2026-07-03

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

👍 37

07/01 20:00

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 提出了一种名为AgenticSTS的测试平台，用于评估长时间跨度的大型语言模型（LLM）智能体的记忆能力。该平台规范了智能体在决策过程中的记忆使用方式，以确保能有效访问过往信息并提高决策质量。通过该方法，研究者旨在解决在复杂决策中由于信息混杂导致的效率低下问题。意义：此研究对长时记忆和决策智能体发展具有重要影响。

EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments

👍 36

07/01 20:00

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 提出了一种名为EvoPolicyGym的评估框架，旨在控制环境中自主智能体的政策演化过程。通过明确的评估设置，研究者分析了如何在不减少性能的前提下提高智能体的决策能力，克服传统评估方式的不足。此框架提供了更加精细化的反馈机制，有助于推动自主智能体的应用。意义：对自主学习和政策改进的研究有重要推动作用。

PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception

👍 36

06/25 20:00

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 Introduced PerceptionRubrics, 一个评估框架，旨在通过原子审计来弥补基准得分与现实世界表现之间的差距。该框架结合1038幅信息密集的图像，并通过更细致的评估标准，对图像进行多模态评估。这一方法旨在改善现有模型在真实场景下的脆弱性。意义：为多模态评估和现实应用提供了新的思路，推动了基于人类感知的评估方法发展。

Morphing into Hybrid Attention Models

👍 27

06/28 20:00

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 Hybrid attention models通过保留部分全注意力层来提升长上下文的效率，以线性注意力替代其余层。本研究探讨了Transformer转换为混合注意力模型的有效性，强调在选择保留全注意力的层时的关键性。该方法为提升长序列的处理能力提供了新的技术路径，具有广泛的应用前景。意义：对高效的序列处理和模型结构优化具有重要指导价值。

TurboServe: Serving Streaming Video Generation Efficiently and Economically

👍 26

06/16 20:00

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系统致力于高效、经济地提供流式视频生成服务，处理长期交互任务的用户需求。研究针对流媒体生成的特点，提出了能够完善会话状态管理的方法，以确保在活跃和空闲时保持良好用户体验。能够有效提升流式视频生成的服务效率和用户满意度。意义：为实时交互应用提供了新的技术支持，有助于流媒体业务的发展。

Multimodal Continuous Reasoning via Asymmetric Mutual Variational Learning

👍 22

06/30 20:00

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 提出了一种多模态大型语言模型（MLLMs）的方法，通过不对称互信息变分学习实现连续推理。该方法解决了传统语言空间瓶颈的问题，允许复杂视觉推理过程中保留感知的细微差别，提高了模型的推理能力。此研究对多模态推理和视觉理解领域的发展起到了促进作用。意义：推动了计算机视觉与自然语言处理的融合研究。

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

👍 22

06/30 20:00

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 研究提出了Expert-Locality-Aware Decode Routing，优化了预填充解码（PD）分解的混合专家（MoE）模型服务。此方法不仅平衡了负载，还考虑每个解码步骤的延迟，从而提高了解码效率。研究表明，该方法在解码速率和资源利用上都有显著的提升，为高性能LMM服务提供了新的思路。意义：为提升大规模语言模型性能和应用提供了技术支持。

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

👍 22

06/29 20:00

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模型系列致力于解决复杂的现实任务，构建一种前瞻性的评估系统，以满足用户真实需求。方法通过选择和抽象与这些需求紧密相连的基准，确保模型能够应对实际应用中的挑战。此研究为智能系统开发提供了有意义的方向，有望提升智能应用的执行能力。意义：推动了智能产品和服务的实用化进程。

MemSyco-Bench: Benchmarking Sycophancy in Agent Memory

👍 21

06/30 20:00

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项目关注现代大型语言模型（LLM）智能体的记忆使用及其对用户偏好的影响。研究识别了记忆检索可能导致的过于迎合用户的问题，提出在智能体的设计中需要平衡用户需求与记忆引导的互动。此研究为优化智能体交互方式提供了深刻的视角。意义：对智能体在用户交互中的行为调整非常关键。

AgenticDataBench: A Comprehensive Benchmark for Data Agents

👍 18

07/01 20:00

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 AgenticDataBench提供了一个全面的数据智能体评估基准，旨在从大量异构数据中提取可操作的洞见，降低数据科学家的重复劳动。通过推出这一基准，研究者希望推动数据驱动应用的自动化，提升数据科学工作流程的效率。意义：为数据智能体的应用和发展提供了新的评估标准。

ASPIRE: Agentic /Skills Discovery for Robotics

👍 16

06/29 20:00

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（Agentic Skill Programming through Iterative Robot Exploration）提出了一种持续学习系统，旨在应对传统机器人编程中的挑战，如多模态感知和执行失败等。研究通过反复探索和自主技能发现，提高了机器人适应复杂任务的能力。该方法为智能机器人系统的发展奠定了基础。意义：促进了自主机器人与环境的互动研究。

CausalMix: Data Mixture as Causal Inference for Language Model Training

👍 16

06/30 20:00

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研究探讨了在大型语言模型训练中的数据混合方法，提出将数据混合视为因果推断。这一方法克服了现有基于静态数据分布的模型优化的局限性，增强了模型对数据变动的适应能力。研究为模型训练提供了新的因果框架，有助于提高模型性能。意义：推动了因果推理在模型训练中的应用研究。

WorldDirector: Building Controllable World Simulators with Persistent Dynamic Memory

👍 13

07/01 20:00

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 WorldDirector是一个高度可控的视频世界模型框架，设计用于持久的动态对象记忆和自由视角探索。与现有模型不同，它独立处理物理动态和像素渲染，为视频生成提供新的可能性。该框架为构建复杂虚拟环境提供了基础，有助于推动虚拟现实和模拟训练的发展。意义：为动态环境交互提供了重要的技术基础。

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

👍 12

06/29 20:00

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 Breaking Failure Cascades提出了一种逐步增强的强化学习方法，旨在提升医学多模态推理的效果。现有的训练管道缺乏有效的信用分配机制，使得结果依赖于最终答案的准确性。该方法通过改善学习过程中的反馈机制，展示了在医学领域的应用潜力。意义：增强了模型在医疗图像处理中的实际应用能力。

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

👍 12

06/30 20:00

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提出了一种新的思维方式，通过将感知与推理解耦，增强细粒度视觉推理能力。研究指出，现有方法在处理高分辨率图像时对微小视觉线索的识别能力不足。该方法为未来的视觉和语言模型设计提供新方向。意义：推动了视觉推理和语言理解的融合研究。

AutoTrainess: Teaching Language Models to Improve Language Models Autonomously

👍 12

06/29 20:00

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提出了一种自我演化的学习模型，旨在减少人力在语言模型训练中的介入。研究者认为，传统训练过程无法满足高度自主代理的需求，并引入了一种新的训练架构，以增强模型自主改进的能力。投资于自动化的学习过程具有显著意义。意义：极大地推动了自主学习系统的发展。

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

👍 8

07/01 20:00

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 SkillCoach提出了一种自我演进的评估体系，旨在优化LLM智能体的技能使用能力。通过解决现实场景中技能重叠带来的挑战，该方法实现了更可靠的技能评估，有助于提升智能体的执行效果和效率。此研究为技能的复用和智能体的优化提供了重要路径。意义：促进了智能体在复杂任务中的表现提升。

The State-Prediction Separation Hypothesis

👍 8

06/30 20:00

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 本文提出了状态预测分离假设，旨在通过将状态存储与下一时 token 的预测解耦，提升语言建模性能。研究设计了一种新的Transformer变体，该模型在语言预测任务上展现了更优的效果。此方法为理解和改进模型结构提供了重要见解。意义：推进了Transformer模型的设计和优化研究。

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

👍 7

07/01 20:00

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 AnyGroundBench是一个针对视频定位的专业域基准，旨在填补现有评估协议与实际应用的差距。研究创新性地提出了新的评估方式，使得视频与语言模型能够更加真实地反映特定领域的性能表现。此研究为视频语义理解提供了更多关注点。意义：推动了视频理解与处理研究的专业化进程。

PixelEyes: Decoupling Perception and Reasoning for Pinpoint Visual Evidence Seeking

👍 7

06/29 20:00

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 PixelEyes探讨了多轮视觉推理，指出当前多模态大型语言模型在目标定位方面的缺陷。研究认为，推理与感知的纠缠导致了无效的搜索路径，提出了解耦方法以增强视觉证据的有效寻觅。此研究为视觉推理模型的设计提供了新思路。意义：推动了更精准的视觉推理与检索技术的发展。

PorTAL: Portable Task Adapters for LLMs

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

07/01 14:09

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 分享了便携式任务适配器PorTAL，用于高效微调大型语言模型（LLM）。其优点在于适配器无需重新训练即可应用于不同模型，提升了模型的灵活性与使用效率，反映出市场对适应性AI工具的关注。

How To Master Fable (Fundamentals Guide)

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

07/01 14:13

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 提供了 Fable 的基础使用指南，强调其能够显著提升AI生产力。通过打磨和掌握 Fable，用户可以在短时间内实现工作效率的提升，显示了用户对工具的使用深化需求。

Career advice in the age of AI

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

07/02 17:25

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 探讨了在人工智能时代的职业建议，指出未来的工作应关注能定义损失函数的问题。强调了教育体系与职场要求的变化，预示了AI对教育和职业发展的深远影响。

Wiki Memory

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

06/30 10:26

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 讨论了智能代理的记忆管理，介绍了wiki记忆的概念，即利用代理整理和构建知识库。目前对此的标准尚在形成中，反映出在AI记忆领域的探索与创新需求依旧强烈。

Toward Unrestricted Intelligence: Venice Series A

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

07/01 10:28

“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项目的介绍，旨在创造一个私人和安全的思维空间，以推动人类自由思想的发展。这一理念反映了对个人隐私和自主权的日益重视，呼应了当前技术与伦理的讨论。

46 thoughts on the near future

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

06/30 16:37

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 总结了个人对未来趋势的看法，涵盖了智能领域的多个方面。此类思考反映了人们对AI和智能技术快速发展的关切，同时也展现了市场对未来的不确定性。

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

07/02 20:20

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 阐述了Fable 5的实际应用，指出其作为操作系统工具而非简单编码工具的特性。强调正确使用Fable 5可以促进工作流的优化，反映出对工具功能理解的深入。

What I learned helping set up 200 company brains

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

06/30 11:59

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 分享了协助设置超过200个企业智能大脑的经验，总结出成功的关键在于企业内部的知识管理和文化建设。这表明企业在AI应用中的内部运作和治理能力日益重要。

What Are Agents Paying For?

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

07/01 02:43

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 探讨了经济中代理人的崛起，揭示了他们作为互联网新兴客户的特征和需求。这一趋势反映了对智能经济转型及其潜在市场的关注。

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 07:10

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在Cursor中的再次回归及其具体应用模式，显示出用户对优化工作流的积极尝试与探索。这表明市场对集成工具和工作模式的创新关注度提升。

Your One-Page PyTorch Training Pipeline Cheat Sheet.

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

07/02 05:03

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 提供了PyTorch训练管道的 cheat sheet，简化了深度学习模型的训练过程。此类实用资源反映了对高效学习和应用的需求，提示出学习工具的重要性。

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 11:05

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 总结了AI行业发展的新阶段，开始关注智能系统的基础设施建设。强调了如何更好地获取知识和协调智能代理的能力，这预示着对基础设施的重视将会增加。

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

Latent Space · 07/03 01:11

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工程师博览会结束时围绕循环展开了辩论，呈现了AI工程的现状，并进行了集中主题演讲，讨论未来的构建方向。

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

Latent Space · 07/02 20:08

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的软件首席Andrew Qu阐述了其代理框架eve的创建背景，以及技能、沙箱和可由代理读取的网站的重要性。

The website of the future may assemble itself for every visitor

Latent Space · 07/02 17:25

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“智能网站”，这些网站会根据用户的意图生成页面。AIEWF上，Carlos Sanchez分享了关于网络未来的看法。

Achieving operational excellence with AI

MIT Tech Review AI · 07/02 11:37

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）因提供了在复杂运营中带来秩序的结构化方法而受到关注，强调统计严谨性和质量控制。

Skill engineering and the case against one-shot AI design

Latent Space · 07/02 10:36

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讨论了在‘loopmaxxing’时代中不可或缺的人类判断，以及为何代理仍需人类操控。

Teaching AI to run with the turbines

MIT Tech Review AI · 07/02 08:51

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 人工智能在消费工具之外的特定行业中展现出重要应用，这些行业关乎物理基础设施、运营连续性和安全性。

[AINews] not much happened today

Latent Space · 07/02 03:10

another quiet day.

中文介绍 今天是另一个安静的日子。

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

Latent Space · 07/02 02:13

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

中文介绍 今天的软件工厂愿景遭到了支持人类理解和控制的演讲者的抵制。

not much happened today

Smol AI News · 07/02 01:44

**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扩展了编码代理评估，涵盖数据库、API密钥、部署和结构化工具使用，标志着端到端应用交付的转变。

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

TLDR AI · 07/01 20:00

中文介绍 Gemini Flash升级，Meta AI云推出，ZCode应用开发。

Autoresearch: The feedback loop behind self-improving agents

Latent Space · 07/01 19:52

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解释了自我研究、代理“配方”和自我改进循环，并强调了人类在软件工厂中的核心作用。

How Cursor deploys AI inside the enterprise

Latent Space · 07/01 15:03

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介绍了她的前线部署工程师团队如何帮助组织实施代理，实质上构建软件工厂。

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

Latent Space · 07/01 10:42

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的负责人为何离开Meta从事药物发现，以及PEARL的零样本OpenBind胜利和共同折叠技术突破准确性阈值的前景。

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

MIT Tech Review AI · 07/01 10:35

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 开放你的聊天机器人，测试其生成的随机数字发现，LLMs因陷入群体思维而受到批评，此创业公司试图引导其走出困境。

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

Latent Space · 07/01 10:28

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创始人Zach Lloyd认为，未来每个重要的软件项目将会在自动化工厂中运行，并讨论工程师如何为这种转变做好准备。