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arXiv · 2603.06616

RACER: Risk-Aware Calibrated Efficient Routing for Large Language Models

Sai Hao·Hao Zeng·Hongxin Wei·Bingyi Jing
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Abstract

Efficiently routing queries to the optimal large language model (LLM) is crucial for optimizing the cost-performance trade-off in multi-model systems. However, most existing routers rely on single-model selection, making them susceptible to misrouting. In this work, we formulate LLM routing as the $\alpha$-VOR problem to minimize expected set size while controlling the misrouting risk, and propose a novel method -- RACER, extending base routers to output model sets that can be subsequently aggregated for improved output. In particular, RACER constructs nested model sets via augmented scoring and utilizes finite-sample concentration bounds to calibrate a threshold that allows for both variable set sizes and abstention. We theoretically prove that RACER achieves rigorous distribution-free risk control on unseen test data in a post-hoc and model-agnostic manner. Extensive experiments verify our theoretical guarantees and demonstrate that RACER consistently enhances downstream accuracy across a wide range of benchmarks.

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Sai Hao, Hao Zeng, Hongxin Wei, Bingyi Jing. 2026-02-20. RACER: Risk-Aware Calibrated Efficient Routing for Large Language Models. https://arxiv.org/abs/2603.06616

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