一句话：提出自适应多智能体设计，按任务复杂度动态确定智能体数量与角色，提升 LLM 调试修复效果。
多智能体设计已被证明能提升大语言模型的调试能力，但增加 LLM 智能体数量会带来运行成本上升以及智能体偏离焦点等弊端。本文提出一种自适应智能体设计：智能体的数量与角色根据待完成任务复杂度动态确定，且角色并非预先定义，而是在分析待解决问题之后生成。初步评估显示，生成的智能体数量依赖于缺陷代码的复杂度——对于仅有语法问题的简单代码，通常只用一个智能体即可修复；对于更复杂的问题，则会创建更多智能体。在修复有效性方面，相比 one-shot prompting 平均提升 11%。作者据此展望未来研究方向，以改进该设计，使自适应软件智能体能够自主规划并执行其软件目标。
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 complexity 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.