{
  "id": "2601.21576",
  "title": "Chain Of Thought Compression: A Theoretical Analysis",
  "authors": "Juncai Li, Ru Li, Yuxiang Zhou, Boxiang Ma, Jeff Z. Pan",
  "first_public": "2026-01-29",
  "added_at": "2026-10-18",
  "venue": "arXiv",
  "arxiv": "2601.21576",
  "paper_url": "https://arxiv.org/abs/2601.21576",
  "lens": [
    "analysis",
    "internalized",
    "learned-tokens"
  ],
  "representation": [
    "hidden-state"
  ],
  "computation": [
    "depth-wise"
  ],
  "training": [
    "supervised",
    "distillation"
  ],
  "questions": [
    "theory",
    "evaluation",
    "limitations",
    "efficiency"
  ],
  "evidence": [
    "formal",
    "empirical",
    "boundary"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "Analyzes how skipping intermediate steps weakens learning signals in a simplified parity-task model. Introduces NatBool-DAG to test logical dependencies and ALiCoT, which supervises learned token states with intermediate logical labels or explicit-CoT representations.",
  "provenance": "Awesome-Latent-CoT; primary sources checked 2026-10-18",
  "author_names": [
    "Juncai Li",
    "Ru Li",
    "Yuxiang Zhou",
    "Boxiang Ma",
    "Jeff Z. Pan"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2601.21576/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2601-21576",
  "citation_date": "2026-01-29",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2601.21576/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2601.21576/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2601.21576/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2601.21576/citation.csl.json"
}