Abstract

A private continuously-learning model as three loops: an inference loop that serves and captures its own interactions, a curation loop that filters/redacts/de-duplicates into safe training data, and a training loop that folds it back into cheap swappable adapters. Formalizes a privacy invariant (no PII token enters the training set under a sound gate) and bounded-forgetting (frozen base → zero base drift), and is honest about model-collapse and delta-leakage failure modes and which components are still research.