LegalMALR:Multi-Agent Query Understanding and LLM-Based Reranking for Chinese Statute Retrieval

2026-01-25

AI Summary

This study addresses the challenge that real-world legal queries often involve multiple intertwined issues and are expressed in informal or ambiguous language, which hinders traditional retrieval methods from accurately recalling relevant statutes. To overcome this limitation, the authors propose a novel paradigm integrating multi-agent query understanding with zero-shot large language model–based re-ranking. The approach leverages multi-perspective query reformulation, iterative dense retrieval guided by Generalized Reinforcement Policy Optimization (GRPO), and natural language legal reasoning to achieve precise statute localization. Evaluated on both the newly constructed CSAID dataset and the public STARD benchmark, the method significantly outperforms existing retrieval-augmented generation (RAG) approaches, demonstrating superior performance in both in-distribution and out-of-distribution scenarios.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Statistical Relational/Logic Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Statute retrieval is essential for legal assistance and judicial decision support, yet real-world legal queries are often implicit, multi-issue, and expressed in colloquial or underspecified forms. These characteristics make it difficult for conventional retrieval-augmented generation pipelines to recover the statutory elements required for accurate retrieval. Dense retrievers focus primarily on the literal surface form of the query, whereas lightweight rerankers lack the legal-reasoning capacity needed to assess statutory applicability. We present LegalMALR, a retrieval framework that integrates a Multi-Agent Query Understanding System (MAS) with a zero-shot large-language-model-based reranking module (LLM Reranker). MAS generates diverse, legally grounded reformulations and conducts iterative dense retrieval to broaden candidate coverage. To stabilise the stochastic behaviour of LLM-generated rewrites, we optimise a unified MAS policy using Generalized Reinforcement Policy Optimization(GRPO). The accumulated candidate set is subsequently evaluated by the LLM Reranker, which performs natural-language legal reasoning to produce the final ranking. We further construct CSAID, a dataset of 118 difficult Chinese legal queries annotated with multiple statutory labels, and evaluate LegalMALR on both CSAID and the public STARD benchmark. Experiments show that LegalMALR substantially outperforms strong Retrieval-augmented generation(RAG) baselines in both in-distribution and out-of-distribution settings, demonstrating the effectiveness of combining multi-perspective query interpretation, reinforcement-based policy optimisation, and large-model reranking for statute retrieval.

Problem

• statute retrieval
• legal query understanding
• colloquial queries
• multi-issue queries
• Chinese legal retrieval

Innovation

• Multi-Agent Query Understanding
• LLM-based Reranking
• Reinforcement Policy Optimization
• Statute Retrieval
• Legal Reasoning