2026-07-04

EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments

👍 39

07/02 08:00

Autonomous agents are increasingly expected to improve executable policies through feedback, yet existing evaluations often collapse this process into a final score or confound it with open-ended software-engineering progress. We introduce Autonomous Policy Evolution, a controlled evaluation setting

中文介绍 提出Autonomous Policy Evolution评估框架，衡量自主智能体在交互环境中通过反馈持续改进策略的能力，避免将过程简化为单一分数或与软件工程进展混淆，提供受控测试平台。

AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents

👍 39

07/02 08:00

Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see. The simplest contract appends past observations, tool calls, and reflections to every prompt, which makes prior context easy to access but also turns it into a jumbled mixture in which the effect of

中文介绍 提出AgenticSTS测试床，研究长程LLM智能体的有界记忆契约。指出简单拼接历史上下文会造成混杂记忆，难以区分有用信息，为评估不同记忆策略提供可控环境。

PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception

👍 36

06/26 08:00

We introduce PerceptionRubrics, a rubric-based evaluation framework that addresses the gap between saturated benchmark scores and real-world brittleness. Shifting evaluation from holistic semantic matching to rigorous atomic auditing, PerceptionRubrics pairs 1,038 information-dense images with over

中文介绍 引入PerceptionRubrics，一种基于量规的多模态评估框架，从整体语义匹配转向原子化审计。配以1038张信息密度图像，旨在校准基准分数与真实世界鲁棒性之间的差距。

Morphing into Hybrid Attention Models

👍 30

06/29 08:00

Hybrid attention models improve long-context efficiency by retaining only a subset of full-attention layers and replacing the remaining layers with linear attention. However, the effectiveness of Transformer-to-hybrid conversion critically depends on which layers preserve full attention. Existing hy

中文介绍 研究如何将Transformer高效转换为混合注意力模型以优化长上下文效率。发现转换效果取决于保留哪些层的全注意力，提出更好的层选择策略，避免现有方法的次优解。

ELDR: Expert-Locality-Aware Decode Routing for PD-Disaggregated MoE Serving

👍 23

07/01 08:00

In prefill-decode (PD) disaggregated LLM serving, each request is assigned to a decode worker after prefill. Existing decode routers balance only load; for mixture-of-experts (MoE) models this is incomplete: equally loaded workers can differ in latency, since each decode step loads the weights of ev

中文介绍 提出ELDR路由方法，针对预填-解码分离的MoE服务，在平衡负载同时感知专家局部性。通过考虑每个解码步骤加载专家权重的延迟差异，减少同负载下的推理延迟波动。

Seed2.0 Model Card: Towards Intelligence Frontier for Real-World Complexity

👍 23

06/30 08:00

We present Seed2.0, a model series that takes a meaningful step toward solving complex, real-world tasks. Our approach begins with identifying users' genuine needs and constructing a reliable, forward-looking evaluation system by selecting and abstracting benchmarks grounded in these needs and in re

中文介绍 发布Seed2.0模型系列，旨在解决复杂现实世界任务。从识别用户真实需求出发，构建前瞻性评估系统，并基于基准选择与抽象指导模型训练，推动通用智能边界。

Multimodal Continuous Reasoning via Asymmetric Mutual Variational Learning

👍 22

07/01 08:00

Multimodal Large Language Models (MLLMs) are often constrained by a language-space bottleneck, forcing complex visual reasoning into discrete tokens which can lose perceptual nuance. A promising alternative is continuous latent reasoning, where the goal is to discover implicit reasoning pathways tha

中文介绍 针对MLLM因语言空间瓶颈将复杂视觉推理强制离散化导致细节损失的问题，提出非对称互变分学习方法，在连续潜在空间发现隐含推理路径，保留感知连续性。

MemSyco-Bench: Benchmarking Sycophancy in Agent Memory

👍 22

07/01 08:00

Memory has emerged as a cornerstone of modern LLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators. However, memory is not always beneficial: retrieved memories often induce a critical issue of sycophancy, causing agents to over-align with the user at t

中文介绍 提出MemSyco-Bench基准，系统评估LLM智能体记忆模块导致的谄媚问题。发现记忆检索会促使智能体过度迎合用户偏好而非客观决策，为可控记忆设计提供评测工具。

AgenticDataBench: A Comprehensive Benchmark for Data Agents

👍 21

07/02 08:00

Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society. Automating this process is essential to reducing labor-intensive efforts for data scientists and enabling scalable data-driven applications. Re

中文介绍 提出AgenticDataBench，全面评估数据科学智能体从异构数据中自动获取洞察的能力，覆盖清洗、分析与建模等任务，旨在减少人力并推动数据驱动应用的可扩展性。

WorldDirector: Building Controllable World Simulators with Persistent Dynamic Memory

👍 16

07/02 08:00

We present WorldDirector, a highly controllable video world model framework designed for persistent dynamic object memory and unrestricted viewpoint exploration. Unlike existing world models that entangle physical dynamics with pixel rendering and rely on continuous visual observation to sustain mot

中文介绍 提出WorldDirector，高度可控的视频世界模型框架，具备持久动态物体记忆和自由视角探索。区别于现有模型将物理动态与像素渲染纠缠，实现场景元素独立控制。

ASPIRE: Agentic /Skills Discovery for Robotics

👍 16

06/30 08:00

Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures. We introduce ASPIRE (Agentic Skill Programming through Iterative Robot Exploration), a continual learning sys

中文介绍 提出ASPIRE，一种持续学习系统，使机器人通过迭代探索自主发现和编程技能。克服传统编程需处理多模态感知、接触动力学等挑战，减少人工干预，提升适应性。

Breaking Failure Cascades: Step-Aware Reinforcement Learning for Medical Multimodal Reasoning

👍 14

06/30 08:00

Recent multimodal large language models have shown great promise in clinical image reasoning, but existing post-training pipelines remain predominantly outcome-centric, relying on final answer correctness or sequence-level preferences. This suffers from sparse credit assignment, making it difficult

中文介绍 针对医学多模态推理中错误累积导致级联失败的问题，提出步骤感知强化学习方法。将奖励分解至每个中间推理步骤，缓解稀疏信用分配，提升临床影像推理的鲁棒性和准确性。

SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use

👍 12

07/02 08:00

Skills are becoming a reusable operational layer for LLM agents, encoding SOPs, domain rules, tool workflows, scripts, and validation routines. In realistic skill repositories, overlapping skills make reliable skill-use difficult. Final verifier success is too coarse for both evaluation and training

中文介绍 提出SkillCoach，通过自进化量规评估和增强LLM智能体的技能使用。针对技能库重叠导致的选择困难，从细粒度技能行为提供反馈，提升技能调用的可靠性与灵活性。

Logit-Contribution Scoring Identifies Non-Literal Retrieval Heads

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07/01 08:00

In long-context use, large language models frequently synthesize answers from the meaning of a relevant context span rather than literally copy-pasting them. Identifying which attention heads perform this synthesis matters for interpreting long-context model behavior. Yet existing detectors miss the

中文介绍 提出Logit-Contribution Scoring方法，识别大语言模型中执行非文字检索合成的注意力头。通过分析logit贡献发现模型从语义中合成答案而非简单复制，增强长上下文模型的可解释性。

InstanceControl: Controllable Complex Image Generation without Instance Labeling

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06/30 08:00

Controllable image generation methods, such as ControlNet, have demonstrated a remarkable capacity to introduce visual conditions(e.g., depth maps) to guide image generation. However, these methods often struggle with complex multi-instance scenes, frequently leading to attribute confusion among ins

中文介绍 提出InstanceControl，无需实例标注即可实现复杂图像中多实例的精确控制。通过解耦条件引导避免属性混淆，在保持生成质量的同时显著提升多实例场景的一致性。

When Search Agents Should Ask: DiscoBench for Clarification-Aware Deep Search

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06/26 08:00

Search agents powered by large language models (LLMs) are increasingly used to solve complex information-seeking tasks, requiring multi-step retrieval and reasoning to fulfill user goals. However, existing benchmarks often assume that user queries are complete and explicit, overlooking the fact that

中文介绍 提出DiscoBench基准，评估搜索智能体在复杂信息寻求任务中主动提出澄清问题的能力。弥补现有基准忽略用户查询不完全的缺陷，推动更真实的交互式搜索评估。

The State-Prediction Separation Hypothesis

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07/01 08:00

Transformers use the same forward computation stream to both predict the next token and store useful state for future token predictions. We formulate the state-prediction separation hypothesis: disentangling the two roles yields better language modeling performance. We design a Transformer variant t

中文介绍 提出状态-预测分离假说：Transformer将下一token预测与状态存储混合在同一前向流中，解耦二者可提升语言建模性能。设计分支分离变体，实验验证其有效性。

AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models

👍 7

07/02 08:00

Vision-Language Models (VLMs) have demonstrated immense promise in Spatio-Temporal Video Grounding (STVG). However, current evaluation protocols are largely confined to zero-shot assessments on general, daily-life benchmarks. This creates a critical disconnect from real-world applications in special

中文介绍 提出AnyGroundBench，面向专业领域的时空视频定位基准。填补当前VLM在通用零样本评估与医疗、工业等专用场景之间的鸿沟，推动模型在真实应用中的鲁棒性。

PixelEyes: Decoupling Perception and Reasoning for Pinpoint Visual Evidence Seeking

👍 7

06/30 08:00

This paper explores multi-turn visual reasoning and observes that MLLMs repeatedly fail to localize the target, leading to long, redundant trajectories. We attribute this failure to the entanglement of reasoning and perception within a single model, the MLLM reasons and localizes simultaneously, and

中文介绍 提出PixelEyes，将感知与推理解耦，解决MLLM在多轮视觉证据查找中反复定位失败的问题。通过分离定位与推理路径，显著减少冗余轨迹，实现精准高效的视觉证据搜索。

Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination

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07/01 08:00

Accelerating materials discovery requires AI systems that can generate scientifically valid hypotheses through multi-step, domain-grounded reasoning. Standard large language models often produce fluent but weakly traceable responses to open-ended materials design problems, making it difficult to det

中文介绍 提出图原生强化学习方法，通过概念重组生成可追溯的科学假说。克服标准LLM产生流畅但弱可追溯性的问题，在材料设计中实现多步领域推理，每个假说源头可查。

PorTAL: Portable Task Adapters for LLMs

@RampLabs · 13.4K 粉丝 · 335.4K 阅 · 507 赞 · 45 转

07/02 02:09

Researcher: Ben Geist Abstract Parameter-efficient fine-tuning (e.g. LoRA) adapts a frozen LLM to a task, but the resulting adapter is locked to one base model. When a new model is released, the

中文介绍 介绍PorTAL方法，解决LoRA等参数高效微调中适配器与基座模型绑定的问题，使任务适配器可跨模型迁移。

How To Master Fable (Fundamentals Guide)

@milesdeutscher · 671.1K 粉丝 · 243.8K 阅 · 500 赞 · 73 转

07/02 02:13

TL;DR: Everything you need to do to get maximum value from Fable. I guarantee that after you're done reading, you'll have all the necessary tools to quite literally 10x your AI productivity with

中文介绍 Fable使用基础指南，提供最大化AI生产力的工具和方法，承诺阅读后能10倍提升效率。

Career advice in the age of AI

@philhchen · 9.1K 粉丝 · 179.1K 阅 · 516 赞 · 34 转

07/03 05:25

AI models get better at anything you can write a loss function for, and school is mostly loss functions: well-defined problems graded against known answers. Therefore, the valuable work of the next

中文介绍 AI时代职业建议：AI擅长有明确损失函数的问题（如学校作业），而未来有价值的工作在于定义问题、处理模糊性。

Toward Unrestricted Intelligence: Venice Series A

@ErikVoorhees · 908.5K 粉丝 · 124.8K 阅 · 529 赞 · 103 转

07/01 22:28

“If others possess your thoughts and constrain your words, then you exist at their permission, and you are not free.” —Anonymous Venice launched just over two years ago to create a private and

中文介绍 Venice项目完成A轮融资，致力于构建私密、无限制的AI智能平台，强调思想自由与去中心化。

How to build a second brain with Fable 5

@EXM7777 · 121.1K 粉丝 · 83.6K 阅 · 518 赞 · 48 转

07/03 22:06

I'm going to show you, step by step, how to turn Fable 5 into a machine that knows your business inside out... and ships outputs that look nothing like what everyone else is getting the tool is a

中文介绍 分步教程：如何用Fable 5构建第二大脑，使其深入了解业务并输出独特成果，不同于常规用法。

THE MOST VALUABLE THING YOU CAN DO WITH FABLE 5 IN THE NEXT 24 HOURS

@AlexFinn · 459.5K 粉丝 · 72.5K 阅 · 565 赞 · 34 转

07/03 08:20

If the first thing you did with Fable 5 was vibe code, you're using it wrong. Fable 5 isn't a vibe coding tool. It's an operating systems tool. Let me explain. Fable 5 is the first model I've ever

中文介绍 强调Fable 5不是「vibe coding」工具，而是操作系统工具。建议用户将其用于更底层的系统操作而非简单代码生成。

Agentic Autonomy Levels

@addyosmani · 404.9K 粉丝 · 53.5K 阅 · 525 赞 · 66 转

07/03 11:29

In most conversations about agentic engineering, the action has changed from prompting to operating. Here's a frontier looking into the fog: software factories, goals, loops, background sessions,

中文介绍 探讨AI智能体自主性层级：从提示工程转向操作系统，提出软件工厂、目标循环、后台会话等前沿概念。

What Are Agents Paying For?

@base · 1.4M 粉丝 · 51.0K 阅 · 501 赞 · 112 转

07/01 14:43

Written by: @Must_be_Ash You've heard the slogans: "the agentic economy is here" and "agents are becoming the internet's newest paying customers." They sound big, but they skip the obvious question:

中文介绍 分析智能体经济中AI代理实际支付的对象，驳斥口号式宣传，探讨代理作为互联网新付费客户的具体场景。

A Field Guide to Fable: Finding Your Unknowns

@trq212 · 299.3K 粉丝 · 40.1K 阅 · 534 赞 · 43 转

07/04 01:43

Working with Claude Fable 5 keeps re-teaching me an old lesson: the map is not the territory. The map, a representation of the work to be done, is my prompts and skills and context, it’s what I give

中文介绍 以「地图不等于领土」比喻，分享使用Claude Fable 5的心得：提示、技能、上下文只是工作的表征，实际需探索未知。

Fable is back, here's how I use it in Cursor

@ericzakariasson · 76.4K 粉丝 · 37.9K 阅 · 567 赞 · 24 转

07/02 19:10

Fable is back in Cursor, and here's a pattern I've been exploring and some other ways I've been getting the most out of the model. Fable as orchestrator, Composer as workers It's easy to put

中文介绍 Fable模型回归Cursor，介绍使用模式：将Fable作为编排器（orchestrator），Composer作为工人（workers），最大化模型效果。

Your One-Page PyTorch Training Pipeline Cheat Sheet.

@0xkozue · 690 粉丝 · 24.9K 阅 · 501 赞 · 46 转

07/02 17:03

The entire engine of Deep Learning works by making tiny, continuous adjustments to a model's weights. This is my PyTorch training pipeline cheat sheet. If you spot any mistakes or have suggestions for

中文介绍 深度学习训练流程单页速查表，涵盖PyTorch训练管道的核心步骤：权重微调、训练循环等，供快速参考。

Building Infra for the Agent-Native Internet: June Recap, July Vision

@OptimaiNetwork · 99.8K 粉丝 · 7.8K 阅 · 540 赞 · 351 转

07/01 23:05

The AI industry is entering a new phase. The conversation is no longer centered on models alone. Increasingly, it is about infrastructure: how intelligent systems access knowledge, coordinate with one

中文介绍 AI行业进入新阶段，重心从模型转向基础设施。Optimai Network分享六月回顾与七月愿景，聚焦智能系统知识访问与协调的底层架构。

Google DeepMind and A24 announce first-of-its-kind research partnership

DeepMind Blog · 07/03 22:25

中文介绍 Google DeepMind与电影公司A24宣布建立首次研究合作伙伴关系。

AIEWF Daily Dispatch: The great loops debate and the state of AI engineering

Latent Space · 07/03 13:11

The AI Engineer World’s Fair ended with a debate about loops, a report on the state of AI engineering, and closing keynotes focused on what to build next.

中文介绍 AI工程师世界博览会（AIEWF）以关于循环的辩论、AI工程现状报告及聚焦下一步构建的闭幕主题演讲结束。

Vercel's Andrew Qu on why agents are a new kind of software

Latent Space · 07/03 08:08

The Vercel Chief of Software explains how its agent framework, eve, was created — and why skills, sandboxes and agent-readable websites now matter.

中文介绍 Vercel软件主管Andrew Qu解释其智能体框架eve的创建过程，并阐述为何技能、沙箱及智能体可读网站如今变得重要。

Meta Watermelon 🍉, Anthropic Samsung chips 🤝, autoresearch in practice 📈

TLDR AI · 07/03 08:00

中文介绍 Meta推出Watermelon项目，Anthropic与三星在芯片领域合作，自研究实践取得进展。

The website of the future may assemble itself for every visitor

Latent Space · 07/03 05:25

Adobe is experimenting with “agentic sites” that generate pages around an individual user’s intent. At AIEWF, we talked to Carlos Sanchez about the Web's future.

中文介绍 Adobe正在试验根据用户意图生成页面的“智能体网站”。在AIEWF上，与会者与Carlos Sanchez探讨了Web的未来。

Achieving operational excellence with AI

MIT Tech Review AI · 07/02 23:37

Frameworks like Lean Six Sigma and business process management (BPM) first gained traction because they promised clarity in the chaos—a structured way to bring order to messy, sprawling operations. Lean Six Sigma emphasized statistical rigor and quality control; BPM created end-to-end maps of how wo

中文介绍 Lean Six Sigma和BPM等框架通过强调统计严谨性和质量控制来提升运营效率，AI可进一步推动运营卓越。

Skill engineering and the case against one-shot AI design

Latent Space · 07/02 22:36

Paul Bakaus talks to us about Impeccable, human judgment in a 'loopmaxxing' era, and why agents still need people to steer them.

中文介绍 Paul Bakaus讨论Impeccable项目，在“循环最大化”时代人类判断的重要性，以及为何智能体仍需人类引导。

Teaching AI to run with the turbines

MIT Tech Review AI · 07/02 20:51

Artificial intelligence may have captured the public imagination through chatbots and image generators, but some of its most consequential use cases are unfolding far from consumer-facing tools. In industries where physical infrastructure, operational continuity, and safety are paramount, AI is beco

中文介绍 AI在消费者工具之外的工业领域有重要应用，如与涡轮机等物理基础设施协同运行，提升运营连续性和安全性。

[AINews] not much happened today

Latent Space · 07/02 15:10

another quiet day.

中文介绍 当日AI领域新闻较少，市场平静。

AIEWF Daily Dispatch: Autoresearch and the tension between AI and human agency

Latent Space · 07/02 14:13

The software factory vision met resistance today from speakers defending human understanding and control.

中文介绍 AIEWF日报：自研究引发关于AI与人类能动性之间的张力，软件工厂愿景遭到主张人类理解与控制的发言者反对。

not much happened today

Smol AI News · 07/02 13:44

**Fullstack Code Arena** extends coding agent evaluation to include **databases, API keys, deployments, and structured tool use**, marking a shift to end-to-end app shipping. **LangChain** released **LangSmith** with unified tracing and **OpenWiki** for auto-generated docs, while **LlamaIndex** demo

中文介绍 Fullstack Code Arena将编码智能体评估扩展至数据库、API密钥等端到端应用交付；LangChain发布LangSmith（统一追踪）和OpenWiki（自动文档生成）。

Gemini Flash upgrade ⚡️, Meta AI cloud 🌐, ZCode 👨‍💻

TLDR AI · 07/02 08:00

中文介绍 Gemini Flash升级，Meta推出AI云服务，以及ZCode相关更新。

Autoresearch: The feedback loop behind self-improving agents

Latent Space · 07/02 07:52

Introspection co-founder Roland Gavrilescu explains autoresearch, agent “recipes,” self-improving loops, and why humans remain central to the software factory.

中文介绍 Introspection联合创始人Roland Gavrilescu解释自研究、智能体“配方”及自我改进循环，强调人类在软件工厂中的核心地位。

How Cursor deploys AI inside the enterprise

Latent Space · 07/02 03:03

Cursor's Pauline Brunet explains how her team of Forward Deployed Engineers help organizations implement agents — essentially setting up software factories.

中文介绍 Cursor的Pauline Brunet解释其前部署工程师团队如何帮助组织实施智能体，实质上是建立软件工厂。

🔬 The Coolest Diffusion Research Isn't in LLMs — Evan Feinberg & Sergey Edunov, Genesis Molecular AI

Latent Space · 07/01 22:42

Why the Llama lead left Meta for drug discovery, PEARL's zero-shot OpenBind win, and what becomes possible when co-folding finally crosses the accuracy threshold.

中文介绍 前Llama负责人离开Meta投身药物发现；PEARL在OpenBind中实现零样本获胜；共折叠技术突破准确度阈值带来新可能。