arXiv:2604.09414v3 Announce Type: replace-cross Abstract: A learning-to-defer (L2D) system decides, for each input, whether to predict on its own or to hand it to one of several available experts. The very well established recipe trains classifier and router jointly by treating the $K$ classes and $J$ experts as competing actions in one shared $(K{+}J)$-action geometry. Subsequent work has proposed a series of incremental fixes within this geometry; we show that each still suffers, to varying severity, from an optimization-level pathology (target distortion, gradient amplification, winner-take
The paper identifies fundamental optimization pathologies in current Learning-to-Defer (L2D) systems, indicating a maturation in the understanding of their limitations and a push for more robust architectures.
Improving L2D systems is crucial for deploying reliable AI in critical applications where human oversight or expert intervention is necessary to prevent errors.
This research suggests a shift away from the 'K classes and J experts as competing actions' paradigm for L2D, potentially leading to more stable and effective methods for AI-human or AI-AI expert collaboration.
- · AI system developers
- · Healthcare sector
- · Financial services
- · Autonomous systems
- · Developers relying on current L2D architectures
- · Systems with high error tolerance
More reliable AI systems capable of deferring to human or other AI experts when uncertain.
Increased trust in AI deployment in sensitive domains due to enhanced safety and accuracy through principled deferral mechanisms.
Reduced liability for AI-driven decisions as deferral mechanisms become more sophisticated and demonstrably robust.
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Read at arXiv cs.LG