Retrieval-Augmented Generation (RAG) is increasingly seen as a solution to the unreliability of legal AI, yet notable failures, such as fabricated court citations and outdated legal information, continue to emerge. This study argues that these issues stem not from scaling language models but from a fundamental mismatch between probabilistic retrieval and the complex structure of legal knowledge. The authors outline the ontological properties of legal knowledge and identify three pathologies of retrieval that hinder its effectiveness. Additionally, they propose four architectural commitments aimed at improving legal retrieval systems, emphasizing the need for a paradigm shift that accommodates the unique characteristics of legal information.
Exploring the Structural and Causal Limitations of RAG in Legal AI
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