根据不确定性分解，智能选择是否调用昂贵模型或直接放弃。
Flexible Routing via Uncertainty Decomposition
- 将不确定性拆分为可减少和不可减少两部分，指导路由决策。
- 在真实与合成数据上，显著降低错误率与成本，提升效率。
- 无需重训练，通过调参即可适配不同任务与成本需求。
现代机器学习系统中，动态将查询路由至低成本模型或高成本的可信源（如大型预训练模型或人工专家），是平衡性能与成本的关键策略。本文提出一种新型不确定性感知路由器，既能避免对固有模糊查询进行不必要的可信源调用，又能通过简单调整超参数，动态适应不同的损失函数和成本参数，无需重新训练。该方法适用于任意分类场景，且每个输入有多组独立标注。其核心思想是利用高阶预测器 [Ahdritz et al., 2025] 将总不确定性分解为不可减少与可减少部分，从而统一处理路由与拒答：当不确定性低时使用弱模型预测；当可减少不确定性高时路由至可信源；当不可减少不确定性高时直接放弃。所提路由器具备强理论保证，其遗憾度相对于最优任务特定路由器有界。实验在合成及真实数据集上均验证了该方法的有效性，尤其在可减少与不可减少不确定性相关性不强的场景下表现更优。
原文摘要 · Abstract (English)
A key strategy for balancing performance and cost in modern machine learning systems is to dynamically route queries to either a low-cost model or a more expensive oracle (such as a large pretrained model or human expert), an approach known as model routing. In this work we present a new uncertainty-aware router that (1) avoids unnecessary oracle calls on inherently ambiguous queries, and (2) adapts dynamically to different loss functions and cost parameters through simple hyperparameter changes, without retraining. Our method, applicable to any classification setting where multiple independent annotations per input are available, is based on decomposing total uncertainty into irreducible and reducible components using higher-order predictors [Ahdritz et al., 2025]. This enables a unified approach to both routing and abstention: predict with the weak model when uncertainty is low, route to the oracle when reducible uncertainty is high, and abstain when irreducible uncertainty is high. Our router comes with strong theoretical guarantees bounding regret relative to optimal task-specific routers. We conduct experiments on both synthetic and real-world datasets that demonstrate the benefits of our approach in suitable regimes -- in particular, whenever reducible and irreducible uncertainty are not too correlated.
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