Can one model handle images, audio, text, and molecules equally well?

A preprocessing step (Equiangular Tight Frame) paired with a tabular foundation model can classify data across wildly different modalities—images, audio, speech, text, molecules, time-series, and tables—without retraining. It stays competitive with specialized models on frozen features while running orders of magnitude faster than backbone fine-tuning. The practical contribution: how to calibrate confidence scores so practitioners can reject uncertain predictions automatically.