{
  "id": "2607.22925",
  "title": "Not All LLM Reasoning is Visible in the Chain-of-Thought",
  "authors": "Vatsal Baherwani, Tom Goldstein, Ashwinee Panda",
  "first_public": "2026-07-24",
  "added_at": "2026-10-22",
  "venue": "arXiv",
  "arxiv": "2607.22925",
  "paper_url": "https://arxiv.org/abs/2607.22925",
  "html_url": "https://arxiv.org/html/2607.22925v2",
  "lens": [
    "learned-tokens",
    "analysis"
  ],
  "representation": [
    "discrete-token",
    "hidden-state"
  ],
  "computation": [
    "token-wise",
    "depth-wise"
  ],
  "training": [
    "analysis-only",
    "inference-only",
    "supervised",
    "reinforcement-learning"
  ],
  "questions": [
    "interpretability",
    "capability",
    "limitations"
  ],
  "evidence": [
    "empirical",
    "mechanistic",
    "probing"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "Tests whether fixed filler tokens improve reasoning without exposing intermediate steps in text. Finds task- and model-dependent effects, studies their internal mechanisms, and reports limits of training models to exploit them.",
  "provenance": "Primary-source literature review, 2026-10-22",
  "author_names": [
    "Vatsal Baherwani",
    "Tom Goldstein",
    "Ashwinee Panda"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2607.22925/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2607-22925",
  "citation_date": "2026-07-24",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2607.22925/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2607.22925/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2607.22925/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2607.22925/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2607.22925"
}