Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation
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
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- PossiblePossibly related (embedding) · 63%agent-tools →
- PossiblePossibly related (embedding) · 53%LazyAGI/LazyLLM →
- PossiblePossibly related (embedding) · 52%kstevica/captain-claw →
- PossiblePossibly related (embedding) · 52%zjunlp/SkillX →
- PossiblePossibly related (embedding) · 51%noetheadynamics/alethea →
- LinkedLinked via arxiv author · 85%Kaiji Zhou →
“Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation”
- LinkedLinked via arxiv author · 85%Ales Leonardis →
“Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation”
- LinkedLinked via arxiv author · 85%Yue Feng →
“Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation”