2026-07-15

Weak-to-Strong Generalization via Direct On-Policy Distillation

👍 93

07/08 08:00

Reinforcement learning with verifiable rewards (RLVR) is a powerful recipe for improving language-model reasoning, but it is expensive to repeat on every new strong model because the target model must generate many rollouts during training. As models scale, post-training itself becomes a bottleneck.

中文介绍 针对RLVR在每个强模型上重复生成rollout的高成本问题，提出直接在线策略蒸馏方法，通过弱模型引导强模型实现高效泛化，降低后训练瓶颈。

ABot-N1: Toward a General Visual Language Navigation Foundation Model

👍 81

07/12 00:21

Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks. Current approaches typically achieve this integration via monolithic policies that map observations directly to actions, yet they often suffer fr

中文介绍 提出ABot-N1通用视觉语言导航基础模型，统一深度推理与空间决策，克服单块策略直接映射观察至动作的局限，提升多样具身任务的泛化能力。

ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory

👍 68

07/11 23:24

Recent VLM and VLA systems have improved robotic perception and action prediction, yet long-horizon embodied agents still require a general runtime layer for reasoning, memory, tool use, verification, and cross-embodiment execution. We present ABot-AgentOS, a general robotic Agent Operating System t

中文介绍 提出ABot-AgentOS通用机器人代理操作系统，集成终身多模态记忆、推理、工具使用与跨本体执行，为长程具身任务提供统一运行时层。

Long-Horizon-Terminal-Bench: Testing the Limits of Agents on Long-Horizon Terminal Tasks with Dense Reward-Based Grading

👍 61

07/09 08:00

AI agents have become capable of autonomously completing short, well-specified tasks. However, existing terminal benchmarks largely focus on simple problems that finish within minutes and are evaluated only by their final outcome. This setup overlooks intermediate progress and partial solutions, yie

中文介绍 提出Long-Horizon-Terminal-Bench长时域终端任务基准，采用密集奖励评分衡量中间进展与部分解，突破仅评估最终结果的局限，全面检测智能体长程任务能力。

Video-Oasis: Rethinking Evaluation of Video Understanding

👍 61

07/02 08:00

The inherent complexity of video understanding makes it difficult to determine whether Video-LLM benchmark performance stems from visual perception, linguistic reasoning, or knowledge priors. While many benchmarks have emerged to assess high-level reasoning, shared criteria for evaluating video unde

中文介绍 提出Video-Oasis评估框架，重新审视视频理解基准，区分视觉感知、语言推理与知识先验的贡献，建立共享评估标准以准确衡量Video-LLM性能。

Video Generation Models are General-Purpose Vision Learners

👍 54

07/10 08:00

Driven by next-token prediction, NLP shifted from task-specific models into powerful generalist foundation models. What, then, is the equivalent catalyst needed to achieve a general-purpose model in computer vision? In this paper, we contend that large-scale text-to-video generation serves as a stro

中文介绍 论证大规模文本到视频生成可作为视觉领域的通用基础模型催化剂，类似NLP中next-token prediction的范式转变，实现通用视觉学习。

Scalable Visual Pretraining for Language Intelligence

👍 49

07/10 08:00

The rapid progress of large foundation models has been driven predominantly by pretraining on large-scale text corpora. However, many forms of knowledge are conveyed through visual representations, where figures, typeset equations, and page layouts carry rich information that cannot be faithfully or

中文介绍 针对文本预训练缺失视觉知识（图表、公式、布局）的问题，提出可扩展视觉预训练方法，利用视觉表示增强语言模型的知识获取与理解能力。

UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks

👍 32

07/09 08:00

The rapid development of large language models and multimodal large language models has accelerated the emergence of proactive agents capable of operating everyday tools and assisting users in real-world environments. However, existing benchmarks struggle to evaluate such agents effectively, as they

中文介绍 提出UniClawBench通用基准，评估多模态主动代理在真实世界任务中操作日常工具的能力，弥补现有基准对交互式、具身场景评估的不足。

LongE2V: Long-Horizon Event-based Video Reconstruction, Prediction, and Frame Interpolation with Video Diffusion Models

👍 31

07/09 08:00

Recovering high-quality video from sparse event streams is a challenging task. Regression methods often blur textures, while existing generative models struggle with long-term stability. We propose LongE2V, a novel approach that leverages pre-trained video diffusion priors to jointly handle event-ba

中文介绍 提出LongE2V方法，利用预训练视频扩散模型先验，联合处理事件流的视频重建、预测与帧插值，解决回归方法纹理模糊和生成模型长期稳定性问题。

Trust Region Policy Distillation

👍 22

07/06 08:00

Big goals are hard to achieve all at once; breaking them into small steps is wiser. We present Trust Region Policy Distillation (TOP-D), which transforms the notoriously unstable, high-variance On-Policy Distillation (OPD) into a stable training paradigm by dynamically constructing a proximal teache

中文介绍 提出信任域策略蒸馏（TOP-D），通过动态构建近端教师将不稳定的在线策略蒸馏转化为稳定训练范式，降低方差并提升强化学习训练效率。

AdvancedMathBench: A Benchmark Suite for Advanced Mathematical Proof Generation and Verification

👍 20

07/14 01:38

Large language models (LLMs) have achieved remarkable performance on high-school and olympiad-style mathematics, yet their capabilities on advanced mathematics remain poorly understood. Existing benchmarks, however, fall short in both scope and evaluation granularity: they provide limited disciplina

中文介绍 提出AdvancedMathBench基准套件，系统评估LLM在高级数学证明生成与验证上的能力，覆盖多学科，弥补现有基准在范围和粒度上的不足。

Enhancing In-context Panoramic Generation via Geometric-aware Pretraining

👍 20

07/09 08:00

In this work, we present Canvas360, a two-stage framework for in-context panoramic generation that combines geometry-aware pretraining with downstream task-specific fine-tuning. To address the lack of large-scale, high-quality training data tailored to in-context panoramic tasks, we propose Canvas36

中文介绍 提出Canvas360两阶段框架，通过几何感知预训练与下游微调实现上下文全景生成，并设计合成数据管道弥补大规模高质量训练数据的缺失。

KronQ: LLM Quantization via Kronecker-Factored Hessian

👍 19

07/08 08:00

Post-training quantization (PTQ) is a widely adopted technique for compressing large language models (LLMs) without retraining. Existing second-order PTQ methods, including GPTQ, construct quantization objectives exclusively from input activation statistics, effectively assuming that all output chan

中文介绍 提出KronQ量化方法，利用Kronecker分解Hessian矩阵构建量化目标，同时考虑输入激活与输出通道影响，提升大模型后训练量化精度。

CineMobile: On-Device Image-to-Video Diffusion for Cinematic Camera Motion Generation

👍 15

07/04 08:00

The growing demand for image-to-video creation on mobile devices has increasingly focused on cinematic motion effects like bullet time, dolly zoom, slow motion, etc. While Diffusion Transformers (DiTs) exhibit strong performance in video generation, their large parameter sizes and multi-step iterati

中文介绍 提出CineMobile，面向移动设备的图像到视频扩散模型，在有限算力下生成子弹时间、推拉变焦等电影级相机运动，克服DiT大参数量和多步迭代的限制。

Metacognition in LLMs: Foundations, Progress, and Opportunities

👍 12

07/14 01:58

Metacognition is a foundational component of intelligence critical to effective learning, problem solving, decision-making, communication, and more. In recent years, it has become increasingly recognized as a cornerstone of capable, transparent AI systems. Yet while LLMs have made significant progre

中文介绍 综述大语言模型中的元认知，涵盖理论基础、现有进展与未来机会，强调元认知对学习、问题解决和透明AI系统的重要性。

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning

👍 11

07/09 20:17

Fine-tuning LLMs to inject new knowledge faces a critical challenge: LLMs can quickly memorize new facts, yet fail to use them for downstream reasoning tasks. We formalize this failure as the \textbf{Knowing--Using Gap}, characterized by an accuracy gap and a temporal lag between memorization and ge

中文介绍 形式化微调中新知识的“知道-使用差距”，揭示LLM记忆新事实后无法迁移到推理任务的现象，分析其准确率差距与时间滞后。

Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents

👍 10

07/09 08:00

In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task requirements, environment facts, prior attempts, diagnoses, and open subgoals can be buried in the context window or pushed bey

中文介绍 提出Proactive Memory Agent主动记忆机制，在长程任务中动态提取和利用分散的状态信息，避免关键信息（任务要求、环境事实、未完成子目标）被上下文窗口掩盖。

Self-Guided Test-Time Training for Long-Context LLMs

👍 9

07/10 08:00

Long-context processing has become increasingly important for large language models (LLMs), but simply extending the context window does not guarantee effective utilization of long inputs. As input length grows, accuracy often degrades, indicating that models still struggle to identify and use the e

中文介绍 提出自引导测试时训练方法，使长上下文LLM在推理阶段主动识别和利用关键信息，缓解窗口扩展带来的精度退化问题。

UP: Unbounded Positive Asymmetric Optimization for Breaking the Exploration-Stability Dilemma

👍 8

07/08 08:00

Reinforcement learning (RL) has become the standard paradigm for enhancing the complex reasoning capabilities of large language models (LLMs). To achieve sample efficiency, modern RL frameworks rely on importance sampling (IS). However, these algorithms suffer from an exploration-stability dilemma.

中文介绍 提出无界正非对称优化（UP），解决强化学习中重要性采样带来的探索-稳定性困境，提升LLM推理训练的效率与稳定性。

Proxy Exploration and Reusable Guidance: A Modular LLM Post-Training Paradigm via Proxy-Guided Update Signals

👍 7

07/13 20:56

Post-training is essential for refining the domain-specific capabilities of large language models (LLMs), yet existing reward optimization and distribution matching methods tightly couple policy exploration with distribution alignment. This coupling forces expensive exploration directly on the polic

中文介绍 提出代理探索与可复用引导（Proxy-Guided Update Signals）模块化后训练范式，解耦策略探索与分布对齐，降低直接在高维策略空间探索的成本。

Making Fable Cheaper Than Opus

@joon_h_lee · 515 粉丝 · 366.3K 阅 · 521 赞 · 43 转

07/14 01:03

We replaced Opus 4.8 with Fable 5, and Devin’s bill went down. Fable 5 costs twice as much per token as Opus 4.8. But when we ran both models on FrontierCode 1.1 using our new Fusion architecture,

中文介绍 使用Fusion架构，用更贵的Fable 5替代Opus 4.8，在FrontierCode 1.1上运行总成本反而降低，展示模型选型与架构创新如何实现降本。

The Most Human Technology Ever Made

@illscience · 30.4K 粉丝 · 332.5K 阅 · 539 赞 · 69 转

07/14 01:05

Over the weekend, Mira Murati wrote a phenomenal essay that you should read in full: “The future worth building is human.” I think it’s one of the best essays of the year, because it gets at the

中文介绍 推荐Mira Murati的文章《The future worth building is human》，认为其触及技术人性化核心，是今年最佳之一，强调技术应服务于人。

The Reverse Information Paradox

@satyanadella · 7.0M 粉丝 · 146.5K 阅 · 1.1K 赞 · 162 转

07/12 23:09

In the age of intelligence, how should firms protect their core IP? Nobel Prize winning economist Kenneth Arrow famously described a paradox in the market for information. “Its value for the

中文介绍 引用Arrow信息悖论，提出智能时代企业保护核心IP的「反向信息悖论」，探讨信息价值与分享的矛盾。

A Framework for Frontier AI and the Dawning of a New Age

@demishassabis · 1.4M 粉丝 · 98.1K 阅 · 1.5K 赞 · 236 转

07/14 17:10

This is a pivotal moment in human history. Artificial General Intelligence (AGI), a system that exhibits all the cognitive capabilities the brain has, is probably only a few short years away. When we

中文介绍 认为AGI几年内即可实现，呼吁建立前沿AI框架以迎接新时代，这是人类历史的转折点。

I Brutally Modified My Front-End Design Skill ~ Now My UIs Don’t Look Like AI Crap

@JoePro · 949 粉丝 · 66.8K 阅 · 537 赞 · 29 转

07/14 11:51

To create a custom Claude skill, navigate to Customize > Skills in your Claude interface, click the + button, and select + Create skill

中文介绍 介绍在Claude中创建自定义技能以改善前端UI设计，避免AI生成的粗糙外观，操作路径为Customize > Skills > + Create skill。

How I'd make $10 million with AI agents

@gregisenberg · 682.7K 粉丝 · 62.2K 阅 · 578 赞 · 36 转

07/14 02:22

2 markets worth trillions are opening at the same time. I've been through 3 platform shifts living in Silicon Valley building/selling VC-backed companies and I've NEVER seen this. Mobile apps came

中文介绍 指出现有两个万亿市场同时开放，AI agents是最大机会，分享如何利用AI agents赚取1000万美元的战略路径。

2 Hermes Workflows I can't live without

@0xJeff · 80.8K 粉丝 · 61.6K 阅 · 502 赞 · 36 转

07/13 19:33

I’ve been consistently using Hermes everyday for the past 4 months and he’s already a core part of my research & investment workflows. Before Hermes, it was just me relying on Grok, Claude, Gemini for

中文介绍 持续使用Hermes 4个月，将两个Hermes工作流融入研究和投资，效率超过单独使用Grok、Claude等工具。

Motion-Graphics Skill

@HeyGen · 106.3K 粉丝 · 58.5K 阅 · 516 赞 · 56 转

07/13 03:00

Day 7 of 30: Motion Graphics by Example /motion-graphics makes short, design-led motion graphics: usually under 10 seconds, no narration, motion is the message. Kinetic type, stat count-ups, charts,

中文介绍 推出Motion Graphics技能，通过示例生成10秒内设计驱动动效，无旁白，动效即信息，适用于动态排版、数据图表等。

How To Actually Design With AI

@LexnLin · 17.6K 粉丝 · 32.3K 阅 · 503 赞 · 27 转

07/13 05:44

Design has always been one of the areas where AI struggles to match human ability. But most people still misunderstand what designing with AI actually means. Designing with AI does not mean asking it

中文介绍 澄清AI设计误区：真正的AI设计不是让AI生成最终方案，而是人机协作，强调人机配合的正确方式。

Your AI says it read your files. Did it really?

@conikeec · 7.5K 粉丝 · 1.1K 阅 · 1.0K 赞 · 121 转

07/14 22:39

AI always answers with confidence — even when it skipped half your documents. waggle shows you which files it really opened, so you know whether to trust the answer, or send it back. You hand an AI

中文介绍 介绍waggle工具，显示AI实际打开的文件，解决AI自信回答却遗漏文档的问题，确保答案可信度。

[AINews] not much happened today

Latent Space · 07/15 07:54

a continuation: Codex adding 1M users a day now.

中文介绍 Codex目前每天新增100万用户。新闻称今日无重大事件。

5 Trends That Defined AI Engineering at World’s Fair 2026

Latent Space · 07/15 07:21

At this year's AIE World’s Fair, AI engineering entered a new phase: building systems around agents, rather than just building with agents.

中文介绍 2026年AI工程世界博览会上，AI工程进入新阶段：围绕智能体构建系统，而非仅使用智能体。

How to manage AI investments in the agentic era

OpenAI News · 07/14 18:00

Learn how enterprises can manage AI investments in the agentic era by measuring useful work per dollar, improving efficiency, and scaling high-value workflows.

中文介绍 OpenAI介绍企业如何在智能体时代管理AI投资，通过衡量每美元有效工作、提升效率并规模化高价值工作流。

not much happened today

Smol AI News · 07/14 13:44

**OpenAI's agent products** saw a **2.5x weekly usage growth** driven by **Codex + ChatGPT Work** and demand for **GPT-5.6 Sol**. JetBrains adopted Codex as a recommended agent, while LangChain enhanced tracing and observability across multiple tools. **PrismML released Bonsai 27B**, a compressed va

中文介绍 OpenAI智能体产品周使用量增长2.5倍，受Codex、ChatGPT Work及GPT-5.6 Sol需求驱动。JetBrains采用Codex为推荐智能体，LangChain增强追踪与可观测性，PrismML发布Bonsai 27。

[AINews] Codex usage up >10x in 6 months to 7M users, +1M in the past ~day; did Codex overtake Claude Code??

Latent Space · 07/14 09:22

a quiet day lets us fact check some numbers against the sound of silence of Claude Code reporting...

中文介绍 Codex在6个月内用户增长超10倍，达700万，过去一天新增100万。新闻对比Claude Code的沉默做出事实核查。

How data science teams use ChatGPT Work

OpenAI News · 07/14 08:00

See how data science teams can use ChatGPT Work to build root-cause briefs, impact readouts, KPI memos, scoped analyses, and dashboard specs from real work inputs.

中文介绍 OpenAI展示数据科学团队如何使用ChatGPT Work从实际工作输入生成根本原因简报、影响报告、KPI备忘录、范围分析和仪表板规格。

How sales teams use ChatGPT Work

OpenAI News · 07/14 08:00

See how sales teams can use ChatGPT Work to create pipeline briefs, meeting prep packets, forecast reviews, account plans, and stalled-deal diagnoses from real work inputs.

中文介绍 OpenAI展示销售团队如何使用ChatGPT Work创建销售管道简报、会议准备包、预测审查、账户计划及停滞交易诊断。

xAI uploads codebases 👨‍💻, Prime Intellect verifiers 🧠, Sakana smart bricks 🧱

TLDR AI · 07/14 08:00

中文介绍 xAI上传代码库，Prime Intellect发布验证器，Sakana推出智能积木。

What Anthropic’s latest AI discovery does—and doesn’t—show

MIT Tech Review AI · 07/14 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. Anthropic—currently the world’s most valuable AI company, with a nearly $1 trillion valuation—has a reputation for publishing strange and heady research. It’s loo

中文介绍 Anthropic目前是全球估值最高AI公司，近1万亿美元，以其奇特研究著称。文章探讨其最新AI发现的意义与局限。

Empowering India’s next generation of innovators with ATL Saathi

DeepMind Blog · 07/13 20:37

Google and AIM launched ATL Saathi, a Gemini-powered AI tool empowering Indian educators in robotics labs.

中文介绍 Google与AIM推出ATL Saathi，一款由Gemini驱动的AI工具，赋能印度教育工作者在机器人实验室中的教学。

not much happened today

Smol AI News · 07/13 13:44

**Prime Intellect** released **verifiers v1**, a redesigned environment stack for **agentic reinforcement learning** and evaluations, improving efficiency by storing rollout traces as **message DAGs** to reduce complexity from **O(n²)** to **O(n)**. This enables practical long-horizon multimodal rol

中文介绍 Prime Intellect发布verifiers v1，为智能体强化学习重新设计环境栈，通过将轨迹存储为消息DAG，将复杂度从O(n²)降至O(n)，提升效率。

Claude Code browser 🌍, Cursor general agent 🤖, Claude Fable extension ⏳

TLDR AI · 07/13 08:00

中文介绍 Claude Code浏览器发布，Cursor通用智能体上线，Claude Fable扩展推出。

[AINews] not much happened today

Latent Space · 07/11 10:53

a quiet day after a week of nonstop model releases

中文介绍 经过一周不间断的模型发布后，今日市场平静，无重大事件。

How Deutsche Telekom is rewiring telecommunications with AI

OpenAI News · 07/10 15:00

How Deutsche Telekom is becoming an AI-native telco with OpenAI-transforming customer service, employee workflows, network operations, and the future of voice.

中文介绍 德国电信正借助OpenAI转型为AI原生电信公司，改造客户服务、员工工作流、网络运营及语音未来。

[AINews] OpenAI launches GPT 5.6 Sol/Terra/Luna, Codex becomes ChatGPT superapp

Latent Space · 07/10 14:19

A big day for OpenAI.

中文介绍 OpenAI发布GPT 5.6 Sol/Terra/Luna，Codex成为ChatGPT超级应用。这是OpenAI的重要一天。