2026-07-09

RynnWorld-Teleop: An Action-Conditioned World Model for Digital Teleoperation

👍 67

07/07 08:00

Scaling robot learning requires massive, diverse trajectory data, yet collection is currently bottlenecked by physical teleoperation, where every demonstration binds operator time to specific hardware and workspaces. We introduce digital teleoperation, a paradigm that decouples data collection from

中文介绍 提出数字遥操作范式，用动作条件世界模型RynnWorld-Teleop将数据收集与物理硬件解耦，实现跨平台机器人轨迹数据的规模化采集。

Hierarchical Sparse Attention Done Right: Toward Infinite Context Modeling

👍 36

07/03 08:00

Scaling modern large language models (LLMs) to long contexts is limited by the quadratic computation cost, and poor length extrapolation of dense attention. Chunk-wise sparse attention offers a promising alternative, but all existing methods fall short of full attention because of their inaccurate c

中文介绍 针对长上下文LLM中稀疏注意力建模不准确的问题，提出分层稀疏注意力方法，实现近乎无限上下文的高效建模，改善计算复杂度和长度外推。

Vision as Unified Multimodal Generation

👍 30

07/07 08:00

We formulate computer vision as unified multimodal generation, where heterogeneous visual tasks are expressed in the native text and image generation spaces of a unified multimodal model, without task-specific architectures. Under this formulation, SenseNova-Vision uses natural-language instructions

中文介绍 将计算机视觉统一为多模态生成任务，SenseNova-Vision用自然语言指令和统一模型空间处理异构视觉任务，无需专用架构。

Bridging Interleaved Multi-Modal Reasoning as a Unified Decision Process

👍 29

07/04 08:00

Unified multi-modal models (UMMs) have shown promising interleaved text-image reasoning capabilities, yet effectively optimizing such multi-turn generation via reinforcement learning (RL) remains an open challenge. Existing approaches apply RL exclusively to text steps, relegating image generation t

中文介绍 针对统一多模态模型（UMM）在图文交错推理中RL优化困难的问题，提出将推理构建为统一决策过程，联合优化文本和图像生成步骤。

Gemma 4 Technical Report

👍 26

07/02 08:00

We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemma 4 model suite features dense and Mixture-of-Experts architectures, ranging from 2.3B to 31B parameters. Alongside impr

中文介绍 介绍Gemma 4系列，包含2.3B~31B参数的稠密和MoE架构，原生多模态，优化计算效率和推理能力。

Light-Omni: Reflex over Reasoning in Agentic Video Understanding with Long-Term Memory

👍 19

07/06 08:00

Agentic video understanding equips models with long-term memory to autonomously process and respond to continuous, long-horizon multimodal streams. However, advanced video agents often rely on ``detective-style'' iterative reasoning for action control (e.g., search) and evidence aggregation, incurri

中文介绍 提出Light-Omni，通过长期记忆和反射式推理替代迭代搜索，实现高效自主的长程视频理解与响应。

DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation

👍 17

07/06 08:00

Speculative decoding accelerates Large Language Model (LLM) inference by decoupling draft generation from target verification. While recent parallel drafters efficiently propose long token sequences in a single forward pass, they suffer from rapid acceptance decay due to a lack of inter-token depend

中文介绍 提出DSpark，利用置信度调度的半自回归投机解码，在保持Token间依赖的同时并行生成，提升大模型推理加速比。

SkillOpt-Lite: Better and Faster Agent Self-evolution via One Line of Vibe

👍 17

07/03 08:00

While skill optimization for autonomous agents has gained traction, existing methods rely on complex pipelines. This leaves a fundamental question unaddressed: What constitutes a minimal viable pipeline for skill optimization, where every component is justified by theory or empirical necessity? We f

中文介绍 探索自主智能体技能优化的最小可行管线，提出SkillOpt-Lite，通过单行反馈（Vibe）实现更优更快的自我进化。

Parallelized Autoregressive Decoding for Omni-Modal Dense Video Captioning

👍 16

07/03 08:00

Dense video captioning aims to generate temporally grounded descriptions of video events, benefiting both event-level video understanding and generation. In this domain, autoregressive video large language models have emerged as a prevalent paradigm due to their strong generative and cross-modal mod

中文介绍 面向全模态密集视频描述，提出并行自回归解码方法，提升视频大语言模型在事件级理解与生成中的效率。

Look Before You Leap: Distilling Tree Search into Action Evaluation for Frozen VLA Models

👍 15

07/04 08:00

Vision-Language-Action (VLA) models acquire broad embodied capabilities through large-scale pretraining, yet their generalization remains far more fragile than that of LLMs and VLMs. The prevailing remedy, post-training via supervised fine-tuning or reinforcement learning, improves task-specific per

中文介绍 针对VLA模型泛化脆弱问题，提出Look Before You Leap方法，通过树搜索蒸馏行动评估，无需额外训练即可提升冻结VLA模型的决策质量。

From Foundation to Application: Improving VLA Models in Practice

👍 11

07/07 08:00

Despite recent progress of VLA foundation models, the disparity between laboratory conditions and real-world applications continues to impede their practical implementation. To bridge this gap, we present LingBot-VLA 2.0, which advances LingBot-VLA through improvements in three functional domains. (

中文介绍 弥合VLA基础模型与实际应用差距，介绍LingBot-VLA 2.0，在感知、规划和执行三方面改进，提升机器人操作实用性能。

TurnOPD: Making On-Policy Distillation Turn-Aware for Efficient Long-Horizon Agent Training

👍 10

07/07 08:00

On-policy distillation (OPD) trains a student policy by matching a stronger teacher on the student's own trajectories, offering a promising framework for language agent training. However, its application to long-horizon agentic tasks remains insufficiently explored. We identify two key inefficiencie

中文介绍 提出TurnOPD，使在线策略蒸馏（OPD）感知回合边界，解决长程智能体训练中策略退化和时间效率低下的问题。

MentalThink: Shaping Thoughts in Mental SVG World

👍 10

07/03 08:00

We introduce MentalThink, a visual-symbolic reasoning paradigm that equips Multimodal LLMs (MLLMs) with an executable mechanism for "mental" visualization. The core of MentalThink is a think-with-SVG pipeline, where the model learns to generate, render, and interpret scalable vector graphics (SVG) c

中文介绍 提出MentalThink视觉-符号推理范式，让多模态LLM通过生成、渲染和解释SVG实现「心理可视化」，增强空间与几何推理能力。

Unified Audio Intelligence Without Regressing on Text Intelligence

👍 10

07/06 08:00

Audio intelligence involves understanding, reasoning about, and generating both audio and speech. In this work, we introduce Nemotron-Labs-Audex-30B-A3B (Audex), a unified audio-text LLM built on Nemotron-Cascade-2-30B-A3B, a strong text-only MoE LLM. Audex adopts a simple unified design with a sing

中文介绍 介绍Audex，基于Nemotron-Cascade-2的30B MoE统一音频文本LLM，在不损失文本智能前提下实现语音/音频理解与生成。

CanvasAgent: Enabling Complex Image Creation and Editing via Visual Tool Orchestration

👍 8

07/06 08:00

Complex image creation and editing often require more than a single generation or editing model. A user request may involve synthesizing images, localizing objects, segmenting regions, editing selected content, compositing intermediate assets, reading text, and enhancing the final result. Such tasks

中文介绍 提出CanvasAgent，通过可视化工具编排（图像合成、分割、编辑、组合等）实现复杂图像创作与编辑的自动化流程。

PointDiT: Pixel-Space Diffusion for Monocular Geometry Estimation

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07/02 08:00

State-of-the-art single-image 3D reconstruction methods often rely on complex hybrid architectures and loss functions, or compress geometry into latent spaces in order to leverage pre-trained latent diffusion models. In this work, we show that such architectural overhead and intricate loss formulati

中文介绍 提出PointDiT，直接在像素空间用扩散模型进行单目几何估计，无需复杂混合架构和潜空间压缩，简化训练流程。

CGGS: Consistency-Augmented Geometric Gaussian Splatting for Ego-centric 3D Scene Generation

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07/04 08:00

Challenges remain in ego-centric 3D scene generation due to limited view overlap and the dominant influence of individual perspectives on scene interpretation. These factors hinder the creation of viewpoint-consistent and semantically aligned visual content, as well as the construction of accurate g

中文介绍 提出CGGS，通过一致性增强的几何高斯泼溅方法，改善自中心3D场景生成中的视角一致性与几何精度。

SeKV: Resolution-Adaptive KV Cache with Hierarchical Semantic Memory for Long-Context LLM Inference

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06/30 08:00

Large language models increasingly operate over long contexts, where the KV cache becomes a dominant memory bottleneck: its size grows linearly with sequence length and must be retained throughout decoding, making full GPU caching prohibitively expensive without compression. Existing KV cache compre

中文介绍 提出SeKV，用分辨率自适应KV缓存和层次语义记忆，在长上下文LLM推理中大幅减少显存占用，保持性能。

TREK: Distill to Explore, Reinforce to Refine

👍 6

07/06 08:00

Group Relative Policy Optimization (GRPO) is effective when the current policy already samples useful reasoning trajectories, but it stalls on hard prompts whose correct solution modes lie outside the student's on-policy support. We propose TREK (Teacher-Routed Exploration via Forward KL), a simple

中文介绍 提出TREK，通过教师前向KL蒸馏探索和改进的GRPO强化，解决GRPO在困难推理问题上策略停滞的问题。

Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding

👍 6

07/07 08:00

We introduce Nemotron-Labs-Diffusion, a tri-mode language model (LM) that unifies AR, diffusion, and self-speculation decoding within a single architecture. Trained with a joint AR-diffusion objective, Nemotron-Labs-Diffusion can switch modes to sustain high throughput across deployment settings and

中文介绍 介绍Nemotron-Labs-Diffusion，统一自回归、扩散和自投机解码三种模式的语言模型，根据部署场景灵活切换以保持高吞吐。

The Task Economy - Data will be the next $1 Trillion Category

@EverettRandle · 19.4K 粉丝 · 484.8K 阅 · 507 赞 · 56 转

07/08 00:15

The Token Economy When we talk about AI today, tokens are king. Specifically, inference tokens have emerged as the primary proxy for tracking the growth of the AI ecosystem. Public companies report

中文介绍 讨论token经济后，预测数据将成为下一个万亿美元市场。推断token是当前AI增长主要指标，但数据资产将主导下一阶段「任务经济」。

Continual Learning for Agents

@pirroh · 15.0K 粉丝 · 314.8K 阅 · 540 赞 · 59 转

07/06 21:10

Everyone talks about Continual Learning as if it means one thing only: updating model weights. But there's an inconvenient truth about the agent ecosystem — the vast majority of agents in production

中文介绍 指出多数人对「持续学习」的理解仅停留在更新权重，但生产环境中的agent需要更复杂的方式。揭示agent生态系统中的真实瓶颈。

Everyone is wrong about open source AI in the enterprise

@thejessezhang · 59.4K 粉丝 · 274.2K 阅 · 512 赞 · 54 转

07/06 23:31

The prevailing story right now is that open source is eating the enterprise. The capability gap between the best closed and open models has shrunk to low single digits. A third of the Fortune 500 has

中文介绍 反驳「开源正在吞噬企业AI」的主流叙事。尽管开源与闭源模型能力差距缩小到个位数，且三分之一财富500强已采用，但实际情况更复杂。

Getting started with loops

@ClaudeDevs · 564.6K 粉丝 · 252.4K 阅 · 2.9K 赞 · 239 转

07/07 03:08

There’s a lot of talk right now about "designing loops" instead of prompting your coding agent. If you spend some time on X trying to pin down what a loop actually is, you'll come across multiple

中文介绍 针对编码agent的「循环设计」概念进行入门讲解。X上对「循环」定义众说纷纭，本文澄清其实际含义与使用场景。

This prompt will change your life

@EXM7777 · 123.5K 粉丝 · 233.6K 阅 · 554 赞 · 38 转

07/07 22:31

for most people, fable has a few hours left to live... this is the smartest model that ever existed and everyone's spending its last night building one more app i made it do something 100x more

中文介绍 在Fable模型即将失效前，分享一个prompt让该模型做更有价值的事——提取其智能用于其他用途，而非构建另一个app。

How to become an applied AI engineer

@eyad_khrais · 20.2K 粉丝 · 225.8K 阅 · 508 赞 · 38 转

07/07 23:42

I wrote this as the guide I would have wanted before making the transition into Applied AI Engineering. The role broadly overlaps with traditional software engineering, but it adds a few important

中文介绍 提供从传统软件工程转型为应用AI工程师的指南。强调该角色与传统开发的重叠以及新增的关键差异，如模型选择、评估、部署等。

The Heretic’s Guide to AI’s Stars Part III: AI Demand, Offshore Financing, & Compression Too

@michaeljburry · 2.0M 粉丝 · 209.2K 阅 · 508 赞 · 51 转

07/08 08:23

“They are just flying empty airplanes around.” - Son NVIDIA is the North Star, Orion, the whole Milky Way. NVIDIA is benefitting from strong demand, but is selling into a concentrated set of buyers

中文介绍 对AI市场的异见分析：尽管NVIDIA受益于强劲需求，但买家高度集中，存在「空转」风险。讨论离岸融资和压缩问题。

Expanding Managed Agents in Gemini API: background tasks, remote MCP and more

@GoogleAIStudio · 184.1K 粉丝 · 165.4K 阅 · 504 赞 · 62 转

07/08 00:38

Today we’re announcing new capabilities in Managed Agents in Gemini API, including background execution, remote MCP server integration, custom function calling and refreshing credentials across

中文介绍 Google AI Studio宣布Gemini API中Managed Agents的新能力：后台执行、远程MCP服务器集成、自定义函数调用和跨服务凭证刷新。面向agent开发者。

You have 1 day to clone Fable 5 into Opus 4.8.

@alex_prompter · 277.4K 粉丝 · 125.9K 阅 · 595 赞 · 77 转

07/07 02:25

Tomorrow is the last day Fable 5 sits inside your plan for free. After July 7 it moves to pay-per-use credits, and most people are about to spend the week arguing over whether it's worth keeping. That

中文介绍 提醒在Fable 5从免费转为按量付费前（7月7日截止），需要将其能力提取或克隆到其他模型（如Opus 4.8）。

Model and effort in Claude Code: knowing more vs. trying harder

@ClaudeDevs · 564.6K 粉丝 · 95.9K 阅 · 1.5K 赞 · 100 转

07/09 00:55

Claude Code gives you two settings that both seem to "make the answer better": the model, and the effort level. But what do these actually do to the output? And how do you know whether to reach for a

中文介绍 解释Claude Code中模型选择与努力程度的实际效果：选对模型是「知道更多」，提高努力是「更努力尝试」。指导如何权衡二者。

Do this on your last day with Fable

@EXM7777 · 123.5K 粉丝 · 86.9K 阅 · 510 赞 · 37 转

07/06 23:48

You can extract Fable 5's intelligence out of the model before it's gone... and i'm going to teach you the 5 workflows that do it, with every prompt ready to paste because tomorrow Fable 5 leaves your

中文介绍 在Fable 5即将离开免费计划前，提供5个工作流和对应prompt，用于从模型中提取其智能并迁移到其他模型使用。

You can go from zero to hireable AI engineer in 4 months. Here's the exact path.

@free_ai_guides · 11.1K 粉丝 · 59.1K 阅 · 503 赞 · 114 转

07/08 00:32

AI engineering is one of the best-paid, fastest-growing jobs in tech right now. And the door to it just got wider than it's ever been. Most guides get this wrong. They hand you a wall of theory, tell

中文介绍 给出从零到可雇佣AI工程师的4个月学习路径。批判常见指南的纯理论路线，提供实践导向的具体步骤。

How to Distill a $10M+ AI Researcher

@JIACHENLIU8 · 2.9K 粉丝 · 48.9K 阅 · 508 赞 · 28 转

07/08 02:16

tldr: Research Process > Research Outcomes Everyone says the best researchers have great intuition. They seem to know which questions are worth asking before the field recognizes them as important.

中文介绍 提炼顶尖AI研究员的思维方式：研究过程比结果更重要。最佳直觉来自不断提问和判断哪些问题值得求解。

Unicity Labs and Quant Partner to Bring Verifiable Execution to Agentic Trading

@unicity_labs · 127.3K 粉丝 · 4.5K 阅 · 520 赞 · 827 转

07/08 22:22

Today we're announcing a partnership between Unicity Labs and @tryquantio to build the secure foundation for agentic finance. Quant - the AI intelligence and execution layer for the agentic future

中文介绍 Unicity Labs与Quant合作，为agent驱动的金融交易提供可验证的执行层。旨在构建agent金融的安全基础。

A Google Knowledge Panel Isn't a Vanity Badge. Here's What It Actually Is - and How I Earned Mine.

@krudhrah · 1.2K 粉丝 · 1.3K 阅 · 517 赞

07/08 00:11

Search my name — "Rudhrah Keshav" — and Google doesn't just show you links. It shows you a panel: my photo, my profession, my date of birth, my education, my podcast, my profiles. All assembled by

中文介绍 解释Google知识面板并非虚荣徽章，而是权威性标识。作者分享获得面板的实践方法，包括资料优化和权威来源建立。

Why AI Infrastructure must evolve for Agent Experience — Akshat Bubna, Modal CTO

Latent Space · 07/09 06:55

2 years after our first coverage, we return with Modal's other cofounder to explore why Agent Experience is working now, and everything they have learned building the new agent cloud.

中文介绍 Modal 联合创始人兼 CTO Akshat Bubna 探讨 AI 基础设施为何需为代理体验演进，分享他们在构建新一代代理云过程中积累的经验。

Data for Agents

Hugging Face Blog · 07/09 01:16

中文介绍 NVIDIA 在 Hugging Face 博客发文，探讨面向 AI 代理的数据策略，但具体内容未详细披露。

Our approach to government and national security partnerships

OpenAI News · 07/08 21:30

Learn how OpenAI approaches government and national security partnerships, with principles for responsible AI use, democratic accountability, and public safety.

中文介绍 OpenAI 公布其与政府和国家安全合作的方法，强调负责任 AI 使用、民主问责和公共安全原则。

Separating signal from noise in coding evaluations

OpenAI News · 07/08 21:00

A new analysis from OpenAI reveals issues in SWE-Bench Pro, a popular coding benchmark, raising concerns about reliability and accuracy in evaluating AI models.

中文介绍 OpenAI 新分析指出，流行编程基准 SWE-Bench Pro 存在可靠性问题，引发对 AI 模型评估准确性的担忧。

Helping K–12 educators build practical AI skills

OpenAI News · 07/08 18:00

OpenAI Academy and the Walton Family Foundation are bringing hands-on AI Skills Jams to help K–12 educators build practical AI skills for the classroom.

中文介绍 OpenAI Academy 与 Walton Family Foundation 合作推出 AI Skills Jams 实践活动，帮助 K-12 教师建立课堂实用 AI 技能。

[AINews] Lilian Weng summarizes 35 papers on Harness Engineering for RSI

Latent Space · 07/08 10:20

a quiet day lets us read some condensed insight

中文介绍 Lilian Weng 总结了 35 篇关于“利用工程实现递归自我改进（RSI）”的论文，提供浓缩洞见。

Introducing GPT-Live

OpenAI News · 07/08 08:00

A new generation of voice models for natural human-AI interaction, now powering ChatGPT Voice.

中文介绍 OpenAI 推出 GPT-Live，新一代语音模型，用于自然的人机交互，现已为 ChatGPT Voice 提供支持。

Native-speed vLLM transformers modeling backend

Hugging Face Blog · 07/08 08:00

中文介绍 Hugging Face 博客发布关于原生速度 vLLM transformers 建模后端的内容，具体细节未提供。

GPT-5.6 Thursday ⭐️, Claude Cowork mobile 📱, Gemini API agents 🤖

TLDR AI · 07/08 08:00

中文介绍 TLDR AI 日报：GPT-5.6 发布、Claude Cowork 移动端上线、Gemini API 代理功能。

From Hugging Face to Amazon SageMaker Studio in one click

Hugging Face Blog · 07/08 05:15

中文介绍 Hugging Face 与 Amazon 合作，推出从 Hugging Face 一键部署到 SageMaker Studio 的功能。

Hugging Face Models on Foundry Managed Compute

Hugging Face Blog · 07/07 23:20

中文介绍 Microsoft Foundry 托管计算现已支持 Hugging Face 模型，简化模型部署与管理。

The foundational elements of AI architecture that IT leaders need to scale

MIT Tech Review AI · 07/07 19:10

With the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the futur

中文介绍 MIT Tech Review 文章分析 AI 架构基础要素，指出随着 AI 能力进步和代理系统兴起，组织扩展用例的同时也面临风险，IT 领导者需谨慎投资。

[AINews] The Field Guide to Fable

Latent Space · 07/07 12:44

a quiet day lets us digest the world's most significant model launch... to date.

中文介绍 [AINews] 解读“Fable”模型发布，被誉为迄今最重要的模型发布，本文提供全面指南。

Australian Payments Plus moves faster with ChatGPT and Codex

OpenAI News · 07/07 08:00

See how Australian Payments Plus uses ChatGPT Enterprise and Codex to move faster through payments complexity. AP+ saves time, improves quality, and keeps human judgment central.

中文介绍 澳大利亚支付公司 Australian Payments Plus 使用 ChatGPT Enterprise 和 Codex 加速处理支付复杂性，节省时间、提升质量，并保留人工判断。

MUFG aims to become AI-native with OpenAI

OpenAI News · 07/07 08:00

MUFG uses ChatGPT Enterprise to build an AI-native organization, improve workflows, and deliver new AI-powered financial services at scale.

中文介绍 日本三菱日联金融集团（MUFG）使用 ChatGPT Enterprise 构建 AI 原生组织，改善工作流程，并大规模提供 AI 驱动的金融服务。