2026-07-16

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

👍 112

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在强模型上重复训练成本高的问题，提出直接在线策略蒸馏（Direct On-Policy Distillation），通过弱模型引导强模型在线生成轨迹，降低训练开销。

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

👍 84

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-N1，一种通用视觉语言导航基础模型，旨在统一深度推理与多样化的具体空间决策任务，解决现有单体策略易受观测噪声影响的问题。

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

👍 72

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，一个通用机器人Agent操作系统，内置终身多模态记忆、推理、工具使用、验证和跨实体执行能力，为长时域任务提供通用运行时层。

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

👍 64

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，用于评估AI Agent在长时域终端任务中的表现，采用密集奖励评分，弥补现有基准只考虑最终结果的不足。

Video Generation Models are General-Purpose Vision Learners

👍 59

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计算机视觉通用基础模型的关键催化剂，表明视频生成模型能学习通用视觉表征，推动向通用视觉学习者转变。

SynthDocBench: Controlled Benchmark for Long-Context Visual Document Understanding

👍 51

07/11 08:00

Vision language models (VLMs) have achieved strong performance on visual document understanding benchmarks such as DocVQA, ChartQA, and MMLongBench-Doc. However, real-world documents combine multiple factors such as length, layout complexity, modality, and question difficulty, which makes it difficu

中文介绍 提出SynthDocBench，一个可控的长上下文视觉文档理解基准，通过合成生成控制长度、布局、模态和问题难度，系统评估VLM在多因素复合下的能力。

Scalable Visual Pretraining for Language Intelligence

👍 50

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 针对纯文本预训练无法捕获图表、公式等视觉知识，提出一种可扩展的视觉预训练方法，通过图文混合训练增强语言模型的多模态理解能力。

Read It Back: Pretrained MLLMs Are Zero-Shot Reward Models for Text-to-Image Generation

👍 42

07/13 08:00

In this paper, we propose SpectraReward, a training-free reward function that turns pretrained MLLMs into off-the-shelf reward models for image-generation reinforcement learning. Instead of asking the MLLM to judge a generated image or answer decomposed verification questions, SpectraReward measures

中文介绍 提出SpectraReward，一种免训练奖励函数，将预训练MLLM直接用作文本到图像生成的奖励模型，通过测量图像与文本的对齐度而非直接判断，提升生成质量。

Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation

👍 27

07/09 08:00

Visual generators excel at rendering, but they confidently fabricate what they do not know. User requests are unbounded, evolving, and deeply long-tailed: new characters, trending entities, post-cutoff events, and more. This world-knowledge bottleneck is structural: generators are trained on fixed c

中文介绍 针对视觉生成模型对长尾知识（新角色、时效事件）的幻觉问题，提出一种可演化的知识边界方法，使生成器能搜索超出训练数据的知识，减少无根据输出。

Trust Region Policy Distillation

👍 26

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将不稳定的在线策略蒸馏转化为稳定训练范式，解决高方差问题，提升收敛速度。

KronQ: LLM Quantization via Kronecker-Factored Hessian

👍 23

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，利用Kronecker因子Hessian矩阵进行LLM后训练量化，相比仅基于输入统计的GPTQ方法，更全面考虑输出通道相关性，提升量化精度。

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

👍 22

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，一套面向高级数学证明生成与验证的基准套件，覆盖多个学科，弥补现有基准在范围和评估粒度上的不足。

Metacognition in LLMs: Foundations, Progress, and Opportunities

👍 17

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LLM中的元认知（Metacognition），梳理其作为智能基础在自省、推理和决策中的进展，并展望提升LLM透明性与能力的机会。

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

👍 12

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 揭示LLM微调中记忆新知识但无法用于下游推理的「知道-使用鸿沟」，形式化该现象为准确率差距与时间滞后，并探索其背后的机制。

Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models

👍 11

07/14 08:00

Coding agents must integrate external tool returns into ongoing reasoning - a capability that standard left-to-right pretraining on code exposes only in its forward direction. We observe that the action-observation-continuation loop of a coding agent is structurally isomorphic to a function call sit

中文介绍 针对编码Agent需整合工具返回与推理的需求，提出函数感知的填充中间训练（FIM），将行动-观察-延续循环映射为函数调用结构，提升Agent基础模型性能。

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

👍 10

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的模块化后训练范式，通过代理引导的更新信号解耦策略探索与分布对齐，降低直接在大模型上探索的成本。

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

👍 10

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（Self-Guided TTT），在长上下文LLM推理阶段动态调整模型表示，帮助模型更有效利用长输入中的关键信息，减少长度导致的性能下降。

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Agent（Proactive Memory Agent），在长时域任务中主动管理并检索关键状态（任务需求、历史尝试等），避免信息被淹没在上下文窗口中。

Multi-Agent LLMs Fail to Explore Each Other

👍 8

07/13 16:34

Exploration is essential for reliable autonomy in multi-agent systems, yet it remains unclear whether large language model (LLM) agents can explore effectively when interacting with one another. We show that modern LLM agents fail to do so, often exhibiting myopic and polarized interaction patterns

中文介绍 发现多Agent LLM系统在交互中缺乏探索能力，表现出短视和极化模式，导致无法有效发现新策略，限制多系统自主性。

MET: Theory-Grounded and Culture-Aware Multilingual Moral Reasoning

👍 7

07/13 23:59

Language models are increasingly used for moral decision-making across diverse linguistic and cultural contexts, yet existing work overlooks multilinguality on three aspects: 1) multilingual evaluation benchmarks use direct translation, failing to adapt culture-specific items; 2) inference-time meth

中文介绍 提出MET框架，实现理论引导且文化感知的多语言道德推理，解决现有评估翻译偏差及推理方法忽视文化适应的问题。

You just hired a million bad employees.

@gsivulka · 10.2K 粉丝 · 591.2K 阅 · 512 赞 · 99 转

07/15 00:41

AI was supposed to replace human labor. It did the opposite. For the first time in history, humans are cheaper than software. And AI is creating more jobs than it eliminates. Technology has

中文介绍 AI并未取代人类劳动，反而使人类历史上首次比软件更便宜，且AI创造的工作多于消除的。这一观点挑战了「AI取代工作」的常见叙事。

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 通过新的Fusion架构，用每token成本两倍于Opus 4.8的Fable 5模型，在FrontierCode 1.1上运行时反而降低了Devin的总账单，关键在架构优化。

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」，称其探讨人性化技术，是年度最佳之一。

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 Demis Hassabis认为AGI可能几年内到来，这是人类历史的关键时刻，并提出了前沿AI的框架。

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 通过自定义Claude Skill功能（路径：Customize > Skills > 创建技能）来改进前端UI设计，避免AI生成的糟糕外观，提升设计质量。

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 两个万亿美元市场同步开启，经历三次平台转型的创业者称前所未见，并分享用AI agent赚取1000万美元的策略。

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 4个月来每天使用Hermes，已成为研究和投资核心。分享两个离不开的工作流，相比之前依赖Grok、Claude、Gemini等工具有显著提升。

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 介绍工具waggle，它能显示AI实际读取了哪些文件，解决AI常自信回答但可能遗漏一半文档的问题，让用户判断答案是否可信。

What building Shippy taught us about building agents

Hugging Face Blog · 07/16 01:29

中文介绍 艾伦人工智能研究所（Allen AI）分享构建Shippy智能体过程中的经验教训，探讨如何设计有效、可靠的自主代理系统。

Model Routing Is Simple. Until It Isn’t.

Hugging Face Blog · 07/16 01:27

中文介绍 IBM Research 指出模型路由看似简单实则复杂，分析了在多模型场景下有效路由的挑战与陷阱。

Meet GPT-Red: an LLM super-hacker OpenAI built to make its models safer

MIT Tech Review AI · 07/16 01:09

OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other models boost their defenses against cyberattacks. Last week the company released the latest version of its flagship LLM, GPT-5.6. OpenAI says that training it against GPT-Red made the model its m

中文介绍 OpenAI 构建了LLM超级黑客GPT-Red，作为陪练帮助其他模型提升防御网络攻击的能力。该公司上周发布旗舰模型GPT-5.6，并称通过GPT-Red训练增强了安全性。

The US is advancing AI safety through state and federal action

OpenAI News · 07/15 20:00

OpenAI outlines a “reverse federalism” approach to AI governance, where state laws help build a national framework for safe, democratic AI.

中文介绍 OpenAI 提出“反向联邦制”AI治理方法，主张各州法律助力构建国家层面的安全、民主AI框架。

GPT-Red: Unlocking Self-Improvement for Robustness

OpenAI News · 07/15 18:00

Explore GPT-Red, OpenAI’s automated red teaming system that uses self-play to improve AI safety, alignment, and prompt injection robustness.

中文介绍 GPT-Red是OpenAI的自动化红队系统，利用自我对弈提升AI安全性、对齐能力和提示注入鲁棒性。

Welcome Inkling by Thinking Machines

Hugging Face Blog · 07/15 08:00

中文介绍 Thinking Machines 在Hugging Face博客上介绍其新模型Inkling，具体细节尚未披露。

Introducing Real World VoiceEQ: Measuring the human quality of voice AI

Hugging Face Blog · 07/15 08:00

中文介绍 Real World VoiceEQ 评测指标发布，旨在衡量语音AI的人类化质量。

DeepSeek IPO plans 📈, Kalshi compute markets ⚡,  Bonsai phone model 📱

TLDR AI · 07/15 08:00

中文介绍 当日AI新闻摘要：DeepSeek计划IPO；Kalshi推出计算能力市场；Bonsai发布手机模型。

[AINews] not much happened today

Latent Space · 07/15 07:54

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

中文介绍 AI新闻简报：当日市场平静，但Codex每日新增用户达100万。

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工程世界博览会总结五大趋势：AI工程进入围绕智能体构建系统的新阶段，而非仅用智能体构建。

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建议企业在智能体时代管理AI投资：衡量每美元有效工作量、提升效率并扩展高价值工作流。

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需求推动。JetBrains将Codex列为推荐智能体；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 OpenAI Codex使用量6个月内增长超10倍，用户达700万，最近一天新增100万。报道质疑Codex是否已超越Claude Code。

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介绍销售团队如何利用ChatGPT Work创建管道简报、会议准备包、预测回顾、客户计划及停滞交易诊断。

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介绍数据科学团队如何利用ChatGPT Work构建根因简报、影响报告、KPI备忘录、范围分析与仪表盘规范。