"我真的厌倦了看到那些急于求成的科技初创公司，为了讨好风投而在数据上撒谎，还贴上'SOTA'的标签。"有网友吐槽。 事情源于高人气开源智能体记忆项目 Mem0 在今年 4 月底发布的一篇论文。论文中，该项目团队为可扩展的、以记忆为核心的架构 Mem0 提出了增强版 本，并声称在 LOCOMO 上打败了所有人，其中，Mem0 在 "LLM-as-a-Judge" 指标上相较于 OpenAI 提高了 26%。（论文地址： https://arxiv.org/abs/2504.19413） 当地时间 8 月 13 日， 另一个高人气的智能体记忆框架 MemGPT 的创始团队 Letta AI ，其联合创始人兼 CTO Sarah Wooders 对此公开指控： 几个月前，Mem0 发布了 MemGPT 的基准测试数据，并声称在记忆方面达到了 "SOTA" 水平。 奇怪的是，我完全不知道他们到底是怎么跑这个基准测试的，如果不对 MemGPT 做重大修改，这个测试根本没法完成（他们没有回应我们关于实验具体 运行方式的询问）。 arXiv 并不是经过同行评审的平台，所以不幸的是，近年来公司可以随意发布任何他们想要的"研究 ...

Core Viewpoint - The article emphasizes the importance of "Context Engineering" as a systematic approach to optimize the input provided to Large Language Models (LLMs) for better output generation [3][11]. Summary by Sections Introduction to Context Engineering - The article highlights the recent popularity of "Context Engineering," with notable endorsements from figures like Andrej Karpathy and its trending status on platforms like Hacker News and Zhihu [1][2]. Understanding LLMs - LLMs should not be anthropomorphized; they are intelligent text generators without beliefs or intentions [4]. - LLMs function as general, uncertain functions that generate new text based on provided context [5][6][7]. - They are stateless, requiring all relevant background information with each input to maintain context [8]. Focus of Context Engineering - The focus is on optimizing input rather than altering the model itself, aiming to construct the most effective input text to guide the model's output [9]. Context Engineering vs. Prompt Engineering - Context Engineering is a more systematic approach compared to the previously popular "Prompt Engineering," which relied on finding a perfect command [10][11]. - The goal is to create an automated system that prepares comprehensive input for the model, rather than issuing isolated commands [13][17]. Core Elements of Context Engineering - Context Engineering involves building a "super input" toolbox, utilizing various techniques like Retrieval-Augmented Generation (RAG) and intelligent agents [15][19]. - The primary objective is to deliver the most effective information in the appropriate format at the right time to the model [16]. Practical Methodology - The process of using LLMs is likened to scientific experimentation, requiring systematic testing rather than guesswork [23]. - The methodology consists of two main steps: planning from the end goal backward and constructing from the beginning forward [24][25]. - The final output should be clearly defined, and the necessary input information must be identified to create a "raw material package" for the system [26]. Implementation Steps - The article outlines a rigorous process for building and testing the system, ensuring each component functions correctly before final assembly [30]. - Specific testing phases include verifying data interfaces, search functionality, and the assembly of final inputs [30]. Additional Resources - For more detailed practices, the article references Langchain's latest blog and video, which cover the mainstream methods of Context Engineering [29].

Core Viewpoint - The article discusses the importance of iterative processes in Electronic Design Automation (EDA) and highlights the challenges posed by decision-making in logic synthesis, emphasizing the need for integrated tools to manage multi-factor dependencies and improve timing convergence [1]. Group 1: EDA Process and Challenges - Iterative loops have been crucial in the EDA process for decades, especially as gate and line delays have become significant [1]. - The consequences of decisions in the EDA process can be far-reaching, affecting multiple other decisions, which complicates achieving acceptable timing [1]. - Serial tool operation can lead to major issues, and achieving timing convergence in logic synthesis is nearly impossible without a concept of iterative learning [1]. Group 2: Integration of Tools - The integration of decision tools, estimators, and checkers into a single tool addresses the issue of multi-factor dependencies, allowing for quick checks during decision-making [1]. - There is a growing need for such integrated functionalities across various fields, enabling users to guide tool operations based on their expertise [1]. Group 3: AI and Verification in EDA - AI hallucinations are recognized as a characteristic rather than a defect, with models generating plausible but not necessarily factual content [3]. - The use of retrieval-augmented generation (RAG) aims to control these hallucinations by fact-checking generated content, similar to practices in EDA [3]. - The industry has a strong emphasis on verification, which is crucial for ensuring the reliability of AI applications in EDA [5]. Group 4: Future Directions and Innovations - The industry is making progress in identifying necessary abstractions for validating ideas efficiently, with examples like digital twins and reduced-order models [6]. - A model generator capable of producing required abstract concepts for verification is deemed essential for mixed-signal systems [6]. - With proper verification, AI could lead to breakthroughs in performance and power efficiency, suggesting a need for a restructuring phase in the industry [6].