2026-07-29

Kimi K3: Open Frontier Intelligence

👍 258

07/27 08:00

We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is built on Kimi Delta Attention and Attention Residuals, which improve information flow across sequence length and model d

中文介绍 提出2.8T参数的MoE模型Kimi K3，104B激活参数，具备原生视觉能力和1M-token上下文窗口。采用Kimi Delta Attention与Attention Residuals，增强长序列和深度模型的信息流。

JarvisHub: An Open Harness for Canvas-Native Multimodal Creative Agents

👍 103

07/26 08:00

Creative AI is moving from single-step asset generation toward long-horizon multimodal production. Although recent generative models can synthesize high-quality images, videos, audio clips, UI elements, storyboards, slides, and other creative assets, real-world creative work requires more than isola

中文介绍 提出JarvisHub，一个面向画布原生多模态创意代理的开放框架，支持图像、视频、音频等多种资产的长周期协同创作，解决孤立生成问题。

From Proprietary to Open-Source: Bridging the Distribution Gap via Multi-Agent Protocol Distillation in Agentic Search

👍 65

07/27 08:00

Agentic search enables large language models to solve knowledge-intensive tasks by interleaving multi-step reasoning with retrieval, yet optimizing this with outcome-based reinforcement learning (RL) provides only sparse supervision. Knowledge distillation can supply denser guidance, and advanced pr

中文介绍 针对代理搜索中结果导向RL监督稀疏的问题，提出多智能体协议蒸馏，将专有模型的知识迁移至开源模型，弥合分布差距，实现更密集的监督信号。

StateAct: Program State, before Pixels, for Long-Horizon Computer-Use Agents

👍 52

07/24 08:00

Computer-use agents are usually improved by strengthening perception: better models for reading a screenshot and choosing where to click. Yet a screenshot is only a lossy rendering of the underlying program state, e.g., the files, application backends, and DOM that hold the task data. Different stat

中文介绍 提出StateAct，直接利用程序状态（文件、后端、DOM）而非像素截图作为输入，为长周期计算机使用代理提供更完整的任务数据，优于纯视觉方法。

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators

👍 49

05/19 08:00

The quality of training data fundamentally determines the capabilities of large language models (LLMs), yet no unified benchmark exists to measure how well LLMs, agents, and data-centric workflows actually prepare training data end to end. We view LLM-driven data preparation as comprising two comple

中文介绍 提出DataPrep-Bench，首个统一基准，评估LLM、代理和数据工作流端到端准备训练数据的能力，涵盖数据清洗、增强等两个互补阶段。

Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills

👍 34

07/24 08:00

LLM training is shifting from manual design and annotation to interaction-driven self-evolution. However, existing self-evolutionary methods face a fundamental dilemma between task diversity and verification reliability: environment-bound methods obtain precise feedback but confine learning to narro

中文介绍 提出Skill Self-Play，通过协同进化技能解决自我进化中任务多样性与验证可靠性的矛盾，无需预定义依赖，推动LLM能力前沿。

Data Pyramid for Embodied Manipulation

👍 31

07/27 08:00

Multimodal foundation models learned to see and to speak by consuming the whole internet. Embodied agents admit no such shortcut, since they require data that couple observations with physical states and actions. These signals can be provided, to varying degrees, by multiple data sources. In this wo

中文介绍 提出Data Pyramid，一种分层数据架构，融合多种数据源（仿真、真实机器人、人类演示等），为具身操作代理提供耦合观测与物理状态/动作的训练数据。

Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement Learning

👍 28

07/22 08:00

Agentic reinforcement learning research is constant algorithm modification, new estimators, new pipeline stages, new rollout schemes, and in mainstream frameworks each change threads through layers of trainer, distributed backend, and rollout glue: the cost lands on the researcher at every iteration

中文介绍 提出Molt，一个原生PyTorch的代理强化学习训练框架，通过统一抽象简化算法修改、分布式后端的成本，支持灵活扩展，降低研究迭代代价。

Scaling Native Multimodal Pre-Training From Scratch

👍 21

07/24 08:00

Although large language models (LLMs) exhibit remarkable reasoning capabilities, their reliance on text-only pre-training restricts the perception of the multimodal physical world. Native multimodal pre-training avoids this limitation by training models from scratch on multimodal inputs, thereby ach

中文介绍 提出从头开始的原生多模态预训练方法，在图像、视频、音频等多模态数据上联合训练模型，无需文本-only预训练，实现跨模态推理，提升物理世界感知。

Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems

👍 20

07/23 08:00

Production AI agents' failures are less often due to an inability to reason well and more often because they cannot manage what is in their reasoning context: conversation histories, large prompts, large tool definitions, and ballooning tool outputs. Agents drown in their own accumulating history wh

中文介绍 将代理上下文管理视为生命周期和架构问题，提出系统性方法管理对话历史、工具定义等，降低成本和错误率，提升推理效率。

The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation

👍 19

07/27 08:00

Multi-turn long-horizon planning is critical for foundation model agents, yet how to fundamentally improve it remains unclear. Existing models are trained on uncontrollable and opaque Internet data, making it difficult to identify how planning ability is acquired, shaped, and integrated. To address

中文介绍 通过单/多教师在线策略代理蒸馏，从预训练到后训练系统性提升多轮长周期规划能力，揭示规划能力的获取与整合机制。

Interactive Training 2: Auditable Control Plane for Live Model Training

👍 17

07/17 08:00

Experiment trackers show how training is progressing, but changing a live run still usually requires trainer-specific code. We present Interactive Training 2, an open-source control plane for steering training through a shared protocol. Training applications declare which settings and actions they e

中文介绍 提出Interactive Training 2，一个开源控制平面，允许通过共享协议实时调整训练参数和操作，实现可审计的活模型训练控制。

LAMAR: An Open Language-Aware Multilingual Alignment Reranker

👍 10

07/24 08:00

In multilingual retrieval augmented generation, a retriever can retrieve relevant documents written in multiple languages, which are subsequently reranked before answer generation. However, it remains unclear whether existing multilingual rerankers consider document language when ordering semantical

中文介绍 提出LAMAR，一个开放的语言感知多语言对齐重排序器，在重排序时显式考虑文档语言，提升跨语言检索和RAG性能。

O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning

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07/20 08:00

Industrial Video Anomaly Detection (IVAD) aims to identify anomalous objects and events in an industrial process, which is crucial for modern manufacturing and quality control systems. Existing VLM-based anomaly reasoning methods are capable of detecting open-ended anomalies in general domains. Howe

中文介绍 提出O-VAD，通过对象中心跟踪与推理实现工业视频异常检测，结合VLM进行开放集异常推理，解决工业场景中的细粒度异常识别。

Multi-Head Latent Control: A Unified Interface for LLM Agent Decision Making

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07/15 08:00

Large language models are increasingly deployed as agents, but reliable agentic behavior requires more than next-token prediction. At inference time, it is preferred that an agent can decide whether to proceed with its current reasoning, defer to a stronger model, request additional information, inv

中文介绍 提出Multi-Head Latent Control，为LLM代理提供统一决策接口，支持在推理时决定继续、委托、请求信息或执行工具，增强代理自主性。

OpenForgeRL: Train Harness-native Agents in Any Environment

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07/23 08:00

Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems. While powerful, these complex harnesses also make agents hard to train end-to-end with open infrastructure, whose SFT/RL stacks can

中文介绍 提出OpenForgeRL，一种训练框架，允许在任意环境中端到端训练原生推理框架代理（如Claude Code），无需复杂适配，降低训练门槛。

SceneActBench: Can Agents Act on the 3D Scenes They See?

👍 6

07/24 08:00

Vision-language model (VLM) agents increasingly use tools to act on 3D scenes rather than only describe them. Existing 3D benchmarks score textual responses or single-object operations, leaving agent action on complete multi-object 3D scenes under evaluated. We present SceneActBench, a benchmark for

中文介绍 提出SceneActBench，评估VLM代理在多物体3D场景中执行行动的基准，涵盖物体操作、空间推理等任务，填补现有评估空白。

Reasoning Denoiser: Denoising Reasoning Traces for Hallucination Detection in Large Reasoning Models

👍 5

07/24 08:00

Large reasoning models (LRMs) generate long reasoning traces before producing final answers. While these traces may contain useful signals for hallucination detection, harnessing them is non-trivial because long trajectories often include noisy steps that obscure the cues relevant to truthfulness as

中文介绍 提出Reasoning Denoiser，通过去噪推理轨迹中的噪声步骤，提取与真实性相关的线索，提升大型推理模型幻觉检测的准确率。

Codifying the Judge: Scalable Evaluation via Program Distillation

👍 5

05/29 08:00

LLM-as-a-judge has become the standard for automated evaluation, but it suffers from high cost, significant latency, and opaque decisions -- limitations that undermine its scalability and reliability. We address these with a simple, efficient alternative: program distillation. Instead of prompting a

中文介绍 提出程序蒸馏，将LLM评估逻辑蒸馏为可执行程序，替代LLM-as-a-judge，大幅降低成本和延迟，同时提供透明可复现的评估。

ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding

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07/27 08:00

Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundamentally a vision-centric challenge: models must absorb knowledge from heterogeneous 2D and 3D medical images, and evaluation protocols must align with

中文介绍 提出ClinFusion，一个以视觉为中心的多模态LLM系统，融合2D/3D医学图像与文本，实现整体医学理解，评估协议对齐临床需求，提升诊断准确性。

More On An Internal OpenAI Model Hacking Into HuggingFace

@TheZvi · 39.0K 粉丝 · 219.4K 阅 · 510 赞 · 75 转

07/27 03:14

We now have more details of what happened. Every time we learn more details, it somehow makes things seem worse. The remaining details may have to wait a bit. OpenAI: We recognize there are a lot of

中文介绍 OpenAI内部模型入侵HuggingFace事件新细节曝光，博主指出每次深入揭示都让情况更糟糕，剩余细节待后续披露。

22580: From GPT2 to Kimi3, Explained

@waterloo_intern · 10.4K 粉丝 · 215.4K 阅 · 660 赞 · 86 转

07/27 23:22

Twenty-two thousand five hundred and eighty. That’s how many GPT-2 (2019) models fit inside KimiK3 (2026). We scaled up by a factor of 22,580 in seven years. But is it just... scale? In this worklog,

中文介绍 从2019年GPT-2到2026年KimiK3，模型规模扩大22580倍，但文章质疑这种增长是否仅是规模扩张，并深入分析背后的实质。

Opus 5 is a really bad model

@HarukaKunori · 241 粉丝 · 129.0K 阅 · 632 赞 · 44 转

07/27 19:07

After trying out Opus 5 for a few hours today, I honestly think Anthropic's benchmark scores are a complete fraud. Sure, the model might have improved in a few areas, but it has regressed unbelievably

中文介绍 博主实测Anthropic Opus 5后批评其基准测试分数欺诈，模型虽有部分提升但退化严重，整体表现糟糕。

We rewrote our agent to run entirely in a Durable Object with Pi, Agents SDK and Code Mode

@Vercantez · 1.9K 粉丝 · 122.3K 阅 · 567 赞 · 44 转

07/29 00:19

We recently finished moving the camelAI agent off of virtual machines. The agent now runs inside a Cloudflare Durable Object, its filesystem lives in SQLite and R2, and it writes JavaScript instead of

中文介绍 camelAI agent完成重构，从虚拟机迁移至Cloudflare Durable Object，文件系统改用SQLite和R2，代码切换为JavaScript，实现更高效运行。

Why Software Factories Fail: Benchmarking the new frontier

@dexhorthy · 25.8K 粉丝 · 115.4K 阅 · 500 赞 · 39 转

07/28 01:43

This is a continuation of Parts 1 and 2 of "Why Software Factories Fail" Part 1: the harness is not enough Part 2: turning the lights back on we got better benchmarks Remember when I said this in Part

中文介绍 软件工厂失败系列第三部分，延续前两部分关于测试框架不足和基准改进的讨论，聚焦基准测试的新前沿。

Here's exactly how to build your company brain (in 5 mins)

@DhravyaShah · 61.2K 粉丝 · 56.0K 阅 · 548 赞 · 36 转

07/28 14:16

Every company will have a company brain, whether they believe it or not. This is the bet that I'm making, and I'm constantly seeing very future forward startups come to us for their own setup. What is

中文介绍 博主断言每家公司未来都需一个「公司大脑」，并分享5分钟搭建方法，已有前沿创业公司采用该方案。

Getting the most out of GPT-5.6: Sol, Terra, and Luna

@cerebras · 64.6K 粉丝 · 48.8K 阅 · 550 赞 · 40 转

07/28 03:44

Authors: @0xSero & Zhenwei Gao (@zhennydez) Your Codex subscription now comes with 3 main models, Sol, Terra, and Luna, each independently trained and served, with reasoning dials. Together, they

中文介绍 Codex订阅新增三个独立训练和服务的模型——Sol、Terra、Luna，配备推理调节旋钮，协同工作以发挥最大效能。

PagedAttention & RadixAttention

@jaga_prasanna · 799 粉丝 · 37.9K 阅 · 502 赞 · 55 转

07/27 17:49

today we look at how two techniques solve memory and compute bottlenecks from two complementary angles virtual memory paging for intra-request memory allocation and prefix tree caching for

中文介绍 博主对比PagedAttention（虚拟内存分页）和RadixAttention（前缀树缓存）两种技术，从互补角度缓解内存与计算瓶颈。

Scientific computing in the age of agentic AI

OpenAI News · 07/29 01:00

A new field report shows how scientists use AI coding agents to modernize scientific computing, accelerating software development and discovery in genomics and beyond.

中文介绍 OpenAI最新报告显示，科学家利用AI编码智能体实现科学计算现代化，加速基因组学等领域的软件开发与发现。

The OlmoEarth Platform: Geospatial inference at planetary scale

Hugging Face Blog · 07/29 00:27

中文介绍 Allen AI推出OlmoEarth平台，实现行星尺度的地理空间推理。

Codex from 0 to 10M Users: Building ChatGPT Work — Akshay Nathan, OpenAI

Latent Space · 07/28 23:26

OpenAI's core product engineering lead on how they are building ChatGPT Work to make AGI accessible to all of humanity: Sites, OpenClaw, Memory, Subagents, Finance, No-Code and advice.

中文介绍 OpenAI核心产品工程负责人Akshay Nathan分享ChatGPT Work从0到1000万用户历程，涵盖Sites、OpenClaw、Memory、子智能体、金融、无代码等功能，旨在让AGI惠及全人类。

LFM2.5-Encoders for Fast Long-Context Inference on CPU

Hugging Face Blog · 07/28 23:01

中文介绍 Liquid AI发布LFM2.5编码器，支持在CPU上快速进行长上下文推理。

Samsung’s chip workers are jumping ship to rival SK Hynix

MIT Tech Review AI · 07/28 17:18

Lee, an engineer at Samsung’s semiconductor division, clocks out when his shift ends. He used to work longer hours, going the extra mile to excel at his projects. But lately, he’s been coming straight home to work on his job application for the chipmaker’s South Korean rival SK Hynix, sharing tips w

中文介绍 三星半导体部门工程师纷纷跳槽至竞争对手SK海力士。一名工程师Lee不再加班，回家后直接准备SK海力士的求职申请，反映人才外流现象。

[AINews] Much ado about Open Weights

Latent Space · 07/28 14:20

Everyone is writing a lot, but only Kimi K3 shipped today

中文介绍 AI新闻摘要：关于开放权重讨论热烈，但当天仅有Kimi K3模型正式发布。

not much happened today

Smol AI News · 07/28 13:44

**Moonshot** released the **Kimi K3**, a **2.8T-parameter MoE** model with **104B active parameters/token**, featuring innovations like **Kimi Delta Attention (KDA)**, **Gated MLA**, and **LatentMoE**. The release includes infrastructure components such as **MoonEP**, **FlashKDA**, and **AgentEnv**,

中文介绍 Moonshot发布Kimi K3模型，参数量2.8T，采用MoE架构，每token激活104B参数，创新包括Kimi Delta Attention、Gated MLA和LatentMoE，同时开源MoonEP、FlashKDA等基础设施组件。

Anthropic on open weights 🔓, Kimi releases K3 weights 🤖, MAI Cyber model 🔐

TLDR AI · 07/28 08:00

中文介绍 Anthropic讨论开放权重，Kimi发布K3模型权重，MAI推出Cyber模型。

OpenAI called the Hugging Face attack unprecedented. But we’ve been here before.

MIT Tech Review AI · 07/28 02:00

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Reading OpenAI’s account last week of how some of its models broke their containment and hacked into the computer systems of Hugging Face, another AI company, was

中文介绍 OpenAI称其模型突破限制攻击Hugging Face系统的事件前所未有，但评论指出类似情况早有先例。

The path to artificial superintelligence

MIT Tech Review AI · 07/27 20:00

Imagine a healthcare system made up of multiple AI agents: one that manages symptom assessment, another scheduling, a third insurance, and a fourth pharmacy. Each is an expert in its domain. But they all have their own distinct knowledge and objectives. Today they can exchange data, but they are not

中文介绍 通往超级智能的路径：以医疗系统为例，多个AI智能体分别负责症状评估、排期、保险和药房，每个智能体拥有独立知识和目标，通过协作实现端到端任务。

Closing the data loop in AI-driven drug discovery

MIT Tech Review AI · 07/27 19:40

Drug discovery is a high-cost, high-risk endeavor that is under growing pressure from a market increasingly defined by first-mover advantage. Since the 1950s, the cost of developing new pharmaceuticals has roughly doubled every nine years—a phenomenon known as Eroom’s Law. Today, bringing a new drug

中文介绍 AI驱动药物发现需要闭环数据。自1950年代以来，新药研发成本每九年翻一番（Eroom定律），市场强调先发优势，AI闭环可降低成本与风险。

Building the enterprise environment for agentic AI

MIT Tech Review AI · 07/27 19:32

For the enterprise, the promise of agentic AI is much more than just a better chatbot. It is software agents that execute business tasks end-to-end across people, business workflows, data, and systems. The platform best-suited to run agents is built with proper CPU capacity, resilient data access, p

中文介绍 企业级智能体AI远超聊天机器人，可端到端执行跨人员、流程、数据和系统的业务任务。最佳平台需具备足够的CPU容量、弹性基础设施等条件。

NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics

Hugging Face Blog · 07/27 17:32

中文介绍 NVIDIA推出Cosmos-H-Dreams，将实时生成式模拟技术应用于手术机器人领域。

not much happened today

Smol AI News · 07/27 13:44

**Moonshot** released the **Kimi K3** open-weights model, a **2.8T-parameter MoE** with **104B active parameters**, **896 experts**, and **1M-token context** featuring native visual understanding. The release includes open-source infrastructure like **FlashKDA**, **MoonEP**, and **AgentENV**, enabli

中文介绍 Moonshot发布Kimi K3开放权重模型，参数2.8T，MoE架构，激活104B参数，896专家，1M token上下文，原生视觉理解。开源基础设施包括FlashKDA、MoonEP等。

How AI is expanding what people do at work

OpenAI News · 07/27 11:30

New OpenAI research shows how AI is expanding what workers do, with ChatGPT users taking on tasks across roles and reshaping job boundaries.

中文介绍 OpenAI新研究表明，AI正扩展员工的工作范围，ChatGPT用户跨角色承担任务，重塑了职业边界。