Discrete Flow Matching achieves non-asymptotic KL and total variation convergence bounds under minimal approximation error assumptions with improved scaling in vocabulary size and dimension.
A dynamic k-center model with known lifetimes achieves deterministic (2+ε)-approximation with amortized updates and linear memory, plus a (6+ε)-approximation with worst-case updates and sublinear memory.