动态调整调试智能体数量与角色，提升效率与准确性。
Towards Adaptive Software Agents for Debugging
- 根据代码复杂度自动决定智能体数量和分工
- 简单问题仅需1个智能体，复杂问题可生成多个
- 相比单次提示平均修复率提升11%，适合自动化调试
使用多个智能体被发现可提升大语言模型的调试能力。然而，增加智能体数量会带来运行成本上升和注意力分散等问题。本文提出一种自适应智能体设计：智能体数量和角色根据任务特征动态确定，角色在分析问题后生成，而非预先设定。初步评估显示，对于仅有语法错误的简单代码，通常只需一个智能体即可修复；而复杂问题则会触发更多智能体生成。在修复效果上，该方法相比单次提示平均提升11%。基于这些结果，本文展望未来研究方向，旨在构建能自主规划并执行软件目标的自适应智能体。
原文摘要 · Abstract (English)
Using multiple agents was found to improve the debugging capabilities of Large Language Models. However, increasing the number of LLM-agents has several drawbacks such as increasing the running costs and rising the risk for the agents to lose focus. In this work, we propose an adaptive agentic design, where the number of agents and their roles are determined dynamically based on the characteristics of the task to be achieved. In this design, the agents roles are not predefined, but are generated after analyzing the problem to be solved. Our initial evaluation shows that, with the adaptive design, the number of agents that are generated depends on the complexity of the buggy code. In fact, for simple code with mere syntax issues, the problem was usually fixed using one agent only. However, for more complex problems, we noticed the creation of a higher number of agents. Regarding the effectiveness of the fix, we noticed an average improvement of 11% compared to the one-shot prompting. Given these promising results, we outline future research directions to improve our design for adaptive software agents that can autonomously plan and conduct their software goals.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。