Core Insights - The article emphasizes the transformative impact of artificial intelligence (AI) on various industries and society as a whole, marking the beginning of a new era in 2025 [1]. Event Overview - The MEET2026 Intelligent Future Conference will focus on cutting-edge technologies and industry advancements, particularly in AI [2]. - The theme of the conference is "Symbiosis Without Boundaries, Intelligence to Ignite the Future," highlighting how AI is becoming a core driving force for societal evolution [3]. Key Topics - The conference will cover hot topics in the tech circle, including reinforcement learning, multimodal AI, chip computing power, AI in various industries, and AI's global expansion [4]. - It will showcase the latest collisions between academic frontiers and commercial applications, featuring leading technological achievements from infrastructure, models, and products [5]. Reports and Awards - The conference will also feature the authoritative release of the annual AI rankings and the Annual AI Trends Report, which will analyze significant trends in AI [6]. Notable Speakers - The event will host prominent figures from academia and industry, including: - Zhang Yaqin, a renowned scientist and entrepreneur in digital video and AI [13]. - Sun Maosong, Executive Vice President of the Tsinghua University AI Research Institute [17]. - Wang Zhongyuan, Director of the Beijing Academy of Artificial Intelligence [21]. - Other notable speakers include experts from various leading tech companies and research institutions [30][35][40][44][48][53][57]. AI Annual Rankings - The "AI Annual Rankings," initiated by Quantum Bit, has become one of the most influential rankings in the AI industry, evaluating companies, products, and individuals across three dimensions [60]. - The submission for the rankings is open until November 17, 2025 [61]. AI Trends Report - The "2025 Annual AI Top Ten Trends Report" will focus on the main themes of technological development in AI, analyzing the maturity, current status, and potential value of various trends [65]. - The case collection for the report is open until November 20, 2025 [66]. Conference Logistics - The MEET2026 Intelligent Future Conference will take place at the Beijing Jinmao Renaissance Hotel, with registration now open [70]. - The conference aims to attract thousands of tech professionals and millions of online viewers, establishing itself as an annual benchmark for the intelligent technology industry [72].

Core Viewpoint - The article discusses Xiaomi's exploration plan for the future of smart homes, emphasizing the integration of large models to enhance user interaction and automate various household tasks, moving beyond traditional rule-based systems [1][5][28]. Group 1: Current Challenges in Smart Home Technology - The current smart home experience is limited by rigid rule presets and insufficient ecosystem collaboration, leading to a cumbersome user experience [9][10]. - Most smart home systems require users to manually configure numerous automation rules, making the interaction process mechanical and tedious [4][10]. Group 2: Introduction of Miloco - Xiaomi's Miloco aims to redefine the smart home paradigm by providing an "AI brain" that understands daily life details and preferences, enabling seamless interaction and automation [11][12]. - Miloco is designed to integrate with various devices and platforms, allowing users to define smart home actions using natural language [14][27]. Group 3: Technological Framework - Miloco's capabilities are built on the Xiaomi MiMo-VL-Miloco-7B edge visual language model, which enhances the system's ability to perceive and understand user needs [15][17]. - The technology supports complex interactions, such as responding to user activities and adjusting settings based on visual cues, marking a shift from simple condition-based triggers to multi-dimensional scene perception [20][19]. Group 4: Open Ecosystem and Privacy - Miloco promotes an open ecosystem, allowing developers to connect with third-party IoT platforms and explore innovative smart home scenarios [27]. - The system prioritizes user privacy by processing visual data locally, ensuring that sensitive information does not leave the home environment [7][27]. Group 5: Future Directions - The launch of Miloco represents Xiaomi's commitment to advancing smart home technology by leveraging large models, aiming for a more user-friendly and intelligent experience [28][31]. - The transition from "users adapting to devices" to "devices adapting to users" is highlighted as a key evolution in smart home interaction [29][30].

Core Insights - The article emphasizes the transformative impact of artificial intelligence (AI) on various industries and society as a whole, marking the beginning of a new era in 2025 [1]. Event Overview - The MEET2026 Intelligent Future Conference will focus on cutting-edge technologies and industry developments related to AI [2]. - The theme of the conference is "Symbiosis Without Boundaries, Intelligence to Ignite the Future," highlighting how AI transcends industry, discipline, and scenario boundaries [3]. - Key topics of discussion will include reinforcement learning, multimodal AI, chip computing power, AI in various industries, and AI's global expansion [4]. Academic and Industry Contributions - The conference will feature the latest advancements from academia and industry, showcasing leading technologies from infrastructure, models, and products [5]. - An authoritative annual AI ranking and trend report will be released during the conference [6]. Notable Speakers - The conference will host prominent figures such as Zhang Yaqin, a renowned scientist and entrepreneur in AI and digital video [12][13]. - Other notable speakers include Sun Maosong, Wang Zhongyuan, Zhao Junbo, and Liu Fanping, all of whom have significant contributions to AI research and development [17][21][27][43]. Awards and Recognition - The "Artificial Intelligence Annual Ranking," initiated by Quantum Bit, has become one of the most influential rankings in the AI industry, evaluating companies, products, and individuals across three dimensions [60]. - The ranking results will be officially announced at the MEET2026 conference [60]. Trend Report - The "2025 Annual AI Top Ten Trends Report" will focus on the main themes of technological development, analyzing the maturity, implementation status, and potential value of AI trends [65]. - The report will nominate representative institutions and best cases related to these trends [65]. Conference Logistics - The MEET2026 conference will take place at the Beijing Jinmao Renaissance Hotel, attracting thousands of tech professionals and millions of online viewers [72].

Core Viewpoint - The article discusses the launch of Baidu's new AI-powered search system, "Baidu Orion," which aims to transform traditional search into a more interactive and intelligent experience, capable of understanding user intent and executing complex tasks [5][10][22]. Group 1: Baidu Orion Overview - Baidu Orion is a multi-agent framework that enhances search capabilities by integrating AI APIs and a rich service ecosystem, allowing for task planning and execution [5][10]. - The system is designed to evolve search from merely providing answers to understanding user needs, remembering past interactions, and engaging in meaningful dialogue [6][13]. Group 2: User Experience Enhancements - Baidu Orion can break down user queries into multiple needs and generate comprehensive plans, making the search experience more intuitive and user-friendly [10][11]. - The system supports multi-modal outputs, providing results in various formats such as images, videos, and even generating custom content based on user requests [11][21]. Group 3: AI Content Generation - The AI capabilities of Baidu Orion allow it to create new content rather than just retrieving existing information, with daily AI content generation exceeding ten million and video model usage surpassing two million [21][22]. - This shift positions search as a creative tool, expanding its role from a simple query-response mechanism to a versatile assistant capable of generating tailored solutions [22]. Group 4: Industry Trends and Open Access - The article highlights a broader industry trend where search engines are evolving into foundational capabilities that can be accessed via APIs, allowing developers to integrate advanced search functionalities into their applications [23][24]. - Baidu has opened up access to Baidu Orion, enabling 625 companies across various sectors to leverage its search AI API, which encapsulates 25 years of search technology and authoritative content resources [23][24].

Core Viewpoint - The article discusses the introduction of InfinityStar, a new method developed by ByteDance's commercialization technology team, which significantly improves video generation quality and efficiency compared to the existing Diffusion Transformer (DiT) model [4][32]. Group 1: InfinityStar Highlights - InfinityStar is the first discrete autoregressive video generator to surpass diffusion models on VBench [9]. - It eliminates delays in video generation, transitioning from a slow denoising process to a fast autoregressive approach [9]. - The method supports various tasks including text-to-image, text-to-video, image-to-video, and interactive long video generation [9][12]. Group 2: Technical Innovations - The core architecture of InfinityStar employs a spatiotemporal pyramid modeling approach, allowing it to unify image and video tasks while being an order of magnitude faster than mainstream diffusion models [13][25]. - InfinityStar decomposes video into two parts: the first frame for static appearance information and subsequent clips for dynamic information, effectively decoupling static and dynamic elements [14][15][16]. - Two key technologies enhance the model's performance: Knowledge Inheritance, which accelerates the training of a discrete visual tokenizer, and Stochastic Quantizer Depth, which balances information distribution across scales [19][21]. Group 3: Performance Metrics - InfinityStar demonstrates superior performance in the text-to-image (T2I) task on GenEval and DPG benchmarks, particularly excelling in spatial relationships and object positioning [25][28]. - In the text-to-video (T2V) task, InfinityStar outperforms all previous autoregressive models and achieves better results than DiT-based methods like CogVideoX and HunyuanVideo [28][29]. - The generation speed of InfinityStar is significantly faster than DiT-based methods, with the ability to generate a 5-second 720p video in under one minute on a single GPU [31].

Core Argument - The article argues that modality following in multi-modal large language models (MLLMs) is a dynamic process influenced by relative reasoning uncertainty and inherent modality preference, rather than a static attribute [1][4][37]. Group 1: Research Contributions - A new toy dataset was constructed to systematically and independently vary the reasoning difficulty of visual and textual inputs, enabling different difficulty combinations for multi-modal inputs [4]. - The study decomposes the explicit behavior of modality following into two core components: case-specific relative reasoning uncertainty and the model's stable inherent modality preference [4][5]. - An empirical finding indicates that the probability of a model following a certain modality decreases monotonically as the relative reasoning uncertainty of that modality increases [5]. Group 2: Framework Design - A controlled dataset was created to validate hypotheses, allowing independent control of visual and textual reasoning complexity [9][10]. - Uncertainty was measured using output entropy, which reflects the model's perceived uncertainty, with lower entropy indicating confident predictions and higher entropy indicating consideration of alternative options [11]. - Relative uncertainty was quantified to measure the confidence gap between text and visual modalities, providing a core metric for subsequent analysis [12]. Group 3: Limitations of Traditional Metrics - Traditional macro metrics like Text Following Rate (TFR) and Visual Following Rate (VFR) were tested on the constructed dataset, revealing confusing patterns that highlight their limitations [14]. - The study identifies a common trend where models perceive text as easier on average, yet exhibit opposite macro preferences, raising questions about the underlying reasons for these discrepancies [15][16]. Group 4: Experimental Paradigm - A new experimental paradigm was designed to decouple model capability from preference, allowing for a clearer understanding of the model's decision-making process [18]. - The researchers grouped data points based on relative uncertainty to create a complete preference curve, reflecting how model preferences change dynamically with relative difficulty [18]. Group 5: Key Experimental Findings - All tested models exhibited a consistent trend where the probability of following text decreases smoothly as text becomes relatively more difficult [19][21]. - The "balance point" was defined as the point where the curve crosses the 50% probability line, serving as a quantifiable measure of inherent modality preference [22]. - The framework successfully explained previous puzzles regarding model behavior by revealing differences in inherent preferences that were not visible in macro metrics [23][24]. Group 6: Internal Mechanisms - The study explored the internal decision-making mechanisms of models, particularly their oscillation behavior when faced with conflicting information near the balance point [29][30]. - The findings indicate that models exhibit higher oscillation counts in ambiguous regions, providing a mechanistic explanation for observed indecision in external behavior [34][36]. Conclusion - The research presents a new framework for understanding modality following in MLLMs, emphasizing the importance of separating model capability from inherent preference, and revealing a robust rule that the likelihood of following a modality decreases with increasing relative uncertainty [37].

Group 1 - Major tech companies are engaging in a competitive AI product battle, with Alibaba, Google, and Tencent making significant moves in the AI space [3][4][31] - Alibaba is planning to revamp its Tongyi app, rebranding it as "Qwen" and integrating AI capabilities to enhance its e-commerce platform [6][7][8] - Google has introduced new AI shopping features aimed at enhancing the online shopping experience, allowing users to search, compare, and check out products using AI [16][18][21] Group 2 - Tencent is focusing on integrating AI into its WeChat platform, with plans to develop an AI agent that can assist users in various tasks within the app [22][30] - Tencent's Q3 financial report highlighted a 15% year-on-year revenue growth, with AI becoming a central theme in its strategic narrative [23][24] - The competition among these companies is centered around creating an "end-to-end closed loop" in user service, redefining the value chain in the internet landscape [33]

Core Insights - Cursor has emerged as a leading player in the AI coding sector, recently achieving a significant milestone with a $2.3 billion Series D funding round, bringing its valuation to $29.3 billion [2][3][6] - The company has rapidly expanded its team to over 300 employees and surpassed an annual revenue of $1 billion, positioning itself as one of the fastest-growing companies in history [8][18][19] - Cursor's unique approach focuses on enhancing the capabilities of top developers rather than making coding accessible to everyone, aiming to integrate deeply into enterprise-level development processes [21][25][26] Funding and Valuation - Cursor completed a $2.3 billion Series D funding round, with a post-money valuation of approximately $29.3 billion, which is nearly three times its valuation during the previous funding round in June [3][6] - The funding round included notable investors such as Google, Nvidia, and Coatue, alongside existing investors like Andreessen Horowitz [5][6] - The company’s valuation trajectory has been remarkable, growing from $4 million in its A round to $29.3 billion in just two years [12][15] Product and Market Position - Cursor's product, an AI programming tool, is designed to significantly enhance coding efficiency, claiming to generate more code than all other large language models combined since the launch of its self-developed model, Composer [8][33] - The tool is currently utilized by millions of developers globally, including teams from major companies like Nvidia, Adobe, and PayPal [24] - Cursor aims to elevate the performance of skilled developers rather than democratizing coding, which differentiates it from other AI coding tools [25][28] Company Culture and Team Dynamics - The internal culture at Cursor is characterized by a strong work ethic, with employees voluntarily dedicating weekends to work on projects, reflecting a commitment to innovation and productivity [37][40] - Despite significant financial success, the company maintains a low-key atmosphere, focusing on continuous improvement and development rather than celebratory events [36][40] - The founders, who were students at MIT when they started Cursor, have seen their personal fortunes rise significantly, with each holding approximately 4.5% of the company, translating to a net worth of at least $1.3 billion each [41][42]

Core Argument - The article argues that modality following in multi-modal large language models (MLLMs) is a dynamic process influenced by relative reasoning uncertainty and inherent modality preference, rather than a static attribute [1][4][37]. Group 1: Contributions and Findings - A new controlled toy dataset was constructed to systematically manipulate the reasoning difficulty of visual and textual inputs [4]. - The study decomposes modality following into two core components: case-specific relative reasoning uncertainty and the model's stable inherent modality preference [4][5]. - A fundamental finding indicates that the probability of a model following a certain modality decreases monotonically as the relative reasoning uncertainty of that modality increases [5]. - The framework provides a more reasonable method for quantifying inherent preference, defining it as the balance point where the model treats both modalities equally [5][22]. - The research explores the internal decision-making mechanisms of models, revealing oscillations in predictions when uncertainty is near the balance point [5][29]. Group 2: Experimental Design - The researchers established a controlled experimental environment using a novel toy dataset that independently controls visual and textual reasoning complexity [9][10]. - A model-centered uncertainty metric, output entropy, was employed to reflect the model's perceived uncertainty [11]. - Relative single-modal uncertainty was introduced to quantify the confidence gap in each conflicting case, serving as a core metric for subsequent analysis [12]. Group 3: Limitations of Traditional Metrics - Traditional macro metrics like Text Following Rate (TFR) and Visual Following Rate (VFR) were tested on the constructed dataset, revealing confusing patterns that highlight their limitations [14]. - The study identifies two puzzles regarding the models' preferences and difficulty perceptions, suggesting that traditional metrics obscure the true motivations behind model decisions [16][23]. Group 4: New Experimental Paradigm - A new experimental paradigm was designed to decouple model capability from preference, allowing for a clearer understanding of the models' decision-making processes [18]. - The researchers grouped data points based on relative uncertainty to create a complete preference curve reflecting how model preferences change with relative difficulty [18]. Group 5: Key Experimental Discoveries - All tested models exhibited a consistent trend: as text becomes relatively more difficult, the probability of following text decreases smoothly [19][21]. - The balance point quantifies inherent preference, indicating whether a model has a visual or textual bias based on its position on the relative uncertainty axis [22]. - The framework successfully explains the previously mentioned puzzles by revealing differences in inherent preferences among models [23][24]. Group 6: Internal Mechanisms - The study investigates why models exhibit oscillations in decision-making when approaching their balance point, providing a mechanism for observed indecision [29][33]. - The distinction between clear and ambiguous regions in input uncertainty is made, with oscillation frequency being significantly higher in ambiguous regions [30][34].

Core Viewpoint - The article discusses the entrepreneurial journey of Cui Baoqiu, a former vice president of Xiaomi, who is now venturing into the field of robotics, specifically focusing on household service robots, marking a shift from his previous role in AI and IoT at Xiaomi [2][4][6]. Group 1: Background and Transition - Cui Baoqiu, known as the "father" of technology at Xiaomi, is now betting on embodied intelligence, a hot trend in the tech industry [2][4]. - After leaving Xiaomi, he initially took a role as the chief technical advisor at a RISC-V chip company, indicating a focus on foundational technology before moving into robotics [8][10]. - His departure from Xiaomi represents a significant shift in his career, moving from a large corporate structure to a more challenging entrepreneurial path [6][12]. Group 2: Vision and Strategy - Cui aims to create a household service robot that embodies the ultimate form of AIoT, integrating various smart devices into a single, interactive entity [7][8]. - He has a vision of transforming his technical blueprint from "connecting everything" to "transforming the physical world" through robotics [4][5]. - His previous experience at Xiaomi, where he was a key player in developing AI and cloud technologies, positions him well for this new venture [15][28]. Group 3: Industry Trends - The trend of creating physical embodiments for AI is gaining traction, with many former tech executives from major companies like Huawei and Horizon also launching similar ventures in robotics [40][42]. - The emergence of embodied intelligence is seen as the next phase in AI development, as software alone is insufficient to realize AI's full potential [40][41]. - This shift reflects a broader trend in the tech industry where former leaders are now focusing on building the physical "bodies" for AI systems, indicating a competitive and high-expectation environment in the robotics sector [45][46].