{
  "id": "2611.05234",
  "title": "ARLoop: Fully-Open Pre-trained Looped Transformers with Attention Residuals",
  "authors": "Yuekun Yao, Yupei Du, Dawei Zhu, Huan Sun",
  "first_public": "2026-11-03",
  "added_at": "2026-11-09",
  "venue": "arXiv",
  "arxiv": "2611.05234",
  "paper_url": "https://arxiv.org/abs/2611.05234",
  "html_url": "https://arxiv.org/html/2611.05234v1",
  "lens": [
    "recurrent"
  ],
  "representation": [
    "hidden-state"
  ],
  "computation": [
    "recurrent",
    "depth-wise"
  ],
  "training": [
    "pretraining"
  ],
  "questions": [
    "architecture",
    "scaling",
    "capability"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "language"
  ],
  "summary": "Combines shared transformer layers with attention over recent residual states across recurrent depths. Evaluates the design in controlled pretraining and releases models trained on open language, math and code data.",
  "provenance": "Primary-source literature review, 2026-11-09",
  "author_names": [
    "Yuekun Yao",
    "Yupei Du",
    "Dawei Zhu",
    "Huan Sun"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2611.05234/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2611-05234",
  "citation_date": "2026-11-03",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2611.05234/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2611.05234/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2611.05234/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2611.05234/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2611.05234"
}