{
  "id": "2610.27628",
  "title": "PAR: Progress-Aligned Recurrence for Length Generalization in State Tracking",
  "authors": "Pan Wang, Yihao Hu, Guangyuan Dong, Yitong Lin, Hang Wang, Zimi Liu, Zhihao Wen, Jianshe Li, Jiang-Ming Yang, Wei Wu, Yongqi Tong",
  "first_public": "2026-10-20",
  "added_at": "2026-10-22",
  "venue": "arXiv",
  "arxiv": "2610.27628",
  "paper_url": "https://arxiv.org/abs/2610.27628",
  "html_url": "https://arxiv.org/html/2610.27628v1",
  "lens": [
    "recurrent"
  ],
  "representation": [
    "hidden-state"
  ],
  "computation": [
    "recurrent",
    "depth-wise"
  ],
  "training": [
    "supervised",
    "self-distillation"
  ],
  "questions": [
    "generalization",
    "architecture",
    "capability"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "Trains each loop of a language model to represent the state after one more operation, while keeping hidden-state norms stable. Combines this supervision with learned stopping and prefix self-distillation to test generalization to longer state-tracking problems without generating intermediate tokens.",
  "provenance": "Primary-source literature review, 2026-10-22",
  "author_names": [
    "Pan Wang",
    "Yihao Hu",
    "Guangyuan Dong",
    "Yitong Lin",
    "Hang Wang",
    "Zimi Liu",
    "Zhihao Wen",
    "Jianshe Li",
    "Jiang-Ming Yang",
    "Wei Wu",
    "Yongqi Tong"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.27628/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-27628",
  "citation_date": "2026-10-20",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.27628/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.27628/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.27628/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.27628/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.27628"
}