{
  "id": "2610.11570",
  "title": "Scaling to Tens of Thousands of Test-Time Iterations with Loop-Native Attention Residuals",
  "authors": "Pengxiang Li, Dilxat Muhtar, Di He, Guinan Su, Lu Yin, Shiwei Liu",
  "first_public": "2026-10-08",
  "added_at": "2026-10-18",
  "venue": "arXiv",
  "arxiv": "2610.11570",
  "paper_url": "https://arxiv.org/abs/2610.11570",
  "lens": [
    "recurrent"
  ],
  "representation": [
    "hidden-state",
    "continuous-state"
  ],
  "computation": [
    "recurrent",
    "depth-wise",
    "stateful"
  ],
  "training": [
    "supervised"
  ],
  "questions": [
    "architecture",
    "scaling",
    "generalization"
  ],
  "evidence": [
    "empirical",
    "scaling"
  ],
  "modality": [
    "symbolic"
  ],
  "summary": "Adds a learned running summary of previous recurrent states so that each update can retain useful earlier work. Uses content-based weighting and temporal decay with constant aggregation memory, and tests whether puzzle-solving improves over much longer inference runs.",
  "provenance": "Primary-source literature search, 2026-10-18",
  "author_names": [
    "Pengxiang Li",
    "Dilxat Muhtar",
    "Di He",
    "Guinan Su",
    "Lu Yin",
    "Shiwei Liu"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.11570/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-11570",
  "citation_date": "2026-10-08",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.11570/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.11570/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.11570/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.11570/citation.csl.json"
}