Why High-Stakes Decisions Need Models Beyond LLMs
An arXiv position paper argues that language is a lossy record of quantitative reality: once source data becomes prose, missing detail cannot be recovered by scaling an LLM. For risk pricing, capital allocation, medical triage, and intrusion response, it proposes “large quantitative models” built around reproducibility, source-data lineage, and calibrated uncertainty. The shift is from generating plausible explanations to producing auditable decisions.