Immune-State Models under Reduced Numerical Precision

Abstract

Immune-State Models under Reduced Numerical Precision addresses a narrow but consequential interpretive problem: numeric compression changing rare state assignments. The article develops a research agenda through critical comparison of published evidence. The starting source uses a conceptual review proposing immunometabolic set points across immune and tissue compartments. Compression changes the computational representation of a decision. Preserving average prediction quality does not guarantee preservation of rare classes, boundary cases, or uncertainty rankings. Match full- and reduced-precision outputs on identical inputs. Inspect decision-boundary transitions and subgroup losses at a fixed deployment budget, with latency measured on the actual hardware. The proposed comparison concerns cell-state classifiers, quantization, shifted cohorts; it would test whether the specific failure scenario is detected without erasing legitimate uncertainty. An integrated account should preserve the distinction between tissue contexts, clinical endpoints, and mechanistic claims and any stronger downstream decision.