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

2026-07-10

AI Summary

Current large language model (LLM) agents rely solely on coarse-grained functional matching for task allocation, neglecting critical differences in performance and cost, which limits both reasoning efficiency and output quality. This work proposes the first incentive-compatible auction mechanism for LLM agent task assignment, treating individual reasoning steps as tradable items bid on by capability-calibrated expert models and tools, thereby enabling capability-driven dynamic routing. The approach allows a single tunable parameter to flexibly balance cost and quality trade-offs and significantly outperforms single-model, static routing, and cascading baselines across five benchmarks, achieving superior reasoning performance even when drawing from the same pool of candidate models.

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.

Problem

• LLM agent reasoning
• task allocation
• expert models
• cost efficiency
• performance variability

Innovation

• auction-based task allocation
• LLM agent reasoning
• incentive-compatible mechanism
• dynamic model routing
• cost-quality trade-off