想提升LLM Agent推理效率？Agora用拍卖机制动态分配任务给专家模型，在五个基准上都比传统路由强，还能平衡成本和效果。
Agora是一种新提出的框架，引入激励相容的拍卖机制，将推理步骤视为可交易物品，让专家模型和工具基于修正后的胜任度进行竞标，从而将关键逻辑分配给最合适的求解器。在五个基准测试上的评估显示，Agora在可比候选池下优于匹配的单模型、路由和级联基线。该框架通过单个拍卖参数实现可控的成本-质量权衡。
Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation
Enhancing the reasoning capabilities of large language model (LLM) agents requires effective orchestration of diverse expert models and tools. However, existing frameworks typically call APIs based on coarse-grained matching between tasks and the functions of expert models or tools, while overlooking critical factors such as performance variability and cost efficiency among functionally similar alternatives. To address this, we propose Agora, a framework that introduces an incentive-compatible auction mechanism for dynamically allocating tasks to expert models and tools. By treating reasoning steps as tradeable items, Agora enables agents to bid based on their rectified competence-ensuring that critical logic is routed to the most capable solver rather than the most overconfident one. Evaluations across five benchmarks show that Agora improves over matched single-model, routing, and cascade baselines under comparable candidate pools, while exposing a controllable cost-quality trade-off through a single auction parameter.