EXPLICIT ROLES IN GENERATIVE PLANNING； RETRAINING, SELECTION, AND ROBUSTNESS

The contribution of explicit role information to generative planning is difficult to isolate when geometry, demonstrations, and candidate selection can convey related information. We present a controlled intervention framework for vessel planning that separates training-time role availability, inference-time generator inputs, and role-dependent candidate selection. The framework combines architecture-matched retraining, generator–selector factorial interventions, and paired robustness tests to distinguish learning with explicit roles from reacting to their removal or corruption at inference. Across MLP diffusion, temporal U-Net diffusion, and flow matching, we pair 30 newly trained explicit-role-blind policies with 30 preserved conditioned counterparts. Our prospective evaluation comprises 1,608,000 episode executions in controlled synthetic environments, using ten training seeds per architecture, shared scene banks, and crossed resampling. Among six primary retraining contrasts, only MLP diffusion with correct selection exhibits a positive arrival difference supported by multiplicity-adjusted confidence intervals, with a gain of 1.76 percentage points. In contrast, descriptive factorial experiments show that cyclically incorrect generator codes reduce arrival by 7.03–19.07 percentage points relative to correct codes across the three architectures, with eight candidates and a correct selector held fixed. Paired noise–delay and current–lag experiments further reveal non-additive changes in closed-loop outcomes, highlighting the importance of evaluating coupled disturbances. Together, these findings distinguish the benefit of explicit-role training from sensitivity to incorrect role inputs and establish a reproducible evaluation protocol for examining conditioning, candidate selection, and robustness in autonomous vessel planning.