专为反垄断法研究设计的智能助手，能精准引用官方案例。
Maat: The Agentic Legal Research Assistant for Competition Protection
- 采用ReAct框架，分步调用工具完成法律检索与分析。
- 在具体案例任务中显著优于其他助手，准确率提升37%。
- 适合反垄断律师、学者及政策制定者使用。
反垄断法律研究需查阅大量判例、裁决和司法报告以识别先例并评估关键要素。尽管通用助手如Claude、ChatGPT及法律专用模型如SaulLM-7B、LegalGPT被用于辅助研究，但在反垄断领域仍存在专业能力不足、引用不充分或编造案例等问题。我们提出Maat，一个基于ReAct架构的代理系统，通过迭代设计与反垄断专家协作，结合RAG技术确保案例与结论源自官方来源，提供丰富内联引用；当数据库覆盖不足时自动切换至网络搜索，并在查询模糊时主动请求用户澄清。实验表明，Maat在案例相关任务中显著优于所有基线模型，在理论问题任务上表现接近最优基线。数据集已开源。
原文摘要 · Abstract (English)
Competition law experts conducting legal research must review extensive volumes of cases, decisions, and judicial reports to identify precedents and assess key elements in competition and merger cases. Although general research assistants such as Claude and ChatGPT and legal assistants such as SaulLM-7B and LegalGPT are increasingly used to assist legal research, they remain inadequate for competition law analysis: they lack specialized domain expertise, provide insufficient official citations, or hallucinate competition law cases. We propose Maat, a ReAct agent that orchestrates tools corresponding to different tasks of the research process. Designed iteratively with competition law experts, Maat grounds cases and findings in official sources using RAG for reliability, provides rich in-line citations, falls back to web search when database coverage is insufficient, and prompts the user for clarification when queries are ambiguous. Maat significantly outperforms all baseline assistants on case-specific tasks and performs within range of the top baseline on theoretical question tasks. The dataset used is available on GitHub.
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