[2509.00761] L-MARS: Legal Multi-Agent Workflow with Orchestrated Reasoning and Agentic Search

Computer Science > Artificial Intelligence arXiv:2509.00761 (cs) [Submitted on 31 Aug 2025 (v1), last revised 30 Mar 2026 (this version, v3)] Title:L-MARS: Legal Multi-Agent Workflow with Orchestrated Reasoning and Agentic Search Authors:Ziqi Wang, Boqin Yuan View a PDF of the paper titled L-MARS: Legal Multi-Agent Workflow with Orchestrated Reasoning and Agentic Search, by Ziqi Wang and 1 other authors View PDF HTML (experimental) Abstract:We present L-MARS (Legal Multi-Agent Workflow with Orchestrated Reasoning and Agentic Search), a multi-agent retrieval framework for grounded legal question answering that decomposes queries into structured sub-problems, retrieves evidence via agentic web search, filters results through a verification agent, and synthesizes cited answers. Existing legal QA benchmarks test either closed-book reasoning or retrieval over fixed corpora, but neither captures scenarios requiring current legal information. We introduce LegalSearchQA, a 50-question benchmark across five legal domains whose answers depend on recent developments that post-date model training data. L-MARS achieves 96.0% accuracy on LegalSearchQA, a 38.0% improvement over zero-shot performance (58.0%), while chain-of-thought prompting degrades performance to 30.0%. On Bar Exam QA (Zheng et al., 2025), a reasoning-focused benchmark of 594 bar examination questions, retrieval provides negligible gains (+0.7 percentage points), consistent with prior findings. These results show that a...