AI RESEARCH
GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval
arXiv CS.AI
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ArXi:2604.23779v1 Announce Type: cross The semantic gap between colloquial user queries and professional legal documents presents a fundamental challenge in Legal Case Retrieval (LCR). Existing dense retrieval methods typically treat LCR as a black-box semantic matching process, neglecting the explicit juridical logic that underpins legal relevance. To address this, we propose GLIER (Generative Legal Inference and Evidence Ranking), a framework that reformulates retrieval as an inference process over latent legal variables. GLIER decomposes the task into two interpretability-driven stages.