Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution

2025-08-07 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The study "Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution" investigates how Large Language Models (LLMs) behave when repeatedly mutating programs without selection pressure. Researchers found that LLM-based mutation consistently converges toward restricted "attractor regions" in program space. This 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. Cycle analysis revealed short cycles and self-loops dominating the transition structure. The rate of convergence varied with prompt wording and model choice (e.g., Claude Sonnet 4 produced as few as 6 unique programs, while GPT-5 Mini with reasoning produced up to 301), but the phenomenon was robust across conditions. A classical GP subtree mutation operator did not exhibit comparable convergence, suggesting this effect is intrinsic to the LLM mutation pipeline.

Key takeaway

For AI Scientists and Machine Learning Engineers developing LLM-driven code generation or evolutionary systems, you must account for the intrinsic bias of LLM mutation operators toward structural homogeneity. Your systems will likely converge to limited program structures, even without selection pressure, potentially hindering open-ended exploration. You should proactively test prompt designs for convergence profiles and integrate explicit diversity-preserving mechanisms to sustain novel program evolution.

Key insights

LLM-driven program mutation without selection pressure consistently converges to restricted structural forms, limiting diversity.

Principles

• LLM mutation inherently biases toward structural homogeneity.
• Prompt wording significantly modulates convergence severity.
• Reasoning-enabled LLMs sustain higher program diversity.

Method

The study analyzed LLM mutation chains in a constrained DSL, tracking unique program and skeleton counts, constructing transition graphs, and performing cycle analysis to measure convergence dynamics.

In practice

• Conduct mutation-chain analysis to identify exploratory prompts.
• Incorporate explicit diversity maintenance mechanisms.
• Consider reasoning-enabled LLMs for higher diversity.

Topics

• LLM-driven Program Evolution
• Genetic Programming
• Program Mutation
• Convergence Dynamics
• Domain-Specific Languages
• Large Language Models

Code references

• can-gurkan/lmca

Best for: AI Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.