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Despite their numerous capabilities, current LLMs are limited when asked to combine concepts. However, concept combination is a fundamental step in problem solving, including circuit design and algorithm development. This paper proposes a Graph-Augmented Generator-Validator optimization methodology to combine concepts by using their linkages in the complex network of concepts to find clusters, which then form the context used for LLM prompting. The method creates simpler combinations with higher linkages of their concepts.

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Concept Combinations with Generator and Validator Agents Prompted Using Insights from Concept Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Hashmath Shaik,
  • Gnaneswar Villuri,
  • Simona Doboli,
  • Alex Doboli

摘要

Despite their numerous capabilities, current LLMs are limited when asked to combine concepts. However, concept combination is a fundamental step in problem solving, including circuit design and algorithm development. This paper proposes a Graph-Augmented Generator-Validator optimization methodology to combine concepts by using their linkages in the complex network of concepts to find clusters, which then form the context used for LLM prompting. The method creates simpler combinations with higher linkages of their concepts.