Language Is an Insufficient Substrate for Quantitative Reasoning, and Consequential Domains Need Large Quantitative Models

The article argues that large language models (LLMs) are inadequate for consequential quantitative tasks such as pricing, risk assessment, and medical triage because language is a lossy representation of quantitative data that cannot be reversed. It formalizes this limitation as a property of the training representation rather than model capacity and identifies three essential properties—reproducibility, traceable lineage to source records, and calibrated uncertainty—that language substrates cannot provide. The authors propose a new class of models, Large Quantitative Models (LQMs), designed to meet these requirements.

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