Quantization in Legal AI: A Theoretical Framework for Fluency-Reasoning Divergence and Traceable Liability

This preprint sets out a theoretical framework for understanding how post-training quantization in large language models used for legal tasks can create hidden liability risks. It argues that many AI “errors” in legal workflows are not random hallucinations, but foreseeable consequences of engineering choices made to reduce inference costs. The work introduces the concepts of Fluency–Reasoning Divergence (FRD) and Reasoning Decay to describe how low-bit quantization can preserve surface-level fluency while progressively degrading multi-step legal reasoning. It connects these technical effects to doctrines of product design defect, failure to warn, and evolving duties of technological competence for lawyers. The framework focuses on deployment-time post-training quantization applied under conditions of supply-chain opacity and nominal equivalence (e.g., treating quantized and non-quantized models as the same “version”). It maps how undisclosed quantization decisions can break the chain between model branding, actual reasoning performance, and client-facing outputs, making legal harm both foreseeable and traceable. The paper concludes by calling for explicit disclosure and certification of quantization levels in legal AI systems, and for governance mechanisms that make FRD and Reasoning Decay observable and auditable. This abstract is part of a larger work in progress on quantization, legal AI, and traceable liability.