证券研究报告 | 2025年11月13日 2026 年 A 股策略展望 "小登"时代，牛途仍在 核心结论：①始于 24 年"924"行情的牛市还未结束，当前进入第二阶段， 驱动力从情绪面转向基本面。②全年维度，科技是主线，演绎路径从算力转 向应用，关注 AI 眼镜、机器人、智驾、AI 编程、AI+生命科学等方向。③牛 市中期有风格轮动，阶段性关注前期滞涨的地产、券商、白酒消费。老红利 有新逻辑，维持底仓配置敞口。 牛途仍在，驱动力转向基本面。完整的牛市三阶段包括孕育期、爆发期和疯 狂期，本轮牛市与 519 行情更相似，目前处于爆发期。市场结构性特征明显， "小登资产"碾压"老登"资产。2026 年牛市驱动力从情绪面转向基本面， 上市企业 ROE 稳步回升，合同负债同比修复，盈利预期持续上修。微观流动 性仍有支撑：1）国内方面，"花存款"趋势延续，中长期定存到期进一步 加速存款搬家进程；2）海外方面，美国中期选举仍存变数，选区重划强博 弈预示后续政治波动加大，美联储预防式降息下半场驱动全球资金流向新兴 市场风险资产。 科技是主线，拥抱"小登"时代。历史上牛市都有主线，从"煤飞色舞"、 "移动互联"到"能源革命" ...

Group 1 - The current bull market is in its second phase, transitioning from emotional drivers to fundamental ones, with a focus on technology as the main theme [1][11][19] - The bull market is characterized by a significant structural differentiation between "small-cap" and "large-cap" assets, with "small-cap" stocks outperforming [30][21] - The technology sector is expected to lead the market, with specific attention on AI applications, robotics, smart driving, and AI in life sciences [2][57][68] Group 2 - The report highlights that the bull market's main line is technology, with significant contributions from major tech companies, particularly in AI and semiconductor sectors [2][63] - Historical bull markets have shown that the main line often correlates with industry cycles, where sectors with high revenue growth tend to outperform [58][60] - The report emphasizes the importance of understanding the differentiation between "old economy" and "new economy" stocks, with a recommendation to maintain exposure to dividend-paying assets amidst a backdrop of financial asset scarcity [2][30][10] Group 3 - The report discusses the impact of macroeconomic policies, including fiscal and monetary measures, on market performance, particularly in relation to the "14th Five-Year Plan" and its focus on high-quality development and technological self-reliance [17][18] - The analysis indicates that the market's valuation structure is healthier compared to previous bull markets, with a lower percentage of stocks trading at high price-to-book ratios [21][25] - The report notes that the trend of "deposit migration" is ongoing, with a shift in funds towards higher-yielding assets as traditional deposit rates decline [35][39]

Core Viewpoint - The article discusses the successful implementation of scientific multitask learning at a cosmic scale through the BigBang-Proton project, proposing the concept of Universe Compression, which aims to pre-train models using the entirety of the universe as a unified entity [1][7]. Group 1: Scientific Multitask Learning - Scientific multitask learning is essential for achieving Universe Compression, as it allows for the integration of highly heterogeneous datasets across various disciplines, which traditional models struggle to converge [2][4]. - The BigBang-Proton project demonstrates that with the right representation and architecture, diverse scientific data can converge, indicating the potential for transfer learning across scales and structures [2][4]. Group 2: Scaling Law and Platonic Representation - The Scaling Law observed in language models can extend beyond language to encompass physical realities, suggesting that the limits of these models may align with the fundamental laws of the universe [5][6]. - The Platonic Representation Hypothesis posits that AI models trained on diverse datasets tend to converge on a statistical representation of reality, which aligns with the findings from the BigBang-Proton project [6][7]. Group 3: Universe Compression Plan - The proposed Universe Compression plan involves creating a unified spacetime framework that integrates all scientific knowledge and experimental data across scales, structures, and disciplines [25][26]. - This approach aims to reveal the underlying homogeneity of structures in the universe, facilitating deep analogies across various scientific fields [26]. Group 4: Next Steps and Hypotheses - The company proposes a second hypothesis that suggests reconstructing any physical structure in the universe through next-word prediction, enhancing the model's ability to simulate complex physical systems [28]. - This hypothesis aims to integrate embodied intelligence capabilities, improving generalization in complex mechanical systems like aircraft and vehicles [28].

Core Insights - "Zijing Zhikang" has completed nearly 100 million yuan in angel round financing, led by Xinglian Capital, with the funds primarily allocated for the development and iteration of the Zijing AI Hospital system [2] - The company aims to leverage advanced large model AI technology to create a virtual medical world system, enhancing smart healthcare applications in the real world [2] - The Zijing AI Hospital's core logic involves simulating real hospital facilities and processes, particularly by creating highly human-like, diverse AI patients to meet initial training data needs [2] Data and Training Challenges - High-quality data and case studies are essential for training AI doctors, but challenges such as data silos and difficulty in data acquisition persist in the real medical world [3] - Zijing Zhikang's core technology team is addressing the cold start problem by synthesizing some case data using AI, creating "evolvable intelligent agents based on simulations" [3] - The AI hospital has constructed over 500,000 AI patients covering various countries, age groups, and disease types, serving as a significant supplement for training AI doctors [3] AI Doctor Evolution - The AI doctors are designed to possess self-evolution capabilities, with a specific memory and reflection algorithm to accumulate "experience" during consultations [5] - The evolution of AI doctors is expected to be faster than that of human doctors, with experimental results indicating that the AI's capability evolution curve aligns with Scaling Law [5] - Zijing Zhikang has developed 42 AI doctors that achieved over 96% accuracy on the MedQA dataset, surpassing the average level of human doctors [5] Product Development and Features - The AI system includes three interfaces: a patient app, a doctor workstation, and a hospital system, facilitating full-cycle health management [5] - Patients can register online, engage in intelligent pre-consultation, and generate structured medical records, while doctors can access these records to save time during consultations [5] - The system is designed to manage health data over time, providing health advice and allowing patients to utilize AI for health consultations and report interpretations [5] Future Plans and Regulatory Alignment - The Zijing AI Hospital system is set to launch publicly by the end of 2025, with initial internal testing already conducted in various departments at Tsinghua University Hospital [6] - The system's development aligns with recent government initiatives aimed at promoting and regulating the application of AI in healthcare, potentially enhancing service capabilities and efficiency in grassroots medical settings [6]

Core Insights - The article discusses the introduction of GEN-0, a new type of embodied foundational model designed for multimodal training based on high-fidelity physical interactions, which aims to enhance robotic intelligence through real-world data [5][9]. Group 1: Model Characteristics - GEN-0 has been developed to capture human-level reflexes and physical common sense, featuring a core characteristic called "harmonic reasoning" that allows seamless training of thinking and action [5]. - The model has surpassed the critical threshold of 7 billion parameters, showing a phase transition where smaller models become stagnant while larger models continue to improve [6][11]. - GEN-0 demonstrates a strong scaling law, indicating that increased pre-training data and computational power predictably enhance the model's performance across multiple tasks [6][11]. Group 2: Data Utilization - The model is pre-trained on over 270,000 hours of real-world heterogeneous manipulation data, with the dataset expanding at a rate of over 10,000 hours per week [22]. - The data collection comes from diverse operational scenarios across thousands of households, warehouses, and workplaces, aiming to cover all conceivable operational tasks [24]. Group 3: Implications for Robotics - GEN-0 signifies a new era in embodied foundational models, where capabilities will grow predictably with real physical interaction data rather than relying solely on text, images, or simulated data [9]. - The findings highlight that smaller models struggle to process complex sensory-motor data during pre-training, while models with over 70 billion parameters can internalize large-scale pre-training data and quickly adapt to downstream tasks with minimal fine-tuning [15][11].

Core Insights - The article discusses the advancements made by the company 超越对称 (Super Symmetry) with their new model BigBang-Proton, which integrates various scientific disciplines and challenges existing AGI approaches [1][2][4]. Group 1: BigBang-Proton Model Innovations - BigBang-Proton successfully unifies multiple scientific problems across different scales, from micro-particles to macro-earth systems, using a next-word prediction paradigm [1]. - The model introduces three fundamental innovations: Binary Patch Encoding, a theory-experiment learning paradigm, and Monte Carlo Attention, which enhances its ability to handle complex scientific tasks [9][12][16]. - The model's pre-training is designed to extend to the entire universe, proposing a concept called "Universe Compression" to consolidate vast amounts of information into a single foundation [5]. Group 2: Performance and Comparisons - BigBang-Proton demonstrates superior performance in arithmetic operations, achieving 100% accuracy in 50-digit addition, significantly outperforming other models like DeepSeek-R1 and ChatGPT-o1 [31][36]. - In particle jet classification tasks, BigBang-Proton achieved an accuracy of 51.29%, competing closely with specialized models, while mainstream LLMs performed poorly [42][44]. - The model also excels in predicting water quality and genomic sequences, achieving competitive results against state-of-the-art models in these domains [59][62]. Group 3: Theoretical and Practical Implications - The introduction of Binary Patch Encoding addresses the limitations of traditional tokenizers, allowing for better numerical analysis and integration of scientific data [11][13]. - The theory-experiment learning paradigm bridges the gap between theoretical knowledge and experimental data, enhancing the model's applicability in real-world scientific research [12][15]. - The advancements made by BigBang-Proton could significantly impact fields reliant on numerical calculations, such as science, engineering, and finance, by resolving long-standing issues related to arithmetic logic [37].

Core Viewpoint - The article discusses the breakthrough achieved by the AI robotics startup Generalist with the introduction of a new embodied foundational model, GEN-0, which is designed for multimodal training on high-fidelity physical interaction data, aiming to enhance robotic intelligence through scalable data and computational power [2][5]. Group 1: GEN-0 Model Features - GEN-0 is built to capture human-level reflexes and physical common sense, with a parameter count exceeding 10 billion [3][4]. - A core feature of GEN-0 is "Harmonic Reasoning," allowing the model to seamlessly think and act simultaneously, which is crucial for real-world physical systems [5]. - The model has demonstrated strong scaling laws, indicating that increased pre-training data and computational power can predictably enhance performance across various tasks [6][10]. Group 2: Data and Training Insights - Generalist has pre-trained GEN-0 on over 270,000 hours of diverse real-world operational data, with the dataset growing at a rate of 10,000 hours per week [23][24]. - The company emphasizes that the quality and diversity of data are more critical than sheer quantity, leading to models with different characteristics based on the data mix used [33]. - The scaling experiments revealed that smaller models exhibit "ossification," while larger models continue to improve, highlighting the importance of model size in absorbing complex sensory-motor data [10][11]. Group 3: Applications and Future Directions - GEN-0 has been successfully tested on various robotic platforms, including humanoid robots with different degrees of freedom [6]. - The company is building the largest and most diverse real-world operational dataset to expand GEN-0's capabilities, covering a wide range of tasks across different environments [28]. - Generalist aims to create a robust infrastructure to support the extensive data collection and processing required for training large-scale robotic models [31].

Core Insights - The article discusses the significant advancements in visual autoregressive models, particularly highlighting the potential of these models in the context of AI-generated content (AIGC) and their competitive edge against diffusion models [2][4][11]. Group 1: Visual Autoregressive Models - Visual autoregressive models (VAR) utilize a "coarse-to-fine" approach, starting with low-resolution images and progressively refining them to high-resolution outputs, which aligns more closely with human visual perception [12][18]. - The VAR model architecture includes an improved VQ-VAE that employs a hierarchical structure, allowing for efficient encoding and reconstruction of images while minimizing token usage [15][30]. - VAR has demonstrated superior image generation quality compared to existing models like DiT, showcasing a robust scaling curve that indicates performance improvements with increased model size and computational resources [18][49]. Group 2: Comparison with Diffusion Models - Diffusion models operate by adding Gaussian noise to images and then training a network to reverse this process, maintaining the original resolution throughout [21][25]. - The key advantages of VAR over diffusion models include higher training parallelism and a more intuitive process that mimics human visual cognition, although diffusion models can correct errors through iterative refinement [27][29]. - VAR's approach allows for faster inference times, with the Infinity model achieving significant speed improvements over comparable diffusion models [46][49]. Group 3: Innovations in Tokenization and Error Correction - The Infinity framework introduces a novel "bitwise tokenizer" that enhances reconstruction quality while allowing for a larger vocabulary size, thus improving detail and instruction adherence in generated images [31][41]. - A self-correction mechanism is integrated into the training process, enabling the model to learn from previous errors and significantly reducing cumulative error during inference [35][40]. - The findings indicate that larger models benefit from larger vocabularies, reinforcing the reliability of scaling laws in model performance [41][49].

Core Insights - The article discusses the intertwining of computing power, capital, and energy in the new global infrastructure driven by AI, emphasizing that AI is not just an algorithmic revolution but a migration of industries influenced by computing power, funding, and geopolitical factors [2] - It highlights the emergence of a "Triangle Deal" among OpenAI, Oracle, and Nvidia, where OpenAI purchases cloud services from Oracle, which in turn buys GPUs from Nvidia, creating a closed-loop system of capital flow [4][5] - The article also points out that controlling data, interfaces, and switching costs is crucial for gaining market power in the AI industry [9] AI Power Struggle - The "Triangle Deal" involves OpenAI purchasing $300 billion worth of cloud services from Oracle over five years, with Nvidia benefiting significantly from GPU sales [4] - Nvidia's investment of up to $100 billion in OpenAI for building AI data centers illustrates the scale of capital required for AI infrastructure [5] - The competition in the AI industry is fundamentally about who controls the data and interfaces, as seen in the dynamics between OpenAI and Microsoft [9] Neo Clouds and Business Models - Neo Clouds represent a new business layer in the AI industry, providing computing power leasing and model hosting services [10] - There are two models for Neo Clouds: short-term contracts with high profit margins but high price risk, and long-term contracts that ensure stable cash flow but depend heavily on counterparty credit [11] - Inference Providers are emerging as key players, offering model hosting and efficient inference services, but they face high uncertainty due to their client base of smaller companies [12][13] AI Arms Race - The article discusses the strategic importance of AI in global power dynamics, particularly for the U.S. to maintain its global dominance [14] - In contrast, China is pursuing a long-term strategy to build a self-sufficient supply chain in semiconductors and AI, with significant government investment [15] Scaling Laws and Technical Challenges - Dylan Patel argues that Scaling Laws will not exhibit diminishing returns, suggesting that increasing computational resources will continue to enhance model performance [16] - The balance between model size and usability is a critical challenge, as larger models can lead to higher inference costs and lower user experience [17] - The need for efficient reasoning and memory systems in AI models is emphasized, with a focus on extending reasoning time to improve performance [22] AI Factory Concept - The AI Factory concept positions AI as an industrial output, where tokens represent the product of computational power and efficiency [28][30] - Companies must optimize token production under constraints of power consumption and model efficiency to remain competitive [30] Talent and Energy Dynamics - The scarcity of skilled individuals who can effectively utilize GPUs is highlighted as a significant challenge in the AI industry [31] - The energy consumption of AI data centers is growing, with projections indicating that AI data centers will consume approximately 624-833 billion kWh by 2025 [32][35] - The U.S. faces challenges in expanding its power generation capacity to meet the rising energy demands of AI infrastructure [36][37] Software Industry Transformation - The traditional SaaS business model is under threat as AI reduces software development costs, leading to a shift towards in-house development [38][39] - Companies with established ecosystems, like Google, may maintain advantages in the evolving landscape, while pure software firms face increasing challenges [40] Company Evaluations - OpenAI is recognized as a top-tier company, while Anthropic is viewed favorably due to its focused approach and rapid revenue growth [41] - Nvidia is seen as a dominant player in the semiconductor space, with significant influence over the AI infrastructure landscape [25] - Meta is highlighted for its potential to revolutionize human-computer interaction through its integrated hardware and software capabilities [42]

Core Insights - The rapid development of large intelligent systems is reshaping industry dynamics, exemplified by OpenAI's recent release of Sora 2, which showcases advancements in model capabilities and the complexity of AI evolution [1][2] - The dialogue between industry leaders, including CSDN's Li Jianzhong and OpenAI's Lukasz Kaiser, focuses on foundational thoughts regarding large models and their implications for future AI development [2][5] Group 1: Language and Intelligence - Language plays a crucial role in AI, with some experts arguing that relying solely on language models for AGI is misguided, as language is a low-bandwidth representation of the physical world [6][9] - Kaiser emphasizes the importance of temporal dimensions in language, suggesting that the ability to generate sequences over time is vital for expressing intelligence [7][9] - The conversation highlights that while language models can form abstract concepts, they may not fully align with human concepts, particularly regarding physical experiences [11][12] Group 2: Multimodal Models and World Understanding - The industry trend is towards unified models that can handle multiple modalities, but current models like GPT-4 already demonstrate significant multimodal capabilities [12][13] - Kaiser acknowledges that while modern language models can process multimodal tasks, the integration of different modalities remains a challenge [13][15] - The discussion raises skepticism about whether AI can fully understand the physical world through observation alone, suggesting that language models may serve as effective world models in certain contexts [14][15] Group 3: AI Programming and Future Perspectives - AI programming is emerging as a key application of large language models, with two main perspectives on its future: one advocating for natural language as the primary programming interface and the other emphasizing the continued need for traditional programming languages [17][18] - Kaiser believes that language models will increasingly cover programming tasks, but a solid understanding of programming concepts will remain essential for professional developers [19][20] Group 4: Agent Models and Generalization Challenges - The concept of "agent models" in AI training faces challenges in generalizing to new tasks, raising questions about whether this is due to training methods or inherent limitations [21][22] - Kaiser suggests that the effectiveness of agent systems relies on their ability to learn from interactions with various tools and environments, which is currently limited [22][23] Group 5: Scaling Laws and Computational Limits - The belief in Scaling Laws as the key to stronger AI raises concerns about potential over-reliance on computational power at the expense of algorithmic and architectural advancements [24][25] - Kaiser differentiates between pre-training and reinforcement learning Scaling Laws, indicating that while pre-training has been effective, it may be approaching economic limits [25][26] Group 6: Embodied Intelligence and Data Efficiency - The slow progress in embodied intelligence, particularly in humanoid robots, is attributed to either data scarcity or fundamental differences between bits and atoms [29][30] - Kaiser argues that advancements in data efficiency and the development of multimodal models will be crucial for achieving effective embodied intelligence [30][31] Group 7: Reinforcement Learning and Scientific Discovery - The shift towards reinforcement learning-driven reasoning models presents both opportunities for innovation and challenges related to their effectiveness in generating new scientific insights [32][33] - Kaiser notes that while reinforcement learning offers high data efficiency, it has limitations compared to traditional gradient descent methods [33][34] Group 8: Organizational Collaboration and Future Models - Achieving large-scale collaboration among agents remains a significant challenge, with the need for more parallel processing and effective feedback mechanisms in training [35][36] - Kaiser emphasizes the necessity for next-generation reasoning models that can operate in a more parallel and efficient manner to facilitate organizational collaboration [36][37] Group 9: Memory Mechanisms in AI - Current AI models' memory capabilities are limited by context windows, resembling working memory rather than true long-term memory [37][38] - Kaiser suggests that future architectures may need to incorporate more sophisticated memory mechanisms to achieve genuine long-term memory capabilities [38][39] Group 10: Continuous Learning in AI - The potential for AI models to support continuous learning is being explored, with current models utilizing context as a form of ongoing memory [39][40] - Kaiser believes that while context learning is a step forward, more elegant solutions for continuous learning will be necessary in the future [40][41]