Use a conditional normalizing flow to replace inner-loop MCMC when sampling states or parameters under progressively tighter neural energy or likelihood constraints. The flow is trained online from recent live sets, and proposals are corrected by importance weighting and resampling, so flow bias does not directly corrupt the nested estimate.
Use the generalized Cramér–Rao relation to adapt the inverse-temperature or noise schedule of an energy-based sampler, diffusion sampler, or stochastic optimizer. The controller limits each temperature change according to the measured energy variance and Fisher information, preventing uncontrolled changes in the sampled energy distribution while allowing larger steps in insensitive regions.
Use the noisy diffusion state to estimate posterior probabilities over data clusters, then route the denoising computation to one or a few cluster-specific score experts. Each expert stores a low-rank covariance or low-rank score correction, so after posterior commitment the denoiser operates at the cluster intrinsic dimension rather than ambient dimension. During the early mixing phase, retain a shared full-dimensional score or evaluate a small weighted set of experts.
Replace many independently equilibrated SGLD runs at different hyperparameters with one controlled sweep in which an auxiliary drift transports particles through the stationary distributions indexed by the swept parameter. Estimate the response of loss, predictions, uncertainty, or weight observables using covariance with the stationary generalized-potential derivative instead of finite differences between separate runs.
Replace confidence-only masked diffusion decoding with an adaptive scheduler that chooses batches whose unrevealed tokens have low conditional total correlation given the already revealed context. The scheduler should preserve large parallel batches when token predictions are conditionally independent, but split highly dependent tokens into separate rounds to reduce forward-KL error.
Use an online estimate of the target distribution's remaining coordinate dependence to choose diffusion step sizes instead of using a uniform timestep grid. Take large jumps when the current state is nearly coordinate-factorized and shorten the step when the denoiser detects strong cross-coordinate dependence, targeting the paper's dependence-adaptive discretization scaling.