Group 1 - The Terahertz concept sector increased by 2.90%, ranking fifth among concept sectors, with 22 stocks rising, including Sifang Electronics which hit the daily limit, and others like Leike Defense and Yaguang Technology showing significant gains of 7.69% and 7.46% respectively [1] - The top gainers in the Terahertz concept sector included Sifang Electronics, ZTE Corporation, and Yaguang Technology, with net inflows of 9953.89 million, 7174.28 million, and 4470.21 million respectively [2][3] - The overall market saw a net inflow of 2.02 billion into the Terahertz concept sector, with 13 stocks receiving net inflows, and 6 stocks exceeding 3000 million in net inflows [1] Group 2 - The leading stocks by net inflow ratio included Sifang Electronics at 17.39%, Tongfang Co. at 10.47%, and Yaguang Technology at 9.35% [2] - The trading volume and turnover rates for the top stocks in the Terahertz concept sector were notable, with Sifang Electronics showing a turnover rate of 7.20% and ZTE Corporation at 1.79% [2][3] - The stocks with the largest declines included Juguang Technology and Shuo Beid, with declines of 0.80% and 0.07% respectively [1]

证券代码（A/H）：000063/00763 证券简称(A/H)：中兴通讯 公告编号：202544 中兴通讯股份有限公司 关于按照《香港上市规则》公布 2025 年 4 月份证券变动月报表的公告 本公司及董事会全体成员保证信息披露的内容真实、准确和完整，没有虚假记载、误导 性陈述或重大遗漏。 中兴通讯股份有限公司根据《香港联合交易所有限公司证券上市规则》（简 称"《香港上市规则》"）规定，在香港联合交易所有限公司披露易网站 （www.hkexnews.hk）刊登了截至 2025 年 4 月 30 日的证券变动月报表。 根据《深圳证券交易所股票上市规则》关于境内外同步披露的要求，特将有 关公告同步披露如下，供参阅。 特此公告。 呈交日期: 2025年5月7日 I. 法定/註冊股本變動 | 1. 股份分類 | 普通股 | 股份類別 | H | | | 於香港聯交所上市 (註1) | 是 | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 證券代號 (如上市) | 00763 | 說明 | | | | | | | | | | 法定/註冊股份數目 ...

Core Viewpoint - The article discusses the development of a novel method for generating high-quality Chain of Thought (CoT) data, focusing on the adaptive difficulty grading of questions for large language models (LLMs) to enhance the reasoning capabilities of smaller models [2][6][41]. Group 1: Research Motivation and Challenges - The emergence of large models like DeepSeek-R1 (671 billion parameters) has highlighted the challenges of deploying such models in real-time systems and edge devices [6]. - There is a pressing need for research on smaller models with fewer than 7 billion parameters, particularly in complex reasoning tasks such as mathematical problem-solving and code generation [7]. - Current CoT data generation methods face challenges, including high computational and annotation costs associated with large-scale data-driven approaches and limited performance gains from high-quality sample-driven methods [8][9]. Group 2: Proposed Methodology - The article introduces a new method called "LLM Adaptive Question Difficulty Grading," which aims to improve the quality of CoT data by dynamically matching model capabilities with data difficulty [12][13]. - The method includes four key innovations: establishing a question difficulty grading system based on inherent model reasoning capabilities, creating an adaptive question bank, designing a difficulty distribution sampling strategy, and generating high-quality CoT data using DeepSeek-R1 [15][18]. Group 3: Experimental Results - The proposed method has shown significant improvements in reasoning performance across various model sizes, with accuracy increases ranging from 6.66% to 26.7% on the AIME24 mathematics competition dataset compared to traditional non-adaptive strategies [18][20]. - Detailed experimental results indicate that models trained with the adaptive CoT data outperform baseline models in multiple mathematical reasoning benchmarks, achieving up to 94.6% accuracy on MATH500 [37]. - The ZCode-32B model demonstrated superior performance across different difficulty levels, indicating that smaller models can achieve competitive results through adaptive data training [38]. Group 4: Conclusion and Future Work - The article concludes that the proposed framework for generating high-quality CoT data is efficient and effective, requiring only about 2,000 high-quality samples to significantly enhance model performance while reducing data and computational costs [41]. - Future work will focus on further integrating reinforcement learning to explore deeper reasoning capabilities and extending applications to more complex cross-domain tasks such as communication fault diagnosis [42].

Investment Rating - The report maintains a "Buy" rating for the company, indicating an expected stock price increase of over 15% relative to the industry index in the next six months [4]. Core Viewpoints - The company is experiencing steady revenue recovery, with a projected revenue of 124.25 billion yuan in 2023, slightly increasing to 150.12 billion yuan by 2027, reflecting a compound annual growth rate (CAGR) of approximately 7.5% from 2025 to 2027 [1]. - The net profit attributable to the parent company is expected to decline from 9.33 billion yuan in 2023 to 8.43 billion yuan in 2024, before gradually increasing to 9.46 billion yuan by 2027, indicating a focus on long-term growth despite short-term challenges [1]. - The company is undergoing a structural transformation, shifting from a primary focus on operator networks to a collaborative development model involving "network + computing power + terminals," with over 35% of revenue coming from the second curve business [2][3]. Financial Summary - Revenue is projected to decrease by 2.4% in 2024, followed by a recovery with a growth rate of 7.0% in 2025 and maintaining similar growth rates through 2027 [1]. - The gross margin is under pressure during the transformation phase, with a reported gross margin of 34.27% in Q1 2025, down 7.75 percentage points year-on-year, primarily due to intense competition in the computing power and terminal markets [2]. - The company anticipates that the computing power business will become a major growth driver, particularly with the launch of DeepSeek, which is expected to shift demand from training to inference, thus expanding the market size [2]. Earnings Forecast - The forecasted net profit for the company is 8.58 billion yuan in 2025, 8.88 billion yuan in 2026, and 9.46 billion yuan in 2027, with corresponding price-to-earnings (P/E) ratios of 17.5, 16.9, and 15.9 respectively [3][4].

Core Insights - The development of large AI models is driving a significant increase in demand for computing power, with a projected annual growth rate of 10 times as AI evolves from version 1.0 to 2.0 [1] - ZTE Corporation has established a dual-driven strategy of "Connectivity + Computing Power" to become a leader in the "network connectivity + intelligent computing power" sector, emphasizing the importance of AI integration in various industries [3][4] - The global AI market is expected to exceed $800 billion by 2028, with a compound annual growth rate (CAGR) of over 32%, indicating substantial investment opportunities in AI technologies [4] Industry Developments - ZTE's revenue for Q1 this year reached 32.97 billion yuan, a year-on-year increase of 7.8%, with over 35% of revenue coming from its second curve business focused on computing power and terminals [5] - The company is focusing on three key areas in AI: enhancing ICT infrastructure solutions, developing AI terminal applications, and promoting AI innovation for broader application [5][6] - ZTE has launched the AiCube DeepSeek intelligent computing integrated machine, which is already being applied in sectors such as education, healthcare, steel, and automotive [5] Challenges and Solutions - Industry clients face challenges such as data pollution, high costs of data collection, and long business cycle times for AI technology updates [9] - ZTE has upgraded its digital cloud platform to address these challenges, facilitating better integration of AI into business operations [9] - The introduction of the "Xingyun Intelligent Agent" marks a new phase in the deployment of enterprise-level intelligent agents, aimed at lowering the technical barriers for AI model commercialization across various industries [10]

Core Viewpoint - The forum focused on how artificial intelligence (AI) can empower the digital transformation of education and explore innovative development paths for "AI + Education" [1] Group 1: AI and Education Transformation - AI is reshaping the educational ecosystem, with digitalization being a key engine for high-quality educational development [3] - The Ministry of Education and nine other departments have issued opinions to accelerate educational digitalization, guiding collaborative innovation between schools and enterprises [3] - ZTE Corporation has planned three main paths: establishing an AI + Education special fund, creating a digital resource pool for education, and deepening industry-education integration [3] Group 2: Innovation and Competitions - The 2025 Second Education Information Technology Application Innovation Competition is set to begin, with ZTE hosting the AI-focused "Target Intelligent Detection Technology Application Practice Competition" [5] - This competition aims to address technical challenges in target detection, combining academic research with practical needs to accelerate technology application [5] Group 3: Industry Collaboration and Solutions - Representatives from leading companies discussed AI education, digitalization, and industry-education integration, launching the "Zhihai AI Education Integrated Machine" for smart teaching solutions [6] - ZTE officially launched the ZTE AI Certification, emphasizing its role as a builder of digital economy and its commitment to educational digitalization [6] - ZTE's "three-in-one" solution focuses on technology foundation, industry-education integration, and ecological co-construction [6] Group 4: Future Directions and Goals - ZTE is actively collaborating with the Ministry of Education on various initiatives, including talent cultivation and international professional standards [7] - The successful forum marks a significant step in promoting the integration of education and technology, contributing to the "Digital China Strategy" [7] - ZTE aims to accelerate the implementation of industry-specific large models and enhance AI talent development to achieve educational reform goals [7]

【环球网科技报道 记者 心月】2025年4月28 - 29日，2025中兴通讯中国生态合作伙伴大会在福州中庚喜来登酒店举行，大会以"数智同兴 共创未来"为主题， 聚焦数字化与智能化融合创新，全力构建开放生态，助力客户加速数智化转型。同期，中兴通讯在第八届数字中国建设峰会上，以"智连山海 数启兴城"为 主题，展示人工智能领域的前沿创新与多元应用。中兴通讯首席发展官崔丽在接受环球网记者采访时分享了中兴在AI技术应用和智算领域的关键进展。 "DeepSeek等开源模型的兴起，让各行各业加速了AI部署，为ICT基础设施建设带来广阔空间。"崔丽称，AI技术的发展和深化应用，将推动智算中心建设、 消费端业务增长，带动网络建设发展，成为未来3 - 5年ICT行业发展的核心驱动力，这也为中兴通讯带来重大机遇。 中兴通讯推出的系列化AiCube DeepSeek智算一体机，在多个行业落地应用，助力企业智能化转型。崔丽介绍道："这款智算一体机是AI大模型在行业落地的 关键一环，它具有全栈开放、超高性能、生态开放且成本低、可靠交付这4大差异化优势。"全栈开放使得客户可自由选择各部件；超高性能体现在支持原生 满血版、最大671B的D ...

Group 1 - The company has positioned the internet market as a strategic focus, aiming to expand cooperation with major firms like ByteDance, Alibaba, and Tencent [1] - In Q1, the company's orders for intelligent computing servers accounted for over 60%, with significant growth in the scale of cooperation [1] - As 5G investments mature, operators are shifting their capital expenditures towards computing power, with a projected increase in computing investments by major telecom operators [1] Group 2 - The company anticipates a 2.38% year-on-year decline in revenue for 2024, with a net profit of 8.425 billion yuan, down 9.66% [2] - The company is confident in returning to revenue growth, driven by changes in AI market focus from training to inference [2] - Organizational adjustments are being made to enhance responsiveness and efficiency in dealing with internet companies [2][3] Group 3 - The company is implementing a "big company, small team" approach, establishing dedicated teams for each client to improve decision-making speed and project efficiency [3] - In Q1, the company's revenue grew by 7.8%, with enterprise revenue doubling and surpassing 20% of total revenue for the first time [3] - The company aims to focus on breakthroughs in telecommunications, internet, finance, and power industries this year [3]

Core Viewpoint - The telecommunications industry is shifting focus from 5G infrastructure to AI infrastructure investment, with companies like ZTE Communications aiming to capitalize on growth opportunities in the AI era [1][2]. Group 1: AI and Infrastructure Investment - ZTE Communications is transitioning from a fully connected model to a "connection + computing power" model, anticipating significant growth driven by AI over the next 3 to 5 years [1][2]. - IDC predicts that the global AI market will exceed $800 billion by 2028, with a compound annual growth rate (CAGR) of over 32%, prompting major internet companies and operators to increase investments in computing infrastructure [1]. Group 2: Revenue and Business Segments - In 2024, ZTE Communications expects a 2.38% year-on-year decline in revenue due to the overall investment environment, but the company is optimistic about returning to growth driven by AI [2]. - The revenue from ZTE's second curve business, which includes computing and terminals, accounted for over 35% in Q1, while the enterprise business revenue doubled, making up over 20% [2]. Group 3: Shift in Revenue Sources - The operator network, previously ZTE's highest revenue source, saw a 15.02% decline in revenue to 703.27 billion yuan in the previous year, influenced by the domestic investment environment [3]. - By 2024, the operator business's revenue share is projected to decrease to 57.98%, while consumer business revenue will rise to 26.72%, and enterprise business revenue will reach 15.3% [3]. Group 4: Growth in Enterprise Business - The surge in AI adoption is driving demand for intelligent computing, with intelligent computing servers becoming a core growth engine for ZTE [4]. - The company anticipates significant growth in the enterprise market, driven by the digitalization needs of industries like short video and live streaming [4]. Group 5: Strategic Focus on Key Industries - ZTE is targeting major clients in the internet, finance, and power sectors, with significant investments in AI infrastructure from leading companies like Alibaba, which plans to invest 380 billion yuan over three years [5]. - The company has adopted a "large enterprise-small team" model to enhance responsiveness to clients, integrating R&D, supply chain, and sales resources [5]. Group 6: Consumer Business Challenges and Strategies - ZTE's consumer business, including smartphones, is viewed as a core growth area, with over 26% of total revenue coming from this segment last year [8]. - The company plans to increase investment in mobile phones, focusing on brand and channel development, and enhancing product competitiveness through collaboration with AI technology firms [9].

Core Viewpoint - The news highlights the performance and potential of state-owned enterprises (SOEs) in China, particularly in the context of ongoing reforms and the integration of artificial intelligence in operations, which may enhance their competitiveness and market performance [2][4]. Group 1: ETF Performance - As of April 29, 2025, the National Enterprise Win-Win ETF (159719) decreased by 0.47%, with a latest price of 1.48 yuan. Over the past week, it has seen a cumulative increase of 0.54% [1]. - The National Enterprise Win-Win ETF had a turnover of 1.2% during the trading session, with a transaction volume of 2.3576 million yuan. The average daily transaction volume over the past year was 17.9394 million yuan [1]. - The Greater Bay Area ETF (512970) increased by 0.43%, with a latest price of 1.16 yuan. Over the past year, it has accumulated a rise of 13.72% [4]. Group 2: Index and Component Stocks - The National Enterprise Win-Win ETF closely tracks the FTSE China National Enterprise Open Win-Win Index, which consists of 100 constituent stocks, including 80 A-share companies and 20 Chinese companies listed in Hong Kong. The top ten constituent stocks are predominantly "China National" stocks [4][6]. - The top ten stocks in the index include China Petroleum, China Petrochemical, China Construction, and China Mobile, with respective weightings of 15.58%, 12.33%, and 8.89% [6]. - The Greater Bay Area Development Theme Index (931000) saw a slight increase of 0.06%, with notable performers including Keda Manufacturing and Weigao Medical, which rose by 6.95% and 4.54%, respectively [4]. Group 3: Policy and Market Outlook - The State-owned Assets Supervision and Administration Commission (SASAC) is actively promoting the "AI+" initiative, focusing on integrating artificial intelligence into enterprise operations to enhance efficiency [1]. - According to Galaxy Securities, SOEs are expected to play a crucial role in China's modernization process, with ongoing reforms aimed at improving core competitiveness and operational efficiency [2]. - The market-oriented operational mechanisms of SOEs are being refined, with plans to implement performance adjustments and exit strategies for underperforming entities by 2025, which may lead to improved profitability [2].