{
  "id": "2609.37379",
  "title": "Looped Transformers as Optimizers",
  "authors": "Yulong Huang, Chen Jiang, Zhanpeng Zhou, Hongtao Zhang, Tianyu Li, Tianyu He, Xiangyu Zhang, Bojun Cheng",
  "first_public": "2026-09-29",
  "added_at": "2026-10-18",
  "venue": "arXiv",
  "arxiv": "2609.37379",
  "paper_url": "https://arxiv.org/abs/2609.37379",
  "summary": "Treats each recurrent hidden-state update as an optimization step and derives loop designs from this view. Introduces OperLoop, combining adaptive step sizes and weight decay.",
  "lens": [
    "recurrent",
    "analysis"
  ],
  "representation": [
    "hidden-state"
  ],
  "computation": [
    "recurrent",
    "depth-wise"
  ],
  "training": [
    "pretraining"
  ],
  "questions": [
    "architecture",
    "theory",
    "capability"
  ],
  "evidence": [
    "formal",
    "empirical"
  ],
  "modality": [
    "language"
  ],
  "provenance": "Primary-source literature search, 2026-10-18",
  "author_names": [
    "Yulong Huang",
    "Chen Jiang",
    "Zhanpeng Zhou",
    "Hongtao Zhang",
    "Tianyu Li",
    "Tianyu He",
    "Xiangyu Zhang",
    "Bojun Cheng"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2609.37379/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-37379",
  "citation_date": "2026-09-29",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2609.37379/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.37379/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.37379/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.37379/citation.csl.json"
}