Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution
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 t...
| Authors | Can Gurkan, Forrest Stonedahl, Uri Wilensky |
|---|---|
| Primary category | cs.AI |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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