arXiv:2504.12988v5 Announce Type: replace Abstract: Existing Learning-to-Defer (L2D) frameworks are limited to single-expert deferral, forcing each query to rely on only one expert and preventing the use of collective expertise. We introduce the first framework for Top-$k$ Learning-to-Defer, which allocates queries to the $k$ most cost-effective entities. Our formulation unifies and strictly generalizes prior approaches, including the one-stage and two-stage regimes, selective prediction, and classical cascades. In particular, it recovers the usual Top-1 deferral rule as a special case while e
This development emerges as AI systems become increasingly complex and critical, requiring more robust and reliable decision-making processes, particularly in scenarios demanding collective intelligence.
It introduces a novel framework for Learning-to-Defer that significantly enhances AI's ability to utilize multiple expert predictions, potentially leading to more accurate, cost-effective, and resilient AI applications.
AI systems can now optimally defer to a collective of experts rather than a single one, improving performance in applications where uncertainty or the need for diverse perspectives is high.
- · AI developers
- · High-stakes AI applications (e.g., medical, financial)
- · Cloud providers
- · Researchers in machine learning
- · Legacy AI solutions relying on single-expert deferral
- · AI systems with limited ensemble capabilities
Improved accuracy and reliability in AI-assisted decision-making across various domains.
Increased demand for specialized AI 'experts' and diverse model architectures to form effective top-kexpert pools.
New competitive landscape for AI platforms offering superior multi-expert aggregation and deferral capabilities, potentially impacting intellectual property around expert orchestration.
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Read at arXiv cs.LG