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.

翻译：摘要：竞争法专家在进行法律研究时，必须审阅大量案件、裁决和司法报告，以识别先例并评估竞争与合并案件中的关键要素。尽管通用研究助手（如Claude和ChatGPT）以及法律助手（如SaulLM-7B和LegalGPT）越来越多地被用于辅助法律研究，但它们在竞争法分析方面仍显不足：缺乏专业领域知识、提供的官方引用不充分，或虚构竞争法案例。我们提出Maat，一种ReAct代理，它能够协调对应于研究过程中不同任务的工具。Maat与竞争法专家迭代设计，利用RAG将案例和发现扎根于官方来源以保证可靠性，提供丰富的内联引用，在数据库覆盖不足时回退至网络搜索，并在查询模糊时提示用户澄清。在案件特定任务上，Maat显著优于所有基线助手；在理论问题任务上，其表现接近最优基线。所使用的数据集可在GitHub上获取。