What Does History Add? Calendar Controls and Information-Timing Audits for University Trajectory Forecasting

Abstract

We audit the predictive value of historical summaries in a rule-based university trajectory simulator. Seventeen ridge specifications and persistence separate current plans, past-state and past-plan summaries, and calendar-only history-length indicators. Across training, validation and two family-held-out evaluations, we reconstruct 16,750 routes and 804,000 monthly records. On the original 40-family set, six-month history lowers six-step normalized RMSE from 0.159234 to 0.151522, but its difference from a length-only control is -0.000895 (descriptive paired 95% interval [-0.001957,0.000248]). Fixed weights on 30 families unused in identified prior tasks reproduce this pattern: 0.156480 to 0.150074 versus current-state prediction, but -0.000558 [-0.001783,0.000728] versus the length control. These uncertain comparisons do not generalize to all-history summaries, which improve on their own calendar control. Exploratory decomposition finds clearer preparation-index and funds benefits, whereas fatigue dominates the six-month model's loss and has an uncertain length-controlled benefit. Moreover, 28.20% of its six-step fatigue predictions violate the simulator's [5,90] support. We document family dependence, prior exposure, strict temporal indexing, matched recursive references and independent numerical replay. The task conditions on recorded future plans; it does not evaluate real-student forecasts, causal action effects, autonomous planning or language-model memory. This is an evaluation audit, not a new memory architecture.