资深投资者Sparks认为，大语言模型（LLMs）正迅速商品化，如同宽带或自来水一样，正被"免费赠送"，其本身并非最终的价值创造者。真正的投资 机会已从基础设施层转移至应用层。未来2-3年的最大机遇在于利用AI技术颠覆传统行业的创新应用。 一位资深投资者通过对冲基金CIO发声，提出AI领域的投资重心应 从基础设施转向应用层。 根据One River Asset Management首席投资官Eric Peters于2025年11月24日发表的文章，企业家、资深投资者Sparks指出，当前市场对大语言模型 （LLM）的认知存在误区，真正的长期投资价值不在于构建这些模型本身，而在于其上的应用生态。 在他看来，LLM开发商扮演的正是当年运营商的角色，他们提供了基础工具，但价值链上的最丰厚利润将流向那些最懂得如何使用这些工具创造实际商 业价值的应用开发者。 这一判断直接影响其投资策略。Sparks表示，自己宁愿"做那个利用搜索引擎赚取真金白银的人"，而不是搜索引擎的开发者。 他正将全部的"前瞻性精力"用于寻找未来2-3年（他称之为"T+3"）的机会。他认为， 最大的机遇是那些能够将AI能力与特定行业深度结合，从而创造 ...

在此情境下，共情指的是顾客认为公司及其代表真诚地试图理解并回应其情绪状态，尤其是在顾客脆弱 的时刻。对于保险客户而言，这可能意味着保险代表不仅处理理赔事宜，还认可客户正在经历的困难， 或者公司事后跟进了解情况。这是一种从客户视角看待问题，并将这种认知转化为关怀和积极回应的能 力。 曾几何时，共情被认为过于温情柔弱，不适用于职场环境。但数十年的研究已打破这一误解。共情包含 三个要素：分享他人经历、尝试理解他人眼中的世界，以及关心他人的福祉。当人们表达共情时，会建 立起更深层次、更具滋养性的关系；当他们感受到共情时，其信任度、士气和幸福感也会随之提升。 职场亦是如此。富有共情力的领导者能够打造出员工敬业度更高、忠诚度更强的团队，在这样的团队 中，员工不仅感觉更良好（体验到更多的快乐、更强的韧性和更高的幸福感），而且工作表现也更出色 （协作更高效、创新能力更强、工作产出更高）。如今，任何一家希望以数据驱动企业文化的公司，都 应确保领导者能够给予共情，员工也能感受到共情。 但企业的顾客又如何呢？在苏黎世保险集团赞助的一项全新全球调查中，我们对11个国家近1.2万人进 行了民意调查，结果发现，大多数顾客希望从与之打交道 ...

Core Insights - The article discusses common mistakes engineers make during code reviews, particularly in the context of increasing AI-generated code and the challenges of reviewing it effectively [3][5]. - It emphasizes the importance of understanding the entire codebase rather than just focusing on code differences (diff) and provides practical advice to improve review efficiency [3][5]. Group 1: Common Mistakes in Code Reviews - Engineers often focus solely on the code differences (diff), missing out on significant insights that come from understanding the broader system [6][7]. - Leaving too many comments during a review can overwhelm the reviewer, making it difficult to identify the most critical feedback [8]. - Using personal coding preferences as a standard for reviews can lead to unnecessary comments and conflicts, as there are often multiple valid solutions to a problem [9][11]. Group 2: Recommendations for Effective Code Reviews - Reviewers should prioritize understanding the context of the code changes rather than just the diff, considering what might be missing from the code [18]. - It is advisable to leave a limited number of well-considered comments instead of a large volume of superficial ones [18]. - Clearly marking reviews as "blocking" when there are significant issues helps clarify the status of the review and prevents confusion about whether changes can be merged [12][13]. Group 3: Review Culture and Practices - Most reviews should ideally result in an approval status, especially in fast-paced environments like SaaS, to avoid bottlenecks in development [13][14]. - High rates of blocking reviews may indicate structural issues within teams, such as over-cautiousness or misalignment of goals between teams [14]. - The article suggests that code reviews should also serve as learning opportunities, fostering knowledge sharing and team growth [17][22].

Group 1 - The core conclusion emphasizes the transformation of information foundations through LLMs, which convert vast amounts of unstructured text into quantifiable Alpha factors, fundamentally expanding the information boundaries of traditional investment research [1] - The technology path has been validated, with a full-stack technology framework for AI-enabled asset allocation established, including signal extraction via LLMs, dynamic decision-making through DRL, and risk modeling with GNNs [1] - AI is evolving from a supportive tool to a central decision-making mechanism, driving asset allocation from static optimization to dynamic intelligent evolution, reshaping the buy-side investment research and execution logic [1] Group 2 - The practical application of AI investment systems relies on a modular collaborative mechanism rather than a single model's performance, as demonstrated by BlackRock's AlphaAgents, which utilizes LLMs for cognition and reasoning, external APIs for real-time information, and numerical optimizers for final asset allocation calculations [2] - Leading institutions are competing on an "AI-native" strategy, focusing on building proprietary, trustworthy AI core technology stacks, as evidenced by JPMorgan's approach, which is centered around "trustworthy AI and foundational models," "simulation and automated decision-making," and "physical and alternative data" [2] - Domestic asset management institutions should focus on strategic restructuring and organizational transformation, adopting a differentiated and focused approach to technology implementation, emphasizing a practical and efficient "human-machine collaboration" system [3] Group 3 - The report discusses the evolution of financial sentiment analysis mechanisms, highlighting the transition from early dictionary-based methods to advanced LLMs that can understand context and financial jargon, underscoring the importance of creating domain-specific LLMs [12][13] - LLMs are being applied in algorithmic trading and risk management, providing real-time sentiment scores and monitoring global information flows to identify potential market risks [14][15] - Despite the promising applications of LLMs, challenges such as data bias, high computational costs, and the need for explainability remain significant barriers to their widespread adoption in finance [15][16] Group 4 - Deep Reinforcement Learning (DRL) offers a dynamic adaptive framework for asset allocation, contrasting with traditional static optimization methods, allowing for continuous learning and decision-making based on market interactions [17][18] - The core architecture of DRL in finance includes various algorithms like Actor-Critic methods and Proximal Policy Optimization (PPO), which show significant potential for investment portfolio management [19][20] - Key challenges for deploying DRL in real financial markets include data dependency, overfitting risks, and the need to integrate real-world constraints into the learning framework [21][22] Group 5 - Graph Neural Networks (GNNs) conceptualize the financial system as a network, allowing for a better understanding of risk transmission and systemic risk, which traditional models often overlook [23][24] - GNNs can be utilized for stress testing and dynamic assessments of the financial system's robustness, providing valuable insights for regulatory bodies [25][26] - The insights gained from GNNs can help investors develop more effective hedging strategies by understanding interdependencies within financial networks [26] Group 6 - BlackRock's AlphaAgents project aims to enhance decision-making by addressing cognitive biases in human analysts and leveraging LLMs for complex reasoning, moving beyond mere data processing [30][31] - The dual-layer decision-making process in AlphaAgents involves collaborative and adversarial debates among AI agents, enhancing the robustness of investment decisions [31][33] - Backtesting results indicate that the multi-agent framework significantly outperforms single-agent models, demonstrating the value of collaborative AI in investment strategies [34][35] Group 7 - JPMorgan's AI strategy focuses on building proprietary, trustworthy AI technologies, emphasizing the importance of trust and security in AI applications within finance [45][46] - The bank is committed to developing foundational models and generative AI capabilities, aiming to control key AI functionalities and ensure compliance with regulatory standards [49][50] - By integrating multi-agent simulations and reinforcement learning, JPMorgan seeks to create sophisticated models that can navigate complex financial systems and enhance decision-making processes [53][54]

Core Insights - The article discusses the evolution and potential of Vision Language Action (VLA) models in robotics, emphasizing their integration of perception, language understanding, and action generation to enhance robotic capabilities [11][17]. Group 1: Introduction and Background - Robotics has traditionally relied on pre-programmed instructions and control strategies, limiting their adaptability in dynamic environments [2][11]. - The emergence of VLA models marks a significant advancement in embodied intelligence, combining visual perception, language understanding, and executable actions into a unified framework [11][12]. Group 2: VLA Methodologies - VLA methods are categorized into four paradigms: autoregressive, diffusion, reinforcement learning, and hybrid/specialized methods, each with unique strategies and mechanisms [8][10]. - The article highlights the importance of high-quality datasets and realistic simulation platforms for the development and evaluation of VLA models [16][18]. Group 3: Challenges and Future Directions - Key challenges identified include data limitations, reasoning speed, and safety concerns, which need to be addressed to advance VLA models and general robotics [10][17]. - Future research directions focus on enhancing the robustness and generalization of VLA models in real-world applications, emphasizing the need for efficient training paradigms and safety assessments [44][47].

Core Insights - The article discusses the emergence of Time Series Reasoning (TSR) as a new field that integrates large language models (LLMs) with time series data analysis, addressing the limitations of traditional methods [2][8][39] - TSR aims to enhance the capabilities of time series analysis by providing explicit reasoning, causal inference, and decision-making abilities, moving beyond mere prediction and classification [2][8][39] Summary by Sections Traditional Time Series Analysis Limitations - Traditional methods like ARIMA and LSTM excel in specific tasks but face three key limitations: lack of interpretability, inability to handle causal relationships, and insufficient dynamic responses [8][14] - LLMs offer new tools to overcome these limitations by providing explicit reasoning processes, generating causal hypotheses, and enabling interaction with external tools [2][8] Emergence of Time Series Reasoning - TSR is defined as the method of performing explicit structured reasoning on time-indexed data using LLMs, integrating multimodal contexts and agent systems [8][39] - A recent survey from a collaborative team outlines a clear definition of TSR and presents a three-dimensional classification framework covering reasoning structure, task objectives, and technical features [3][9] Three-Dimensional Classification Framework - The framework categorizes TSR into three dimensions: reasoning topology (how reasoning is conducted), core objectives (why reasoning is performed), and attribute labels (auxiliary features of methods) [9][24] - Reasoning topology includes three types: direct reasoning, linear chain reasoning, and branch-structured reasoning, each with varying complexity and capabilities [12][22] Reasoning Topology - Direct reasoning is the simplest form, providing results without showing intermediate steps, which limits interpretability [15] - Linear chain reasoning introduces ordered steps, enhancing interpretability and modularity [18] - Branch-structured reasoning allows for multiple paths and self-correction, increasing flexibility and adaptability [22] Core Objectives of Time Series Reasoning - The core objectives of TSR are categorized into four types: traditional time series analysis, explanation and understanding, causal inference and decision-making, and time series generation [24][28] - Each objective aims to enhance the performance and flexibility of traditional tasks through LLM integration [28] Attribute Labels - Attribute labels provide additional features for classifying methods, including control flow operations, execution agents, information sources, and LLM alignment methods [29][30] - These labels help researchers refine their work and understand the nuances of different approaches [29] Resources and Tools - The article emphasizes the importance of resources and tools for advancing the field, categorizing them into reasoning-first benchmarks, reasoning-ready benchmarks, and general-purpose benchmarks [33][36] - These resources are essential for researchers to test and validate their methodologies effectively [33] Future Directions and Challenges - The field faces several challenges, including standardizing evaluation metrics for reasoning quality, integrating multimodal data, and ensuring the robustness and safety of agent systems [38][39] - Addressing these challenges will define the future trajectory of time series reasoning, aiming for large-scale reliability in critical sectors like finance, healthcare, and energy [39]

Core Insights - The article discusses the application of large language models (LLMs) and multimodal large language models (MLLMs) in the paradigm shift for autonomous driving trajectory prediction, enhancing the understanding of complex traffic scenarios to improve safety and efficiency [1][20]. Summary by Sections Introduction and Overview - The integration of LLMs into autonomous driving systems allows for a deeper understanding of traffic scenarios, transitioning from traditional methods to LFM-based approaches [1]. - Trajectory prediction is identified as a core technology in autonomous driving, utilizing historical data and contextual information to infer future movements of traffic participants [5]. Traditional Methods and Challenges - Traditional vehicle trajectory prediction methods include physics-based approaches (e.g., Kalman filters) and machine learning methods (e.g., Gaussian processes), which struggle with complex interactions [8]. - Deep learning methods improve long-term prediction accuracy but face challenges such as high computational demands and poor interpretability [9]. - Reinforcement learning methods excel in interactive scene modeling but are complex and unstable [9]. LLM-Based Vehicle Trajectory Prediction - LFM introduces a paradigm shift by discretizing continuous motion states into symbolic sequences, leveraging LLMs' semantic modeling capabilities [11]. - Key applications of LLMs include trajectory-language mapping, multimodal fusion, and constraint-based reasoning, enhancing interpretability and robustness in long-tail scenarios [11][13]. Evaluation Metrics and Datasets - The article categorizes datasets for pedestrian and vehicle trajectory prediction, highlighting the importance of datasets like Waymo and ETH/UCY for evaluating model performance [16]. - Evaluation metrics for vehicles include L2 distance and collision rates, while pedestrian metrics focus on minADE and minFDE [17]. Performance Comparison - A performance comparison of various models on the NuScenes dataset shows that LLM-based methods significantly reduce collision rates and improve long-term prediction accuracy [18]. Discussion and Future Directions - The widespread application of LFMs indicates a shift from local pattern matching to global semantic understanding, enhancing safety and compliance in trajectory generation [20]. - Future research should focus on developing low-latency inference techniques, constructing motion-oriented foundational models, and advancing world perception and causal reasoning models [21].

Core Insights - The article discusses the transition from static large language models (LLMs) to self-evolving agents capable of continuous learning and adaptation in dynamic environments, paving the way towards artificial superintelligence (ASI) [3][4][46] - It emphasizes the need for a structured framework to understand and design self-evolving agents, focusing on three fundamental questions: what to evolve, when to evolve, and how to evolve [6][46] Group 1: What to Evolve - Self-evolving agents can improve various components such as models, memory, tools, and architecture over time to enhance performance and adaptability [19][20] - The evolution of these components is crucial for the agent's ability to handle complex tasks and environments effectively [19][20] Group 2: When to Evolve - The article categorizes self-evolution into two time modes: intra-test-time self-evolution, which occurs during task execution, and inter-test-time self-evolution, which happens between tasks [22][23] - Intra-test-time self-evolution allows agents to adapt in real-time to specific challenges, while inter-test-time self-evolution leverages accumulated experiences for future performance improvements [22][23] Group 3: How to Evolve - Self-evolution emphasizes a continuous learning process where agents learn from real-world interactions, seek feedback, and adjust strategies dynamically [26][27] - Various methodologies for self-evolution include reward-based evolution, imitation learning, and population-based approaches, each with distinct feedback types and data sources [29][30] Group 4: Applications and Evaluation - Self-evolving agents have significant potential in various fields, including programming, education, and healthcare, where continuous adaptation is essential [6][34] - Evaluating self-evolving agents presents unique challenges, requiring metrics that capture adaptability, knowledge retention, and long-term generalization capabilities [34][36] Group 5: Future Directions - The article highlights the importance of addressing challenges such as catastrophic forgetting, knowledge transfer, and ensuring safety and controllability in self-evolving agents [40][43] - Future research should focus on developing scalable architectures, dynamic evaluation methods, and personalized agents that can adapt to individual user preferences [38][44]

Core Viewpoint - The emergence of large language models (LLMs) represents a transformative change in software development, comparable to the shift from assembly language to the first generation of high-level programming languages [5][10]. Group 1: Impact of LLMs on Programming - LLMs not only enhance the level of abstraction in programming but also compel a reevaluation of what it means to program with non-deterministic tools [7][10]. - The transition from deterministic to non-deterministic programming paradigms expands the dimensions of programming practices [8][10]. Group 2: Historical Context of Programming Languages - High-level programming languages (HLLs) introduced a new level of abstraction, allowing programmers to think in terms of sequences, conditions, and iterations rather than specific machine instructions [8][9]. - Despite advancements in programming languages, the fundamental nature of programming has not changed significantly until the advent of LLMs [6][9]. Group 3: Embracing Non-Determinism - The introduction of non-deterministic abstractions means that results from LLMs cannot be reliably reproduced, contrasting with the consistent outcomes from traditional programming [10][13]. - The industry is experiencing a radical transformation as developers learn to navigate this non-deterministic environment, which is unprecedented in the history of software development [13].

Group 1 - The U.S. tech job market has undergone significant changes since the launch of ChatGPT in November 2022, with some positions experiencing drastic declines while others remain in high demand [1] - The largest wave of layoffs in U.S. history began in 2023, impacting the IT job market, but hiring activities are gradually recovering, albeit with limited new positions [2] - The average tenure of software engineers at major tech companies has increased significantly, indicating a slowdown in hiring and a reluctance among employees to change jobs [6][80] Group 2 - The demand for AI engineers has surged since mid-2023, making it the hottest position in the tech industry, with a notable increase in job openings [29] - Major tech companies like Apple, IBM, and Amazon are leading in job openings, with Apple having the highest number at 2,177 positions [13] - Over half of the open positions are at senior levels, and there is a notable decrease in vacancies for senior engineers, prompting them to apply for lower-level positions [21][24] Group 3 - The San Francisco Bay Area remains the dominant hub for tech jobs, accounting for nearly 20% of global tech job openings, with a total of 9,072 positions [72][74] - The average tenure at major tech companies has increased by about two years over the past three years, reflecting a more stable workforce amid hiring slowdowns [80] - The trend of internal mobility among major tech firms is prevalent, with companies primarily hiring from each other, leading to longer tenures [85] Group 4 - Remote job opportunities have decreased, with the proportion of remote positions falling from 25% to 20% over the past year, although AI engineering roles still see a slight increase in remote opportunities [98][100] - The salary for remote positions has generally declined by 10-15%, as supply exceeds demand, making high-paying remote jobs a rare privilege [102]