于恩 投稿 量子位 | 公众号 QbitAI 超越YOLOv3、Faster-RCNN，首个在COCO2017 val set上突破30AP的 纯多模态开源LLM 来啦！ 华中科技大学、北京邮电大学等多所高校研究团队共同推出的 Perception-R1 （PR1） ，在视觉推理中最基础的感知层面，探究rule- based RL能给模型感知pattern带来的增益。 PR1重点关注当下主流的 纯视觉 （计数，通用目标检测） 以及 视觉语言 （grounding，OCR） 任务，实验结果展现出在模型感知策略上 的巨大潜力。 然而，在识别物体和真正以细致入微的理解和逻辑感知视觉世界之间存在微妙的差异。虽然MLLM在一般的视觉问答方面越来越出色，但它们 在需要精确物体定位、准确计数多个物体、在复杂布局中完美阅读文本或执行复杂视觉推理的任务上常常表现不佳。这就像知道图片中有一只 猫和能够精确指出它的耳朵、计算它的胡须或理解它与其他物体的互动之间的区别。 强化学习的崛起与Perception-R1的诞生 强化学习 （Reinforcement Learning, RL） 引发了语言模型的范式转变。像RLHF （来自人 ...

Group 1 - The emergence of DeepSeek, a large model comparable to ChatGPT, has created significant waves in the global technology and capital markets, igniting enthusiasm for innovation and investment opportunities in the tech sector [3] - Tangxing Capital focuses on discovering and nurturing high-growth potential hard tech companies, aiming to drive industrial upgrades and regional economic development through a comprehensive support system [3][4] - The investment team at Tangxing Capital possesses deep industry backgrounds and professional investment capabilities, allowing them to accurately grasp technology development trends and identify quality projects [3][4] Group 2 - Young entrepreneurs like Liang Wenfeng and Wang Xingxing exemplify the characteristics of contemporary tech leaders, showcasing strong learning abilities and rapid application of new technologies [4][5] - These entrepreneurs break traditional thinking and industry boundaries, integrating resources across sectors to create new application scenarios and business models [5][6] - Key traits admired in successful entrepreneurs include innovation spirit, cross-disciplinary integration ability, strategic vision, and focus on core business areas [6] Group 3 - The investment style of Tangxing Capital is characterized by "insightful decisiveness," emphasizing the ability to quickly identify and act on investment opportunities [7] - A notable investment decision involved a significant investment in Plater, a key player in the 3D printing industry, despite market uncertainties, which later yielded a tenfold return [9] - Plater's technology addresses complex manufacturing needs in aerospace, automotive, and medical sectors, significantly contributing to China's manufacturing transformation [8][9] Group 4 - The current bull market is driven by a combination of macroeconomic stability, loose monetary policy, and positive market sentiment, creating a conducive environment for investment [10][11] - The bull market enhances the financing environment for primary markets, encouraging entrepreneurship and accelerating company growth through increased funding [12][13] - The interaction between primary and secondary markets fosters a cycle of investment and exit opportunities, optimizing resource allocation and enhancing economic vitality [14]

市场消息：苹果公司CEO库克称仍然对公司的人工智能（AI）和大语言模型（LLM）路线图感到兴 奋。 ...

苹果公司CEO库克：仍然对公司的人工智能（AI）和大语言模型（LLM）路线图感到兴奋。 ...

Group 1 - The report by Zhong An Technology and Zhong An Financial Technology Research Institute explores the application of large language models (LLMs) in the financial and insurance industries, concluding that LLMs present new opportunities but face challenges in implementation that require multi-party collaboration [1] - The development of large model technology is diversifying globally, with vertical models emerging to provide tailored industry solutions. China has made progress in computing autonomy and data optimization, leading to a trend of functional differentiation and specialization in its ecosystem [1][24] - New technologies are driving down the costs of training, operation, and inference for large models, prompting a restructuring of processes in the financial industry. Financial enterprises need to balance acquisition, inference, and operational costs while selecting appropriate deployment models and roles [1][12] Group 2 - Domestic models like DeepSeek and Tongyi Qianwen have achieved breakthroughs in cost control and inference performance, providing better technical options for insurance institutions while ensuring data security and compliance [1][15] - Insurance institutions are accelerating the integration of large models, focusing on internal efficiency improvements across the entire insurance business chain and back-office management. Caution is advised during pilot applications to address data security and AI hallucination issues [1][16] - The value of data elements is becoming more prominent, with the financial and insurance industries building high-quality datasets through horizontal, vertical, and government-enterprise collaboration mechanisms to promote intelligent transformation [1][19] Group 3 - The application of large language models in the financial and insurance sectors is transitioning from pilot exploration to systematic integration, with initial deployments focusing on low-risk, low-intervention auxiliary business scenarios such as intelligent customer service and smart claims [6][7] - The introduction of large language models is not only enhancing process efficiency but also driving a deep transformation in information processing paradigms and decision-making logic within the industry [8][9] - The rise of large language models is reshaping the operational philosophies, business logic, and value creation models of financial institutions, leading to trends such as precision financial services and cross-industry ecological collaboration [9][10] Group 4 - The evolution of large model technology is characterized by a shift from purely algorithmic breakthroughs to the construction of systemic capabilities that integrate model deployment, business processes, and system interfaces [29][30] - The deployment capabilities of large models are transitioning from "usable" to "adaptable," with future competition likely focusing on building flexible deployment mechanisms across architectures and scenarios [31] - The emergence of vertical large models is addressing the specific needs of industries like finance and healthcare, enhancing precision and efficiency in tasks such as risk assessment and compliance checks [40][41]

点击按钮 ▲立即预约 民营经济促进法获表决通过 4月30日消息，十四届全国人大常委会第十五次会议表决通过民营经济促进法，自2025年5月20日起施行。民营经济促进法共9章78条，包括总 则、公平竞争、投资融资促进、科技创新、规范经营、服务保障、权益保护、法律责任和附则。 据悉，作为我国第一部专门关于民营经济发展的基础性法律，民营经济促进法将进一步优化民营经济发展环境，保证各类经济组织公平参与市场 竞争，促进民营经济健康发展和民营经济人士健康成长，构建高水平社会主义市场经济体制，发挥民营经济在国民经济和社会发展中的重要作 用。（界面新闻） ｜点评｜ 在当前科技浪潮中，敏锐洞察技术风向的民营企业，更是推动创新的重要源动力。为民营经济立法，从法律上认可民营经济的地位， 实有必要。面对市场环境变化，民营企业往往表现得更加敏感，其健康发展也需要得到法律的支持。 民营经济一路走来，依然面临着不少壁垒和约束。其活力释放，尤其依赖宽松、公平的市场环境。从这点出发，法律更需要给予民营企业被保 护的安全感，而非被限制过多的条条框框。同时，出台法律只是一个起点，支持民营经济，更要重视民企的实际经营需求。在此过程中，法律 的內容也需 ...

以下文章来源于刘聪NLP ，作者周星星 ，恢复了 PPO 的原始目标，采用蒙特卡罗回报估计优势，并设置无偏基线，从而 有效避免了优化偏差，在提升令牌效率的同时，还能维持模型的推理性能。 4. 推理能力的提升是渐进的，没有明显的"顿悟时刻" 6. 避免"长度作弊"需自然扩展响应。 刘聪NLP . NLP刘聪，如货币般流通！这里的刘聪，不会rapper，只发paper！长期关注AIGC前沿内容！还写过两 本书：ChatGPT原理与实战、大型语言模型实战指南！欢迎来讨论AI！ 上篇 R1复现小记：在业务场景的两类NLP任务上有显著效果 提到在业务场景中复现 DeepSeek-R1，也简单 记录下最近阅读一些论文过程中积累的 insight。 [1]Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning [2]An Empirical Study on Eliciting and Improving R1-like Reasoning Models [3]Understanding R1-Zero-Like Training: ...

Uxin Limited (UXIN) Q4 2025 Earnings Call April 30, 2025 08:00 AM ET Company Participants Jack Wang - Managing DirectorDai Kun - Founder, Chairman & CEOFeng Lin - Chief Financial Officer Conference Call Participants Fei Dai - Analyst Operator and welcome to the Yixin Fourth Quarter and Full Year twenty twenty four Earnings Conference Call. At this time, all participants are in listen only mode. A question and answer session will follow the formal presentation. As a reminder, this conference is being recorde ...

Core Viewpoint - BabelDOC has developed a PDF translation tool that effectively addresses common issues in machine translation, such as formatting errors and layout inconsistencies, allowing for precise PDF output. Group 1: Product Features - BabelDOC achieved a top-three ranking in the GitHub Trending list for all development languages shortly after its release [2] - The tool supports multiple languages, enabling translations from Latin-based languages to Simplified Chinese, Traditional Chinese, Japanese, and Korean, as well as mutual translations among Chinese, Japanese, and Korean [2] - Free users can process up to 1,000 pages per month, while Pro users can process up to 10,000 pages and access advanced translation models [3] Group 2: Technical Implementation - BabelDOC can extract and translate embedded elements in PDFs, such as charts, footnotes, and formulas, ensuring pixel-level layout alignment with the original document [7] - The tool utilizes AI layout recognition technology to identify text layout, paragraph structure, and complex formatting, which is crucial for maintaining the integrity of professional documents [7][9] - After recognizing the layout, the extracted text is translated using a large language model, and the translated text is matched with the original formatting to ensure consistency [8][9] Group 3: Understanding PDF Complexity - PDF (Portable Document Format) was invented by John Warnock in the early 1990s to ensure consistent document display across different devices [13] - PDF documents have unique advantages, such as strong cross-platform compatibility and high-quality printing, but they are less editable compared to DOCX formats [14] - The structure of a PDF is complex, resembling a tree with various components, including a file header, page tree, cross-reference table, and content flow, which complicates the translation process [16][19]

医疗大模型知识覆盖度首次被精准量化！ 在医疗领域，大语言模型（LLM）的潜力令人振奋，但其知识储备是否足够可靠？腾讯优图实验室天衍研究中心的最新研究给出了答案。 他们提出的 MedKGEval框架 ，首次通过医疗知识图谱（KG）的多层级评估，系统揭示了GPT-4o等主流模型的医学知识覆盖度。 该研究已被WWW 2025会议Web4Good Track录用为口头报告（oral）。目前，WWW 2025正在悉尼举行，会议时间从4月28日持续至5月2 日。 MedKGEval团队 投稿 量子位 | 公众号 QbitAI 背景 大语言模型（LLM）在医疗领域的快速发展凸显了其知识存储与处理的潜力，但其临床部署前的可靠性验证亟需更系统化的评估框架。 当前主流的Prompt-CBLUE、Medbench和MedJourney等评估体系虽通过医学问答基准测试LLM的任务执行能力，却存在三个明显的局限： 1）其长尾数据分布导致罕见病症覆盖不足，评测结果存在偏差； 2）任务导向的设计聚焦疾病预测、用药咨询等单一场景，难以量化模型内在医学知识储量； 3）传统问答形式局限于表面对错判断，无法捕捉医学概念间的复杂拓扑关联。 为解决这 ...