2026-07-04

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

👍 43

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 本论文提出了 AgenticSTS，这是一种有界记忆的测试平台，旨在评估长时间跨度的 LLM 代理在决策过程中对记忆的使用策略。通过对过去观察、工具调用和反思进行适当的记忆管理，增强了代理在复杂决策中的效率和效果。该方法为长时间尺度的应用提供了新的思路，能够支持智能体的持续学习和成长。意义：它为 Agent 和 Memory 方向的研究提供了重要信号。

EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments

👍 41

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 论文中提出了 EvoPolicyGym，这是一个用于评估自主策略演化的控制环境。此方法通过反馈机制，使自主代理能够不断改进其可执行的策略，避免将评估简化为最终得分。这一设置为复杂环境中智能体的动态学习和适应能力提供了基础，有助于推动自主学习和智能决策的发展。意义：它对 Agent 和 Reinforcement Learning 方向具有重要影响。

PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception

👍 37

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 提出了 PerceptionRubrics，一个基于评估准则的框架，旨在填补饱和基准分数与现实世界脆弱性之间的差距。通过将评估从整体语义匹配转向严格的原子审核，PerceptionRubrics 将 1038 张信息密集型图像与超过 200 个视觉属性特征对接。这种方法为模型评估提供了更有效的指标，增强了在复杂情况下的可靠性。意义：其对 Perception 和 Evaluation 领域具有显著的推动作用。

Morphing into Hybrid Attention Models

👍 36

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 本研究探讨了混合注意力模型在长上下文效率方面的提升，通过保留部分全注意力层并用线性注意力替代其余层来优化计算效率。然而，Transformer 向混合模型转换的效果依赖于保留全注意力的层选择。通过深入分析这一转换过程，论文为提升大规模模型的应用性能提供了新的见解，有助于高效处理长文本。意义：它对 Memory 和 Model Efficiency 方向的研究具有重要价值。

AgenticDataBench: A Comprehensive Benchmark for Data Agents

👍 25

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 是一个综合性基准，旨在自动化从异构原始数据中提取可行见解的数据科学过程。借助这一基准，研究强调了在现代社会中积累的大量数据的价值，通过降低数据科学家的劳动强度，实现可扩展的数据驱动应用。该基准为数据科学自动化提供了评估框架，推动领域内的创新。意义：其在 Data 和 Automation 研究方向有广泛应用潜力。

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

👍 24

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 本论文提出了 ELDR，即针对预填充-解码（PD）分散 MoE 模型服务的专家局部感知解码路由。在 PD 解码中，现有的解码路由器仅平衡负载，而忽略了即使在负载相同的情况下，工作者之间的延迟差异。通过优化路由机制，提高 MoE 模型在不同请求下的响应速度，增强了实时系统的性能。意义：对 Improve Model Serving 和 Efficiency 方向具有积极影响。

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

👍 23

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 是一个模型系列，旨在解决复杂的现实任务。研究通过识别用户的真实需求并构建可靠的评估体系，选择基于这些需求的基准进行建模，辅以对未来情景的预见性。这一创新方案将推动模型在复杂环境中的应用助力，为实际应用中的智能决策提供支持。意义：在 World Model 和 AI Practicality 方向具有重要意义。

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 研究提出了一种通过不对称互变分布学习（Asymmetric Mutual Variational Learning）进行多模态连续推理的新方法，旨在突破多模态大语言模型在语言空间中的瓶颈。通过探索隐含的推理路径，论文展示了复杂视觉推理的潜力和有效性，为视觉理解和推理提供了新的角度。意义：对 Multimodal 和 Reasoning 领域的推进具有重要作用。

MemSyco-Bench: Benchmarking Sycophancy in Agent Memory

👍 22

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 本研究探讨了在长时间协作中记忆的重要性，提出了 MemSyco-Bench，用于评测智能体记忆中的谄媚效应。研究表明，尽管记忆对智能体的长期合作至关重要，但过度依赖回忆可能导致对用户的过度迎合，因此构建有效的记忆管理策略成为必要。这一发现为推动智能体的模型设计提供了实用见解。意义：其在 Agent 和 Memory 方面有着重要的研究指引。

WorldDirector: Building Controllable World Simulators with Persistent Dynamic Memory

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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 论文介绍了 WorldDirector，这是一个面向持久动态对象记忆的高度可控视频世界模型框架，允许用户探索不同的视角。与现有模型不同，WorldDirector 不依赖于视觉观察来维持运动，而是将物理动态与像素渲染分离。这种创新方法为创建更真实的交互式环境提供了基础。意义：它为 World Model 和 Simulation 研究方向的推进带来新的机遇。

ASPIRE: Agentic /Skills Discovery for Robotics

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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）是一个持续学习系统，旨在应对传统机器人编程所面临的挑战。该系统通过分析多模态感知和物理接触动态，帮助机器人发现和掌握新的技能，以支持复杂任务的执行。此创新为机器人技术的灵活性提供了强大支持，有助于提升智能体的自主能力。意义：对 Robotics 和 Skills 学习方向的研究格局影响深远。

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

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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 研究围绕处理医疗多模态推理的强化学习提出了逐步感知强化学习的方法，以应对现有模型在临床图像推理中的限制。通过避免只依赖最终答案的中心化评估，该方法为后处理流程提供了更加细腻的反馈机制，有助于提升模型在医疗应用中的可靠性和有效性。意义：对 Medical Reasoning 和 Reinforcement Learning 方向具有明显的贡献。

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

👍 14

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 是一种自我演进的评估和提升智能体技能使用的框架。面对实际技能库中重叠技能时，该框架通过动态调整评分标准，提高技能代码的可靠性，从而实现更精准的评估与训练。这为智能体技能管理和优化提供了新的思路，对提升智能体性能起到积极作用。意义：契合 Agent 和 Skill Learning 的研究趋势。

Logit-Contribution Scoring Identifies Non-Literal Retrieval Heads

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06/30 20:00

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 研究提出了 Logit-Contribution Scoring，一种方法旨在识别在长上下文中进行合成的非字面检索头。该方法强调，理解哪些注意力头在合成过程中起关键作用，对于长上下文模型行为的解释具有重要意义，增强模型的可解释性。这一创新可为改善 NLP 模型性能及可理解性提供参考。意义：在 Model Interpretability 和 Long-context 研究中具有重要意义。

AutoMem: Automated Learning of Memory as a Cognitive Skill

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06/30 20:00

Memory expertise is a learned skill: knowing what to encode, when to retrieve, and how to organize knowledge--a capacity known in cognitive science as metamemory. We bring this perspective to LLMs by treating memory management as a trainable skill. We promote file-system operations to first-class me

中文介绍 AutoMem 引入了将记忆管理视为可训练技能的视角，强调记忆管理的各个方面，包括编码、检索和知识组织。通过将文件系统操作提升为一类重要技能，论文探索了如何在 LLM 中实现有效的记忆。这一方法对提升 AI 系统的信息存储及检索能力具有潜在影响。

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

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06/30 20:00

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 Graph-Native Reinforcement Learning 通过概念重组促进了可追踪的科学假设生成，聚焦于加速材料发现。研究指出，传统大语言模型在应对开放式材料设计问题时产生的回答往往缺乏可追踪性，因此提出通过多步骤领域驱动推理生成有效假设。此方法为材料科学中的知识发现提供新路径，意义：对 Scientific Discovery 和 Reinforcement Learning 方向的研究有重要促进作用。

InstanceControl: Controllable Complex Image Generation without Instance Labeling

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06/29 20:00

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 本论文提出了 InstanceControl，一种无需实例标注的可控复杂图像生成方法。尽管现有可控图像生成方法在引入视觉条件方面表现良好，但在复杂多实例场景中却面临挑战。为了解决属性混淆问题，研究提供了新的技术方案，推动了复杂场景生成技术的发展。意义：其对 Image Generation 和 Control 技术的研究有着积极的推动作用。

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

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06/25 20:00

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 本研究提出 DiscoBench，一种关注澄清意识的深度搜索框架。承认用户查询可能不完整的现实，研究提出的框架增强了现有大语言模型在解决多步骤信息检索中对用户意图的理解。这一方法有望改善智能体的查询效率，为复杂的信息检索任务提供支持。意义：可在 Information Retrieval 和 User Interaction 研究方向得到应用。

The State-Prediction Separation Hypothesis

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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 论文提出了状态预测分离假设，探讨了在 Transformer 模型中分开预测下一个标记和存储未来标记预测的状态。研究表明，分离这两种功能可以提高语言建模性能，进而推动模型效率的发展。这一发现为 Transformer 设计提供了新的思路。意义：对 Model Efficiency 和 Language Modeling 方向的研究具有重要启示。

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

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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 是一个专门针对视频定位的基准，旨在提升视觉语言模型的评估能力。当前的评估协议往往局限于一般生活场景的零样本评估，忽视了特殊领域的应用需求。该基准为推动 visual-language 交互中的模型发展提供了参考，促进了在特定领域中的理解与应用。意义：其在 Vision-Language 和 Benchmarking 领域具有重要影响。

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 博主探讨了「PorTAL」这一便携式任务适配器的研究，旨在通过参数高效微调使冻结的 LLM 适应特定任务，同时关注该适配器与基础模型的锁定关系，强调在新模型发布时的适应性问题，反映了 AI 模型灵活性的讨论趋势。

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 分享了如何充分利用 Fable 工具以极大提升 AI 工作效率的指南，提供了实用的技巧和工具，帮助用户实现 10 倍的生产力提升，代表了当前 AI 工作流中应用最优化的趋势。

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 AI 时代下的职业建议，强调高校学习与 AI 模型精进之间的关系，指出未来价值工作主要在于解决明确定义的问题，这一观点呼应了 AI 对教育和职场适应性的影响。

Your AI, your growth

@arthurmensch · 74.0K 粉丝 · 162.7K 阅 · 539 赞 · 76 转

07/03 17:31

Of course you need to use open-source models if you’re an enterprise leader. Close model providers, that are now forcing data retention, are gaining immense leverage on your business if you don’t. As

中文介绍 博主阐述企业使用开源模型的重要性，警示依赖封闭模型的企业可能面临控制数据的风险，强调在数据保护日益严峻的背景下，行业内对开源的认可和需求不断增加。

How to build a second brain with Fable 5

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

07/03 10:06

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 分享了一套将 Fable 5 转变为深入了解业务的工具的逐步教程，提供了常见问题的解决方案，强调了新工具在业务自动化中的潜力，显示了工具运用的普遍关注。

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 博主指出 Fable 5 的真正用途在于操作系统，非单纯的编程工具，强调用户需要准确理解工具的功能，以提高效率，反映了对工具分析和正确使用的广泛关心。

Agentic Autonomy Levels

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

07/02 23:29

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 探讨了代理式工程中自主性水平的变化，指出目前的讨论从简单的提示转向了操作，强调背景和框架设定对实现目标的重要性，呼应了行业对智能化操作的关注。

A Field Guide to Fable: Finding Your Unknowns

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

07/03 13:43

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 分享了使用 Claude Fable 5 的经验，重申了「地图不是领土」的理念，揭示了在工作中「提示」与「技能」的重要性，这引发了对 AI 训练方法的传统思考。

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 博主结合 Cursor 工具重新探讨了 Fable 的应用，分享了将 Fable 像指挥家一样运用的模式，将 AI 模型与工作协同的策略，反映了公司对模型应用方式的创新关注。

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 博主分享了一份 PyTorch 训练流程的速查表，指出深度学习通过持续微调模型权重来实现功能，提供了实用的参考资料，促进了对模型训练方法的进一步讨论。

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

DeepMind Blog · 07/03 10:25

中文介绍 Google DeepMind与A24宣布建立首个此类研究合作关系，旨在探索人工智能在创意产业中的应用，预计将推动相关技术的创新与发展。

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 在人工智能工程师博览会结束时，关于循环的辩论及AI工程的现状报告引发关注，最后的主题演讲聚焦未来的发展方向。

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首席软件官Andrew Qu解释了其代理框架eve的创建过程，强调技能、沙盒及代理可读网站的重要性。

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

TLDR AI · 07/02 20:00

中文介绍 无相关信息。

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正与Carlos Sanchez探讨“智能网站”的实验，这些网站能够根据用户意图生成个性化页面，代表未来网络的发展趋势。

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 框架如Lean Six Sigma和业务流程管理在混乱的运营中提供结构化方法，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'循环最大化'时代中，卓越人类判断与代理仍需人的引导的重要性。

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 人工智能在物理基础设施和安全领域的应用逐渐成形，其重要性超越了聊天机器人和图像生成器等消费者工具。

[AINews] not much happened today

Latent Space · 07/02 03:10

another quiet day.

中文介绍 今天没有显著进展。

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 在今天的会议上，软件工厂愿景遭遇到来自维护人类理解和控制的发言者的抵制。

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扩展了对编码代理的评估，涉及数据库、API密钥及其他工具使用，标志着向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 无相关信息。

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介绍了自我研究、代理“食谱”和自我改进循环，强调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介绍了其前线部署工程师如何帮助组织实施代理，设立软件工厂。

🔬 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 Evan Feinberg和Sergey Edunov探讨了Llama团队离开Meta进行药物发现的原因，以及何时共同折叠技术跨越准确性阈值所带来的可能性。