TIMEGATE: Sustainable Time-Boxed Promotion Gates for Continual ML Adaptation Under Resource Constraints

As machine learning(ML) systems evolve to continual adaptation, each re-training cycle uses compute, annotation, and energy. We introduce TimeGate, a policy layer managing adaptation by budgeting time, labeling, training, and evaluation. TimeGate emits a metric-availability signal $M$ for partial vs. full-evaluation decisions. We validate: (i) labeling outperforms training by $2.3\times$ on Adult tabular; (ii) it transfers to LLaMA-3.1-8B + QLoRA on SST-2 (accuracy $0.80 \to 0.96$; $M{=}1$ in 35/36 runs); (iii) $M$ is informative, 28-cell sensitivity shows $M$ drops to $0.81$ at tight thresholds; (iv) 100-cycle simulation achieves $66\%$ evaluation-compute savings with no silent mis-promotions; (v) $10\%$-slice evaluation on LLaMA uses $89\%$ less wall-clock and energy on a single H200 (ratios agree to $0.2\%$). The mechanism is model-agnostic, validated on XGBoost/Adult and LLaMA-3.1-8B/SST-2; extensions are discussed in the appendix.