When an LLM repeatedly mutates a program, does it explore new forms or circle back to the same ones? We study this question by analyzing LLM-driven mutation chains in the absence of selection pressure within a domain-specific language, varying prompt design, model family, and stochastic replication. We find that LLM-based mutation consistently converges toward restricted attractor regions in program space. Convergence is especially severe at the structural level: in 87% of chains, over 93% of mutations revisit a previously seen structural form, with most variation confined to terminal substitutions within recurring templates. Cycle analysis reveals short cycles and self-loops dominating the transition structure. The rate of convergence varies with prompt wording and model choice, but the phenomenon is robust across conditions. A classical GP subtree mutation operator does not exhibit comparable convergence, suggesting that the effect is intrinsic to the LLM mutation pipeline. These findings reveal a tension at the heart of LLM-driven program evolution: the same capabilities that enable semantics-aware program transformation also carry a systematic bias toward structural homogeneity that must be accounted for if such systems are to sustain open-ended exploration. Source code is available at https://github.com/can-gurkan/lmca.

翻译：当大语言模型反复变异程序时，它会探索新形态还是回归旧形态？我们通过在无选择压力的领域特定语言中分析大语言模型驱动的变异链来研究该问题，实验变量包括提示设计、模型族和随机复制。研究发现，基于大语言模型的变异始终趋向于程序空间中的受限吸引子区域。结构层面的收敛尤为严重：在87%的变异链中，超过93%的变异重复了先前已出现的结构形态，大部分变异局限于重复模板内的终端替换。循环分析显示短循环和自循环主导了转移结构。经典遗传规划子树变异算子未表现出可比拟的收敛性，表明该效应是大语言模型变异流程的内在特性。这些发现揭示了大语言模型驱动程序演化的核心张力：其语义感知变换能力与系统性的结构同质化倾向相伴相生，若要使此类系统维持开放式探索，必须对这种偏置加以考量。源代码见 https://github.com/can-gurkan/lmca。