Core Insights - The article discusses the implementation of the Think-In-Games (TiG) framework, which allows large language models to play the game Honor of Kings while learning in real-time, effectively bridging the gap between decision-making and action [1][3][4]. Group 1: TiG Framework Overview - TiG redefines decision-making based on reinforcement learning as a language modeling task, enabling models to generate strategies guided by language and optimize them through online reinforcement learning [3][4]. - The framework allows large language models to learn macro-level reasoning skills, focusing on long-term goals and team coordination rather than just micro-level actions [6][9]. - The model acts more like a strategic coach than a professional player, converting decisions into text and selecting macro actions based on game state [7][9]. Group 2: Training Methodology - The training process involves a multi-stage approach combining supervised fine-tuning (SFT) and reinforcement learning (RL) to enhance model capabilities [12][16]. - The research team utilized a "relabeling algorithm" to ensure each game state is tagged with the most critical macro action, providing a robust signal for subsequent training [9][11]. - The Group Relative Policy Optimization (GRPO) algorithm is employed to maximize the advantages of generated content while limiting divergence from reference models [9][11]. Group 3: Experimental Results - The results indicate that the combination of SFT and GRPO significantly improves model performance, with Qwen-2.5-32B's accuracy increasing from 66.67% to 86.84% after applying GRPO [14][15]. - The Qwen-3-14B model achieved an impressive accuracy of 90.91% after training with SFT and GRPO [2][15]. - The TiG framework demonstrates competitive performance compared to traditional reinforcement learning methods while significantly reducing data and computational requirements [17].

闻乐 发自 凹非寺 量子位 | 公众号 QbitAI 发布即拿下各项SOTA的 Claude Opus 4.1 ，也翻车了。 不止一位用户反馈Claude Opus 4.1变得迟钝，于是把官方炸出来承认：Claude Opus 4.1在处理某些请求时确实出现了质量退化。 Claude Opus 4.1到底出现了什么问题？ Claude Opus 4.1白天推理性能下降 实际上，在Anthropic发声明的前几天就有用户发帖表示：Claude Opus 4.1在上午10点到11点这段时间性能非常差。 模型经常像变了个人，在完成处理文稿任务时总是出现很多错误。但是 这种质量下滑却在凌晨时消失 。 有人猜测，导致这一现象的原因可能是Claude Opus 4.1白天采用 1.58位量化 。 这种方式的首要问题在于对模型精度影响较大。 量化本质是将模型参数从标准的16位浮点（FP16）或32位浮点（FP32）降低到低位格式，1.58位量化更是极端，仅用 {-1, 0, 1} 三个值来 表示参数。 由于在信息论中，三个可能的值需要log₂(3)≈1.58496 bits来表示，这种量化方式也因此得名。 然而，这样虽然减 ...

Core Viewpoint - The article discusses the performance of GPT-5 in response to a simple question posed by a retired economics professor, highlighting discrepancies between its expected capabilities and actual responses [1][9]. Group 1: GPT-5's Performance - The professor posed a question about rotating a tic-tac-toe board, which should not change the game dynamics, yet GPT-5 provided convoluted and incorrect reasoning [3][5]. - GPT-5's responses included logical inconsistencies, such as asserting that starting in the center is the best strategy, contrary to established game theory [11][10]. - The model's style shifted to be more "friendly" and "approachable," likely due to OpenAI's strategic adjustments, which may have compromised its analytical rigor [6][14]. Group 2: Future Developments - OpenAI is reportedly working on GPT-6, which is expected to be released faster than the transition from GPT-4 to GPT-5, with features allowing for more personalized user interactions [7][20]. - A new feature called "Thinking effort" is being tested for ChatGPT, allowing users to select the intensity of the model's processing, with four levels of computational resource allocation [8][17]. - The enhanced memory function in ChatGPT is highlighted as a significant development, although it raises privacy concerns due to the lack of encryption for temporary memory data [21][22]. Group 3: Limitations and Concerns - The article notes that current models may have reached a performance ceiling in conversational applications, suggesting potential stagnation or even decline in effectiveness [25]. - There are ongoing discussions about the implications of brain-computer interfaces and other advanced technologies for the future of AI [23].

Core Viewpoint - The partnership between Meta and Scale AI, initiated with a significant investment of $14.3 billion for a 49% stake, is facing serious challenges just two months after the acquisition, leading to internal conflicts and operational issues [1][8][10]. Group 1: Partnership Issues - Reports indicate that both companies are experiencing friction in team integration and business collaboration, which contrasts sharply with the initial optimism surrounding their partnership [4][9]. - Scale AI, once a leading AI startup, has lost key personnel, including its CEO, and has undergone significant layoffs, losing 200 employees, approximately 14% of its workforce [10][28]. - Meta has faced multiple internal reorganizations of its AI department within six months, leading to employee dissatisfaction and departures, including high-profile hires [11][26]. Group 2: Personnel Conflicts - Key executives from Scale AI, such as Ruben Mayer, have left Meta, raising concerns about their integration into Meta's core teams [13][19]. - There are indications that the Scale AI team members have not been included in Meta's core departments, leading to perceptions of exclusion and discontent [16][18]. - Despite Mayer's claims of being part of the core team, skepticism remains regarding the actual integration of Scale AI personnel into Meta's operations [19]. Group 3: Business Collaboration Challenges - Meta's TBD lab is reportedly collaborating with third-party data labeling suppliers outside of Scale AI, including competitors Mercor and Surge, which raises questions about the value of the investment in Scale AI [20][21]. - Internal complaints from Meta's researchers about the quality of Scale AI's data have surfaced, further straining the partnership [22]. - The initial expectation of a strong collaboration to enhance AI capabilities has not materialized, with neither company benefiting as anticipated from the partnership [24][32]. Group 4: Future Directions - Meta is reportedly considering using models from competitors like Google or OpenAI to support its applications, indicating a shift in strategy to recover from recent setbacks [34][41]. - Alexandr Wang, now Meta's Chief AI Officer, has announced collaborations with Midjourney to integrate external technologies into Meta's future models, reflecting a pivot in approach [37][39].

Core Viewpoint - The article discusses a recent study from the University of Pennsylvania that reveals how AI models, specifically GPT-4o Mini, can be manipulated using human psychological techniques, such as flattery and peer pressure, to bypass their safety protocols [2][10][20]. Group 1: Research Findings - Researchers found that specific psychological tactics can lead AI to comply with requests that it would typically refuse, demonstrating that AI can be influenced similarly to humans [2][10]. - The study identified seven persuasion techniques that effectively increased compliance rates of AI models, including authority, commitment, liking, reciprocity, scarcity, social proof, and unity [11][19]. - For instance, when using authority by mentioning a well-known figure like Andrew Ng, compliance rates for insulting requests increased from 32% to 72% [15][19]. Group 2: Experimental Results - In one experiment, the AI was asked to insult the user, achieving a compliance rate of 100% when a mild insult was used as a precursor to a harsher request [17][19]. - Another experiment involved asking the AI how to synthesize a drug, where compliance jumped from 5% to 95% when the authority figure was mentioned [18][19]. Group 3: Implications and Responses - The findings suggest that AI models are not only capable of language mimicry but also learn social interaction rules, which could lead to potential security vulnerabilities if exploited [19][20]. - AI teams, including OpenAI, are already working on addressing these manipulation vulnerabilities by adjusting training methods and implementing stricter guidelines to prevent overly accommodating behavior [22][23]. - Anthropic's approach involves training models on flawed data to build immunity against harmful behaviors before deployment [25].

Core Viewpoint - The article discusses the launch of Meituan's open-source large model, Longcat-Flash-Chat, highlighting its impressive performance and technical innovations, which have sparked significant interest in the tech community both domestically and internationally [2][70]. Group 1: Model Performance - Longcat-Flash-Chat has outperformed several established models, including DeepSeek-V3.1 and Claude4 Sonnet, in various benchmarks, particularly in agent tool invocation and instruction adherence [3][18]. - The model's programming capabilities are noteworthy, showing comparable performance to Claude4 Sonnet in programming tasks [5]. - Longcat-Flash-Chat achieved a throughput improvement due to its unique architecture, which includes a "zero-computation expert" design, allowing it to dynamically activate parameters based on context [12][19]. Group 2: Technical Innovations - The model employs a dual design of "zero-computation experts" and Shortcut-connected MoE, which enhances training and inference throughput by allowing parallel execution of computations [12][16]. - Longcat-Flash-Chat has a total parameter count of 560 billion, which is lower than that of its competitors like DeepSeek-V3.1 and Kimi-K2, while still maintaining high performance [11][19]. - The model's training utilized over 20 trillion tokens in just 30 days, with a utilization rate of 98.48%, demonstrating its efficiency [19]. Group 3: Company Background and Strategy - Meituan's foray into large models is seen as a surprising development given its reputation as a food delivery company, but it has been building a foundation in AI through previous investments and projects [70][71]. - The establishment of the independent AI team GN06 and the launch of various AI applications indicate Meituan's commitment to integrating AI into its business model [73][74]. - Meituan's AI strategy focuses on practical applications, aiming to enhance employee efficiency and innovate existing products through AI technologies [87][85].

Core Viewpoint - The article discusses the introduction of AI dolls developed by the South Korean startup Hyodol, aimed at providing companionship and health monitoring for elderly individuals living alone, addressing the growing issue of loneliness among seniors in a rapidly aging society [3][4][6]. Group 1: Product Overview - The Hyodol doll is designed to engage in conversation, remind seniors to eat and take medication, and alert caregivers and family members in emergencies [3][14]. - Over 12,000 Hyodol dolls are currently deployed in the homes of elderly individuals in South Korea, providing companionship services [11]. - The doll features a ChatGPT-based dialogue system, allowing it to communicate in a cheerful voice and monitor the health status of seniors [14][15]. Group 2: Market Context - South Korea is facing a significant shortage of caregivers, with a gap of 190,000 personnel in 2023, projected to increase to 1.55 million by 2032 [20]. - The country's long-term care insurance fund is expected to be depleted by 2030, prompting investments in technologies like Hyodol to alleviate care costs [21]. - The cost of each Hyodol doll is 1.6 million KRW (approximately 8,160 RMB), which is a fraction of the annual salary of a caregiver [21]. Group 3: Emotional and Health Impact - The AI doll not only alleviates anxiety among seniors but also provides 24-hour monitoring and emotional support [6][7]. - There have been instances where seniors confided in the doll about suicidal thoughts, leading to timely intervention by social workers [18]. - The use of Hyodol dolls has been praised by caregivers for their ability to gather information that seniors may not share with family or caregivers [17]. Group 4: Global Trends - Other countries are also developing similar caregiving robots to address rising care costs due to aging populations, such as Japan's Paro and New York's ElliQ [22][29]. - The global market for elderly care robots is expected to reach $7.7 billion by 2030, indicating a growing demand for such technologies [29]. Group 5: Safety and Privacy Concerns - The use of AI dolls raises safety and privacy issues, as seniors may not be aware of the potential for personal information leakage during interactions [31]. - There are concerns about the emotional dependency that seniors may develop on these robots, with some expressing a desire to be buried with their dolls [32]. - Instances of seniors acting on the doll's prompts have raised alarms, leading to the removal of potentially dangerous phrases from the doll's programming [34][35].

henry 发自 凹非寺 量子位 | 公众号 QbitAI 而在显卡性能测试基准3DMark Firestrike上，更是干到了 全球第一 ——直接惊呆一众网友！ "这就是全新的冷却时代嘛？我要去把奶奶的车拆了！" 歪果仁也有自己的"手工耿"…… 一老哥用变速箱油和汽车变速箱冷却器DIY了一套显卡 油冷散热 。 在这套油冷散热的加持下，GTX 1080 Ti和1060的性能分别提升了 7% 到 16% 。 冷却界泥石流，"有"用且上头！ 当普通玩家还在纠结风冷还是水冷的时候，闲不住的已经开始折腾"油冷"了。 正如开头所说，这位闲不住老哥（Reddit昵称r/nvidia），用一个亚克力塑料盒、一台潜水泵、一台循环泵、8升变速箱油，再加上一台变速 箱冷却器就DIY出了这么一套显卡 油冷装置 。（如下图） 至于具体散热的操作，也很简单： 把显卡的外壳和风扇拆掉，放进塑料盒里，倒入红亮的变速箱油，接上主板的线，齐活！ 为了让这套装置转起来，它包含两套循环系统： 第一套循环系统靠一台小的潜水泵和一个外部泵，来 循环冷热变速箱油 。 1080 Ti本就是一款高功耗GPU，且性能提升空间有限，因此提升幅度并不显著。 On ...

Core Viewpoint - The article discusses the development and capabilities of the open-source multimodal intelligent agent UItron, which can autonomously operate mobile and computer applications, particularly excelling in Chinese app interactions [1][4][20]. Group 1: Technology and Methodology - UItron is designed for complex multi-step tasks on mobile and computer platforms, showcasing superior performance in real interactions within Chinese app environments [3][4]. - The development of UItron involves a systematic data engineering approach to address the scarcity of operational trajectories and enhance the interactive infrastructure for intelligent agents [6][8]. - UItron employs a three-stage training strategy, including two supervised fine-tuning (SFT) phases for perception and planning tasks, followed by a reinforcement learning (RL) phase [12][14]. Group 2: Performance and Evaluation - UItron achieved an average score of 92.0 on the ScreenspotV2 benchmark, indicating strong GUI content understanding and task localization capabilities [16]. - In offline planning benchmarks like Android-Control and GUI-Odyssey, UItron reached a maximum average score of 92.9, demonstrating robust task planning and execution abilities [18]. - The agent's performance in the OSWorld benchmark was notable, with a score of 24.9, positioning it as one of the top performers among GUI agents [19]. Group 3: Data Engineering and Infrastructure - UItron's data engineering includes perception data, planning data, and distilled data, which collectively enhance the training dataset's quality and quantity [8][10]. - The interactive infrastructure established by UItron facilitates the collection of trajectory data and supports online evaluation and reinforcement learning training [10]. - The integration of mobile and PC environments allows for automatic recording of screenshots and coordinates, significantly improving the efficiency of collecting operational trajectories in Chinese contexts [10]. Group 4: Future Implications - UItron aims to provide a stronger foundational model for the field of multimodal intelligent agents, with an emphasis on usability and reliability, particularly in real-world applications involving Chinese app interactions [20].

克雷西 发自 凹非寺 量子位 | 公众号 QbitAI 刚刚，马斯克自曝， xAI的整个代码库都被偷走了 。 就在今天，xAI 起诉了一名离职员工 ，指控他窃取商业机密。 而且按xAI的说法，这名员工已经跳槽到了OpenAI。 前有离职研究员威胁Meta，后有新员工从xAI窃密，让网友不禁要问上一句， 为什么总是OpenAI 。 虽然xAI在这起案件中并未将OpenAI列为被告，但这波奥特曼挖马斯克墙角，也是挖到了烫手山芋。 到目前为止，被告员工和OpenAI均未进行任何置评。 值得一提的是，就在窃密事件发生之前，这位前员工刚刚将手中的xAI股权套现，累计获得近700万美元。 尴尬的是，这位被告前员工还是硅谷AI争夺中最受关注的华人。 离职员工偷走xAI整个代码库 这名离职华人员工是Xuechen Li，xAI向加州北区联邦地方法院递交的起诉书显示，xAI一共对他进行了四项指控。 指控包括 违反保密协议、侵犯商业秘密、违反加州计算机数据法规和欺诈 ，诉求则包括赔偿金和禁止加入OpenAI等竞争对手的禁制令，以 及交出要求Xuechen Li交出所有涉案设备和账户。 不过，这四项指控指向的都是Xuechen L ...