一水 发自 凹非寺 量子位 | 公众号 QbitAI 春晚还没来，但机器人春晚已经刷屏了（doge）！ 没错，已经明确不上马年春晚的 智元机器人 ，昨儿个自己办了场"春晚"—— 推出全球首个大型机器人晚会，不仅让机器人当主演，甚至连台下观众都全是机器人 （os：没见过，雀食没见过） 。 而且不得不说，这200多个机器人聚在一起是真能整活儿啊—— 机器人跳街舞就算了，现在还直接端上了机器人小品、机器人魔术、机器人武术…… （连晓明哥和开心麻花都成了助演嘉宾） 网友们一边蹲直播一边刷屏，讨论那叫一个热烈： 机器人过年，人类在一旁欣慰地看着。 好好好，首届机器人晚会到底有多精彩？咱这不得去瞅瞅。 演员和观众，全被机器人承包了！ 大戏一开场，就在大家准备登台的时刻，结果有位朋友紧张到晕倒了（bushi。 忽略这一小插曲，其余机器人赶紧整装，准备开幕秀 （镜头一扫后台还有正在做妆发的） 。 而随着气势磅礴的音乐一响，由 智元灵犀X2 组成的机器人军团就开始表演开场舞了。 瞧瞧这整齐划一的动作，来跟我左边一起画个龙，右边画一道彩虹~ 舞蹈收尾还有集体killing part，全都干拔来了个后空翻： 而热完场子之后，由 智 ...

Core Viewpoint - The article discusses the launch of a new AI model called "Pony Alpha" by OpenRouter, which has generated significant interest and speculation regarding its capabilities and potential identity as a Chinese model, especially with the upcoming Lunar New Year [2][5][23]. Group 1: Model Features and Performance - Pony Alpha is described as a "stealth model" with a context window of 200K and a maximum output of 131K, optimized for coding, reasoning, and role-playing [6][7][4]. - The model has demonstrated impressive front-end capabilities, comparable to top models like Claude Opus 4.6, achieving complex tasks with single prompt inputs [8]. - Users have successfully created applications such as a global radio broadcasting website and a music player, showcasing Pony Alpha's ability to generate extensive code and sophisticated UI designs [10][12]. Group 2: Speculations and Comparisons - There is widespread speculation about the true identity of Pony Alpha, with guesses including various models like GLM-5, DeepSeek-V4, and Claude Opus 5.3, but no consensus has been reached [20][23]. - Evidence suggesting Pony Alpha may be a variant of GLM-5 includes user tests revealing similarities in tokenizer usage and stylistic features in generated outputs [23][25][26]. - The timing of the model's release aligns with announcements from other Chinese AI companies, indicating a competitive landscape leading up to the Lunar New Year [27][28].

Core Insights - The article highlights the success of ByteDance's self-developed technology, specifically the GUI Agent model UI-TARS, which has topped GitHub's trending list and surpassed 26k stars, outperforming OpenAI's official Skills [1][3]. Group 1: Technology Overview - UI-TARS is a multi-modal AI agent that can perform complex operations on various software through natural language commands, mimicking human interactions with screens [5][9]. - The core logic of UI-TARS is "purely vision-driven," allowing the AI to observe screens like a human eye, enabling it to operate regardless of whether APIs are available or interfaces are complex [11][12]. - The technology includes two main projects: Agent TARS, which operates in both web UI and server environments, and UI-TARS-desktop, a desktop application for local computer and browser operations [6][8]. Group 2: Development and Evolution - UI-TARS aims to equip agents with four key capabilities: perception, action, reasoning, and memory [21]. - The project began a year ago and has evolved significantly, with the initial version leveraging 6 million high-quality tutorial data to enhance its deep thinking capabilities [20][24]. - Subsequent iterations, such as UI-TARS-1.5 and UI-TARS-2, have improved the agent's performance, addressing data bottlenecks and enhancing its ability to integrate various functionalities [26][28]. Group 3: Market Impact and Future Prospects - The article notes that UI-TARS has become one of the most popular open-source multi-modal agents, with significant attention from industry leaders [30]. - The technology is positioned to revolutionize how AI interacts with users, as highlighted by industry figures who predict that products like UI-TARS will significantly impact the market by 2025 [32][34]. - The article concludes by emphasizing the potential of GUI agents to bridge the gap between AI capabilities and human tasks, suggesting a transformative effect on productivity and efficiency [37][38].

Core Viewpoint - The loyalty of employees in Silicon Valley has diminished, with significant "acqui-hire" events occurring, indicating a shift towards a "mercenary" culture in the tech industry [1][3]. Group 1: Major Acqui-Hire Events - In June 2025, Meta invested $14.3 billion to acquire Alexandr Wang from Scale AI [1]. - In July 2025, Google spent $2.4 billion to acquire technology from Windsurf, bringing in its founder Varun Mohan and research team into DeepMind [1]. - In December 2025, NVIDIA reached a $20 billion agreement with Groq to acquire its core inference technology and CEO Jonathan Ross along with key executives [1]. Group 2: Talent Mobility and Motivations - Talent mobility is categorized into "voluntary" and "involuntary" job changes, with motivations including high salaries, access to cutting-edge resources, and the pursuit of promising technologies [4]. - The trend of researchers moving from Google to OpenAI began in early 2023, with at least five Google Brain researchers joining OpenAI before the launch of ChatGPT [6][7]. Group 3: High Salaries and Recruitment Strategies - Meta's aggressive recruitment strategy included a compensation package of up to $300 million over four years, with the first year's salary exceeding $100 million [15]. - The competition for AI talent has led to a "mercenary culture," where employees prioritize financial incentives over loyalty to their companies [23][24]. Group 4: Acqui-Hire as a Strategy - Acqui-hire has become a popular strategy among Silicon Valley giants, allowing companies to acquire talent without the complexities of full mergers [40]. - The case of Google acquiring Windsurf illustrates the potential fallout from such strategies, as remaining employees felt abandoned and betrayed [44]. Group 5: Cultural Shifts in the Tech Industry - A cultural shift is occurring in the tech industry, where employees are increasingly wary of long-term commitments to a single company, driven by rapid technological advancements [54][57]. - The speed of innovation in AI means that working for a startup can yield experience equivalent to several years in traditional tech roles [57]. Group 6: Domestic Talent Wars - The competition for AI talent is not limited to Silicon Valley; domestic companies are also aggressively recruiting from top labs, with Tencent and ByteDance making significant hires from OpenAI and Google DeepMind [60][62]. Group 7: The Value of AI Talent - The scarcity of top AI talent makes them a strategic asset for companies, with the potential to significantly impact model training costs and performance [64].

BiPS团队 投稿 量子位 | 公众号 QbitAI 随着视觉-语言模型 （VLM） 推理能力不断增强，一个隐蔽的问题逐渐浮现： 很多错误不是推理没做好，而是"看错了"。 在复杂视觉任务中，模型往往能正确识别对象、理解问题，甚至给出完整的推理链，却因捕捉了错误的视觉证据，得出自信却错误的答案。 现有方法通常在推理阶段"指路"——例如生成视觉提示或调用外部工具，以临时对齐证据。这类策略虽有效，却面临明显局限：视觉线索形式 受限、高度依赖具体任务，且推理开销大。更重要的是，它引出一个根本性问题： 如果模型始终需要外部提醒才知道"看哪儿"，它是否真的理解了视觉世界？ 为此，微软亚洲研究院与清华大学提出 BiPS （Bi-directional Perceptual Shaping） ，从源头重塑模型的"看图方式"。 BiPS不在推理时临时提示关注区域，而是在训练阶段就教会模型： 面对特定问题，哪些视觉细节必须关注，哪些可以忽略 。通过系统性地对 齐问题与视觉证据，BiPS促使模型内化一种核心能力—— 带着问题去看图 。因此，在推理时无需任何额外提示，模型也能自动聚焦于真正决 定答案的关键区域与细节。 实验表明，这种 ...

一水 发自 凹非寺 量子位 | 公众号 QbitAI 获陶哲轩转发，arXiv上的一篇新论文正在引起巨大关注！ 挤进前排后发现，原来这是一项 由11位全球顶尖数学家发起的AI实验 —— 让AI在规定期限内，解决他们各自在真实研究过程中产生的10道"研究级"难题，以此探索"AI+数学"的能力边界。 而且走的还是高斯时代的路子——人类先证明出来，但先不公布答案和过程，等到了合适时间再公开，避免AI偷偷看答案。 以前这是一项为保护数学家证明自己优先解决某道问题的做法，而在AI时代却有了新玩法。 在陶哲轩看来，这项实验非常有意思： 当前"一次性"AI提示似乎难以解决这些问题，但它们已被人类领域专家攻克。可以预见，配备AI工具的其他领域专家也能解决其中相当 一部分。 这些问题的技术门槛相当高，非领域专家难以验证AI生成的任何输出结果 。 因此在我看来，要让非专家解决其中任何一个问题都极具挑战性——当然，意外惊喜也并非不可能。在截止期限前，这项实验能否产生 任何显著成果，将十分值得关注。 好好好，既然老陶如此安利了，咱这就开扒完整实验过程（doge）。 解完10道数学题，然后…藏起证明过程 概括而言，通过提出一套名为Fi ...

所有岗位不同能力层级职位均在开放，欢迎结合个人履历和经验申请。 岗位均为全职，工作地点：北京中关村。 岗位面向： 加入我们，你可以获得： 以下是岗位详情： 编辑部 发自 凹非寺 量子位 | 公众号 QbitAI AI热潮还在汹涌，但如果你还不知道如何参与……那为什么不来 量子位 呢？ 我们是一家以 追踪AI新进展 为核心的内容平台，经过8年积累，目前拥有顶流影响力，广泛且备受认可的产业资源，以及时代风口的最佳观 测和学习生态位。 目前，我们有 三大方向 岗位招聘，希望你是 （或者能成为） 这三个方向的内容专家： AI产业方向 岗位职责： AI产业方向 ：关注基建层创新，包含芯片、AI Infra、云计算； AI财经方向 ：关注AI领域创投和财报，跟踪产业链资本动向； AI产品方向 ：关注AI在应用和硬件终端方向的进展。 社招：覆盖编辑、主笔、主编各个层级，按能力匹配岗位； 校招：应届毕业生，接受实习且可转正。 站在AI浪潮之巅 ：第一时间接触和了解AI领域最新技术和产品，构建完整的AI认知体系。 玩转AI新工具 ：将各种AI新技术、新工具应用于工作，提升工作效率和创造力。 打造个人影响力 ：通过撰写独家原创内 ...

克雷西 发自 凹非寺 量子位 | 公众号 QbitAI 系统性能优化领域顶级专家 Brendan Gregg ，正式官宣加入OpenAI。 入职后，他将加入 ChatGPT性能团队 ，在澳大利亚远程办公，向团队负责人Justin Becker汇报工作。 Brendan被技术圈尊称为 "性能之神" ，他的到来，受到了OpenAI总裁Brockman的亲自欢迎。 Brockman甚至表示，自己就是Brendan多年以来的老粉丝。 同时他还是Linux内核核心技术 eBPF 的主要推动者，一手构建了现代云计算的性能分析工具箱…… 网友们评价，Brendan的这些作品绝对是next level。 | foundrceo @ @foundrceo · 21小时 | | | | | | --- | --- | --- | --- | --- | | that's sick man, brendan's work is next level for sure | | | | | | U | | 3 | ılıl 402 | 1, | | Ton77 ▽ @ @TRJ 77 · 15小时 | | | | | | Greg a ...

henry 发自 凹非寺 量子位 | 公众号 QbitAI 国内博士毕业，居然不用死磕论文了！ 最近，Nature专门发文关注了这一动向—— 由中国首次授予的 实践型博士 学位： 不看论文，只认硬核实践产出！ | nature | | | | | --- | --- | --- | --- | | Explore content ▼ | About the journal ✓ | Publish with us ▼ | Subscribe | 报道里也把这么做的目的说得很明白：就是要培养顶尖工程师，把国家整体创新能力直接拉上一个台阶。 而且，这不光是嘴上说说而已。 顺着Nature的线索深挖我们发现，这种不要毕业论文，靠实践成果拿下博士学位的还真不少，而且还都在去年9月后扎堆出现。 有意思的是，360创始人 周鸿祎 在2023年考入的清华大学 创新领军工程博士 项目，也正好落在这一制度框架之内。 换句话说，这并不是某几所高校的"个案探索"，而是一条已经被制度正式打开的通道。 这下，应该没有AI公司的CEO不是博士的了吧？（doge） 第一批没有论文的博士毕业了 据公开资料显示，自去年9月以来，全国至少已有11 ...

梦晨 发自 凹非寺 量子位 | 公众号 QbitAI 用Claude Code写代码的人，终于不用每次开新会话都从头解释项目背景了。 顶GitHub开源热榜的一款持久化记忆系统Claude-Mem，直击AI编程助手最致命的痛点：跨会话失忆。 Claude-Mem本身100%免费，还能帮你省token钱。 它通过"三层渐进式披露"的检索架构，常规使用下能节省90% Token，测试阶段的"无尽模式"更是能把Token消耗砍掉95%，工具调用次数上 限直接拉高20倍。 给Claude Code装上"长期记忆" 传统AI编程助手有个绕不开的问题，每次新会话都是一张白纸。 昨天刚聊完的架构设计、上周敲定的编码规范、刚刚那些踩过的坑，AI统统不记得。开发者只能一遍遍重复解释，时间和Token都在这种"复 读"中白白流失。 Claude-Mem的解法是在本地环境搭建一套完整的记忆系统。 它采用事件驱动架构，通过五个生命周期钩子 （SessionStart、UserPromptSubmit、PostToolUse、Stop、SessionEnd） 在后台静默运 行。 每当Claude Code执行文件读写、代码编辑、命令执 ...