Researchers propose Agora, a framework that employs an incentive-compatible auction mechanism to dynamically allocate tasks to expert models and tools for large language model agents.
- Agora treats reasoning steps as tradeable items, allowing agents to bid based on rectified competence rather than overconfidence.
- The system routes critical logic to the most capable solver by considering performance variability and cost efficiency among functionally similar alternatives.
- Evaluations across five benchmarks demonstrate that Agora outperforms matched single-model, routing, and cascade baselines under comparable candidate pools.
- The framework exposes a controllable cost-quality trade-off through a single auction parameter.
This approach addresses the limitations of existing frameworks that rely on coarse-grained matching, offering improved reasoning capabilities and efficiency for LLM agents.