Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation

Coordinating multiple AI models and tools within a single reasoning pipeline is challenging when systems rely on crude task-matching rather than accounting for cost and performance differences. This paper proposes an auction-based framework where reasoning steps are treated as tradeable tasks, and expert models "bid" based on calibrated competence, routing work to the most capable rather than most confident solver. Evaluated across five benchmarks, it outperforms standard routing and cascade baselines while allowing a tunable cost-quality trade-off. Applications include more efficient orchestration of AI agent ecosystems, especially in enterprise settings needing controllable cost versus quality trade-offs. Authors: Kaiji Zhou, Ales Leonardis, Yue Feng Paper: https://arxiv.org/abs/2607.09600v1