How Modal Used Evals and Inference-Time Compute Scaling to Generate Scannable QArt Codes

Modal used evals and inference-time compute scaling to improve its QArt code system, boosting QR scan rate to its 95% service-level objective while improving aesthetic quality and returning codes in under 20s at p95. The work follows the standard ML playbook of collecting human judgments, aligning to them, then scaling, addressing the "it worked on my prompt" gap that leaves many generated QR codes unscannable.