{
  "id": "2606.31779",
  "title": "Bridging the Gap Between Latent and Explicit Reasoning with Looped Transformers",
  "authors": "Ying Fan, Anej Svete, Kangwook Lee",
  "first_public": "2026-06-30",
  "added_at": "2026-10-23",
  "venue": "arXiv",
  "arxiv": "2606.31779",
  "paper_url": "https://arxiv.org/abs/2606.31779",
  "html_url": "https://arxiv.org/html/2606.31779v2",
  "code_url": "https://github.com/yingfan-bot/lotus",
  "lens": [
    "internalized",
    "continuous",
    "recurrent",
    "beyond-sequential"
  ],
  "representation": [
    "continuous-state"
  ],
  "computation": [
    "recurrent",
    "parallel",
    "depth-wise"
  ],
  "training": [
    "supervised",
    "curriculum"
  ],
  "questions": [
    "capability",
    "efficiency",
    "interpretability"
  ],
  "evidence": [
    "empirical",
    "mechanistic"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "LOTUS repeatedly refines blocks of continuous reasoning states in parallel, training each position to predict a token from a worked solution. It generates answers from those states and studies accuracy, latency and how much of the reasoning can be read back.",
  "provenance": "Primary-source literature review, 2026-10-23",
  "author_names": [
    "Ying Fan",
    "Anej Svete",
    "Kangwook Lee"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2606.31779/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2606-31779",
  "citation_date": "2026-06-30",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2606.31779/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2606.31779/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2606.31779/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2606.31779/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2606.31779"
}