2026-07-14

Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning

👍 85

07/08 08:00

Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization. Mechanistically explaining these relationships requires interpreting structural evidence through sc

中文介绍 提出深度原生结构推理（Deep Native Structural Reasoning）方法，通过空间、化学与周期性组织解释结构-性质关系，实现生物学、化学与材料科学中更准确、跨学科且透明的理解。

Video-Oasis: Rethinking Evaluation of Video Understanding

👍 58

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 重新审视视频理解评估体系，指出现有基准难以区分视觉感知、语言推理与知识先验的贡献，提出共享评估准则以更准确衡量Video-LLM的真实能力。

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

👍 47

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基准，专门测试AI智能体在长周期终端任务上的表现，采用密集奖励评分以评估中间进度和部分解决方案，避免仅依赖最终结果的局限性。

Scalable Visual Pretraining for Language Intelligence

👍 41

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 针对文本预训练难以捕获图表、公式等视觉知识的问题，提出可扩展的视觉预训练方法，使语言模型能从视觉表示中获取知识，提升语言智能。

Video Generation Models are General-Purpose Vision Learners

👍 40

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通用基础模型的催化剂，使视频生成模型成为通用型视觉学习者，类似NLP中next-token prediction的作用。

Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation

👍 32

07/09 08:00

Scientific ideas rarely start from a blank page. They inherit mechanisms, repair known limitations, and recombine pieces of earlier work, much like biological genomes. Current benchmarks still say little about whether AI systems can follow this inheritance structure. We present IdeaGene-Bench (IG-Be

中文介绍 提出IdeaGene-Bench基准，评估AI系统遵循科学思想继承结构（继承机制、修复局限、重组先期工作）的能力，推动思想谱系推理与谱系驱动的创意生成。

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

👍 31

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，用于评估能操作日常工具并辅助用户的主动智能体，解决现有基准缺乏真实世界交互评估的不足。

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

👍 29

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先验联合处理基于事件的视频重建、预测与帧插值，克服回归方法纹理模糊和生成模型长期不稳定的问题。

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，结合几何感知预训练与下游任务微调，实现文生上下文全景生成，并构建大规模高质量训练数据以弥补数据不足。

Trust Region Policy Distillation

👍 17

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 提出信任区域策略蒸馏（TOP-D），通过动态构建近端教师网络，将不稳定高方差的在线策略蒸馏转化为稳定训练范式，实现复杂目标的分步学习。

OpenCoF: Learning to Reason Through Video Generation

👍 17

07/09 08:00

Reasoning has become a core capability for large models, especially when reliable decisions require understanding logical consequences. Recent video generation models offer a reasoning path distinct from previous Chain-of-Thought (CoT): reasoning can unfold through temporally connected frames, known

中文介绍 提出OpenCoF方法，将推理过程通过视频生成展开（推理通过时间连续帧可视化），为大型模型提供区别于链式思维（CoT）的新推理路径。

KronQ: LLM Quantization via Kronecker-Factored Hessian

👍 16

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后训练量化方法，引入Kronecker分解的海森矩阵作为二阶信息，改进GPTQ等仅依赖输入激活统计量的方法，实现更好的LLM压缩效果。

Jet-Long: Efficient Long-Context Extension with Dynamic Bifocal RoPE

👍 16

07/08 08:00

Modern LLMs are increasingly deployed in long-context applications such as retrieval-augmented generation, repository-level coding, and agentic workflows whose accumulated reasoning and tool traces routinely push the input an order of magnitude past the pretraining window, making zero-shot context e

中文介绍 提出Jet-Long长上下文扩展方法，采用动态双焦RoPE（Dynamic Bifocal RoPE），高效处理远超预训练窗口的输入，适用于检索增强生成、仓库级编码等场景。

Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning

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07/08 08:00

Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs). Previous RL pipelines for LLMs were mostly synchronous and batch-interleaved, which is inefficient for long-horizon agentic tasks. Recently, asynchronous RL has emerged as a more efficient

中文介绍 提出单rollout异步优化框架用于智能体强化学习，解决传统同步批处理RL在长时任务中的低效问题，提升LLM后训练的效率和灵活度。

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

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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，专为子弹时间、推拉变焦等电影级相机运动生成设计，克服DiT参数大和多步迭代在设备端的限制。

Automating the Design of Embodied Agent Architectures

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07/03 08:00

Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules. This modularity exposes a large architectural design space, but current systems still rely on researcher intuition to choose where information is stored, how observations are proces

中文介绍 提出自动设计具身智能体架构的方法，将感知、记忆、规划、行动模块组合视为可搜索的设计空间，减少对研究者直觉的依赖，自动发现高效架构。

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 提出主动记忆智能体（Proactive Memory Agent），在长时任务中自动检索并关注决策相关的状态信息，避免关键信息被历史轨迹淹没，提升长期任务表现。

Sparse Delta Memory: Scaling the State of Linear RNNs through Sparsity

👍 10

07/08 08:00

Linear attention models allow a fixed state size and a fixed amount of compute per token. However, due to their limited state size, linear attention models fall behind in long-context recall compared to softmax-attention-based transformer architectures. Increasing the state size of linear attention

中文介绍 提出稀疏增量记忆（Sparse Delta Memory），通过引入稀疏性扩展线性RNN的状态大小，在不显著增加计算量的前提下提升长上下文召回能力，缩小与softmax Transformer的差距。

AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation

👍 8

07/07 08:00

We present AgentLens, a production-assessed benchmark for interactive code agents. Most code-agent benchmarks reduce a run to a single bit -- did the task pass? -- but the people who actually use these agents experience the entire trajectory: how the agent follows instructions, uses its tools, verif

中文介绍 提出AgentLens生产评估基准，不仅记录代码智能体任务是否通过，还通过生产级日志分析整个轨迹（指令遵循、工具使用、验证行为等），提供细粒度评估。

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

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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LLM微调中新知识「知道-使用」鸿沟，表现为准确率差距和记忆与泛化之间的时间滞后，并探讨其机制理解，为提升知识泛化提供方向。

The Great Wave Has Arrived (from GLM CEO Jie Tang)

@bingxu_ · 6.5K 粉丝 · 318.6K 阅 · 505 赞 · 88 转

07/11 23:10

-- Bing Xu's Note --- I came across an internal GLM letter on the Chinese app RedNote, purportedly written by @jietang, and translated the Chinese text in the images into English. --- Letter Start

中文介绍 GLM 首席执行官 Jie Tang 的内部信被曝光，Bing Xu 将其从中文翻译为英文。信中宣告 AI「大浪潮」已至，代表智谱 AI 对行业格局的判断，引发关注。

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 Satya Nadella 引用诺贝尔奖得主 Arrow 的信息悖论，探讨智能时代企业保护核心 IP 的困境。提出「逆向信息悖论」：信息价值在披露前无法评估，披露后则受损。

gpt-5.6-sol without hitting limits

@theo · 356.6K 粉丝 · 77.0K 阅 · 774 赞 · 27 转

07/12 06:59

I've burned over $200,000 of tokens with gpt-5.6-sol. It's a great model. That said, it's a bit too easy to hit your limits on the $200 Codex Pro sub. OpenAI has been generous about resets, but that

中文介绍 theo 在使用 gpt-5.6-sol 上烧掉超 20 万美元 token，指出 $200/月的 Codex Pro 订阅极易触及限制。虽 OpenAI 提供重置，但高消耗用户仍需应对配额瓶颈。

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 Greg Eisenberg 称两个万亿级市场同步开放，AI 代理商机前所未有。他基于三次平台迁移经验，分享如何用 AI 代理赚取 1000 万美元，认为这是比移动应用更大的机遇。

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 HeyGen 推出「Motion-Graphics Skill」：AI 生成 10 秒内设计驱动的动效图形，无旁白，运动即信息。支持动态文字、统计图表等，适合短视频场景。

GPT‑5.6 by our community

@OpenAIDevs · 373.8K 粉丝 · 58.5K 阅 · 532 赞 · 34 转

07/12 03:14

GPT‑5.6 has only been in builders’ hands for a couple of days. Here’s what the community is already building with it: A voxel Manhattan, built autonomously with GPT‑5.6 Sol: GPT‑5.6 Sol got to

中文介绍 GPT-5.6 发布数日，社区已用它自主构建体素曼哈顿等创意应用。OpenAIDevs 展示模型在自动化生成方面的突破，体现新版本在创造力和效率上的提升。

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 LexnLin 澄清「用 AI 设计」的常见误解：并非让 AI 直接出图。真正用法是 AI 作为协作工具辅助人类决策，而非替代设计师。这是对 AI 设计流程的重新定义。

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（估值近1万亿美元）发布最新AI发现，揭示了模型内部运作的机制，但也引发对理解局限的讨论。该研究展示了AI推理过程的某些方面，但并不能解释一切。

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

TLDR AI · 07/13 08:00

中文介绍 TLDR AI 2026年7月13日新闻摘要：Claude Code浏览器版上线、Cursor推出通用智能体、Claude Fable扩展发布，均为AI工具链的重要更新。

not much happened today

Smol AI News · 07/11 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，为智能体强化学习和评估提供全新环境栈。通过将轨迹存储为消息DAG，复杂度从O(n²)降至O(n)，支持高效的长程推理。

[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 在连续一周密集模型发布后，当日AI领域相对平静，未有重大事件或新模型发布，市场进入消化期。

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 德国电信与OpenAI合作，利用AI改造客户服务、员工工作流、网络运营及语音技术，目标成为AI原生电信公司。

[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，分为Sol/Terra/Luna三层，并将Codex升级为ChatGPT超级应用。此次发布意义重大，标志着模型分层和功能整合的新阶段。

not much happened today

Smol AI News · 07/10 13:44

**OpenAI** rolled out **GPT-5.6** featuring a new model stratification with tiers **Luna / Terra / Sol** and effort levels including **Max** and **Ultra**, introducing complex configuration options. The launch faced UX challenges with the **ChatGPT Work / Codex** split, prompting rapid corrective ac

中文介绍 OpenAI推出GPT-5.6，引入Luna/Terra/Sol分层及Max/Ultra努力级别，配置选项复杂。同时ChatGPT Work与Codex拆分，带来用户体验挑战。

Getting started with ChatGPT

OpenAI News · 07/10 08:00

Learn how to use ChatGPT, start your first conversation, and discover simple ways to write, brainstorm, and solve problems with AI.

中文介绍 OpenAI Academy发布入门指南，教授如何使用ChatGPT开始对话，并利用AI进行写作、头脑风暴和问题求解，适合新手快速上手。

Profiling in PyTorch (Part 3): Attention is all you profile

Hugging Face Blog · 07/10 08:00

中文介绍 Hugging Face博客发布PyTorch性能分析系列第三部分，聚焦注意力机制的性能优化，通过性能分析工具提升模型效率。

GPT-5.6 🚀, Muse Spark 1.1 ✨, ChatGPT Work 💼

TLDR AI · 07/10 08:00

中文介绍 TLDR AI 2026年7月10日摘要：OpenAI发布GPT-5.6，Muse推出Spark 1.1，ChatGPT Work作为生产力工具上线运行。

Anthropic found a hidden space where Claude puzzles over concepts

MIT Tech Review AI · 07/10 04:22

The AI firm Anthropic has developed a technique that has given it the clearest glimpse yet at what’s really going on inside large language models as they answer questions or carry out tasks. What they found ranges from the mundane to the unnerving. Researchers at the company built a tool called the

中文介绍 Anthropic开发新技术，发现大型语言模型内部存在一个隐藏空间，Claude在此处理概念。这一发现揭示了AI推理的内部过程，既有平凡之处也有令人不安的方面。

GPT-5.6 is now the preferred model in Microsoft 365 Copilot

OpenAI News · 07/09 21:00

Learn how GPT-5.6 powers Microsoft 365 Copilot with stronger AI capabilities across Word, Excel, PowerPoint, Chat, and Cowork for faster, higher-quality work.

中文介绍 OpenAI宣布GPT-5.6成为Microsoft 365 Copilot的首选模型，为Word、Excel、PowerPoint等应用带来更强的AI能力，提升工作质量和效率。

GPT-5.5 Bio Bug Bounty

OpenAI News · 07/09 18:00

Details about the OpenAI Bio Bounty program

中文介绍 OpenAI推出GPT-5.5生物漏洞赏金计划，鼓励研究人员发现并报告AI在生物学领域可能被滥用的风险，以确保安全。

GPT-5.6: Frontier intelligence that scales with your ambition

OpenAI News · 07/09 18:00

More intelligence from every token, stronger performance per dollar, and more capability on demand for your hardest work.

中文介绍 OpenAI发布GPT-5.6，宣称每个token提供更多智能，性能成本比更高，按需提供更强能力，适用于最困难的任务和雄心勃勃的项目。

ChatGPT is now a partner for your most ambitious work

OpenAI News · 07/09 18:00

ChatGPT Work is an agent that can take action across your apps and files, stay with a project for hours if needed, and turn a goal into finished work.

中文介绍 OpenAI推出ChatGPT Work智能体，可在应用和文件间行动，持续数小时处理项目，将用户目标直接转化为成品。