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LLMs show distinct self-modification behaviors based on model identity

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper investigates how large language models engage in iterative self-improvement by modifying their own system prompts. The study tested six models, including Claude Opus 4.5, GPT-4o, and Gemini 3-Pro, across different optimization modes. A key finding is that the models' tendency to stop self-modification is more closely tied to their inherent identity than the assigned optimization goal. Claude models consistently converged, while GPT-4o showed extensive modification, with other models falling in between. AI

IMPACT Reveals that LLM self-modification convergence is an intrinsic property, potentially impacting how future models are designed and controlled.

RANK_REASON The cluster contains an academic paper detailing empirical research on LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs show distinct self-modification behaviors based on model identity

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The cluster contains an academic paper detailing empirical research on LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Oleg Stroganov ·

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

    arXiv:2611.01304v1 Announce Type: new Abstract: What happens when language models are given tools to iteratively modify their own system prompts? We present an empirical study of LLM self-modification, comprising 54 independent experiments across six language models (Claude Opus …

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