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 提出自主策略进化评估框架EvoPolicyGym，在受控交互环境中测试智能体从反馈改进策略的能力，避免与开放式软件工程进展混淆，为策略迭代提供标准化评测。

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 设计有界内存测试床AgenticSTS，研究长时程LLM智能体内存合约（如简单拼接历史）如何影响决策，揭示内存组织对长期规划性能的关键作用。

PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception

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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 引入基于评分标准的评估框架PerceptionRubrics，将多模态评估从整体语义匹配转为原子级审计，配对1,038张高密度图像，校准模型表现与人类感知差距。

Morphing into Hybrid Attention Models

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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Transformer转混合注意力模型时全注意力层的最优保留位置，提出转换策略，在长上下文效率与性能间取得平衡，优于现有启发式方法。

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

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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 针对预填充-解码分离的MoE服务提出ELDR路由，不仅均衡负载更感知专家局部性（不同专家权重加载延迟差异），显著降低解码延迟。

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

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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，基于用户真实需求构建前瞻评估体系，在复杂现实任务（推理、规划等）上取得显著进展，迈向通用智能。

Multimodal Continuous Reasoning via Asymmetric Mutual Variational Learning

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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 提出非对称互变分学习方法，实现多模态连续潜在推理，突破语言空间瓶颈，保留视觉细节并在复杂推理任务上超越离散Token方法。

MemSyco-Bench: Benchmarking Sycophancy in Agent Memory

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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 构建MemSyco-Bench基准，系统评估LLM智能体记忆引发谄媚（过度迎合用户）的问题，发现记忆检索加剧该现象，需谨慎设计记忆机制。

AgenticDataBench: A Comprehensive Benchmark for Data Agents

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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 推出数据科学智能体综合基准AgenticDataBench，覆盖从异构数据到洞察的全流程评估，推动自动化数据科学能力标准化测评。

WorldDirector: Building Controllable World Simulators with Persistent Dynamic Memory

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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视频世界模型框架，具备持久动态对象记忆和自由视角探索，解耦物理动态与渲染，实现高可控视频生成。

ASPIRE: Agentic /Skills Discovery for Robotics

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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机器人技能持续学习系统，通过迭代探索自主发现并编程可复用技能，无需人工干预，在多种操作任务中提升泛化性。

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

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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 面向医疗多模态推理提出步骤感知强化学习，解决结果导向训练的稀疏奖励问题，在临床图像推理中有效阻断级联失败，提升诊断准确性。

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

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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，利用自进化评分标准评估和增强LLM智能体技能使用能力，应对仓库中技能重叠问题，优于粗粒度最终验证器。

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评分方法，识别LLM中执行非文字检索（语义合成而非复制）的注意力头，深化对长上下文模型行为的理解。

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方法，无需实例标注即可实现可控复杂图像生成，通过间接条件控制解决多实例属性混淆问题，生成质量优于现有方法。

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 构建DiscoBench基准，评估搜索智能体在模糊查询下的澄清能力，揭示现有基准忽略交互式澄清需求，推动深度搜索改进。

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设：将Transformer的预测与状态存储解耦可提升语言建模性能，设计的变体模型在多个任务上验证了有效性。

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

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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 推出AnyGroundBench基准，评估VLM在专业领域（非日常生活）的时空视频定位能力，填补现有基准与实际应用间的鸿沟。

PixelEyes: Decoupling Perception and Reasoning for Pinpoint Visual Evidence Seeking

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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，解耦感知与推理模块解决MLLM多轮视觉推理中定位失败问题，实现精准视觉证据查找，轨迹更简洁高效。

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 提出原生图强化学习方法，用于材料科学假设生成，通过概念重组实现可追溯、多步推理，生成高质量科学假设优于标准LLM。

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 参数高效微调（如LoRA）产生的适配器通常绑定特定基座模型，新模型发布后需重新训练。PorTAL提出便携式任务适配器，可跨不同基座模型迁移，提升灵活性与复用性。

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

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

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

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

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

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,

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:

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

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

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

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

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

DeepMind Blog · 07/03 22:25

中文介绍 谷歌DeepMind与独立电影公司A24宣布一项开创性研究合作。

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）闭幕，围绕循环（loops）展开辩论，发布AI工程现状报告，闭幕主题演讲聚焦下一步构建方向。

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，以及技能、沙箱和智能体可读网站为何变得重要。

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

TLDR AI · 07/03 08:00

中文介绍 Meta发布Watermelon模型，Anthropic与三星在芯片领域合作，自动研究（autoresearch）在实践中应用。

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正在实验“智能体网站”，可根据用户意图生成个性化页面。在AIEWF上，Carlos Sanchez讨论了网络的未来。

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和业务流程管理（BPM）等框架曾在混乱中提供结构化方法，文章探讨AI如何帮助实现运营卓越。

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讨论技能工程，在“循环最大化”时代人类的判断力，以及智能体仍需人类指导的原因。

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除了聊天机器人和图像生成，在物理基础设施、运营连续性和安全等工业领域也有重要应用，文章探讨教AI与涡轮机协同运行。

[AINews] not much happened today

Latent Space · 07/02 15:10

another quiet day.

中文介绍 当日AI领域平静，无重大事件。

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：自动研究（Autoresearch）引发AI与人类能动性之间的张力，软件工厂愿景遭到反对，演讲者捍卫人类理解与控制。

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扩展编码智能体评估至数据库、API密钥等；LangChain发布LangSmith统一追踪和OpenWiki自动生成文档。

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

TLDR AI · 07/02 08:00

中文介绍 Gemini Flash升级，Meta AI云服务，ZCode开发者工具。

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解释自动研究（autoresearch）、智能体“配方”、自我改进循环，以及人类为何仍是软件工厂的核心。

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企业实施智能体，本质上建立软件工厂。

🔬 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 Genesis Molecular AI的Evan Feinberg和Sergey Edunov讨论扩散研究在药物发现中的应用，PEARL的零样本OpenBind胜利，以及共折叠突破精度阈值带来的可能性。