Performance-Gated Adaptation Freeze

Integrate the mechanism as a wrapper around an optimizer in an online-learning loop, initially targeting only an adaptation head or the last layer of an LSTM, SSM, MLP predictor, or neural controller. At each update compute a performance error from a delayed or held-out sample, rather than using exactly the minibatch loss that drives the gradient. Pseudocode is: initialize q=0, counter=0, frozen=False; compute e=clip(performance_error,-e_max,e_max); update q=alpha*q+(1-alpha)*e*e and r=sqrt(q). If frozen=False, backpropagate and execute optimizer.step(). If r <= eps_off, increment counter; otherwise reset it to zero. Set frozen=True when counter >= Nd. While frozen, continue inference and RMS monitoring but skip gradient and optimizer updates. Require r >= eps_on for Nu consecutive steps before unfreezing. Compute the EW-RMS and counters exactly; estimate $\tau$ from the desired memory horizon and choose thresholds from a clean validation stream or target control RMS. The first cheap test should use an online LSTM on Mackey-Glass or Lorenz-63 data, with a stationary low-excitation phase followed by observation noise and a distribution shift. Compare Adam, ordinary early stopping, and the gate. Measure parameter displacement, error, and recovery. The quantitative prediction is that parameter velocity becomes approximately zero after the threshold remains satisfied for $N_d$ updates, while adaptation resumes only after the upper hysteresis threshold is crossed.