Replacing Experts Without Retraining: Contract-Aware Composition of Sleep Models

Many real-world AI applications require multiple models for different tasks within a larger system. The growing availability of pretrained and task-specific models makes it possible to assemble such systems from existing experts. While this reduces the amount of training during initial deployment, these systems should also evolve as stronger models, new architectures, and revised preprocessing become available. Ideally, replacing one expert in the system should not require retraining every other component, yet independently developed models may differ in their input and output contracts. We study this problem in medical sleep analysis, where related time-series tasks use data from the same patient but require different modalities and temporal resolutions. We propose an explicit model contract that specifies the required inputs and temporal meaning of a model’s predictions. We use these contracts to execute models over a common prediction envelope and to properly align class probabilities. We study four interacting polysomnography tasks with experts from three different model families. In our experiments, we score a total of 62.8 million windows from 1,820 recordings. Across all three families and four tasks, our contract-based alignment improves the F1 score by up to 0.2 points compared to naive alignment. More importantly, we show that zero-shot exchange of a strong expert from one family into another one can improve the performance of the system by up to 0.17 F1 score without impacting other tasks. To our knowledge, this is the first complete-night study of zero-shot replacement among heterogeneous, interacting sleep experts and the first systematic study towards model replacement in ML systems without fine-tuning or retraining.