机器之心发布 如果把用户在互联网上留下的每一个足迹都看作一段记忆，那么现在的推荐系统大多患有 "短期健忘症"。 受限于算力和存储，那些沉睡在数年前的点击、收藏与购买，往往被粗暴地截断或遗忘。即便被召回，它们在模型眼中也只是一串串冰冷且互不相识的 ID 代码。 但事实上，真正有趣的东西也往往藏在这些被遗忘的 "长尾" 之中。如何唤醒这 10 万级 的沉睡数据，并读懂它们背后的视觉与语义关联？ 阿里妈妈与武汉大学团队给出的答案是 MUSE（MUltimodal SEarch-based framework） 。这不仅仅是一个新的 CTR 模型，更像是一个给推荐系统安装的 "多模 态海马体"。它利用图像与文本的语义力量，重构了用户跨越时空的兴趣图谱。 甚至，他们还开源了构建这个 "数字大脑" 的基石： Taobao-MM 数据集 。 对于推荐系统长久以来技术演进路线，这一突破可谓是一次深刻的反思与重构！ 论文标题：MUSE: A Simple Yet Effective Multimodal Search-Based Framework for Lifelong User Interest Modeling 在搜推 ...

Core Viewpoint - The article discusses the introduction of the MUSE framework, which aims to enhance the capabilities of LLM agents by enabling them to accumulate experience and evolve continuously, addressing the challenges of long-horizon tasks and memory limitations [1][5]. Group 1: MUSE Framework Overview - MUSE stands for Memory-Utilizing and Self-Evolving, designed to create a closed-loop system for LLM agents that allows them to learn from experience and evolve over time [5]. - The framework consists of a hierarchical memory module that organizes different levels of experience, including strategic, procedural, and tool memory [7][8]. Group 2: Key Mechanisms of MUSE - The first step involves a hierarchical memory module that allows agents to retain and apply historical knowledge, overcoming the "forgetfulness" of traditional LLMs [7]. - The second step is self-reflection, where agents evaluate their task execution and convert raw execution trajectories into structured experiences, refining their standard operating procedures (SOPs) [10][11]. - The third step focuses on self-evolution, enabling agents to continuously improve through a cycle of planning, execution, reflection, and experience extraction [13][15]. Group 3: Experimental Results - MUSE demonstrated state-of-the-art (SOTA) performance in the TAC benchmark, achieving a score of 51.78%, surpassing existing methods that used larger models [16]. - The framework's ability to accumulate experience leads to improved performance over time, showcasing its potential for long-term productivity tasks [19]. Group 4: Future Prospects - The MUSE framework signifies a new phase of experience-driven lifelong learning for AI agents, moving beyond static testing models [29]. - Future research directions include optimizing memory, enriching experience sources, integrating human feedback, and developing comprehensive evaluation standards for long-term tasks [30][31].

As filed with the Securities and Exchange Commission on September 23, 2025 Registration No. 333-289926 UNITED STATES SECURITIES AND EXCHANGE COMMISSION Washington, D.C. 20549 _______________________________ AMENDMENT NO. 1 TO FORM F-1 REGISTRATION STATEMENT Under The Securities Act of 1933 _______________________________ Robo.ai Inc. (Exact name of Registrant as specified in its charter) Not Applicable (Translation of Registrant's name into English) | | | (State or other jurisdiction of incorporation or org ...

Group 1 - The core issue is the overwhelming demand for telescope time, particularly for the MUSE instrument at the European Southern Observatory (ESO), leading to a significant backlog of applications [1][3] - The traditional peer review system is under strain due to the increasing volume of academic papers, resulting in declining research quality and innovative ideas being overlooked [5][7] - The COVID-19 pandemic has exacerbated the situation, with a surge in paper submissions further stressing the peer review system [7][8] Group 2 - ESO has implemented a new "applicant peer review" system where applicants must also review their competitors' proposals, aiming to alleviate the burden on traditional reviewers [3][10] - Various methods are being explored to incentivize peer reviewers, including non-monetary rewards and integrating peer review contributions into performance evaluations [13][14] - The debate over whether to pay peer reviewers continues, with proponents arguing it reflects the value of their work, while opponents warn of potential conflicts of interest [15][17] Group 3 - Recent experiments with paid peer review have shown mixed results, with one journal reporting a slight increase in acceptance rates and reduced review times, while another experienced significant improvements in processing speed and quality [21][22][24] - Funding agencies are also struggling to find qualified reviewers, even when offering substantial compensation [26][28] - A successful trial in the UK demonstrated that a new review model could double the speed of funding application reviews while mitigating concerns about bias [29][30] Group 4 - The need to expand the pool of reviewers is critical, as the number of papers is increasing, particularly from emerging research countries, while the reviewer base remains limited [31][33] - Collaborative review models pairing senior scholars with junior researchers are gaining traction, providing training opportunities while increasing reviewer capacity [34] - Structured peer review methods, which involve specific questions for reviewers, have shown promise in improving consistency and quality of reviews [36][38] Group 5 - Transparency in the peer review process is being advocated, with suggestions to publish review reports alongside final papers and to attribute reviews to individual reviewers [41][42] - This push for transparency is believed to enhance the quality of reviews, as reviewers may be more diligent knowing their work will be publicly accessible [42]

As filed with the Securities and Exchange Commission on August 29, 2025 Registration No. 333- UNITED STATES SECURITIES AND EXCHANGE COMMISSION Washington, D.C. 20549 _______________________________ FORM F-1 REGISTRATION STATEMENT Under The Securities Act of 1933 _______________________________ Robo.ai Inc. (Exact name of Registrant as specified in its charter) Not Applicable (Translation of Registrant's name into English) _______________________________ Cayman Islands 5900 Not Applicable (State or other jur ...