Do Language Models Converge to Themselves? Recursive Self-Refinement as Textual Relaxation
Large language models are increasingly used in recursive refinement workflows, where an initial draft is repeatedly revised by the same model. Despite their growing use, the long-term dynamics of such workflows remain poorly understood. Does repeated refinement continue to improve outputs indefinitely, or does it converge toward a stable textual form? We study recursive self-refinement as a dynamical process in which repeated LLM revision drives text toward a model-preferred soft fixed-point region. Using GPT-5.5, we generate 10-step refinement trajectories for 50 ICML 2025 abstracts under both default-temperature and deterministic decoding, and additionally evaluate 15 ICML 2020 abstract...
| Authors | Xuening Wu, Qianya Xu, Yanlan Kang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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