The Phoropter: A Biological Assay Framework for Behavioral Drift Detection and Deceptive Output Detection in Distilled Language Models

The proliferation of distilled language models in production environments has created a critical gap in MLOps tooling: existing drift detection methodologies rely primarily on output content analysis, measuring what a model says rather than how a model responds. This singular reliance on output content as the primary measurement signal creates a fundamental blindspot - a model experiencing distillation drift or producing deceptive outputs may do so in ways that are not detectable through content analysis alone. Just as the human visual system processes incoming light through a series of refractive layers, converting raw optical stimulus into neural signal and ultimately conscious perception, a language model processes input tokens through successive transformation layers, converting raw text into probability distributions and ultimately generated output. The structural parallel - stimulus intake, layered processing, conscious response, and involuntary physiological signal - forms the theoretical basis for applying ophthalmic diagnostic methodology to language model behavioral analysis. We propose The Phoropter, a diagnostic framework that applies digitized representations of ophthalmic refraction and perturbation testing methodologies to language model behavioral analysis, establishing a multi-signal measurement layer that operates independently of and in conjunction with model output content. The Phoropter emits structured perturbation stimuli and measures behavioral response signals including latency profiles, token probability distributions, and output consistency under controlled variation - analogous to the clinical distinction between subjective reporting and objective physiological measurement in ophthalmology. We further introduce an automated closed-loop calibration system that iteratively adjusts distillation parameters in response to measured behavioral drift until calibration targets are met, and extend the framework to establish a theoretical basis for deceptive output detection by identifying divergence between conscious output layer responses and involuntary reflexive signals. Empirical validation is pending deployment on target inference hardware.