While conceptually elegant, realizations of this adaptive (on-the-fly or self-learning) kMC idea stand and fall with the number of system states and how well an already visited state is recognized. An intractable total number of TS searches along the kMC trajectory is only avoided, if state revisits quickly become dominant. State revisits are also much easier recognized, if the system is confined to a lattice model. Yet, it might a priori not be clear whether a lattice model might suit a system, or if so which one. If complete off-lattice kMC is performed, imperfect state recognition can instead quickly lead to a strong redundancy of TS search efforts for in principle actually equivalent system configurations. In practice, applications of such adaptive kMC have therefore to date been restricted to rather simple systems or to systems where state recognition is readily achieved.