研究人员首次系统综述了大型语言模型（LLM）在法律领域的应用，提出创新的双重视角分类法，融合法律推理框架（经典的法律论证型式框架）与职 业本体（律师/法官/当事人角色），统一梳理技术突破与伦理治理挑战。论文涵盖LLM在法律文本处理、知识整合、推理形式化方面的进展，并指出幻 觉、可解释性缺失、跨法域适应等核心问题，为下一代法律人工智能奠定理论基础与实践路线图。 既解剖图尔敏论证框架下的九类任务的技术进展，又映射争议解决全场景的大模型真实工作流。 当法律严谨性碰撞人工智能的生成浪潮，如何驾驭LLM的颠覆性潜力？ 传统法律人工智能受限于符号主义和小模型方法，面临知识工程瓶颈、语义互操作性不足及碎片化推理等挑战。Transformer架构的LLM凭借上下文推 理、少样本适应和生成式论证能力，突破了早期系统的局限性。 法律领域对复杂文本处理、多步骤推理和流程自动化的需求与LLM的涌现能力高度契合。 但技术落地伴随伦理风险（如偏见放大、专业权威弱化），亟需系统性研究框架整合技术、任务与治理。 来自中国政法大学、香港理工大学等不同学科的法律科技交叉团队完成了首部系统整合法律推理与LLM技术的全面综述，以开创性「双重视角分类 ...

Core Insights - The article discusses the rapid development and integration of AI Agents in various sectors, highlighting their potential to transform workflows and user experiences [1][3] - It raises critical questions about the current capabilities and limitations of AI Agents, as well as the evolving human-AI relationship [1][3] User Perspective: Ideal vs. Reality - AI Agents are defined by their ability to use tools, make autonomous decisions, and engage in iterative processes [3][5] - The relationship between humans and AI Agents is characterized as a partnership rather than a contractual one, emphasizing collaboration [5][6] User Experiences with AI Agents - Users categorize AI Agents into three types: coaching, secretarial, and collaborative, each serving different functions in their daily tasks [9][10] - Specific examples of AI tools like CreateWise and Manus demonstrate their capabilities in audio editing and task management, respectively [12][14] User Complaints - Users express concerns about AI Agents' inability to follow instructions accurately and the tendency for AI to overcomplicate tasks [18][20] - The lack of "human-friendly" design in AI products is noted, as they often fail to capture the nuances of human interaction [21][23] Builder Responses: Technical Challenges and Solutions - Developers acknowledge the need for AI Agents to manage user expectations and improve their decision-making capabilities through experience [30][32] - The importance of user feedback in refining AI performance is emphasized, likening AI to inexperienced interns who need guidance [32][33] Technical Innovations and Market Strategies - The article discusses the potential for multi-Agent collaboration to enhance problem-solving capabilities [41][42] - It highlights the necessity for AI products to focus on specific industries to accumulate valuable user data and insights [46][49] Business Perspective: Competitive Landscape - New data generated by AI Agents can disrupt traditional SaaS models, providing startups with a competitive edge [53][55] - The article suggests that startups should focus on niche markets and specific user needs to avoid direct competition with large model companies [67][68] Philosophical and Future Considerations - The widespread adoption of AI Agents is expected to reshape human-machine relationships and societal structures [70]