{
  "id": "2504.07986",
  "title": "SEAL: Steerable Reasoning Calibration of Large Language Models for Free",
  "first_public": "2025-04-07",
  "paper_url": "https://arxiv.org/abs/2504.07986",
  "authors": "Runjin Chen, Zhenyu Zhang, Junyuan Hong, Souvik Kundu, Zhangyang Wang",
  "arxiv": "2504.07986",
  "added_at": "2026-11-09",
  "venue": "arXiv",
  "provenance": "Primary-source literature review, 2026-11-09",
  "html_url": "https://arxiv.org/html/2504.07986v3",
  "lens": [
    "analysis"
  ],
  "representation": [
    "hidden-state",
    "text-latent-hybrid"
  ],
  "computation": [
    "depth-wise",
    "token-wise"
  ],
  "training": [
    "inference-only"
  ],
  "questions": [
    "interpretability",
    "controllability",
    "efficiency"
  ],
  "evidence": [
    "empirical",
    "mechanistic"
  ],
  "modality": [
    "language"
  ],
  "summary": "Separates execution, reflection, and transition steps in written CoT using hidden representations. Adds a steering vector at thought boundaries to adjust how often the model reflects or changes direction, then measures accuracy and token use.",
  "author_names": [
    "Runjin Chen",
    "Zhenyu Zhang",
    "Junyuan Hong",
    "Souvik Kundu",
    "Zhangyang Wang"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2504.07986/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2504-07986",
  "citation_date": "2025-04-07",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2504.07986/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2504.07986/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2504.07986/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2504.07986/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2504.07986"
}