arXiv:2601.22538v2 Announce Type: replace Abstract: Learning-to-defer (L2D) routes each decision to a system's own predictor or to an external expert. Streaming time-series settings break the offline-L2D assumptions: the data are non-stationary, expert availability shifts over time, and the internal predictor is trained online. We propose L2D-SLDS, a one-stage online L2D framework based on a factorized switching linear-Gaussian state-space model over all potential residuals: a discrete regime, a shared global factor, and per-expert idiosyncratic states. The always-observed internal residual co
This research addresses the growing need for robust AI decision-making in real-time, non-stationary environments, which is critical for increasingly dynamic AI agents.
Advanced 'learning to defer' capabilities will enhance the reliability and adaptability of autonomous AI systems, reducing errors and improving performance in complex operational settings.
AI systems can now more effectively manage uncertainty and delegate tasks between internal models and human experts in fluctuating, real-time data streams.
- · AI Agent developers
- · Automation industries
- · Real-time decision systems
- · Outdated online learning algorithms
- · Static AI deployment models
More resilient and trustworthy AI deployments in critical applications like finance, logistics, and resource management.
Accelerated adoption of AI agents in environments where human-AI collaboration on decision-making is paramount.
Reduced burden on human experts by enabling AI to intelligently triage and manage tasks, potentially leading to new human-AI organizational structures.
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Read at arXiv cs.LG