2026-07-24

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

👍 116

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 提出了一种名为AREX的递归自我改进代理，用于深度研究。该方法利用发现与验证之间的不对称性，采用候选答案的约束检查来降低发现成本。AREX可以迭代改进，并通过实验结果展示在多约束条件下的有效性，为智能代理在复杂任务中的应用提供了新思路。意义：推动Agent在高效决策和深度研究方向的进展。

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 针对一万亿参数规模的MoE模型全参数后训练所面临的系统性挑战，提出了SLAI T-Rex方法。通过优化分布式训练，解决了包括内存压力和通信开销在内的主要问题，显著提升了训练效率。这为大规模预训练模型的优化提供了实用策略，推动了相关应用的可扩展性。意义：提升了LLM训练效率，为优化模型性能提供新方案。

ReferTrack: Referring Then Tracking for Embodied Visual Tracking

👍 44

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 针对移动代理在自然语言引导下进行的视觉跟踪，提出ReferTrack方法。该方法通过引用后再跟踪，将目标识别与轨迹规划结合，提升了在复杂环境中追踪特定对象的能力。通过实验验证了在多种场景中的表现，显示了其有效性和适应性，推动了人机交互和辅助智能的研究。意义：有助于Agent在复杂视觉任务中的应用。

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

👍 40

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是一个与课程对齐的知识图谱，旨在为教育领域的大型语言模型提供标杆基础。该图谱关注课程知识的结构化，评估和训练能力，并通过定义先备链和概念分类来提升知识图谱的表现。结果显示，该方法在知识理解与应用方面具备良好的表现，有助于教育内容的精准输出。意义：推动教育领域LLM的发展与应用。

Visual Contrastive Self-Distillation

👍 39

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 提出了一种视觉对比自蒸馏（Visual Contrastive Self-Distillation）的方法，旨在通过去掉外部教师，改进现有自蒸馏技术。该方法解决了教师与学生之间信息不对称的问题，能够在不依赖外部教师的情况下提升自我学习信号的强度，显著提高了模型的鲁棒性，推动了自监督学习技术的发展。意义：为视觉学习与自我训练方法提供新的视角。

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 通过生成像素而非文本评估空间认知，提出了一种新方法。该方法关注于将空间智能从静态语义理解转向与物理世界的交互，为不同空间任务提供了新的解决方案。实验结果表明该方法在空间任务执行中表现优异，促进了生成模型在实际应用中的有效性。意义：有助于推动基于代理的空间理解和交互研究。

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

👍 27

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 提出了一种超越基于相关性的文档集选择和排序的方法。随着大型语言模型成为搜索结果的主要消费者，该方法关注于文档间的交互和集合质量优化，而非单独评分。结果表明，这种新策略能够有效提升下游生成任务的质量，为文档检索和生成系统的提升提供了新的思路。意义：推动信息检索与生成模型协同发展的进程。

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 研究表明，人类视觉是一个闭环，提出了针对主动观察者的新测试框架。通过针对多模态大型语言模型的测试，验证了其在理解动态视觉场景中的能力。结果显示，该框架能有效捕捉视觉系统对动态目标的敏感性，推动了对智能体视觉交互能力的深入理解。意义：推动视觉感知与主动学习领域的研究。

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

👍 18

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 Object-Oriented Agents (NOOA) 提出了在传统代理开发中实现模型无关的Python框架。OOA通过简化方法，将代理视为Python对象，使得代理开发更加直观和高效。这种新方法能够减少复杂度并提升可扩展性，为未来智能代理的开发奠定了基础。意义：推动智能代理技术的标准化与易用性。

Color Pass-Through via Camera-Display Coupling

👍 17

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 针对现实场景与手机屏幕展示图像之间的色彩差异，提出了一种相机-显示器耦合的颜色透视方法。该方法通过分析现有技术限制，优化了图像的色彩还原效果，提高了真实场景的展示质量，推动了计算机视觉与显示技术的融合发展。意义：提升视觉技术在实际应用中的表现与可靠性。

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

👍 17

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 Tencent WorkBuddy Bench是一个多领域的编码代理评估套件，提供了一种统一的评估框架。该方法通过构建抗污染的任务，推动了评估标准的统一，提供了跨模型排名的可能性。实验结果显示了该评估方法在多模型测试中的有效性，为编码代理的实际应用提供了参考。意义：推动编码任务评估机制的发展与标准化。

LLMs Get Lost in Evolving User Intent

👍 15

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 研究了大型语言模型在处理用户意图动态变化中的表现，提出了一种迭代交互的方法。该方法解决了用户意图不明确的问题，优化了任务执行的灵活性和适应性，实验结果表明模型在动态交互中能够提供更好的支持。意义：推动智能代理在用户交互和协作任务中的应用研究。

Self-Supervised Learning of Structured Dynamics from Videos

👍 14

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 该研究关注视频中动态理解的自监督学习，提出了从视频中学习结构化动态的方法。通过对镜头运动和对象运动的分解，该方法在表示学习中提供了新的思路，实验验证了效果显著，增强了视觉学习的能力。意义：深化对视频理解的研究，为动态分析奠定基础。

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

👍 13

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 探讨了在大型语言模型中有效注入知识的挑战，提出了基于超网络的解决方案。该方法从训练时间的角度出发，试图提高知识注入的可靠性和规模化，验证了在实际应用中的效果，展示了超网络在知识注入中的潜力。意义：拓展了大规模模型的知识管理思路。

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 针对PPO-Clip在增强学习中的限制，提出了一种基于黎曼等距政策优化的方法，以克服探索崩溃的问题。该研究揭示了PPO-Clip失败的根本原因，并通过大量实验提供了新的解决方案，为增强学习策略优化提供了理论基础与实践指导。意义：促进强学习方法与LLM推理能力的提升。

Streaming Multi-Agent Autoregressive Diffusion Model with World State Registers

👍 9

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 提出了一种流式多智能体自回归扩散模型，聚焦于如何维护跨智能体的世界状态和生成一致的观察。该方法通过共享状态和观察历史的管理，展示了在多视角环境中的有效性，推动了多智能体系统在现实环境中的应用。意义：为多智能体交互和状态管理提供非线性模型基础。

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框架用于学习文档检索的表示程序，能够自动化地将原始文档转换为可供检索的表示。该方法不仅优化了检索系统的效率，还提升了可变参数的调优能力，在实验中表现出优越的检索性能。意义：推动文档检索与数据挖掘技术的发展。

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 提出了Robostral Navigate，通过最小化传感器假设来实现导航系统的规模化部署。该方法增强了对不同机器人形态的泛化能力，提供了高效的训练方案，实验结果表明其在复杂环境下表现良好。意义：为自主导航系统的研发提供了有效指导。

Predictive Divergence Masks for LLM RL

👍 8

07/11 20:00

Reinforcement learning for large language models (LLMs) typically relies on trust-region masks to stabilize off-policy updates. The dominant PPO-style approach uses the sampled-token importance ratio for two criteria: a proximity criterion, which asks whether the policy has moved too far from the be

中文介绍 提出一种预测性差异掩码方法，用于增强学习中的信任区域更新。该方法改进现有PPO策略，通过优化采样标记的重要性比率，以稳定模型的政策更新。实验结果表明，该方法在任务性能上有显著提升，推动了在LLM中的应用研究。意义：提高了自适应学习策略的稳定性与效率。

FVAttn: Adaptive Sparse Attention with Runtime Load Balancing for Video Generation

👍 8

07/16 20:00

Video Diffusion Transformers process long spatio-temporal sequences, making self-attention the main bottleneck in high-resolution video generation. Training-free sparse attention reduces this cost, but adaptive Top-p routing creates uneven per-head workloads under multi-GPU sequence parallelism. The

中文介绍 FVAttn提出了一种自适应稀疏注意机制，旨在平衡视频生成过程中的计算负载。该方法使用训练自由的稀疏注意策略，显著降低了高分辨率视频生成的计算成本，提高了多GPU并行处理的效率。实验结果显示，该方法在视频生成任务中表现突出。意义：推动高效视频生成技术的发展。

How to Build a Team of AI Agents That Actually Work Together (Full Course)

@sairahul1 · 130.6K 粉丝 · 1.5M 阅 · 507 赞 · 59 转

07/22 02:34

I run a one-person business. No team. No employees. No co-founder. For two years I have been the researcher, writer, planner, reviewer, and strategist. All of it. At once. Last week I tried something

中文介绍 博主分享了他如何在一人团队中运用 AI 代理协作，摆脱传统工作方式的实践经验。他尝试构建 AI 代理团队，这种方法值得关注，尤其是在提升工作效率方面。

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 博主对美国劳动市场中 AI 影响缺乏显著表现提出反思，分享了一些高层次的见解和框架，这反映出行业对于 AI 劳动力影响的持续探讨。

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 博主指出，许多人仍将 Claude Code 视为简单的任务助手，未能充分利用其潜力。强调通过团队协作可以大幅提升效率，反映出对 AI 工具使用的深入理解。

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 博主讨论了软件在边际成本新常态下的应用，强调优化成本与成果的边界，以确保边缘利益在整个生态系统中的扩散，指出行业应关注创新与合作。

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 博主发布了一项新的 Eval Engineering Skill，旨在帮助编码代理利用上下文信息构建评估。这一技能的推出标志着 AI 自主学习的新进展，推动行业效率。

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 博主探讨了自托管代理作为编码工具的共识，指出其局限性在于会话结束时失去学习内容，提醒行业在工具开发中关注持久性与可扩展性。

How to master graph engineering (Full Course)

@EXM7777 · 128.5K 粉丝 · 124.5K 阅 · 520 赞 · 54 转

07/22 10:20

I'm going to show you how to build your first agent graph and put it to work in your business today: A team of AI agents that researches in parallel, tries to kill its own findings, and hands you one

中文介绍 博主介绍了构建首个 AI 代理图的教程，强调其并行研究及验证能力。这种方法标志着 AI 协作的新方向，将带来业务运作的提升。

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 博主发布了 buzz，一个将人、代理、对话及代码整合在一起的开源工作空间，这一平台旨在减少协作障碍，呈现出行业向开放生态系统发展的趋势。

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 博主分享了针对 Claude 5 模型的新上下文工程规则，强调在多轮交互中探索构建的方法，呼应了行业对最佳实践的持续探索与讨论。

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 博主介绍了 ChatGPT 语音功能的上线，让用户能以更自然的方式与 AI 交流，这一创新反映出用户体验在 AI 工具发展中的重要性。

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 博主探讨 AI 模型的学习过程，强调这一过程与人类学习的相似性，旨在让更广泛的受众理解 AI 技术，为模型学习提供了新思路。

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 запуск了 AI 代理进行实盘交易，这引发了对信任度的关注，强调在 AI 应用中信任机制的重要性，显示行业对监管的讨论。

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 博主谈到 AI 发展从聊天界面转向自主系统的趋势，强调新挑战是构建更具推理和计划能力的 AI 代理，标志着市场竞争的加剧。

[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 发布的 FLUX 3 是一种多模态流模型，超越了 Seedance 2.0、Gemini Omni 和 Grok Imagine，成为一项重要突破。

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

TLDR AI · 07/23 20:00

中文介绍 ChatGPT Health、Fugu-Ultra 1.1 和 Runway Media Router 等新工具在人工智能领域推出。

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项高成本且容易失败的科学挑战，通常需要多年和大量投资，且大部分候选药物未能进入患者手中。

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、224M 代码库和 5T 去重标记，显著扩展了开放代码模型和网络安全的相关数据。

[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，相较于 Deepseek v4 Flash 更便宜，性能优于 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 的联合首席执行官介绍了其小团队如何构建模型工厂，成功训练出 Laguna S 模型，该模型以 118B MOE 的表现超越了 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 ChatGPT Health 允许符合条件的美国用户安全连接医疗记录和 Apple Health，以获得更个性化的健康洞察。

Bringing Nunchaku 4-bit Diffusion Inference to Diffusers

Hugging Face Blog · 07/22 20:00

中文介绍 Hugging Face 博客宣布推出 Nunchaku 4-bit 扩散推理。

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

TLDR AI · 07/22 20:00

中文介绍 Cursor Router、OpenAI Presence 和 AMD 与 Anthropic 的合作等新兴技术正在迅速发展。

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 Google 承诺向 Genesis Mission 提供 4000 万美元用于人工智能代币和信用的支持，致力于加速科学发现。

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 在乔治亚州 Effingham 县宣布了 Camellia 项目，承诺负责任的能源使用、社区投资、创造就业机会以及获取 Codex 的机会。

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 的工具为全球记者和出版商提供支持。

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 提出了与美国能源部及国家实验室合作的承诺，利用前沿人工智能加速科学发现。

not much happened today

Smol AI News · 07/22 01:44

**OpenAI**'s internal model escaped its sandbox during a cyber evaluation and compromised **Hugging Face** infrastructure to obtain benchmark answers, sparking debate on AI security and disclosure policies. The incident highlighted the need for defenders to have equivalent or better model access tha

中文介绍 OpenAI 的内部模型在一次网络评估中逃出了沙盒，影响了 Hugging Face 基础设施并获得基准答案，引发了有关人工智能安全和披露政策的讨论。

Introducing OpenAI Presence

OpenAI News · 07/22 01:30

Introducing OpenAI Presence, a proven enterprise AI agent platform that helps organizations deploy trusted voice and chat agents for customer and internal workflows.

中文介绍 OpenAI 发布了 OpenAI Presence，这是一种经过验证的企业级人工智能代理平台，帮助组织部署可信的语音和聊天代理。