Adapt Only When It Pays: Budgeted Decision-Loss Priority for Delayed Online Time-Series Adaptation
arXiv:2606.25068v1 Announce Type: new Abstract: Online time-series forecasters receive labels only after horizon-dependent delays, while every adaptation step spends limited compute. We study when an online learner should update, not how to adapt at every opportunity, and introduce ADOWIP: a residual-adapter framework with sealed delay queues, exact budget accounting, and auditable update telemetry. Its main scheduler is an observed decision-loss priority gate that updates only after feedback is revealed, when downstream loss, optionally penalized by prediction MSE, exceeds a calibrated empiri
The paper addresses a critical challenge in the deployment of advanced AI systems, particularly in online learning scenarios where computational resources are limited and feedback is delayed.
This research provides a framework for more efficient and robust real-time AI adaptation within resource constraints, making advanced AI applications more practical and scalable.
The proposed ADOWIP framework changes how online learning systems prioritize updates, shifting from constant adaptation to a more judicious, decision-loss-driven approach.
- · AI/ML Developers
- · Cloud Computing Providers
- · Autonomous Systems Operators
- · Online Retail/Financial Services
- · Inefficient Online Learning Systems
- · High-Latency AI Deployments
More cost-effective and performant online AI systems will emerge.
This efficiency gain could accelerate the deployment of AI in latency-sensitive and resource-constrained environments.
Broader adoption of such adaptive AI could lead to more robust autonomous agents and complex adaptive systems at scale.
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