Core Insights - Artificial intelligence is significantly transforming both personal and professional environments, moving from consumer applications to business solutions [1] - Minglue Technology has launched its proprietary model line, DeepMiner, aimed at addressing the challenges of accuracy, transparency, and verifiable decision-making in business data analysis [1][2] - DeepMiner utilizes a multi-agent architecture to enhance human-machine collaboration, providing analytical support across various sectors such as advertising, retail, and cross-border e-commerce [1] Company Overview - Minglue Technology has received approval for overseas listing through the Hong Kong Stock Exchange under the 18C rule, with a planned IPO date of August 29, 2025 [2] - The company has raised a total of $616 million from investors including Tencent, Sequoia China, Temasek, Jintuo Capital, and Huaxing Capital from 2010 to 2024 [2] - The main business segments of Minglue Technology include marketing intelligence, operational intelligence, and industry solutions [2] Financial Performance - Revenue figures for Minglue Technology from 2022 to 2024 are as follows: 1.269 billion yuan in 2022, 1.462 billion yuan in 2023, and a projected 1.381 billion yuan in 2024, indicating a year-on-year decline of 5.5% for 2024 [2] - The net profit for the company is expected to decline by 97.5% year-on-year in 2024 [2]

Core Viewpoint - Minglue Technology's proprietary GUI model, Mano, has achieved record-breaking SOTA results in the recognized benchmarks Mind2Web and OSWorld, establishing a new paradigm for GUI intelligent agents through innovations in online reinforcement learning and automatic data collection [1][14][23]. Group 1: Performance Achievements - Mano achieved a success rate of 40.1% in the OSWorld-Verified benchmark, surpassing other models such as qwen and GUI-Owl [10][19]. - In the Mind2Web benchmark, Mano demonstrated superior performance across various metrics, including element accuracy and step success rate, significantly outperforming all other SOTA methods [18][15]. - The model's success rate in OSWorld-Verified reached 41.6±0.7%, marking an approximate 7 percentage point improvement over competitors [21][19]. Group 2: Innovations and Methodology - Mano introduces online reinforcement learning as a novel training paradigm in the GUI interaction field, enhancing its performance in dynamic environments [22][23]. - The model's architecture consists of three main components: exploration module, processing flow, and optimization process, which collectively improve its reasoning and adaptability [25][26]. - The automatic data collection method developed by the technical team significantly enhances the efficiency and accuracy of data acquisition, allowing for the generation of high-quality interaction trajectory data [48][49]. Group 3: Market Context and Future Directions - The demand for AI agents is expected to surge by 2025, positioning Mano as a key player in differentiated competition by accessing data sources that other agents cannot reach [59][63]. - Minglue Technology plans to continue exploring areas such as data collection, training integration, and CAPTCHA handling to further optimize Mano for real-world applications [66].