2026-07-03

Does VLA Even Know the Basics? Measuring Commonsense and World Knowledge Retention in Vision-Language-Action Models

👍 54

06/17 08:00

Embodied Vision-Language-Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data, yet it is unclear how much commonsense and factual knowledge they retain after adaptation. Failures on knowledge-sensitive tasks are ambiguous, conflating missing knowledge w

中文介绍 提出评估VLA模型在机器人适应后保留常识和世界知识的方法，发现微调会导致知识遗忘，并量化知识保留程度，为模型改进提供依据。

PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception

👍 33

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 PerceptionRubrics通过原子级细则评估多模态模型，从全局语义匹配转向严格原子审计，包含1038张信息密集图像，揭示基准高分与真实脆弱性之间的差距。

SkillHone: A Harness for Continual Agent Skill Evolution Through Persistent Decision History

👍 24

06/23 08:00

Agent skills extend language-model agents with task-specific procedures, scripts, and references, but the tasks and environments they target continually change. Existing methods improve skills in bounded runs and retain only the final artifact, discarding the decision history that later agents need

中文介绍 SkillHone利用持久决策历史让智能体技能持续进化，解决现有方法丢弃中间决策导致无法适应任务变化的问题，提升长期任务表现。

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

👍 19

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 ELDR通过感知专家局部性进行解码路由，优化MoE模型在预填充-解码分离服务中的负载和延迟，相比仅负载均衡路由显著降低解码延迟。

TurboServe: Serving Streaming Video Generation Efficiently and Economically

👍 19

06/17 08:00

Streaming video generation is emerging as a new serving workload in which users interact with long-lived sessions that generate video progressively, chunk by chunk. Unlike offline video generation or typical LLM serving, streaming video generation must preserve session state across active and idle p

中文介绍 TurboServe高效服务流式视频生成，通过状态管理和调度策略支持长会话，降低生成延迟和成本，优于现有离线或LLM服务方案。

Managing Procedural Memory in LLM Agents: Control, Adaptation, and Evaluation

👍 19

06/22 08:00

Procedural memory is increasingly used to improve LLM agents on recurring workplace tasks, yet its ability to produce reusable skills remains poorly understood. We introduce AFTER, a benchmark of 382 realistic enterprise tasks spanning six professional roles and 22 procedural skills, designed to eva

中文介绍 引入AFTER基准，包含382个企业任务和22项程序技能，系统评估LLM智能体对程序性记忆的控制、适应和重用能力，揭示当前方法的不足。

MemSyco-Bench: Benchmarking Sycophancy in Agent Memory

👍 18

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 MemSyco-Bench系统评测LLM智能体在记忆检索中产生的谄媚现象，发现记忆会诱导智能体过度迎合用户观点，损害事实性，为构建诚实智能体提供参考。

DataEvolver: Self-Evolving Multi-Agent Data Construction for Text-Rich Image Generation

👍 18

06/30 08:00

Text-rich image generation is one of the most challenging settings in image generation, since models must simultaneously produce visually realistic images and render legible, semantically aligned, and layout-consistent text. Existing data pipelines usually follow a static crawl-filter-freeze paradig

中文介绍 DataEvolver通过多智能体协作自进化构建训练数据，克服静态数据管线局限，在文本丰富图像生成中提升文本渲染准确性和视觉质量。

Multimodal Continuous Reasoning via Asymmetric Mutual Variational Learning

👍 16

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，让多模态模型在连续潜空间中推理，保留感知细节，超越离散token的语言瓶颈，提升视觉推理能力。

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

👍 16

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 Seed2.0模型系列通过识别用户真实需求并构建前瞻性评估系统，在复杂真实任务上取得进步，涵盖多模态、推理等能力，迈向通用智能。

Are We Measuring Strategy or Phrasing? The Gap Between Surface- and Approach-Level Diversity in LLM Math Reasoning

👍 16

06/29 08:00

Diversity in LLM mathematical reasoning is critical for exploration, but common diversity metrics mostly capture surface-level variation rather than differences in how a problem is solved. We address this gap by introducing approach-level diversity: variation in strategies across correct solutions t

中文介绍 提出方法层面多样性指标，区分LLM数学推理中的策略差异与表述差异，发现常规指标高估多样性，方法层面多样性更反映真实探索能力。

CausalMix: Data Mixture as Causal Inference for Language Model Training

👍 14

07/01 08:00

In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model performance. Recent methods optimize mixture weights via proxy models, but they rely on the assumption of static data distributions. As a result, when the underlying data pool shifts, these methods require

中文介绍 CausalMix将数据混合建模为因果推断，自适应调整混合权重，无需重训代理模型，在数据分布变化时仍保持高效，提升LLM训练效果。

ASPIRE: Agentic /Skills Discovery for Robotics

👍 12

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 ASPIRE使机器人通过持续探索自主发现和编排技能，适应新配置和失败，无需手工编程，实现技能库的自动扩展。

Perceive-to-Reason: Decoupling Perception and Reasoning for Fine-Grained Visual Reasoning

👍 11

07/01 08:00

Fine-grained visual reasoning remains challenging for vision-language models, especially when small but critical visual cues are buried in high-resolution images. Existing approaches rely on repeated cropping or test-time visual search to introduce local evidence, but they typically do not explicitl

中文介绍 Perceive-to-Reason通过解耦感知和推理阶段，先定位关键视觉线索再进行精细推理，在细粒度视觉推理任务上显著提升准确率。

VideoSearch-R1: Iterative Video Retrieval and Reasoning via Soft Query Refinement

👍 11

07/01 08:00

As video corpora continue to expand in both scale and task complexity, there is increasing demand for approaches that retrieve relevant videos from large-scale corpora (inter-video reasoning) and subsequently perform fine-grained, query-conditioned tasks (intra-video reasoning) within the retrieved

中文介绍 VideoSearch-R1通过软查询优化迭代检索视频，并在检索到的视频内进行精细推理，同时处理跨视频和视频内推理任务，提升视频问答性能。

QVal: Cheaply Evaluating Dense Supervision Signals for Long-Horizon LLM Agents

👍 9

06/30 08:00

LLM agents increasingly act over long horizons, where a single trajectory can contain hundreds or thousands of actions. In these settings, outcome-only rewards provide too sparse guidance, failing to inform the model about the goodness of intermediate actions. Dense supervision methods aim to solve

中文介绍 QVal通过廉价方式评估长程LLM智能体的中间动作质量，提供密集奖励信号，优于纯结果奖励，有效指导长时域任务学习。

AutoTrainess: Teaching Language Models to Improve Language Models Autonomously

👍 8

06/30 08:00

Training language models (LMs) remains a highly human-intensive process, even as frontier language model agents become increasingly capable at software engineering and other long-horizon tasks. A central challenge is that autonomous post-training is not just a coding problem: it requires the agent t

中文介绍 AutoTrainess让语言模型自主进行后训练，包括数据生成、监督和评估，减少人工干预，在多个基准上实现性能提升，推动自动化训练。

MOPD: Multi-Teacher On-Policy Distillation for Capability Integration in LLM Post-Training

👍 8

06/29 08:00

Modern large language models (LLMs) rely on reinforcement learning during post-training to push specific capabilities, yet integrating multiple capabilities into one model remains hard. Existing methods, such as Off-Policy Finetune and Mix-RL, are either inefficient or lose performance. In this work

中文介绍 MOPD通过多教师在线策略蒸馏，在LLM后训练中高效集成多种能力（如数学、代码），避免性能损失，优于Off-Policy方法和Mix-RL。

The State-Prediction Separation Hypothesis

👍 7

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，将Transformer中状态存储与下一个token预测解耦，设计新变体，在语言建模任务上获得更好性能。

BioInsight: Multi-Agent Orchestration for Interactive Biomedical Knowledge Discovery

👍 7

06/19 08:00

Biomedical researchers increasingly use AI-generated analyses and reports to interpret protein-level signals, but static outputs are often insufficient for research decision-making, where users need to inspect evidence, assess uncertainty, compare mechanisms, and refine hypotheses. We present BioIns

中文介绍 BioInsight通过多智能体协调，提供交互式生物医学知识发现，支持证据检查、不确定性评估和假设细化，优于静态报告。

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，一种便携式任务适配器，解决LoRA等参数高效微调方法仅绑定单一基座模型的问题。当新模型发布时，适配器无法迁移，PorTAL旨在实现跨模型迁移。针对LLM适配器的可移植性痛点。

The AI Economy: The Next Chapter

@rickyho_1989 · 9.7K 粉丝 · 296.6K 阅 · 508 赞 · 69 转

06/30 20:23

Part I: The Economics of Intelligence Why the AI industry is about to optimize for intelligence per dollar rather than intelligence itself I have become increasingly convinced that the artificial

中文介绍 探讨AI经济新篇章，核心观点是AI行业正从追求智能本身转向优化单位成本智能（intelligence per dollar）。基于对AI经济学演变的观察，预测未来产业重点。

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 发布Fable使用指南，承诺教读者如何最大化利用Fable工具并实现10倍AI生产力提升。内容涵盖基础操作到高级技巧，适合入门和进阶用户。

Wiki Memory

@hwchase17 · 115.7K 粉丝 · 129.7K 阅 · 500 赞 · 57 转

06/30 22:26

Memory for agents is still early, with little to no standards. “Memory” means something different to everyone. But one common pattern is emerging: wiki memory. The idea is simple: use an agent to turn

中文介绍 介绍Agent记忆的新模式——Wiki Memory。当前Agent记忆缺乏标准，而wiki memory思路是用Agent将对话历史转化为结构化知识库，实现记忆共享和持久化。亮点：提出一种通用模式。

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宣布A轮融资，致力于提供私密、无限制的AI服务。创始人引用匿名者话语强调思想自由。项目旨在突破内容审查和隐私限制，打造不受约束的智能平台。

46 thoughts on the near future

@bayeslord · 41.6K 粉丝 · 86.2K 阅 · 511 赞 · 43 转

07/01 04:37

This list is based on a thread I posted on June 4th. A few edits and additions here and there. Several people asked me to make the thread easier to read, so here it is. Intelligence I think people are

中文介绍 整理46条关于近期未来的思考，涵盖智能、经济、技术趋势等主题。该清单基于之前帖子，经编辑和补充后呈现，视角独到，适合关注未来的读者。

What I learned helping set up 200 company brains

@jacob_posel · 28.7K 粉丝 · 54.5K 阅 · 501 赞 · 30 转

06/30 23:59

I have been directly or indirectly involved in helping set up over 200 company brains and context layers. Here are the three most important lessons. 1. The Biggest Predictor Of Success Is An Internal

中文介绍 分享帮助200多家公司搭建「大脑」和上下文层的经验教训。最大成功预测因素是内部知识库的质量，其余两点包括工具选择与组织文化。实战总结，对AI知识管理有参考价值。

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 探讨AI Agent经济中的实际支付对象。作者指出「Agent经济」口号忽略了根本问题：Agent到底在为什么付费？分析Agent作为新付费用户的消费模式与价值交换。

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 介绍Fable重回Cursor后的使用模式：将Fable作为编排器（orchestrator），Composer作为工作节点（workers）。分享具体工作流和提升效率的技巧，适合Cursor用户借用。

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行业焦点从模型转向基础设施。该帖总结六月进展并展望七月，重点讨论智能系统如何获取知识、协调行动，为Agent原生互联网构建底层支撑。

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「代理式网站」，可根据用户意图动态生成页面。在AIEWF上，Carlos Sanchez讨论了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与业务流程管理（BPM）等框架因在混乱中带来清晰而兴起，强调统计严谨和质量控制。AI正助力运营卓越。

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与Impeccable讨论了在「循环最大化」时代中人类判断的作用，指出代理仍需人类引导。

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在工业领域（如物理基础设施、运营连续性和安全）有重大应用，远不止聊天机器人和图像生成器。

[AINews] not much happened today

Latent Space · 07/02 15:10

another quiet day.

中文介绍 今日AI新闻平淡，没有重大进展。

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每日简报：自动化研究遭遇阻力，多位演讲者捍卫人类理解与控制。

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

TLDR AI · 07/02 08:00

中文介绍 TLDR AI今日摘要：Gemini Flash升级、Meta AI云、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解释了自动化研究、代理「配方」、自我改进循环以及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介绍了其前向部署工程师团队如何帮助企业实施代理，实质是搭建软件工厂。

🔬 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 Llama负责人离开Meta投身药物发现，PEARL的零样本OpenBind获胜，以及共折叠跨过精度阈值后的可能性。

LLMs are stuck in a groupthink groove. This startup is trying to get them out.

MIT Tech Review AI · 07/01 22:35

Let’s start with a game. Open up your chatbot of choice—Claude, ChatGPT, Gemini—and type “Give me a random number between 1 and 10.” You’re going to get 7. Almost always. Now type “Another” and you’ll get 3 or 4. Type “Another” again and you’ll get 8 or 9. That won’t work every time—but if it…

中文介绍 大语言模型存在群体思维问题：让AI生成1-10的随机数，总是得到7。一家初创公司正试图打破这一模式。

Warp CEO Zach Lloyd on why software factories are the next phase of coding

Latent Space · 07/01 22:28

Warp's founder thinks every major software project will soon run on an automated factory. He discusses why and how engineers should prepare for this shift.

中文介绍 Warp CEO Zach Lloyd认为，每个大型软件项目都将很快在自动化工厂中运行，工程师应为此做好准备。

not much happened today

Smol AI News · 07/01 13:44

**Anthropic** re-enabled **Claude Fable 5** with updated cybersecurity safeguards routing some requests to **Opus 4.8**. The relaunch influenced tooling adoption by **Cursor**, **Devin**, and **Perplexity**. Builders are adapting to frontier-model constraints by employing **multi-model orchestration

中文介绍 Anthropic重新启用Claude Fable 5并更新了网络安全保护措施，部分请求路由至Opus 4.8。Cursor、Devin和Perplexity等工具随之调整。

AIEWF Daily Dispatch: Loops, Software Factories & Forward Deployed Engineers

Latent Space · 07/01 12:46

On Tuesday at the AI Engineer World's Fair, there was a lot of talk about loops, agent engineering, and the emergence of software factories. Also a hot topic: open models.

中文介绍 AIEWF周二关注循环、代理工程和软件工厂的兴起，开放模型成为热门话题。

[AINews] Sonnet 5 today, and Fable 5 tomorrow

Latent Space · 07/01 11:01

Everything is open again!

中文介绍 今日Sonnet 5发布，明日Fable 5即将问世，一切重新开放。