{
  "id": "2610.31943",
  "title": "Agreement rather than normalisation keeps depth reusable in looped language models",
  "authors": "Ivan Viakhirev, Kirill Borodin, Maxim Abramov, Grach Mkrtchian",
  "first_public": "2026-10-22",
  "added_at": "2026-10-26",
  "venue": "arXiv",
  "arxiv": "2610.31943",
  "paper_url": "https://arxiv.org/abs/2610.31943",
  "html_url": "https://arxiv.org/html/2610.31943v1",
  "lens": [
    "recurrent",
    "analysis"
  ],
  "representation": [
    "hidden-state"
  ],
  "computation": [
    "recurrent",
    "depth-wise"
  ],
  "training": [
    "analysis-only",
    "supervised"
  ],
  "questions": [
    "architecture",
    "generalization",
    "limitations"
  ],
  "evidence": [
    "empirical",
    "mechanistic",
    "probing"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "Tests normalization between passes through a looped transformer. Removing it from pretrained Ouro models breaks predictions, while small models trained without it generalize better across loop counts, showing why the computation used during training and inference must agree.",
  "provenance": "Primary-source literature review, 2026-10-26",
  "author_names": [
    "Ivan Viakhirev",
    "Kirill Borodin",
    "Maxim Abramov",
    "Grach Mkrtchian"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.31943/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-31943",
  "citation_date": "2026-10-22",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.31943/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.31943/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.31943/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.31943/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.31943"
}