Latency-Aware Allostatic Objective Controller

1. Integration point: implement the fast loop as ordinary SGD or Adam on a model with parameters $\theta$. Expose one or more explicit reference variables $r$, for example a vector of task-loss weights in multi-task learning, the desired calibration-risk tradeoff, an RL target return, or an auxiliary-loss coefficient. Keep $r$ separate from ordinary optimizer state so it is a visibly governed object. Every $d$ training steps, pause reference updates but not model updates, and evaluate the model on a held-out evidence buffer collected since the previous reference change. 2. Pseudocode: initialize $r\in\mathcal{R}$; repeat fast steps: compute $L(\theta;r)$, update $\theta\leftarrow\theta-\eta\nabla_\theta L$; append outcome statistics to a buffer; after $d$ steps compute $E=(1/K)\sum_q(u_q-v_q)$; estimate the finite-difference gradient of the evidence objective with respect to $r$ or use a learned linear surrogate; update $r\leftarrow\Pi_{\mathcal{R}}(r-\gamma\nabla_r\widehat J)$; clear the buffer. Add hysteresis so no update occurs unless the mismatch exceeds threshold $h$. 3. Computed versus estimated: the fast gradient and held-out outcomes are computed directly; $\tau_e=d\Delta t$ is known; the environmental or objective-drift timescale $\tau_r$ must be estimated by measuring the autocorrelation decay or change-point spacing of task performance, label frequencies, or reward statistics. The critical ratio is not supplied as a theorem by the paper and must be tested. 4. First cheap experiment: use a two-task MLP on rotated-MNIST or a synthetic regression problem whose task-weight optimum switches every $T$ steps. Compare fixed weights, immediate noisy adaptation, and delayed allostatic adaptation for $d\in\{1,10,50,100,200\}$ and $\gamma$ values. Plot excess validation loss against $d/T$. The predicted signature is a crossover near $d/T\approx1$: adaptation should help for $d/T<1$, while for $d/T>1$ its excess loss should rise and eventually exceed the fixed-reference baseline. Repeat with the same average update count but shuffled evidence to distinguish useful delayed evidence from merely reduced update frequency.