{
  "id": "2609.21605",
  "title": "Trading Depth for Time in Recurrent Transformers",
  "authors": "Zeyi Huang, Xuehai He, Yelong Shen, Yong Jae Lee",
  "first_public": "2026-09-18",
  "added_at": "2026-10-18",
  "venue": "arXiv",
  "arxiv": "2609.21605",
  "paper_url": "https://arxiv.org/abs/2609.21605",
  "lens": [
    "recurrent",
    "learned-tokens"
  ],
  "representation": [
    "hidden-state",
    "continuous-state"
  ],
  "computation": [
    "recurrent",
    "token-wise",
    "stateful"
  ],
  "training": [
    "pretraining"
  ],
  "questions": [
    "architecture",
    "efficiency",
    "scaling"
  ],
  "evidence": [
    "empirical",
    "scaling"
  ],
  "modality": [
    "language"
  ],
  "summary": "Inserts a hidden-state refinement step between consecutive text tokens in a recurrent transformer. Compares this extra thinking time with adding physical layers or transformer loops under matched computation budgets.",
  "provenance": "Primary-source literature search, 2026-10-18",
  "author_names": [
    "Zeyi Huang",
    "Xuehai He",
    "Yelong Shen",
    "Yong Jae Lee"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2609.21605/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-21605",
  "citation_date": "2026-09-18",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2609.21605/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.21605/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.21605/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.21605/citation.csl.json"
}