Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation
Kaiji Zhou, Ales Leonardis, Yue Feng
Why It Matters
What makes this one worth your time
This approach could lead to more efficient and effective orchestration of expert models, potentially improving the performance of LLM agents in complex reasoning tasks.
Agora uses auction-based task allocation to improve LLM agent reasoning.
Summary
The paper proposes Agora, a framework that uses an auction-based mechanism to allocate tasks to expert models and tools, aiming to enhance the reasoning capabilities of large language model agents by considering performance variability and cost efficiency.
Key contributions
- Introduction of an auction mechanism for task allocation in LLM agents.
- Demonstration of improved performance over existing baselines across five benchmarks.
Notable insights
- Treating reasoning steps as tradeable items allows for dynamic task allocation based on competence rather than confidence.
- The framework introduces a controllable cost-quality trade-off through a single auction parameter.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2607.09600v1 Announce Type: new Abstract: 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.