Sampling ideas

Diffusion step schedules, solvers, guidance and stopping rules that cut function evaluations or improve sample quality.

△ Mechanism confirmed, baseline not beaten 2026

Lipschitz-Inflated Conformal Trajectory Tube

Wrap a neural ODE, recurrent state-space model, or learned world model with a split-conformal prediction tube that is valid between irregularly sampled observations. Calibrate a pointwise residual quantile at observed times and inflate it at an unobserved time according to its distance from the nearest observed time and an estimated bound on the true and predicted trajectory slopes.

Useful8/10
Difficulty4/10
Novelty7/10
Paper: Conformal Prediction Regions for Continuous-Time Trajectories under Random Sampling arXiv:2608.29559
△ Mechanism confirmed, baseline not beaten 2026

CVaR-tail active residual correction

Train a cheap neural surrogate globally, then use an ensemble or bootstrap covariance to identify inputs near the estimated upper-tail boundary and inputs where high-fidelity correction is uncertain. Fit a Tikhonov-regularized residual model on the acquired expensive labels and use the corrected predictor for CVaR estimation or risk-constrained optimization. The acquisition policy deliberately ignores easy central-region samples unless they influence the tail threshold.

Useful7/10
Difficulty5/10
Novelty6/10
Paper: Risk-averse design optimization with CVaR constraints via multifidelity tail-region correction arXiv:2608.29222