Drift-to-Action Controllers: Budgeted Interventions with Online Risk Certificates

• URL: http://arxiv.org/abs/2603.08578v1
• Date: Mon, 09 Mar 2026 16:34:12 GMT
• Title: Drift-to-Action Controllers: Budgeted Interventions with Online Risk Certificates
• Abstract summary: Drift2Act is a drift-to-action controller that treats monitoring as constrained decision-making with explicit safety.<n>It achieves near-zero safety violations and fast recovery at moderate cost on WILDS Camelyon17, DomainNet, and a controlled synthetic drift stream.
• Score: 1.565870461096057
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deployed machine learning systems face distribution drift, yet most monitoring pipelines stop at alarms and leave the response underspecified under labeling, compute, and latency constraints. We introduce Drift2Act, a drift-to-action controller that treats monitoring as constrained decision-making with explicit safety. Drift2Act combines a sensing layer that maps unlabeled monitoring signals to a belief over drift types with an active risk certificate that queries a small set of delayed labels from a recent window to produce an anytime-valid upper bound $U_t(δ)$ on current risk. The certificate gates operation: if $U_t(δ) \le τ$, the controller selects low-cost actions (e.g., recalibration or test-time adaptation); if $U_t(δ) > τ$, it activates abstain/handoff and escalates to rollback or retraining under cooldowns. In a realistic streaming protocol with label delay and explicit intervention costs, Drift2Act achieves near-zero safety violations and fast recovery at moderate cost on WILDS Camelyon17, DomainNet, and a controlled synthetic drift stream, outperforming alarm-only monitoring, adapt-always adaptation, schedule-based retraining, selective prediction alone, and an ablation without certification. Overall, online risk certification enables reliable drift response and reframes monitoring as decision-making with safety.

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