Error-Aware Reverse Auction Mechanism for Large Language Model Routing

2026-08-12

AI Summary

This work addresses the limitations of centralized prediction-based routing in large language models (LLMs), which often leads to misaligned information risks and scalability bottlenecks. To overcome these issues, the paper introduces, for the first time, a reverse auction mechanism into LLM routing and proposes the error-aware EA-RAM framework. In this framework, model providers autonomously bid their success rates and costs, while explicit modeling of dual sources of noise—arising from both prediction and evaluation—enables robust handling of uncertainty. The mechanism is shown to be Bayesian incentive-compatible and individually rational, with a provable upper bound on social welfare loss. Empirical results demonstrate that EA-RAM consistently outperforms centralized baselines across both simulated and real-world benchmarks, maintaining robustness under dual-error conditions and significantly advancing the cost-performance Pareto frontier.

Abstract

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 propose a market-based routing paradigm that shifts ex-ante prediction to LLM providers via a reverse auction, where providers bid with self-predicted success probabilities and execution costs. To account for inherently noisy provider predictions and center evaluations, we introduce the \textit{\textbf{E}rror-\textbf{A}ware \textbf{R}everse \textbf{A}uction \textbf{M}echanism} (EA-RAM), which explicitly models this inherent Dual Error. We prove that EA-RAM is Bayesian incentive compatible and individually rational under the Dual Error, establish sufficient conditions for center rationality, and derive an explicit welfare-loss bound. We further identify robustness effects: opposite-signed errors can cancel, vanishing-tail link functions (e.g., logistic) stabilize clear-cut cases via saturation, and extra noise smooths belief maps, reducing the gains from marginal manipulation. Experiments on simulations and real-world benchmarks show that EA-RAM is robust to the Dual Error and achieves a better cost--performance Pareto frontier than centralized baselines, with additional gains when providers contribute local information, validating its practical effectiveness.

Problem

• Large Language Model Routing
• Reverse Auction
• Error-Aware Mechanism
• Scalability Bottleneck
• Information-Risk Mismatch

Innovation

• Reverse Auction
• Large Language Model Routing
• Error-Aware Mechanism
• Bayesian Incentive Compatibility
• Dual Error