Core Insights - The article presents a comprehensive review of the application of Large Language Models (LLMs) in the legal field, introducing an innovative dual perspective classification method that integrates legal reasoning frameworks with professional ontology [1][3][5] - It highlights the advancements of LLMs in legal text processing, knowledge integration, and formal reasoning, while also addressing core issues such as hallucinations, lack of interpretability, and cross-jurisdictional adaptability [1][5][12] Group 1: Technological Advancements - Traditional legal AI methods are limited by symbolic approaches and small model techniques, facing challenges such as knowledge engineering bottlenecks and insufficient semantic interoperability [6][8] - The emergence of LLMs, powered by Transformer architecture, has successfully overcome the limitations of earlier systems through context reasoning, few-shot adaptation, and generative argumentation capabilities [6][12] - The legal sector's demand for complex text processing, multi-step reasoning, and process automation aligns well with the emerging capabilities of LLMs [8][12] Group 2: Ethical and Governance Challenges - The practical application of technology in the legal field is accompanied by ethical risks, such as the amplification of biases and the weakening of professional authority, necessitating a systematic research framework to integrate technology, tasks, and governance [3][8][11] - The review systematically analyzes ethical challenges faced by legal practitioners, including technical ethics and legal professional responsibilities, expanding user-centered ontology research for LLM deployment [11][12] Group 3: Research Contributions - The study employs an innovative dual perspective framework that combines legal argumentation types with legal professional roles, significantly advancing research in the field [9][12] - It constructs a legal reasoning ontology framework that aligns the Toulmin argument structure with LLM workflows, integrating contemporary LLM advancements with historical evidence research [9][10] - A role-centered deployment framework for LLMs is proposed, merging litigation and non-litigation workflows to meet the demand for smarter tools in legal practice [10][12] Group 4: Future Directions - Future research should prioritize multi-modal evidence integration, dynamic rebuttal handling, and aligning technological innovations with legal principles to create robust and ethically grounded legal AI [13] - The article advocates for a legal profession-centered strategy, positioning LLMs as supportive tools rather than decision-makers, ensuring human oversight at critical junctures [13]

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]