MC@NLO's negative-weight events get a learned fix: decision trees and normalizing flows cut the negative fraction in most processes
Negative-weight events from MC@NLO matching erode the statistical power of simulations, and full folding suppresses them only at prohibitive cost. This work learns a local correction with decision trees and normalizing flows whose integral over radiation variables vanishes, and adds it to the radiation-event short-distance cross section; across several electron- and hadron-collision processes the negative-weight fraction falls in most cases while total rates and Born-level distributions stay unchanged, at a modest extra computational cost.