From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation
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...
| Authors | Mingfei Lu, Yi Zhang, Mengjia Wu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 April 2026 |
| Last updated at source | 12 April 2026 |
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