{
  "id": "2610.25076",
  "title": "P2N: Reusing Deep Representations for Greater Effective Depth",
  "authors": "Yiqin Hao, Boyi Zeng, Zitong Wang, He Li, Shixiang Song, Feichen Song, Yifan Liu, Ran Tao, Bryan Dai, Zhouhan Lin",
  "first_public": "2026-10-18",
  "added_at": "2026-11-02",
  "venue": "arXiv",
  "arxiv": "2610.25076",
  "paper_url": "https://arxiv.org/abs/2610.25076",
  "html_url": "https://arxiv.org/html/2610.25076v2",
  "lens": [
    "recurrent"
  ],
  "representation": [
    "hidden-state"
  ],
  "computation": [
    "recurrent",
    "depth-wise",
    "parallel"
  ],
  "training": [
    "pretraining"
  ],
  "questions": [
    "architecture",
    "capability",
    "efficiency",
    "scaling"
  ],
  "evidence": [
    "empirical",
    "scaling"
  ],
  "modality": [
    "language"
  ],
  "summary": "Feeds the previous token’s deep hidden state into earlier layers for the next token. A shared middle block and parallel Jacobi updates train this recurrence without serializing all training tokens.",
  "provenance": "Primary-source literature review, 2026-11-02",
  "author_names": [
    "Yiqin Hao",
    "Boyi Zeng",
    "Zitong Wang",
    "He Li",
    "Shixiang Song",
    "Feichen Song",
    "Yifan Liu",
    "Ran Tao",
    "Bryan Dai",
    "Zhouhan Lin"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.25076/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-25076",
  "citation_date": "2026-10-18",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.25076/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.25076/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.25076/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.25076/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.25076"
}