{
  "id": "2412.01113",
  "title": "LLMs Faithfully and Iteratively Compute Answers During CoT: A Systematic Analysis With Multi-step Arithmetics",
  "first_public": "2024-12-02",
  "paper_url": "https://arxiv.org/abs/2412.01113",
  "authors": "Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi, Shusaku Sone, Masaya Taniguchi, Ana Brassard, Keisuke Sakaguchi, Kentaro Inui",
  "arxiv": "2412.01113",
  "added_at": "2026-11-09",
  "venue": "arXiv",
  "provenance": "Primary-source literature review, 2026-11-09",
  "html_url": "https://arxiv.org/html/2412.01113v4",
  "lens": [
    "analysis"
  ],
  "representation": [
    "hidden-state"
  ],
  "computation": [
    "token-wise",
    "depth-wise"
  ],
  "training": [
    "analysis-only"
  ],
  "questions": [
    "interpretability",
    "evaluation"
  ],
  "evidence": [
    "empirical",
    "probing",
    "mechanistic"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "Uses linear probes and activation patching on multi-step arithmetic to test when answers are computed. Finds that the tested models form intermediate answers during written CoT and that those generated steps causally affect the final answer.",
  "author_names": [
    "Keito Kudo",
    "Yoichi Aoki",
    "Tatsuki Kuribayashi",
    "Shusaku Sone",
    "Masaya Taniguchi",
    "Ana Brassard",
    "Keisuke Sakaguchi",
    "Kentaro Inui"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2412.01113/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2412-01113",
  "citation_date": "2024-12-02",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2412.01113/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2412.01113/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2412.01113/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2412.01113/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2412.01113"
}