@inproceedings{geng-etal-2026-markovian,
title = "{M}arkovian Generation Chains in Large Language Models",
author = "Geng, Mingmeng and
Mohamed, Amr and
Shang, Guokan and
Vazirgiannis, Michalis and
Poibeau, Thierry",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2188/",
pages = "46847--46865",
ISBN = "979-8-89176-462-0",
abstract = "The widespread use of large language models (LLMs) raises an important question: how do texts evolve when they are repeatedly processed by LLMs? In this paper, we define this iterative inference process as Markovian generation chains, where each step takes a specific prompt template and the previous output as input, without including any prior data. In iterative rephrasing and round-trip translation experiments, the output either converges to a small recurrent set or continues to produce novel sentences within a finite horizon. Through sentence-level Markov chain modeling and analysis of simulated data, we show that iterative process can either increase or reduce the diversity depending on factors such as the temperature parameter and the initial input sentence. These results offer valuable insights into the dynamics of iterative LLM inference and their implications for multi-agent LLM systems."
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<abstract>The widespread use of large language models (LLMs) raises an important question: how do texts evolve when they are repeatedly processed by LLMs? In this paper, we define this iterative inference process as Markovian generation chains, where each step takes a specific prompt template and the previous output as input, without including any prior data. In iterative rephrasing and round-trip translation experiments, the output either converges to a small recurrent set or continues to produce novel sentences within a finite horizon. Through sentence-level Markov chain modeling and analysis of simulated data, we show that iterative process can either increase or reduce the diversity depending on factors such as the temperature parameter and the initial input sentence. These results offer valuable insights into the dynamics of iterative LLM inference and their implications for multi-agent LLM systems.</abstract>
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%0 Conference Proceedings
%T Markovian Generation Chains in Large Language Models
%A Geng, Mingmeng
%A Mohamed, Amr
%A Shang, Guokan
%A Vazirgiannis, Michalis
%A Poibeau, Thierry
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F geng-etal-2026-markovian
%X The widespread use of large language models (LLMs) raises an important question: how do texts evolve when they are repeatedly processed by LLMs? In this paper, we define this iterative inference process as Markovian generation chains, where each step takes a specific prompt template and the previous output as input, without including any prior data. In iterative rephrasing and round-trip translation experiments, the output either converges to a small recurrent set or continues to produce novel sentences within a finite horizon. Through sentence-level Markov chain modeling and analysis of simulated data, we show that iterative process can either increase or reduce the diversity depending on factors such as the temperature parameter and the initial input sentence. These results offer valuable insights into the dynamics of iterative LLM inference and their implications for multi-agent LLM systems.
%U https://aclanthology.org/2026.findings-emnlp.2188/
%P 46847-46865
Markdown (Informal)
[Markovian Generation Chains in Large Language Models](https://aclanthology.org/2026.findings-emnlp.2188/) (Geng et al., Findings 2026)
ACL
- Mingmeng Geng, Amr Mohamed, Guokan Shang, Michalis Vazirgiannis, and Thierry Poibeau. 2026. Markovian Generation Chains in Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 46847–46865, Budapest, Hungary. Association for Computational Linguistics.