2026-07-20

LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget

👍 175

07/16 08:00

A growing gap separates inference context lengths from RL post-training: inference systems are approaching million-token contexts, while post-training workloads often remain at 256K tokens or below and rely on length generalization at deployment. The gap is especially important for AI agents, whose

中文介绍 提出LongStraw，在固定GPU预算下将RL后训练扩展到2M tokens以上，弥合推理与训练上下文长度差距，尤其面向AI agent场景。

VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding

👍 145

07/16 08:00

Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world applications. Despite this progress, current open-source models remain limited in several ways. They often struggle to generalize across diverse video types, making

中文介绍 提出VideoChat3，完全开源的视频多模态大模型，致力于高效通用视频理解，在多种视频类型上提升泛化能力，弥补现有开源模型局限。

Boogu-Image-0.1: Boosting Open-Source Unified Multimodal Understanding and Generation

👍 126

07/14 08:00

We introduce Boogu-Image-0.1, an open-source unified multimodal understanding and generation model family, comprising Base, Turbo, Edit, and Edit-Turbo variants. It delivers competitive performance in high-quality text-to-image generation, fast inference, instruction-based editing, and bilingual (Ch

中文介绍 推出Boogu-Image-0.1开源统一多模态理解与生成模型家族（Base、Turbo、Edit等），在高质量文生图、快速推理、指令编辑及中文支持上表现优异。

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning

👍 91

07/14 08:00

Reinforcement learning with verifiable rewards without human-annotated data, often referred to as zero RL, has emerged as a powerful paradigm for eliciting chain-of-thought reasoning. However, due to computational constraints, existing studies are largely restricted to small models, leaving the trai

中文介绍 提出Ring-Zero，将零强化学习（Zero RL）扩展到万亿参数规模，激发涌现推理能力，突破先前小模型限制，首次在大型模型上实现可验证奖励RL训练。

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning

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07/16 08:00

Large language models are increasingly trained as interactive agents for long-horizon tasks involving multi-turn interaction, tool use, and environment feedback. Outcome-based reinforcement learning (RL) provides a practical optimization paradigm, but its sparse trajectory-level rewards offer limite

中文介绍 提出SEED，自进化在策略蒸馏方法用于智能体强化学习，通过自进化机制缓解基于结果的RL中稀疏奖励问题，提升长程任务中多轮交互与工具使用效果。

SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration

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07/16 08:00

Recent advances in Tool-Integrated Large Language Models have made web search a core capability of information-seeking agents. However, as interaction histories grow, agents increasingly struggle to track task progress. When search attempts fail to yield useful evidence, current single- and multi-ag

中文介绍 提出SearchOS-V1，鲁棒的开域信息检索智能体协作框架，通过多智能体协作解决交互历史增长导致的任务追踪难题，提升搜索效率和证据收集。

BadWAM: When World-Action Models Dream Right but Act Wrong

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07/16 08:00

World-action models (WAMs) are emerging as a promising foundation for embodied control: rather than predicting actions alone, they learn representations that couple action generation with future world prediction. This coupling is often viewed as a source of robustness, interpretability, and safety,

中文介绍 揭示世界-动作模型（WAM）的潜在缺陷：未来预测正确但动作生成可能错误，分析耦合不总能带来鲁棒性，指出其失败模式。

AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities

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07/15 08:00

As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering. To address this, we introdu

中文介绍 提出AgentCompass，统一智能体能力评估基础设施，解决现有评估流程碎片化、可重复性低的问题，标准化评估框架，减少冗余工程。

KeyFrame-Compass: Towards Comprehensive Evaluation of Keyframe-Conditioned Video Generation

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07/15 08:00

Video generation increasingly relies on keyframe-based workflows, where creators specify a sequence of reference images to guide generation. Although recent models support multi-keyframe conditioning, it remains unclear whether they can faithfully reproduce the prescribed keyframes while maintaining

中文介绍 提出KeyFrame-Compass，综合评估关键帧条件视频生成模型，系统衡量模型是否忠实复现给定关键帧同时保持视频连贯性。

PolicyShiftGuard: Benchmarking and Improving Policy-Adaptive Image Guardrails

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07/07 08:00

Image guardrails are typically trained and evaluated under a fixed safety policy, implicitly treating safety as an intrinsic property of an image. Real deployments are different: the same image may be allowed in one product, restricted in another, and newly disallowed when a policy boundary changes.

中文介绍 提出PolicyShiftGuard，评估和提升策略自适应图像安全防护，强调安全策略的动态性，构建跨策略边界基准，改进模型对策略变化的适应能力。

MetaView: Monocular Novel View Synthesis with Scale-Aware Implicit Geometry Priors

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07/13 08:00

Current visual generation models are capable of producing high-quality content, yet they lack a coherent perception of the spatial structure. Existing generative novel view synthesis methods typically introduce explicit geometry priors, which enforce spatial consistency but inherently restrict gener

中文介绍 提出MetaView，单目新视角合成方法，引入尺度感知隐式几何先验，避免显式几何先验限制，在保证空间一致性同时提升生成泛化能力。

MultiRef-Compass: Towards Comprehensive Evaluation of Multi-Reference-to-Audio-Video Generation

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07/15 08:00

Multi-reference-to-audio-video (MR2AV) generation aims to generate coherent audio-video content conditioned on multiple references and textual instructions. Existing benchmarks mainly focus on text-driven generation, single-reference subject preservation, or isolated audio-video alignment, leaving t

中文介绍 提出MultiRef-Compass，多参考音频-视频生成综合评估基准，针对多参考条件与文本指令，系统评估模型生成连贯音视频内容的能力。

Concurrent Image Understanding and Generation: Self-Correcting Coupled Markov Jump Processes

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07/14 08:00

Human cognition does not separate understanding and generation. A teacher at a whiteboard speaks and draws together, each modality reshapes the other. In this paper, we bring this coupled loop to artificial systems. Masked Diffusion Models (MDMs) are ideally suited to this task, yet existing sampler

中文介绍 提出自校正耦合马尔可夫跳过程，基于掩码扩散模型（MDM）实现图像理解与生成同时进行，两种模态相互修正，更贴近人类认知。

UniVR: Thinking in Visual Space for Unified Visual Reasoning

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07/14 08:00

Learning broad world knowledge directly from raw visual data is a fundamental capability of intelligence. We introduce UniVR, the first investigation into simultaneously learning complex reasoning, fine-grained physical dynamics, and long-term planning from pure visual demonstrations. At its core, U

中文介绍 提出UniVR，在纯视觉演示中同时学习复杂推理、精细物理动力学和长期规划，统一视觉推理能力，无需语言标注。

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch

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07/15 08:00

World Action Models (WAMs) improve robot policy learning by jointly modeling actions and future visual observations, using future scene evolution as dense supervision for physically grounded action generation. However, a common design in existing WAMs is to explicitly generate future videos at infer

中文介绍 提出GigaWorld-Policy-0.5，基于AutoResearch加速和增强的世界-动作模型（WAM），避免推理时显式生成未来视频，降低计算开销并提升机器人策略学习。

Self-Improvements in Modern Agentic Systems: A Survey

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07/14 08:00

Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, from experience with minimal or even no human input. This survey frames modern self-improving agents as adaptive systems that convert experience into a

中文介绍 综述现代自主智能体自我改进方法，将智能体视为自适应系统，从经验中实现可控演化，梳理从原型到部署的关键技术与挑战。

RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination

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07/15 08:00

Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved. We introduce Hy-Embodied-RxBrain, an embodied cognition foundation model with joint language-visual reasoning and imagination. Unlike vision-language models that emphasize scene understan

中文介绍 提出RxBrain，具身认知基础模型，联合语言-视觉推理与想象，将高层任务推理与物理状态连接，支持具身智能，超越传统VLM。

Spectral Rewiring for Exploration, Purification, and Model Merging

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07/03 08:00

Reinforcement learning has become a standard post-training recipe for large language models, but dense full-parameter updates create two deployment-relevant bottlenecks: suppressed reasoning performance, often reflected by premature saturation of test-time scaling, and interference when consolidatin

中文介绍 提出频谱重连（Spectral Rewiring）方法，用于LLM后训练中的探索、净化与模型合并，解决密集全参数更新导致的推理性能抑制和合并干扰。

Demystifying On-Policy Distillation: Roles, Pathologies, and Regulations

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07/15 08:00

On-policy distillation (OPD) has become a key paradigm in LLM post-training, yet its training dynamics remain poorly understood. We present a systematic study examining the role, pathologies, and regulations of OPD. We first clarify the role of OPD as an exploration catalyst: it steers the student t

中文介绍 系统研究在策略蒸馏（OPD）在LLM后训练中的角色、病理与调控，揭示OPD作为探索催化剂的作用，并分析其训练动态与调节策略。

Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation

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07/15 08:00

While recent advances in 3D generation have enabled impressive visual synthesis, existing methods often rely on 2D diffusion supervision without explicit mechanisms for geometric consistency, leading to spatial hallucinations such as duplicated structures and misaligned geometry. These issues become

中文介绍 提出Hallo4D，缓解多模态幻觉以生成一致时空内容，针对2D扩散监督缺乏几何一致性问题，引入显式几何约束，减少空间重复和错位。

Maybe Intelligence Ain't All That

@CliffordSosin · 16.4K 粉丝 · 197.0K 阅 · 514 赞 · 49 转

07/19 05:36

Superintelligence arrived. You probably didn't notice, because it turned out to be kind of incremental. Don't believe me? Run the test. Talk to Fable 5 for an hour, then talk to your ten smartest

中文介绍 讨论超级智能的真相：作者认为它可能并未引起注意，因为进步是渐进的。提出一个测试——与Fable 5对话一小时后，再与十个最聪明的人交流，感受智能的真实差距。

How to build your first team of agents:

@EXM7777 · 126.7K 粉丝 · 173.6K 阅 · 521 赞 · 51 转

07/18 21:48

I'm going to show you how to build your first team of AI agents... and how to wire them into a self improving loop inside a shared workspace the team we're building has 5 agents, one for each function

中文介绍 分享构建首个AI智能体团队的教程：5个智能体分别负责不同功能，在共享工作区内形成自我改进循环，实现团队协作与自动优化。

Kimi K3 on KernelBench

@elliotarledge · 40.7K 粉丝 · 69.3K 阅 · 506 赞 · 45 转

07/17 17:24

This is the Kimi K3 post you guys have been waiting for. I got some early access to this model and have been testing it on kernels, and even before seeing the benchmark scores I was impressed with its

中文介绍 抢先评测Kimi K3模型在KernelBench上的表现：作者获得早期访问权限，测试内核编程，认为其能力令人印象深刻，甚至还未查看基准分数。

From Loop Engineering to Graph Engineering?

@IntuitMachine · 62.3K 粉丝 · 50.7K 阅 · 552 赞 · 47 转

07/18 18:00

What the shift in AI agent architecture is really about Peter Steinberger just posted nine words that gathered thousands of likes: https://x.com/steipete/status/2078277297791189132 "Are we still talking loops or did we

中文介绍 探讨AI智能体架构从循环工程到图工程的转变。引用Peter Steinberger的帖子（九个字获数千赞），引发对架构演进方向的热议。

[AINews] not much happened today

Latent Space · 07/18 12:30

a quiet day

中文介绍 据Latent Space报道，当日AI行业未出现重大新闻或进展，整体市场较为平静。投资者和从业者普遍持观望态度，等待新的催化剂。

Fine-tune video and image models at scale with NVIDIA NeMo Automodel and 🤗 Diffusers

Hugging Face Blog · 07/17 23:57

中文介绍 NVIDIA与Hugging Face合作推出NeMo Automodel与🤗 Diffusers的集成方案，支持大规模微调视频和图像模型。该工具旨在简化训练流程，提升效率。

A scorecard for the AI age

OpenAI News · 07/17 18:00

Sarah Friar, CFO of OpenAI, introduces a practical AI scorecard to measure ROI through useful work, cost per successful task, dependability, and return on compute.

中文介绍 OpenAI首席财务官Sarah Friar提出AI记分卡，通过有用工作量、单次成功任务成本、可靠性和计算回报四个维度衡量AI投资回报率，为企业提供实用评估工具。

The risk of weather data sabotage is rising

MIT Tech Review AI · 07/17 16:57

Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on the same thing: a weather forecast. While these forecasts are something that most people glance at for two seconds, weather predictions influence major strategic decisions in many industries, wit

中文介绍 MIT科技评论指出，天气数据被破坏的风险正在上升。航空调度员、电网运营商和农民每日依赖天气预报做出关键决策，数据安全漏洞可能带来重大影响。

not much happened today

Smol AI News · 07/17 13:44

**Moonshot's Kimi K3 release** has sparked a reassessment of **Chinese open-weight models**' proximity to the frontier, with strong performance in coding, agentic tasks, and long-horizon knowledge work. The strategic focus has shifted from a "compute moat" to an "efficiency stack" involving **MoE ro

中文介绍 Smol AI News报道，Moonshot AI的Kimi K3模型发布促使业界重新评估中国开源模型与前沿水平的差距。该模型在编程、智能体任务和长程知识工作中表现突出，战略焦点从「计算护城河」转向「效率堆栈」。

[AINews] Kimi K3 2.8T-A50B: the largest open model ever released; Opus 4.8-class at Sonnet 5 pricing

Latent Space · 07/17 09:46

a great week for open models continues.

中文介绍 Latent Space报道，Kimi K3（2.8T-A50B参数）成为迄今最大的开源模型，性能达到Opus 4.8级别，定价与Sonnet 5相当。本周开源模型领域持续突破。

Kimi K3 🌕, Gemini 3.5 delayed ⏳, crushing ARC-AGI 3 🤖

TLDR AI · 07/17 08:00

中文介绍 TLDR AI总结当日AI动态：Kimi K3正式发布；谷歌Gemini 3.5模型被推迟；ARC-AGI 3基准测试被攻克。多项重要进展同时出现。

Why teens deserve access to safe AI

OpenAI News · 07/17 00:00

Learn how OpenAI is making ChatGPT safer for teens with age-appropriate protections, learning tools, parental controls, and expert partnerships.

中文介绍 OpenAI宣布为青少年提供更安全的ChatGPT访问，内容包括年龄适宜的保护措施、学习工具、家长控制功能，并与专家合作，确保AI使用安全。

🔬 The Lab of the Future Should Feel Like a Data Center — Andy Beam & Rafa Gómez-Bombarelli, Lila Sciences

Latent Space · 07/16 21:30

Lila is betting that science, not the internet, is the last untapped source of training data. We went to find out what that actually looks like in a room full of robots.

中文介绍 Lila Sciences的Andy Beam和Rafa Gómez-Bombarelli认为，科学领域是最后一块未开发的训练数据来源。他们的未来实验室如同数据中心，布满机器人，致力于挖掘科学数据。

Newer Models, Same Advantage

Hugging Face Blog · 07/16 19:49

中文介绍 Dharma AI在Hugging Face博客发文，阐述其新模型在保持原有优势的基础上，进一步提升了性能。具体细节尚未披露。

Our approach to bioresilience

DeepMind Blog · 07/16 17:30

Google DeepMind and Isomorphic Labs are sharing our joint approach to bioresilience and AI models.

中文介绍 Google DeepMind与Isomorphic Labs联合公布其在生物韧性（bioresilience）及AI模型方面的研究方法。该策略旨在利用AI应对生物威胁，增强系统抵抗力。

[AINews] Thinky's Inkling: 975B-A41B multimodal, new best American Apache 2.0 open model (with Inkling-Small, 276B-A12B)

Latent Space · 07/16 14:18

Thinky's first full LLM release is a banger and bonus: it's open weights!

中文介绍 Thinky发布首个完整LLM模型Inkling（975B-A41B多模态），以及小版本Inkling-Small（276B-A12B），成为美国Apache 2.0许可下的最佳开源模型，权重完全开放。

not much happened today

Smol AI News · 07/16 13:44

**Moonshot AI** launched **Kimi K3**, a frontier-class open-weights model with **2.8T parameters**, **1M-token context window**, and **native multimodal input**. It features novel **Kimi Delta Attention (KDA)** enabling up to **6.3x faster decoding** and **Attention Residuals** for **~25% higher tra

中文介绍 Moonshot AI推出前沿级开源模型Kimi K3，拥有2.8T参数、100万token上下文窗口和原生多模态输入。其创新的Kimi Delta Attention（KDA）机制使解码速度提升最多6.3倍。

How Cars24 scales conversations and builds faster with OpenAI

OpenAI News · 07/16 08:00

Cars24 uses OpenAI-powered voice and chat agents to handle 1M+ monthly conversation minutes, recover 12% of lost leads, and bring agentic workflows to teams across the company.

中文介绍 Cars24借助OpenAI的语音和聊天代理，每月处理超过100万分钟对话，成功挽回12%的流失线索。该公司在全团队部署了代理工作流，显著提升效率。

Security incident disclosure — July 2026

Hugging Face Blog · 07/16 08:00

中文介绍 Hugging Face发布2026年7月安全事件披露公告，详细说明了此次安全问题的性质、影响范围及已采取的修复措施。用户被建议更新相关凭证。