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全部 AI 动态
Qwen@Alibaba_QwenAI 评分2323引用QwenCloud@qwen_cloud
Qwen@Alibaba_QwenAI 评分5050千问推出 E-Commerce Bench,用于评测长周期自主商业运营:智能体以 ¥100,000 启动资金经营网店 365 天,需处理选品、议价、定价、促销、库存与现金流。
AI at Meta@AIatMeta精选AI 评分7272推荐理由:Meta 公布实时语音感知模型的能力清单与榜单排名,可据此判断流式 ASR 的当前水平。
Hugging Face@huggingfaceAI 评分1818
DeepSeek@deepseek_ai精选AI 评分7575DeepSeek 在 API 平台上线实验性多模态模型 DeepSeek-V4-Flash-Vision-Exp,调用时使用 model='deepseek-v4-flash-vision-exp'。
推荐理由:官方给出新模型与 V4-Flash 的文本能力对照,以及多模态智能体基准上的提升幅度,便于判断是否值得接入。
Hugging Face@huggingfaceAI 评分2020我们 Hub 上的模型数量刚刚突破 300 万 🤗 社区正加速迈向一个开放、分布式的未来——开放 AI 无处不在,人人可用 🚀

DeepSeek@deepseek_ai精选AI 评分8080推荐理由:官方给出 Harness 的插件化架构与 MIT 开源仓库,读者可据此判断它能否接入现有智能体工作流。
DeepSeek@deepseek_ai精选AI 评分8080推荐理由:官方给出 V4-Pro 的 Agent 能力升级、三档推理强度和 Codex 接入方式,可据此判断实际使用成本与场景。
Hugging Face@huggingfaceAI 评分2222引用Jess Fields@jessalanfieldsQwen 3.8 27b is being released at 11:00AM EST on Wednesday Folks are lined up for miles to see what this fancy new model is capable of
Hugging Face@huggingfaceAI 评分3333AI 智能体如何复现 ICML 2026 论文 https://x.com/i/broadcasts/1OxwbbdvRygJB
Hugging Face@huggingfaceAI 评分4040我们正与 Open Secure Alliance 合作制定安全事件学习指南,共同为安全事件建立更好的审查、披露与控制机制。
引用NVIDIA@nvidiaSecurity is strongest when organizations collaborate openly and move quickly. Now over 120 members strong, the Open Secure AI Alliance is sharing new open source security contributions, including new proposed SAFE guidelines, for turning confidential incident findings into stronger protection across the ecosystem.
Andrej Karpathy@karpathyAI 评分4242
Mira Murati@miramuratiAI 评分4242我们分享了对开放权重发布的思路、我们如何评估 Inkling、为什么安全取决于模型及其所进入的生态系统、以及测试、分阶段访问和更强的防御如何能开辟一条通往更大开放度的道路。
引用Thinking Machines@thinkymachinesReleasing weights indiscriminately isn't safe. Neither is keeping capable models inside a few labs. We think there's a path between them. We haven't mapped all of it. Our new post covers the part we can see: how we assessed Inkling, and why access should widen in stages. https://thinkingmachines.ai/blog/a-safe-path-to-open-weights
DeepSeek@deepseek_ai精选AI 评分7878推荐理由:官方公布 V4-Flash API 公测,并给出 Agent 能力提升与 Codex 适配的配置入口。
Mira Murati@miramurati精选AI 评分7878引用Thinking Machines@thinkymachinesToday, we are releasing Inkling-Small. Inkling-Small achieves comparable performance to Inkling at a quarter of its size. It features 276B total parameters, 12B active. We are making the full weights available. https://thinkingmachines.ai/news/inkling-small/ Fine-tune it on Tinker today, or chat with it in text, image, and audio on Tinker Playground.
推荐理由:Inkling-Small 以四分之一体量达到与 Inkling 相当的性能,并开放完整权重,读者可据此判断小模型的部署与微调空间。
Berkeley AI ResearchAI 评分5858 K-Search 扩展 MLX 后端:把 CUDA 内核经验迁移到 Apple Silicon
Berkeley Sky Lab 团队为 K-Search 增加 MLX 后端和结构化 CUDA-to-MLX 翻译层,让进化式内核搜索以现有 CUDA 内核为知识库生成 Apple Silicon 内核。
Hugging Face@huggingfaceAI 评分3838训练智能体 3:学习如何通过强化学习训练一个本地/开放权重智能体 https://x.com/i/broadcasts/1PKqrrbanaZGb
Hugging Face@huggingfaceAI 评分2222引用MiniMax (official)@MiniMax_AIOpen weights. Open research. Open innovation.🫶 Marching for an open future.🤍
Berkeley AI ResearchAI 评分3838 ABBEL:通过信念状态监督让 LLM 高效完成长程交互
伯克利 AI 研究团队提出 ABBEL 框架,将 LLM 的交互摘要建模为自然语言"信念状态"并对其内容单独监督。在协作编程基准 CollabBench 上,该方法把与全上下文模型的性能差距缩小约 50%,训练步数从 100 步降至 50 步,峰值上下文 token 数也明显低于全上下文设置。
Mira Murati@miramuratiAI 评分2222让 AI 变得有用的知识是分散的。它存在于科学家、工程师、临床医生和企业之中。要让 AI 从分布式知识中受益,它自身也必须是分布式的。同意 Jensen 的观点,这是一个值得建设的未来。
引用Jensen Huang@JensenHuangFor my first post, I’m sharing a letter @NVIDIA signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. The world needs both frontier closed models and frontier open models. https://images.nvidia.com/pdf/Open-Weights-and-American-AI-Leadership.pdf
Mira Murati@miramurati精选AI 评分7878引用Thinking Machines@thinkymachinesToday, we are introducing Inkling. Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available. https://thinkingmachines.ai/news/introducing-inkling/ Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
推荐理由:Thinking Machines 首款模型从零训练并开放权重,读者可据此了解其多模态推理定位与微调入口。
Mira Murati@miramuratiAI 评分5858引用Mira Murati@miramuratiThinking Machines Lab exists to empower humanity through advancing collaborative general intelligence. We're building multimodal AI that works with how you naturally interact with the world - through conversation, through sight, through the messy way we collaborate. We're excited that in the next couple months we’ll be able to share our first product, which will include a significant open source component and be useful for researchers and startups developing custom models. Soon, we’ll also share our best science to help the research community better understand frontier AI systems. To accelerate our progress, we’re happy to confirm that we’ve raised $2B led by a16z with participation from NVIDIA, Accel, ServiceNow, CISCO, AMD, Jane Street and more who share our mission. We’re always looking for extraordinary talent that learns by doing, turning research into useful things. We believe AI should serve as an extension of individual agency and, in the spirit of freedom, be distributed as widely and equitably as possible. We hope this vision resonates with those who share our commitment to advancing the field. If so, join us. https://thinkingmachines.paperform.co/
Berkeley AI ResearchAI 评分4242 智能免费之后怎么办?面向智能体、由智能体构建的数据系统
UC Berkeley 团队提出,推理成本已从 GPT-4 级约 $30/百万 token 降至不足 $1,部分厂商低于 $0.10,基准测试中推理价格每年下降 9x 至 900x、中位数约 50x。作者据此提出三类数据系统挑战:为智能体设计的数据系统、支撑智能体群体运行的数据系统、以及由智能体合成的数据系统。
Berkeley AI ResearchAI 评分2222 2026 届 BAIR 博士毕业生展示:从机器人到 LLM 推理的研究版图
伯克利人工智能研究实验室(BAIR)展示 2026 届博士毕业生,研究覆盖机器人、LLM 推理、计算机视觉、生成模型、AI 安全与医疗等方向。毕业生去向包括 OpenAI、Mistral AI、Physical Intelligence、Amazon 等机构,以及 UCLA、芝加哥大学教职和自创初创公司。
Andrej Karpathy@karpathyAI 评分5858引用Claude@claudeaiIntroducing Claude Tag, a new way for teams to work with Claude. In Slack, Claude joins as a team member with access to the channels and tools you choose. Tag Claude in and delegate tasks to it while you focus on other work.
Andrej Karpathy@karpathy精选AI 评分8282Andrej Karpathy 评价 Claude Fable 5 发布,称其与 Mythos 是同一底层模型但增加了安全防护,基准测试全面 SOTA 且领先明显。
引用Claude@claudeaiFable 5 is state-of-the-art on nearly all tested benchmarks, with exceptional performance in software engineering, knowledge work, scientific research, and vision. The longer and more complex the task, the larger Fable 5’s lead over our other models.
推荐理由:Karpathy 以第一手使用体验评价 Claude Fable 5 在长任务和复杂问题上的跃升,并给出对软件需求变化的判断。
DeepSeek@deepseek_aiAI 评分4242我们已将折扣永久化!🎉 尽情使用 DeepSeek-V4-Pro 构建,让你的创新想法成为现实!🚀
引用DeepSeek@deepseek_aiThe DeepSeek-V4-Pro discount has been extended until May 31, 2026, 15:59 UTC!
Mira Murati@miramuratiAI 评分2222协作式 AI 建立在交互性之上:机器与人,实时协作,覆盖每一种模态。解决这个问题需要社区的力量,加入我们。
引用Thinking Machines@thinkymachinesWe are offering grants of $100,000 + Tinker credits to researchers advancing the field of human-AI interactivity. Submit your proposals by June 19th! https://thinkingmachines.ai/news/interactivity-research-grants/
Andrej Karpathy@karpathyAI 评分2222个人动态:我已加入 Anthropic。我认为未来几年处于 LLM 前沿的工作将尤其具有塑造性。我非常兴奋能加入这里的团队,重回研发工作。我依然对教育怀有深厚的热情,并计划适时恢复这方面的工作。
Mira Murati@miramuratiAI 评分6262Mira Murati 表示团队正在分享其在交互模型上的工作,这是一类从零训练、原生处理实时交互的新模型,而非在回合制模型上外挂交互能力。推文附有一段视频链接。
Andrej Karpathy@karpathyAI 评分3838引用Thariq@trq212http://x.com/i/article/2052796100608974848
Berkeley AI ResearchAI 评分3636 自适应并行推理:高效推理扩展的下一个范式
伯克利 AI 研究团队综述并行推理进展,重点分析自适应并行推理——让模型自行决定何时分解并并行化子任务、开多少并发线程以及如何协调。现有方法如 self-consistency、Best-of-N、Tree of Thoughts、MCTS、ParaThinker、GroupThink 和 Hogwild! Inference 的并行结构多由外部设定,而非模型自主选择。