Legal-R1: Agentic Retrieval for Evidence-Based Legal Reasoning
We introduce Legal-R1, an agentic retrieval framework for evidence-based legal reasoning.
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Key points
- Legal question answering requires answers that are not only plausible, but also grounded in legally valid and citable evidence.
- Standard retrieval-augmented generation often relies on one-shot semantic matching, which is insufficient when legal evidence must be selected according to authority, applicability, and diversity across heterogeneous sources.
- Legal-R1 decomposes legal reasoning into a multi-agent workflow, where a Lawyer agent learns when to retrieve, which legal source to query, what evidence to seek, and when the collected evidence is sufficient.
- Experiments on multiple Chinese legal benchmarks show that Legal-R1 improves over general-purpose, legal-specific, and retrieval-augmented baselines.
Sources (1)
- [1]Legal-R1: Agentic Retrieval for Evidence-Based Legal ReasoningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 03:59 PM
We introduce Legal-R1, an agentic retrieval framework for evidence-based legal reasoning.
Legal question answering requires answers that are not only plausible, but also grounded in legally valid and citable evidence.
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