From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation

• URL: http://arxiv.org/abs/2604.10470v1
• Date: Sun, 12 Apr 2026 05:49:37 GMT
• Title: From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation
• Abstract summary: Legal consultation question answering (Legal CQA) presents unique challenges compared to traditional legal QA tasks.<n>We construct JurisCQAD, a large-scale dataset of over 43,000 real-world Chinese legal queries annotated with expert-validated positive and negative responses.<n>We propose JurisMA, a modular multi-agent framework supporting dynamic routing, statutory grounding, and stylistic optimization.
• Score: 7.115887917718381
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Legal consultation question answering (Legal CQA) presents unique challenges compared to traditional legal QA tasks, including the scarcity of high-quality training data, complex task composition, and strong contextual dependencies. To address these, we construct JurisCQAD, a large-scale dataset of over 43,000 real-world Chinese legal queries annotated with expert-validated positive and negative responses, and design a structured task decomposition that converts each query into a legal element graph integrating entities, events, intents, and legal issues. We further propose JurisMA, a modular multi-agent framework supporting dynamic routing, statutory grounding, and stylistic optimization. Combined with the element graph, the framework enables strong context-aware reasoning, effectively capturing dependencies across legal facts, norms, and procedural logic. Trained on JurisCQAD and evaluated on a refined LawBench, our system significantly outperforms both general-purpose and legal-domain LLMs across multiple lexical and semantic metrics, demonstrating the benefits of interpretable decomposition and modular collaboration in Legal CQA.

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