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

arXiv: https://arxiv.org/abs/2605.29183 · HTML: https://mlsanity.com/paper/2605.29183/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 13 of 13)

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- [From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate](https://mlsanity.com/paper/2501.16548/) (2025)
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- [Cost-Effective Retraining of Machine Learning Models](https://mlsanity.com/paper/2310.04216/) (2023)
- [AgentDevel: Reframing Self-Evolving LLM Agents as Release Engineering](https://mlsanity.com/paper/2601.04620/) (2026)
- [Reducing Unintended Bias of ML Models on Tabular and Textual Data](https://mlsanity.com/paper/2108.02662/) (2021)
- [Towards MLOps: A DevOps Tools Recommender System for Machine Learning System](https://mlsanity.com/paper/2402.12867/) (2024)
- [Machine Learning Operations: A Mapping Study](https://mlsanity.com/paper/2409.19416/) (2024)
- [Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy](https://mlsanity.com/paper/2506.13838/) (2025)

## Abstract (arXiv)

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 2.3x 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%).