Title: ChemHAS: Hierarchical Agent Stacking for Enhancing Chemistry Tools Show Chinese title

Title: ChemHAS：用于增强化学工具的分层代理堆叠

Abstract: Large Language Model (LLM)-based agents have demonstrated the ability to improve performance in chemistry-related tasks by selecting appropriate tools. However, their effectiveness remains limited by the inherent prediction errors of chemistry tools. In this paper, we take a step further by exploring how LLMbased agents can, in turn, be leveraged to reduce prediction errors of the tools. To this end, we propose ChemHAS (Chemical Hierarchical Agent Stacking), a simple yet effective method that enhances chemistry tools through optimizing agent-stacking structures from limited data. ChemHAS achieves state-of-the-art performance across four fundamental chemistry tasks, demonstrating that our method can effectively compensate for prediction errors of the tools. Furthermore, we identify and characterize four distinct agent-stacking behaviors, potentially improving interpretability and revealing new possibilities for AI agent applications in scientific research. Our code and dataset are publicly available at https: //anonymous.4open.science/r/ChemHAS-01E4/README.md. Show Chinese abstract

Abstract: 基于大型语言模型（LLM）的智能体在通过选择合适的工具来提升化学相关任务性能方面已经展现出能力。然而，它们的有效性仍然受到化学工具固有预测错误的限制。本文进一步探索了如何利用这些基于LLM的智能体反过来减少工具的预测错误。为此，我们提出了ChemHAS（化学分层智能体堆叠），这是一种简单但有效的方法，它通过从有限的数据中优化智能体堆叠结构来增强化学工具。ChemHAS在四个基础化学任务中实现了最先进的性能，表明我们的方法能够有效地弥补工具的预测错误。此外，我们还识别并刻画了四种不同的智能体堆叠行为，这可能提高可解释性，并揭示AI智能体在科学研究中应用的新可能性。我们的代码和数据集可在https://anonymous.4open.science/r/ChemHAS-01E4/README.md公开获取。