{
  "id": "2610.19768",
  "title": "Learning to Compute Is Not Learning to Retain or Reuse in Looped Transformers",
  "authors": "Peilong Zhou, Hongtao Zhang, Yulong Huang, Cangyuan Li, Mingyu Chen, Ying Wang",
  "first_public": "2026-10-14",
  "added_at": "2026-10-18",
  "venue": "arXiv",
  "arxiv": "2610.19768",
  "paper_url": "https://arxiv.org/abs/2610.19768",
  "summary": "Separates computing an answer, preserving it through additional loops, and keeping its hidden state usable for later steps. On state-machine and maze tasks, shows that training can improve one ability without securing the others.",
  "lens": [
    "analysis",
    "recurrent"
  ],
  "representation": [
    "hidden-state"
  ],
  "computation": [
    "recurrent",
    "depth-wise"
  ],
  "training": [
    "supervised",
    "analysis-only"
  ],
  "questions": [
    "interpretability",
    "generalization",
    "limitations"
  ],
  "evidence": [
    "empirical",
    "mechanistic",
    "boundary"
  ],
  "modality": [
    "symbolic"
  ],
  "provenance": "Primary-source literature search, 2026-10-18",
  "author_names": [
    "Peilong Zhou",
    "Hongtao Zhang",
    "Yulong Huang",
    "Cangyuan Li",
    "Mingyu Chen",
    "Ying Wang"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.19768/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-19768",
  "citation_date": "2026-10-14",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.19768/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.19768/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.19768/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.19768/citation.csl.json"
}