Technology mapping is a critical yet challenging stage in logic synthesis. While Large Language Models (LLMs) have been applied to generate optimization scripts, their potential for core algorithm enhancement remains untapped. We introduce MappingEvolve, an open-source framework that pioneers the use of LLMs to directly evolve technology mapping code. Our method abstracts the mapping process into distinct optimization operators and employs a hierarchical agent-based architecture, comprising a Planner, Evolver, and Evaluator, to guide the evolutionary search. This structured approach enables strategic and effective code modifications. Experiments show our method significantly outperforms direct evolution and strong baselines, achieving 10.04\% area reduction versus ABC and 7.93\% versus mockturtle, with 46.6\%--96.0\% $S_{overall}$ improvement on EPFL benchmarks, while explicitly navigating the area--delay trade-off. Our code and data are available at https://github.com/Flians/MappingEvolve.

翻译：技术映射是逻辑综合中关键且具挑战性的阶段。尽管大语言模型（LLM）已被用于生成优化脚本，但其在核心算法增强方面的潜力尚未被充分挖掘。本文提出MappingEvolve——一个开创性地利用LLM直接进化技术映射代码的开源框架。该方法将映射过程抽象为独立的优化算子，并采用分层智能体架构（包括规划器、进化器和评估器）指导进化搜索。这种结构化方法实现了战略性且有效的代码修改。实验表明，我们的方法显著优于直接进化方法与强基线：与ABC相比实现10.04%的面积缩减，与mockturtle相比实现7.93%的优化，在EPFL基准测试中达到46.6%–96.0%的$S_{overall}$综合改进，同时显式平衡面积-延迟权衡。相关代码与数据发布于https://github.com/Flians/MappingEvolve。