证券简称：万兴科技 证券代码：300624 万兴科技集团股份有限公司 2025 年员工持股计划 （草案） 二〇二五年六月 万兴科技集团股份有限公司 2025 年员工持股计划（草案） 声明 公司及全体董事保证本员工持股计划内容不存在虚假记载、误导性陈述或者 重大遗漏，并对本员工持股计划内容的真实性、准确性、完整性承担个别和连带 的法律责任。 1 万兴科技集团股份有限公司 2025 年员工持股计划（草案） 风险提示 （一）本员工持股计划经公司股东会审议批准后方可实施，能否获得公司股 东会批准，存在不确定性。 （二）本员工持股计划的资金来源、股份来源、参加对象等要素均属初步结 果，能否完成实施，存在不确定性。 （三）如参加对象的认购资金不足，本员工持股计划存在不能成立或者低于 预计规模的风险。 （四）股票价格受公司经营业绩、宏观经济周期、国际/国内政治经济形势 及投资者心理等多种复杂因素影响。因此，股票交易是有一定风险的投资活动， 投资者对此应有充分准备。 （五）公司将根据有关规定及时披露本员工持股计划进展情况，敬请广大投 资者谨慎决策，注意投资风险。 2 万兴科技集团股份有限公司 2025 年员工持股计划（草案） ...

万兴科技集团股份有限公司 关于召开2025年第一次临时股东会的通知 本公司及董事会全体成员保证信息披露的内容真实、准确、完整，没有 虚假记载、误导性陈述或重大遗漏。 证券代码：300624 证券简称：万兴科技 公告编号：2025-042 3、会议召开的合法、合规性：第五届董事会第二次会议审议，决定召开2025 年第一次临时股东会，本次股东会的召开符合有关法律、行政法规、部门规章、 规范性文件和《公司章程》的规定。 4、会议召开的日期、时间： （1）现场会议召开时间：2025年6月26日（星期四）下午15:00 （2）网络投票时间：通过深圳证券交易所（以下简称"深交所"）交易系 统进行网络投票的具体时间为：2025年6月26日9:15-9:25，9:30-11:30，下午13:00- 15:00；通过深交所互联网投票系统进行网络投票的具体时间为：2025年6月26日 9:15至15:00期间的任意时间。 5、会议召开方式：本次股东会采用现场表决与网络投票相结合的方式召开。 万兴科技集团股份有限公司（以下简称"公司"）第五届董事会第二次会议 决定于2025年6月26日召开公司2025年第一次临时股东会（以下简称" ...

万兴科技集团股份有限公司 董事会薪酬与考核委员会关于 2025 年员工持股计划有关事项的 核查意见 （四）公司实施本次员工持股计划有利于建立、健全激励约束机制，充分调 动公司骨干人员的积极性和创造性，促进公司持续、健康发展，不存在损害公司 及全体股东利益的情形，亦不存在以摊派、强行分配等方式强制员工参与本次员 工持股计划的情形。 综上，董事会薪酬与考核委员会认为，公司实施本次员工持股计划符合公司 长远发展的需要，不存在损害公司及全体股东利益的情形。 万兴科技集团股份有限公司 董事会薪酬与考核委员会 万兴科技集团股份有限公司（以下简称"公司"）全体薪酬与考核委员会委 员在认真审阅第五届薪酬与考核委员会第二次会议相关会议资料的基础上，经充 分、全面的讨论与分析，依据相关规定，就公司 2025 年员工持股计划相关事项 发表核查意见如下： （一）公司不存在《关于上市公司实施员工持股计划试点的指导意见》等法 律、法规规定的禁止实施员工持股计划的情形，公司具备实施本次员工持股计划 的主体资格。 （二）本次员工持股计划在公告前，已通过召开职工代表大会充分征求员工 意见，本次员工持股计划的制定及其内容符合《关于上市公司实施员 ...

证券代码：300624 证券简称：万兴科技 公告编号：2025-041 万兴科技集团股份有限公司 第五届董事会第二次会议决议公告 本公司及董事会全体成员保证信息披露的内容真实、准确、完整，没有 虚假记载、误导性陈述或重大遗漏。 一、董事会会议召开情况 万兴科技集团股份有限公司（以下简称"公司"）第五届董事会第二次会议 （以下简称"董事会"）于 2025 年 6 月 4 日以通讯、邮件等方式向全体董事发 出会议通知，并于 2025 年 6 月 9 日在深圳市南山区海天二路软件产业基地 5 栋 D 座 10 楼会议室以现场与通讯相结合方式召开。本次会议由公司董事长吴太兵 先生主持，应出席会议董事 5 人，实际出席会议董事 5 人。公司部分高级管理人 员列席了会议。本次会议的召集、召开符合法律、行政法规、部门规章、规范性 文件、《万兴科技集团股份有限公司章程》（以下简称《公司章程》）和《万兴 科技集团股份有限公司董事会议事规则》的规定。 二、董事会会议审议情况 经全体董事审议并表决，一致形成决议如下： 1、审议《关于公司〈2025 年员工持股计划（草案）〉及其摘要的议案》 为完善公司、股东和员工之间的利益共享机制， ...

Investment Rating - The industry investment rating is "Positive" [5] Core Insights - AI Agents represent the third stage of AI development, transitioning from simple Q&A and content generation to becoming true "executors" capable of completing actual work tasks independently by 2025 [1][17] - The Model Context Protocol (MCP) is redefining the paradigm for AI Agents, acting as a crucial infrastructure that enhances the interaction between AI models and external services, making it more natural and precise [2][22] - Major tech companies are actively developing AI Agent products, indicating a shift from technical competition to ecological value reconstruction in the AI Agent industry [3][36] Summary by Sections MCP Protocol Restructuring AI Agent Paradigm - AI Agents are defined as the third stage of AI development, capable of representing users in actions [10] - The MCP protocol standardizes tool interfaces, allowing for cross-platform interoperability and enhancing AI model capabilities [19][22] Acceleration of AI Agent Applications - Tech giants like ByteDance and Alibaba are focusing on AI Agent products, with rapid iterations expected from Q4 2024 to early 2025 [3][36] - The market shows a strong preference for general-purpose AI Agents, with significant funding differences between general and vertical industry AI startups [39] Investment Recommendations - The MCP protocol is likened to the "HTTP protocol" of the AI era, marking a transition to a standardized phase of AI development [46] - Recommended companies to watch include: 1) Business platform BIP: Yonyou Network; 2) Office: Kingsoft Office; 3) AIGC: iFlytek, Wanjun Technology [46][47]

Investment Rating - The investment rating for the industry is "Positive" [5] Core Insights - AI Agents represent the third stage of AI development, transitioning from simple Q&A and content generation to becoming true "executors" capable of completing actual work tasks independently by 2025 [1][15] - The Model Context Protocol (MCP) is redefining the paradigm for AI Agents, serving as a crucial infrastructure that enhances the interaction between AI models and external services, making it more natural and precise [2][20] - Major tech companies are actively investing in AI Agent products, indicating a shift from technical competition to ecological value reconstruction in the AI Agent industry [2][34] Summary by Sections MCP Protocol Restructuring AI Agent Paradigm - AI Agents are identified as the third stage of AI development, with capabilities to represent users in actions [1][8] - The MCP protocol standardizes tool interfaces, allowing for seamless data interaction and decision execution across platforms [17][20] Acceleration of AI Agent Applications - Tech giants are rapidly deploying AI Agent products, with a noticeable shift towards ecological value reconstruction [34] - The market shows a strong preference for general-purpose AI Agents, with significant funding differences compared to vertical industry-focused agents [37] Investment Recommendations - The MCP protocol is likened to the "HTTP protocol" of the AI era, marking a transition to a standardized era of AI development [3][44] - Recommended companies to focus on include: Yonyou Network (commercial platform), Kingsoft Office (office solutions), iFlytek, and Wankong Technology (AIGC) [3][44] Industry Key Company Profit Forecasts - Profit forecasts for key companies indicate a positive outlook, with expected net profits for Yonyou Network, Kingsoft Office, iFlytek, and Wankong Technology showing growth from 2025 to 2027 [45]

证券代码：300624 证券简称：万兴科技 公告编号：2025-039 万兴科技集团股份有限公司 关于回购公司股份的进展公告 本公司及董事会全体成员保证信息披露的内容真实、准确、完整，没有 虚假记载、误导性陈述或重大遗漏。 万兴科技集团股份有限公司（以下简称"公司"）于 2025 年 4 月 25 日召开 第四届董事会第二十四次会议审议通过了《关于回购公司股份方案的议案》，同 意使用自有资金和自筹资金通过深圳证券交易所股票交易系统以集中竞价交易 方式回购公司股份，用于员工持股计划或股权激励计划，回购股份的期限自董事 会审议通过本次回购股份方案之日起 12 个月内，回购股份的价格为不超过人民 币 89.85 元/股（含），回购的资金总额不低于人民币 2,500 万元（含），不超过 人民币 5,000 万元（含）。具体内容详见公司于 2025 年 4 月 28 日在巨潮资讯网 上披露的《关于回购公司股份方案的公告》（公告编号：2025-019）。 根据《上市公司股份回购规则》《深圳证券交易所上市公司自律监管指引第 9 号——回购股份》等相关规定，回购期间，公司应当在每个月的前三个交易日 内披露截至上月末的回购进展 ...

万兴科技集团股份有限公司 关于回购公司股份的进展公告 本公司及董事会全体成员保证信息披露的内容真实、准确、完整，没有 虚假记载、误导性陈述或重大遗漏。 万兴科技集团股份有限公司（以下简称"公司"）于 2024 年 8 月 15 日召开 第四届董事会第十九次会议，审议通过了《关于回购公司股份方案的议案》，使 用自有资金以集中竞价方式回购公司部分 A 股股份，用于员工持股计划或股权 激励计划；回购股份的资金总额不低于人民币 2,000 万元（含）且不超过人民币 4,000 万元（含），回购股份的价格不超过人民币 71.78 元/股（含），回购期限 自公司董事会审议通过回购股份方案之日起 12 个月内。具体内容详见公司于 2024 年 8 月 16 日在巨潮资讯网上披露的《关于回购公司股份方案的公告暨回购 报告书》（公告编号：2024-054）。 证券代码：300624 证券简称：万兴科技 公告编号：2025-040 证券代码：300624 证券简称：万兴科技 公告编号：2025-040 公司回购股份的时间、回购股份的价格以及集中竞价交易的委托时段等均符 合公司回购股份方案及《深圳证券交易所上市公司自律监管指引第 ...

Core Insights - The article discusses the core technologies of multimodal large models, focusing on representation learning, translation, alignment, fusion, and collaborative learning [1][2][7][11][14]. Representation Learning - Representation learning is fundamental for multimodal tasks, addressing challenges such as combining heterogeneous data and handling varying noise levels across different modalities [1]. - Prior to the advent of Transformers, different modalities required distinct representation learning models, such as CNNs for computer vision (CV) and LSTMs for natural language processing (NLP) [1]. - The emergence of Transformers has enabled the unification of multiple modalities and cross-modal tasks, leading to a surge in multimodal pre-training models post-2019 [1]. Translation - Cross-modal translation aims to map source modalities to target modalities, such as generating descriptive sentences from images or vice versa [2]. - The use of syntactic templates allows for structured predictions, where specific words are filled in based on detected attributes [2]. - Encoder-decoder architectures are employed to encode source modality data into latent features, which are then decoded to generate the target modality [2]. Alignment - Alignment is crucial in multimodal learning, focusing on establishing correspondences between different data modalities to enhance understanding of complex scenarios [7]. - Explicit alignment involves categorizing instances with multiple components and measuring similarity, utilizing both unsupervised and supervised methods [7][8]. - Implicit alignment leverages latent representations for tasks without strict alignment, improving performance in applications like visual question answering (VQA) and machine translation [8]. Fusion - Fusion combines multimodal data or features for unified analysis and decision-making, enhancing task performance by integrating information from various modalities [11]. - Early fusion merges features at the feature level, while late fusion combines outputs at the decision level, with hybrid fusion incorporating both approaches [11][12]. - The choice of fusion method depends on the task and data, with neural networks becoming a popular approach for multimodal fusion [12]. Collaborative Learning - Collaborative learning utilizes data from one modality to enhance the model of another modality, categorized into parallel, non-parallel, and hybrid methods [14][15]. - Parallel learning requires direct associations between observations from different modalities, while non-parallel learning relies on overlapping categories [15]. - Hybrid methods connect modalities through shared datasets, allowing one modality to influence the training of another, applicable across various tasks [15].

Core Insights - The multi-modal large model market in China is projected to reach 15.63 billion yuan in 2024, an increase of 6.54 billion yuan from 2023, and is expected to grow to 23.48 billion yuan in 2025, indicating strong market demand and government support [1][6][19] Multi-Modal Large Model Industry Definition and Classification - Multi-modal large models are AI systems capable of processing and understanding various data forms, including text, images, audio, and video, using deep learning technologies like the Transformer architecture [2][4] Industry Development History - The multi-modal large model industry has evolved through several stages: task-oriented phase, visual-language pre-training phase, and the current multi-modal large model phase, focusing on enhancing cross-modal understanding and generation capabilities [4] Current Industry Status - The multi-modal large model industry has gained significant attention due to its data processing capabilities and diverse applications, with a market size projected to grow substantially in the coming years [6][19] Application Scenarios - The largest application share of multi-modal large models is in the digital human sector at 24%, followed by gaming and advertising at 13% each, and smart marketing and social media at 10% each [8] Industry Value Chain - The industry value chain consists of upstream components like AI chips and hardware, midstream multi-modal large models, and downstream applications across various sectors including education, gaming, and public services [10][12] Competitive Landscape - Major players in the multi-modal large model space include institutions and companies like the Chinese Academy of Sciences, Huawei, Baidu, Tencent, and Alibaba, with various models being developed to optimize training costs and enhance capabilities [16][17] Future Development Trends - The multi-modal large model industry is expected to become more intelligent and humanized, providing richer and more personalized user experiences, with applications expanding across various fields such as finance, education, and content creation [19]