When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval
Researchers propose a self-evolving framework where an LLM-based agent automatically generates query rewriting rules to enhance BM25 for legal case retrieval, without parameter training. Evaluated on LeCaRD-v2, it outperforms non-evolutionary baselines, especially with a high-capacity LLM.
[2606.17220] When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval
[Submitted on 15 Jun 2026]
Title:When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval
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Abstract:Legal case retrieval remains challenging due to the complexity of legal language and the need for precise lexical alignment between queries and relevant cases. Although dense retrieval models have achieved notable progress, empirical studies show that BM25 continues to serve as a strong baseline in this domain. It motivates us to propose a self-evolving framework for rule-driven query rewriting that enhances BM25 without any parameter training. The framework equips an LLM-based agent with an automatic evaluation environment, enabling it to iteratively create rewriting rules, plan validation experiments over rule combinations, and eliminate ineffective rules based on historical feedbacks. We evaluate our method on the Chinese legal case retrieval benchmark LeCaRD-v2. Experimental results demonstrate that the proposed framework outperforms non-evolutionary baselines, including human-designed rules and greedy rule selection, particularly when powered by a highcapacity core LLM. We also conduct detailed analyses to investigate the mechanisms underlying self-evolution. Our findings reveal that LLM's capabilities to leverage previous experimental results and its intrinsic knowledge of rule elimination play critical roles in refining the rule set via self-evolution.
Comments: To appear in ACL 2026
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.17220 [cs.AI]
(or arXiv:2606.17220v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.17220
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mingxu Tao [view email] [v1] Mon, 15 Jun 2026 19:09:31 UTC (553 KB)
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