Error-Aware Reverse Auction Mechanism for Large Language Model Routing
Routing each query to a cost-effective large language model (LLM) is critical for balancing quality and cost, yet most routers rely on a centralized task center to predict model performance, creating an information-risk mismatch and a scalability bottleneck as the model pool grows. We formulate LLM routing as a market-based allocation problem among strategic providers and propose a routing paradigm that shifts ex-ante prediction to LLM providers via a reverse auction, where providers submit self-predicted acceptance probabilities and execution costs. To account for noisy provider predictions and center evaluations, we introduce the \textit{\textbf{E}rror-\textbf{A}ware \textbf{R}everse \t...
| Authors | Haolong Chen, Zhengyuan Xin, Liang Zhang et al. |
|---|---|
| Primary category | cs.GT |
| Published | 13 August 2026 |
| Last updated at source | 28 September 2026 |
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