Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation
Kaiji Zhou, Ale\v{s} 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:2607.09600v2 Announce Type: replace 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 uses a confidence-calibrated auction to dynamically allocate tasks to expert models and tools. By treating reasoning steps as tradeable items, Agora bases allocation on calibrated competence rather than raw confidence. Across five main benchmarks, Agora improves or remains competitive with single-model, routing, and cascade baselines under matched candidate pools.