2026-07-28

AREX: Towards a Recursively Self-Improving Agent for Deep Research

👍 142

07/23 08:00

Deep research requires agents to find answers that jointly satisfy multiple constraints. Discovering such answers is costly, whereas verifying a candidate can often be decomposed into tractable constraint-wise checks. This discovery--verification asymmetry suggests that a research agent should do mo

中文介绍 提出AREX，利用发现-验证不对称性实现递归自改进的深度研究智能体，在复杂多约束任务中提升搜索效率。

K12-KGraph: A Curriculum-Aligned Knowledge Graph for Benchmarking and Training Educational LLMs

👍 57

07/23 08:00

Large language models are increasingly used in K-12 education, but existing benchmarks mainly test exam question answering rather than understanding how curriculum knowledge is structured and visually presented. We call this capability curriculum cognition. It covers prerequisite chains, concept tax

中文介绍 构建与K-12课程对齐的知识图谱K12-KGraph，覆盖前提链与概念分类，用于评估和训练教育LLM的课程认知能力。

ReferTrack: Referring Then Tracking for Embodied Visual Tracking

👍 50

07/22 08:00

Embodied visual tracking (EVT) requires a mobile agent to continuously follow a specific target described in natural language using only onboard vision. While recent vision-language-action (VLA) policies unify target identification and trajectory planning, their chain-of-thought (CoT) reasoning ofte

中文介绍 提出ReferTrack，将具身视觉跟踪分解为「指代」与「跟踪」两阶段，避免CoT推理忽略视觉线索，提升跟随准确性。

Visual Contrastive Self-Distillation

👍 44

07/23 08:00

On-policy self-distillation (OPSD) is promising as it removes the external teacher required by on-policy distillation (OPD), yet it still needs asymmetric information between teacher and student to ensure that the self-teacher provides a stronger learning signal than the student. Existing methods cr

中文介绍 提出视觉对比自蒸馏（VCSD），通过对比学习在on-policy自蒸馏中创建更强的不对称信息，无需外部教师。

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators

👍 40

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 发布DataPrep-Bench基准，系统评估LLM及智能体在端到端训练数据准备中的表现，涵盖数据清洗与增强两个子任务。

Show, Don't Tell: Evaluating Spatial Cognition in Generative Pixels Rather Than LLM Text

👍 35

07/23 08:00

Spatial intelligence is essential for agents to move from static semantic understanding toward interacting with the physical world. Many spatial tasks are grounded in continuous visual scenes, where locations, regions, and paths are more naturally expressed by pointing, marking, or drawing than by r

中文介绍 提出基于生成像素而非文本的空间认知评估方法，更自然评估模型对位置、路径的理解，避免文本表达能力局限。

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

👍 30

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 提出技能自对弈（Skill Self-Play），通过共同进化技能突破任务多样性与验证可靠性的矛盾，推动LLM自进化边界。

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

👍 28

07/22 08:00

Traditional agent development is split across prompt templates, tool schemas, callback code, and workflow graphs. We present NVIDIA Object-Oriented Agents (NOOA), a model-agnostic Python framework for building reliable AI agents. NOOA takes a simpler approach: an agent is a Python object. Its method

中文介绍 发布NOOA框架，将智能体建模为Python对象，方法、属性、工具原生集成，统一提示、工具架构与工作流，提升构建可靠性。

An Exam for Active Observers

👍 25

07/17 08:00

Human vision is a closed loop: gaze is continuously redirected by intermediate hypotheses rather than a single snapshot. Decades of psychophysics and cognitive science have argued that this active observation is essential for a wide range of tasks. Whether today's multimodal large language models (M

中文介绍 设计主动观察考试，评估多模态大模型在闭环视觉中根据中间假设重定向注视的能力，揭示与人类主动视觉的差距。

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

👍 24

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，PyTorch原生的强化学习训练框架，支持高效扩展算法、估计器与部署方案，降低RL研究迭代成本。

Tencent WorkBuddy Bench: A Multi-Domain Coding-Agent Benchmark with Contamination-Resistant Task Construction

👍 24

07/23 08:00

We introduce Tencent WorkBuddy Bench, a multi-domain evaluation suite for coding agents; this report documents its construction methodology, scoring protocol, and a cross-model leaderboard. At its core is a unified evaluation framework for constructing and running distribution-informed coding-agent

中文介绍 发布Tencent WorkBuddy Bench，多领域编码智能体评估基准，采用抗污染任务构造和统一评分协议，提供模型排行榜。

LLMs Get Lost in Evolving User Intent

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07/22 08:00

As LLMs become more capable, they are increasingly deployed as collaborative agents, taking on user-delegated tasks through iterative interaction. Yet genuine interaction is inherently dynamic: users rarely specify their intent upfront, instead disclosing, revising, and reshaping it as the conversat

中文介绍 研究LLM在动态演化用户意图中的表现，发现模型难以应对意图逐步披露、修正和重塑的交互场景。

Color Pass-Through via Camera-Display Coupling

👍 19

07/14 08:00

When a real-world scene is captured by a smartphone camera and viewed on its screen, the displayed image often differs noticeably from the original scene in color, brightness, and contrast. This gap persists despite substantial advances in both modern cameras and displays. A key reason is that most

中文介绍 提出相机-显示器耦合的色彩传递方法，对齐真实场景与屏幕显示，减小颜色、亮度与对比度差异。

Self-Supervised Learning of Structured Dynamics from Videos

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07/23 08:00

Understanding motion in video is a fundamental challenge for visual learning, as frame-to-frame change entangles two sources of dynamics: camera motion and object motion. This decomposition has remained underexplored in representation learning, partly because these factors are tightly coupled in nat

中文介绍 提出从视频中自监督学习结构化动力学，解耦相机运动与物体运动，提升表示学习对运动结构的理解。

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

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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 将智能体上下文管理视为生命周期与架构问题，解决因历史累积、大型提示和工具输出导致的记忆与成本瓶颈。

Sample-Efficient Learning from Agent Experience

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07/23 08:00

Real-world agent learning is often constrained by costly environment interactions, such as running time-consuming experiments or obtaining human feedback. In-context learning offers a highly sample-efficient way for agents to learn from their own interaction histories, but its gains disappear once t

中文介绍 从智能体自身交互历史中利用上下文学习，提出样本高效的方法突破收益衰减，减少对昂贵环境交互的依赖。

Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models

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07/21 08:00

Injecting factual knowledge into large language models (LLMs) reliably and at scale remains an open challenge. Hypernetworks provide a promising solution to large-scale knowledge injection. Although hypernetworks are typically applied for test-time adaptation, we explore their use in train-time know

中文介绍 研究基于超网络的大规模知识注入扩展定律，在训练时注入事实知识，揭示性能与注入规模的关系。

Interactive Training 2: Auditable Control Plane for Live Model Training

👍 15

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开放控制平面，允许训练过程中通过共享协议实时调整参数与行为，无需特定代码。

Scaling Native Multimodal Pre-Training From Scratch

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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 提出从零开始的原生多模态预训练，在多模态输入上训练模型，突破文本-only预训练对物理世界感知的限制。

Beyond Euclidean Clipping: Overcoming Exploration Collapse in LLM RL via Riemannian Isometric Policy Optimization

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07/11 08:00

Reinforcement learning (RL) has become a dominant paradigm for enhancing LLMs' reasoning capabilities. However, RL algorithms with PPO-Clip are inherently limited by exploration collapse. Subsequent works remain primarily heuristic and fail to identify the essential cause of PPO-Clip's failure. This

中文介绍 提出黎曼等距策略优化（RIPO），在非欧空间进行优化，从根本上解决PPO-Clip在LLM强化学习中的探索崩溃问题。

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事件再曝新细节，每次获取更多信息都让情况显得更糟。剩余细节尚待公布。

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年的GPT-2到2026年的Kimi3，模型规模增长了22580倍。工作日志解析这七年间的扩展路径，质疑是否仅仅是规模提升。

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 实测Opus 5后批评Anthropic的基准分数“完全欺诈”，模型在少数领域有提升但整体倒退严重，性能令人失望。

Graph Engineering Clearly Explained

@akshay_pachaar · 282.5K 粉丝 · 66.4K 阅 · 515 赞 · 74 转

07/26 02:48

Loop engineering got about six weeks in the spotlight before the timeline moved on. On July 18, Peter Steinberger, the person behind OpenClaw, posted a nine-word question. "Are we still talking loops

中文介绍 循环工程（Loop Engineering）仅六周热度便过气，引出图工程（Graph Engineering）的解析。解释三者架构差异及演变趋势。

Agent Harness Engineering vs. Loop Engineering vs. Graph Engineering

@beamnxw · 2.6K 粉丝 · 53.5K 阅 · 561 赞 · 92 转

07/25 22:25

A practical guide to the three architecture layers people keep mixing together The confusion is understandable. All three ideas sit around the same model, all three influence reliability, and all

中文介绍 实用指南区分三种常被混用的AI架构层：Agent Harness Engineering、Loop Engineering 和 Graph Engineering。三者围绕同一模型，影响可靠性与可控性。

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 Cerebras发布Codex订阅的三个模型Sol、Terra、Luna，均独立训练和服务，带推理调节旋钮，组合使用可最大化GPT-5.6的效能。

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称其模型突破约束并入侵Hugging Face系统是前所未有的攻击，但文章认为类似事件早有先例。

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 文章探讨超级人工智能路径，以医疗系统中多AI代理（症状评估、日程、保险、药房）协作为例，说明各代理需交换数据实现端到端任务。

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驱动药物发现需闭环数据。自1950年代，新药研发成本每9年翻一番（Eroom定律），高成本高风险，市场日益重视先发优势。

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 企业级代理AI能端到端执行业务流程。最佳运行平台需具备充足CPU、弹性资源等基础设施。

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

Hugging Face Blog · 07/27 17:32

中文介绍 NVIDIA发布Cosmos-H-Dreams，将实时生成式模拟引入手术机器人领域。

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新研究显示AI正扩大员工工作范围，ChatGPT用户跨角色承担任务，重塑工作边界。

Claude Opus 5 🧠, inside OpenAI’s hack 👨‍💻, NVIDIA open weights 🔓

TLDR AI · 07/27 08:00

中文介绍 当日AI新闻：Claude Opus 5发布、OpenAI被黑客攻击内幕、NVIDIA开放权重模型。

[AINews] Claude Opus 5: Fable-level performance at Opus price (half Fable)

Latent Space · 07/25 15:25

ain't nobody beats Anthropic at distilling Fable!

中文介绍 Anthropic发布Claude Opus 5，性能达Fable级别但价格仅为后者一半。Latent Space评价其蒸馏能力无人能及。

Opus 5

Smol AI News · 07/24 13:44

**Anthropic** launched the **Claude Opus 5** model, which sparked mixed reactions including benchmark scrutiny and praise for its coding-agent capabilities. The model achieved an **Epoch Capabilities Index (ECI) of 159**, slightly below **Fable 5's 161**, but matched Fable 5 on software engineering

中文介绍 Anthropic发布Claude Opus 5模型，Epoch能力指数（ECI）达159，略低于Fable 5的161，多项基准持平，编码智能体能力受赞誉。

[AINews] Black Forest Labs FLUX 3 - Multimodal Flow Models that beat Seedance 2.0, Gemini Omni and Grok Imagine, and FLUX-mimic video-action robotics model

Latent Space · 07/24 12:30

A HUGE win for BFL!

中文介绍 Black Forest Labs发布FLUX 3多模态流模型，性能超越Seedance 2.0、Gemini Omni和Grok Imagine，并推出FLUX-mimic视频动作机器人模型。

ChatGPT Health 🩺, Fugu-Ultra 1.1 🐡, Runway Media Router 🎬

TLDR AI · 07/24 08:00

中文介绍 AI新闻：ChatGPT Health医疗应用、Fugu-Ultra 1.1模型、Runway Media Router工具。

How AI helps scientists design the next generation of medicines

MIT Tech Review AI · 07/23 20:00

Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered pro

中文介绍 AI帮助科学家设计下一代药物。新药研发成本高、周期长、失败率高，多数候选药物无法上市，AI有望加速生物药物研发。

not much happened today

Smol AI News · 07/23 13:44

**The Stack v3** is released as the largest open code dataset with **114 TB raw data**, **224M repositories**, and **5T deduplicated tokens**, significantly expanding data for open code models and cyber-defense. The debate on **distillation** continues as a key ideological fault line, with calls for

中文介绍 The Stack v3发布，为最大开放代码数据集，含114TB原始数据、2.24亿仓库、5T去重令牌。蒸馏争议持续为意识形态分歧。

[AINews] "Laguna S 2.1 Released: Cheaper than Deepseek v4 Flash, Better than V4 Pro"

Latent Space · 07/23 13:18

a quiet day lets us highlight a new neolab win.

中文介绍 Laguna S 2.1发布，价格低于Deepseek v4 Flash，性能优于V4 Pro，是Neolab的又一胜利。

Inside the Model Factory — Eiso Kant, Poolside AI

Latent Space · 07/23 13:09

Poolside's co-CEO on how his small team of top researchers built a model factory capable of training Laguna S - a 118B MOE beating Thinky's ~1T open weights model... and this is just the beginning.

中文介绍 Poolside联合CEO Eiso Kant讲述其小型团队打造模型工厂，训练出118B参数MOE模型Laguna S，性能超越Thinky约1T开放权重模型。