PAC transition-cover training monitor

Attach the monitor to a learned dynamics model used for model-predictive control, world modeling, or long-horizon RNN prediction. Discretize the reachable latent-state and input space into cells, maintain a replay buffer indexed by cell, and allocate fresh rollouts according to coverage deficits. For each cell, construct an empirical successor envelope using coordinatewise quantiles, conformal radii, or interval propagation, reserving held-out transitions for evaluation. Given target miss probability epsilon_i and confidence failure delta_i, require n_i at least log(1/delta_i) divided by epsilon_i before declaring a cell covered. Pseudocode: identify the current cell; collect or simulate transitions; fit B_i; evaluate held-out successors; if the violation rate exceeds the allowed threshold, enlarge B_i, collect more samples, or increase the loss weight for that cell. The paper supplies the conservative transition and PAC coverage mechanism; the loss and sampling scheduler are neural-network adaptations. The first experiment should use a small MLP predicting the next state of a noisy pendulum or Lorenz system, comparing uniform data collection with cell-indexed PAC collection at equal transition budgets. The predicted signature is that held-out miss rates remain below epsilon_i except in approximately a delta_i fraction of cells, while under-covered cells exhibit larger long-horizon rollout error.