aiqbench analysis
Agora introduces a novel auction-based mechanism to optimize task allocation among expert models, prioritizing calibrated competence over raw confidence scores to improve LLM agent reasoning. This approach addresses critical inefficiencies in existing frameworks by dynamically selecting tools based on performance variability and cost efficiency rather than coarse-grained matching. The method demonstrates competitive or superior performance across five benchmarks compared to standard routing and cascade baselines.
Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation
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.
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.
Accepted to Findings of the Association for Computational Linguistics: EMNLP 2026. 13 pages, 5 figures
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2607.09600 [cs.AI]
(or arXiv:2607.09600v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.09600 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 223 words
