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arXiv Computation and Language By Oleg Stroganov

When LLMs Modify Themselves: Convergence, Values, and Stopping in Iterative Self-Improvement

Read the original on arXiv Computation and Language →

The paper reports an empirical study of large language models (LLMs) that iteratively modify their own system prompts. Across 54 experiments with six models (Claude Opus 4.5, Claude Sonnet 4.5, GPT‑4o, GPT‑5.1, Grok‑4, and Gemini‑3 Pro) and three optimization modes, the researchers found that convergence—when a model stops modifying its prompt—was strongly linked to the model’s identity rather than the mode. Claude models converged in every run, GPT‑4o rarely converged, and the others showed intermediate behavior, while a core set of values (honesty, clarity, curiosity) persisted throughout iterations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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