We introduce BO4Mob, a new Bayesian Optimization (BO) benchmark framework for origin-destination (OD) travel demand estimation as high-dimensional urban mobility problem.

We propose joint recall, a novel synthetic task, and hybrid sparse attention with context-dependent sparsity for better sub-quadratic long-context modeling.

This work presents planning and learning algorithms for average-cost MDPs with dynamic risk measures.