Core Insights - The article discusses the limitations of current recommendation systems, which often suffer from "short-term amnesia" due to computational and storage constraints, leading to the neglect of valuable long-tail data [1][3] - MUSE (Multimodal Search-based framework) is introduced as a solution to enhance user interest modeling by leveraging multimodal information, effectively acting as a "digital hippocampus" for recommendation systems [1][4] - The framework has been successfully implemented in Alibaba's advertising system, demonstrating a significant CTR increase of 12.6% [6][36] Summary by Sections Background and Evolution - The evolution of CTR modeling has transitioned from short-term behavior analysis to long-term behavior modeling, but improvements have plateaued as historical behavior length increases [2][3] - Users accumulate extensive behavior sequences, often exceeding one million actions, but current models typically utilize only a few thousand recent actions due to limitations in processing and storage [3][4] MUSE Framework - MUSE focuses on reorganizing user behavior data through multimodal information to improve the quality and usability of lifelong interest modeling [6][20] - The framework consists of two main components: GSU (General Search Unit) for initial retrieval and ESU (Exact Search Unit) for detailed modeling, both enhanced by multimodal embeddings [20][24] Implementation and Results - MUSE has been fully deployed in Alibaba's advertising system, capable of modeling user behavior sequences of up to 100,000 actions, with ongoing improvements to extend this to millions [6][36] - The implementation has shown that using high-quality multimodal embeddings significantly enhances retrieval and modeling accuracy, leading to improved business outcomes [6][36] Engineering Considerations - The design of MUSE allows for controlled latency despite the complexity of handling long sequences and multimodal data, primarily by decoupling the GSU from the main processing path [31][36] - The system's architecture emphasizes efficient data retrieval and processing, minimizing the impact of network and storage delays on overall performance [36][39] Industry Implications - MUSE offers valuable insights for industries involved in advertising, content recommendation, and e-commerce, suggesting a shift towards integrating multimodal embeddings and enhancing user interest modeling [37][39] - The framework encourages a reevaluation of existing systems, advocating for a focus on quality embeddings and efficient data handling to unlock new performance improvements [45][47]

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 ...