@inproceedings{zhou-etal-2026-curse,
    title = "The Curse of Verbalization: How Presentation Order Constrains {LLM} Reasoning",
    author = "Zhou, Yue  and
      Zou, Henry Peng  and
      Di Eugenio, Barbara  and
      Zhang, Yang",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.218/",
    pages = "4175--4185",
    ISBN = "979-8-89176-386-9",
    abstract = "This paper delves into the factors that contribute to the difficulty of problems for large language models (LLMs). We begin with a pilot test evaluating LLMs' understanding of esoteric programming languages and find that LLMs struggle significantly when programs execute in an order that is unaligned with how the program is presented. This phenomenon leads to the hypothesis that LLM performance on reasoning correlates with the alignment between the order in which information is presented and the order in which it should be utilized. We demonstrate that this hypothesis holds broadly in mathematical reasoning: restructuring problems to align the order of information presentation with the order of utilization consistently improves performance across state-of-the-art models. We conjecture this occurs because LLMs acquire a strong tendency to verbalize information in presentation order during training on human text, a tendency detrimental in reasoning domains where the optimal utilization order often diverges from the presentation order. To provide further evidence, we construct pseudo-mathematical problems with nonsensical terms and quantify the verbalization flexibility of LLMs without interference from mathematical knowledge. Across twelve representative LLMs, we find that this flexibility exhibits a strong correlation (p = 0.87) with general reasoning performance rankings on LMArena."
}