发表机构
Anhui University; Tsinghua University（安徽大学; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
LawCompass是一个基于证据的法律助手，通过法律问答、专业检索和深度研究三种功能，实现从标准问答到多智能体深度研究的转变，并保持引用链接以增强可信度。
AI 中文摘要
大型语言模型（LLM）和检索增强生成（RAG）的最新进展显著促进了法律信息的普及。然而，大多数现有的法律助手仍局限于多轮对话式问答，无法支持需要系统性证据检索、多步推理和报告级综合的复杂法律任务。在本文中，我们提出了LawCompass，一个基于证据的法律助手，它引导从标准法律问答向多智能体深度研究的转变。LawCompass提供三种面向任务的功能：法律问答，为法律问题提供精确且有证据支持的答案；专业检索，通过查询重写实现对法规和司法案例的结构化探索；以及深度研究，采用多智能体工作流来分解复杂法律任务并综合生成全面的研究报告。至关重要的是，LawCompass在所有模块中保持明确的引用链接，使用户能够直接对照原始法律来源验证系统输出。评估结果表明，LawCompass为将对话式AI转变为可信且基于证据的法律研究辅助提供了一种实用且可扩展的范式。
英文摘要
Recent advances in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) have significantly democratized access to legal information. Nevertheless, most existing legal assistants remain confined to multi-turn conversational QA, failing to support complex legal tasks that require systematic evidence retrieval, multi-step reasoning, and report-level synthesis. In this paper, we present LawCompass, an evidence-grounded legal assistant that navigates the transition from standard Legal QA to multi-agent deep research. LawCompass provides three task-oriented functions: Legal QA, which delivers precise, evidence-backed answers to legal questions; Professional Retrieval, which enables structured exploration of statutes and judicial cases via query rewriting; and Deep Research, which employs a multi-agent workflow to decompose complex legal tasks and synthesize comprehensive research reports. Crucially, LawCompass maintains explicit citation links across all modules, empowering users to directly verify system outputs against original legal sources. Evaluation results demonstrate that LawCompass provides a practical and scalable paradigm for transforming conversational AI into trustworthy and evidence-grounded legal research assistance.
