LawCompass: Navigating from Legal QA to Multi-Agent Deep Research with Grounded Evidence

arXiv 2610.01027 ↗First posted 1 Oct 2026cs.CLcited by 0

On the map: Retrieval-augmented generation → Legal and financial RAG

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LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Legal and financial RAG, one of 15 directions in Retrieval-augmented generation. Builds on 11 of its 17 references that are in the map. First posted on arXiv 1 Oct 2026.

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2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 877
2023Large Language Models in Finance: A Surveycited by 223
2023Large Language Models in Law: A Surveycited by 128
2023Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecturecited by 119
2025Multi-Agent Simulator Drives Language Models for Legal Intensive Interactioncited by 22
2025CoCoLex: Confidence-guided Copy-based Decoding for Grounded Legal Text Generationcited by 6
2024ELLA: Empowering LLMs for Interpretable, Accurate and Informative Legal Advicecited by 5
2025LegalCore: A Dataset for Event Coreference Resolution in Legal Documentscited by 3

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