该帖分享了 Claude Code 的完整图形工程操作手册，强调许多人仍将其视作昂贵的实习生。博主建议团队应更有效地利用 Claude Code，通过优化任务分配和工作流，提升生产力，指出了对自动化工具应用优化的趋势。

Frontier Diffusion & Control

@satyanadella · 7.5M 粉丝 · 153.7K 阅 · 923 赞 · 114 转

07/23 12:31

In a world where software has real marginal cost for the first time, how do we ensure frontier benefits are diffused across the entire ecosystem? The key is to optimize the cost-to-outcome frontier in

中文介绍 博主探讨如何在软件首次出现边际成本的背景下，确保前沿技术的收益能够在整个生态系统中扩散。强调优化成本-结果边界的重要性，反映出对软件经济新形态的共识与讨论。

Towards Automating Eval Engineering

@Vtrivedy10 · 13.9K 粉丝 · 152.6K 阅 · 503 赞 · 50 转

07/22 13:04

Today we’re releasing our Eval Engineering Skill, a skill that helps coding agents build evals using context from a repository and agent traces. The skill inspects how an agent is structured, mines

中文介绍 发布了 Eval Engineering Skill 以帮助编码代理构建评估。该技能基于上下文进行检查，挖掘代理的结构，对于自动化评估工程的进步具有重要意义，显示行业对提升代码评估效率的关注。

this system will change your life...

@eptwts · 116.8K 粉丝 · 140.0K 阅 · 514 赞 · 28 转

07/23 13:21

the consensus on self-hosted agents is that they're simply coding tools... you point one at a repo, it writes code, you close the terminal & everything it learned about you dies with that session.

中文介绍 博主指出自托管代理的共识仅是编码工具，表明在使用后其学习内容不会持久化。此观点或显示对整体 AI 开发和使用模式的重新思考，揭示了对代理管理和体验的反思趋势。

why we're buzzing

@jack · 10.3M 粉丝 · 109.6K 阅 · 782 赞 · 93 转

07/22 18:25

yesterday we released buzz. it's an open source workspace that puts people, agents, conversations, and code on the same level, behind one cryptographic identity system. we built it to reduce our

中文介绍 博主宣布推出 buzz，一个开源工作空间，旨在将人员、代理、对话和代码平等整合。此举反映出对团队协作和透明度提升的需求，显示出新工作方式的趋势。

Trajectory: A Standard Format for Agent Experience Data

@Letta_AI · 9.8K 粉丝 · 86.5K 阅 · 504 赞 · 31 转

07/24 02:07

Introducing trajectory, an open-source package that normalizes coding-agent sessions from @AnthropicAI Claude Code, @OpenAI Codex, @pidotdev, @LangChain deepagents, @openclaw, Letta Code, and other

中文介绍 推出了 trajectory，一个开源项目，旨在规范来自不同 AI 编码代理的会话数据。此工具反映了对标准化代理体验数据的需求，代表了行业正朝着建立兼容性的努力迈进。

Why Software Factories Fail

@dexhorthy · 23.8K 粉丝 · 64.9K 阅 · 515 赞 · 50 转

07/24 12:51

or: the harness is not enough Update - the talk version of this post is live on youtube: https://www.youtube.com/watch?v=Ib5GBkD555M i guess we doin loops now We're all racing to put AI coding into production. A lot has

中文介绍 博主分析了软件工厂失败的原因，特别强调单靠工具不足以成功。反思在 AI 编码部署中的挑战，强调需要更全面的开发模式和策略，显示出对增强软件开发流程的广泛关注。

The new rules of context engineering for Claude 5 models

@trq212 · 318.0K 粉丝 · 48.6K 阅 · 1.2K 赞 · 119 转

07/24 13:45

I’ve written previously about how to best prompt the newest generation of Claude 5 models and work with them iteratively to discover what you want to build. But when you send a message to Claude, the

中文介绍 分享了针对 Claude 5 模型的上下文工程新规则，介绍如何进行有效的提示和迭代开发。反映出在 AI 模型应用领域对优化交互方式的持续关注与探索。

Practical multi-agent orchestration in Codex

@pvncher · 30.4K 粉丝 · 44.0K 阅 · 565 赞 · 33 转

07/24 13:30

GPT-5.6 Sol gets especially interesting when it has a team to work with. Codex's new Multi-Agent V2 tools give Sol and Terra a natural way to delegate tasks, share updates, and coordinate through

中文介绍 博主介绍了 Codex 的新多代理 V2 工具如何增强团队协作，提升任务分配和更新共享的效率，反映出多代理系统在 AI 开发中日益受到重视的趋势。

I Can Finally Talk to ChatGPT the Way I Actually Think

@derrickcchoi · 9.1K 粉丝 · 40.1K 阅 · 516 赞 · 31 转

07/23 16:08

This must be a little like how Tony Stark feels talking to Jarvis. I'm really excited that we just launched ChatGPT Voice across Work and Codex in the ChatGPT desktop app. I don't know about you but

中文介绍 发布了 ChatGPT Voice 功能，让用户可以更自然地与 ChatGPT 互动，增强了实时交流的体验。此开发显示出对用户交互方式人性化的关注和技术发展的新方向。

How we teach AI models

@leerob · 272.8K 粉丝 · 32.9K 阅 · 550 赞 · 51 转

07/23 21:39

How do AI models learn new skills and behaviors? The process is surprisingly human and easy to understand, even if you don’t have a machine learning background. Helpful colleagues AI models,

中文介绍 博主解释了 AI 模型如何学习新技能和行为的过程，即使对于非机器学习背景的人也容易理解。表明对 AI 教育领域的深入探索和对普及化的追求。

Robinhood let AI agents trade. Hood AI makes sure they can be trusted.

@HoodAI0x · 5.6K 粉丝 · 15.1K 阅 · 633 赞 · 293 转

07/22 16:39

Robinhood Just Let AI Agents Trade With Real Money. Trust Wasn't Part Of The Update. Connect Claude, Grok, or ChatGPT through Robinhood's official MCP server, open a dedicated agentic account, and a

中文介绍 博主提到 Robinhood 允许 AI 代理进行真实货币交易，强调信任问题的重要性。反映出对 AI 在金融领域应用的风险与管理关注，显示行业内的警觉与反思。

Building the Next Layer of the Agent Economy on BNB Chain

@OptimaiNetwork · 99.3K 粉丝 · 6.1K 阅 · 509 赞 · 112 转

07/23 11:10

Over the past year, AI has moved beyond chat interfaces toward autonomous systems capable of reasoning, planning, and taking action. The next challenge is no longer building more capable agents - it

中文介绍 博主探讨了 AI 发展超越聊天界面的新趋势，强调自主系统在推理和计划能力方面的进步。此内容展现了 AI 经济层面的新挑战，反映出对以 Agent 为核心的未来发展的共识。

[AINews] Claude Opus 5: Fable-level performance at Opus price (half Fable)

Latent Space · 07/25 03:25

ain't nobody beats Anthropic at distilling Fable!

中文介绍 Anthropic推出了Claude Opus 5模型，该模型在基准评测中表现出色，获得了159的Epoch Capabilities Index (ECI)，略低于Fable 5的161，但在某些能力上与Fable 5持平。

Opus 5

Smol AI News · 07/24 01:44

**Anthropic** launched the **Claude Opus 5** model, which sparked mixed reactions including benchmark scrutiny and praise for its coding-agent capabilities. The model achieved an **Epoch Capabilities Index (ECI) of 159**, slightly below **Fable 5's 161**, but matched Fable 5 on software engineering

中文介绍 Anthropic发布Claude Opus 5模型，该模型引发了各种反应，有人称赞其编程能力，并且在基准测试中获得了159的Epoch Capabilities Index (ECI)，略低于Fable 5的161。

[AINews] Black Forest Labs FLUX 3 - Multimodal Flow Models that beat Seedance 2.0, Gemini Omni and Grok Imagine, and FLUX-mimic video-action robotics model

Latent Space · 07/24 00:30

A HUGE win for BFL!

中文介绍 Black Forest Labs的FLUX 3模型表现优异，超越了Seedance 2.0、Gemini Omni和Grok Imagine，标志着公司的一次重大胜利。

ChatGPT Health 🩺, Fugu-Ultra 1.1 🐡, Runway Media Router 🎬

TLDR AI · 07/23 20:00

中文介绍 该条目暂无具体内容信息。

How AI helps scientists design the next generation of medicines

MIT Tech Review AI · 07/23 08:00

Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered pro

中文介绍 新药的设计和开发是一个昂贵且易失败的科学挑战。研发新药通常需要数年时间和巨额投资，而大多数候选药物最终无法上市。

not much happened today

Smol AI News · 07/23 01:44

**The Stack v3** is released as the largest open code dataset with **114 TB raw data**, **224M repositories**, and **5T deduplicated tokens**, significantly expanding data for open code models and cyber-defense. The debate on **distillation** continues as a key ideological fault line, with calls for

中文介绍 The Stack v3发布，成为最大的开放代码数据集，包含114 TB原始数据、224百万个代码库和5万亿去重标记，显著扩大了开放代码模型和网络防御的数据基础。

[AINews] "Laguna S 2.1 Released: Cheaper than Deepseek v4 Flash, Better than V4 Pro"

Latent Space · 07/23 01:18

a quiet day lets us highlight a new neolab win.

中文介绍 Laguna S 2.1发布，其价格低于Deepseek v4 Flash，同时性能超越V4 Pro。

Inside the Model Factory — Eiso Kant, Poolside AI

Latent Space · 07/23 01:09

Poolside's co-CEO on how his small team of top researchers built a model factory capable of training Laguna S - a 118B MOE beating Thinky's ~1T open weights model... and this is just the beginning.

中文介绍 Poolside AI的联席首席执行官介绍了如何建立模型工厂，该工厂能够训练Laguna S模型，其参数达到118B，超越Thinky的约1T开放权重模型。

Launching Health in ChatGPT

OpenAI News · 07/22 20:00

Health in ChatGPT now lets eligible U.S. users securely connect medical records and Apple Health to get more personalized insights and better understand their health.

中文介绍 OpenAI在ChatGPT中推出Health功能，符合条件的美国用户可以安全连接医疗记录和Apple Health，以获取个性化健康洞察。

Bringing Nunchaku 4-bit Diffusion Inference to Diffusers

Hugging Face Blog · 07/22 20:00

中文介绍 该条目暂无具体内容信息。

Cursor Router 🔀, OpenAI Presence 🤖, AMD + Anthropic deal 🤝🏻

TLDR AI · 07/22 20:00

中文介绍 该条目暂无具体内容信息。

Accelerating the frontiers of scientific discovery: Google’s $40M commitment to the Genesis Mission

DeepMind Blog · 07/22 09:38

Google commits $40M in AI tokens and credits for the Genesis Mission

中文介绍 谷歌承诺向Genesis Mission项目提供4000万美元的人工智能代币和信用。

Building AI infrastructure with the Effingham County community

OpenAI News · 07/22 09:00

OpenAI announces Project Camellia in Effingham County, Georgia, with commitments to responsible energy, community investment, jobs, and access to Codex.

中文介绍 OpenAI宣布在乔治亚州Effingham县启动Project Camellia，承诺负责的能源使用、社区投资、创造就业机会及Codex访问权限。

How news organizations are using AI to advance their vital missions

OpenAI News · 07/22 09:00

News organizations are using AI to strengthen reporting, grow audiences, and improve business operations, with OpenAI tools supporting journalists and publishers worldwide.

中文介绍 新闻机构正在利用人工智能加强报道、扩大受众和改善业务运营，OpenAI的工具支持全球的记者和出版商。

Advancing the next era of national science

OpenAI News · 07/22 08:00

OpenAI outlines its commitment to advancing American science working with the U.S. Department of Energy and national labs to use frontier AI to accelerate discovery.

中文介绍 OpenAI概述了与美国能源部和国家实验室合作，利用前沿人工智能加速科学发现的承诺。