Motivated by these findings we return to scrutinize the tacit notion of a separation of time scales in surface catalysis. This is enabled by two recent methodological advances. On the one hand, the emergence of powerful machine-learned interatomic potentials (MLIPs) leads to a substantial reduction in the computational costs and allows for more extensive samplings of the PES without sacrificing the first-principles accuracy. On the other hand, inspired by seminal adaptive kinetic Monte Carlo approaches we have recently developed an automatic process exploration (APE) framework. This framework overcomes the human bias toward intuitive elementary processes and systematically identifies low-barrier processes at minimized PES sampling.