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Area of Science:

  • Artificial Intelligence
  • Machine Learning

Background:

  • AI Agents now handle complex tasks beyond content recommendation.
  • Tool-augmented Large Language Models (LLMs) integrate external tools for enhanced capabilities.
  • Existing research underutilizes the diversity of available tools for LLM augmentation.

Purpose of the Study:

  • To propose a method for simultaneously calling multiple tools of the same type.
  • To leverage diverse external tools for improved LLM inference accuracy.
  • To develop an efficient method for combining enhanced and existing LLM models in tool-augmented systems.

Main Methods:

  • Categorization of external tools by type.
  • Simultaneous invocation of same-type tools during LLM inference.
  • Multi-stage reasoning approach with optimized model selection for planning and tool invocation.

Main Results:

  • Achieved accuracy improvements of 4.4-9.3% compared to existing studies.
  • Demonstrated enhanced LLM inference by utilizing diverse, simultaneously called tools.
  • Reduced response errors by up to 9% through efficient model combination strategies.

Conclusions:

  • Simultaneous use of diverse tools significantly boosts AI Agent performance.
  • Optimized model selection in multi-stage reasoning is crucial for cost-effective, high-accuracy tool-augmented LLMs.
  • This research advances the development of more capable and efficient AI Agents.