@inproceedings{kudo-etal-2026-llms,
    title = "{LLM}s Faithfully and Iteratively Compute Answers During {C}o{T}: A Systematic Analysis With Multi-step Arithmetics",
    author = "Kudo, Keito  and
      Aoki, Yoichi  and
      Kuribayashi, Tatsuki  and
      Sone, Shusaku  and
      Taniguchi, Masaya  and
      Brassard, Ana  and
      Sakaguchi, Keisuke  and
      Inui, Kentaro",
    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.59/",
    pages = "1114--1153",
    ISBN = "979-8-89176-386-9",
    abstract = "This study investigates the internal information flow of large language models (LLMs) while performing chain-of-thought (CoT) style reasoning.Specifically, with a particular interest in the faithfulness of the CoT explanation to LLMs' final answer, we explore (i) when the LLMs' answer is (pre)determined, especially before the CoT begins or after, and (ii) how strongly the information from CoT specifically has a causal effect on the final answer.Our experiments with controlled arithmetic tasks reveal a systematic internal reasoning mechanism of LLMs.They have not derived an answer at the moment when input was fed into the model.Instead, they compute (sub-)answers while generating the reasoning chain on the fly.Therefore, the generated reasoning chains can be regarded as faithful reflections of the model{'}s internal computation."
}