{
  "id": "2610.33360",
  "title": "LSTM-Style Looped Transformers with Shared KV Caches",
  "authors": "Andreas Kirsch",
  "first_public": "2026-10-23",
  "added_at": "2026-11-02",
  "venue": "arXiv",
  "arxiv": "2610.33360",
  "paper_url": "https://arxiv.org/abs/2610.33360",
  "html_url": "https://arxiv.org/html/2610.33360v1",
  "lens": [
    "recurrent"
  ],
  "representation": [
    "hidden-state",
    "cache-memory"
  ],
  "computation": [
    "depth-wise",
    "recurrent",
    "stateful"
  ],
  "training": [
    "pretraining"
  ],
  "questions": [
    "architecture",
    "efficiency"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "language"
  ],
  "summary": "Adds an LSTM-style gated memory across transformer loops and shares one KV cache across the recurrent core. Aligns cache entries from two training passes with a consistency loss to reduce the mismatch during autoregressive decoding.",
  "provenance": "Primary-source literature review, 2026-11-02",
  "author_names": [
    "Andreas Kirsch"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.33360/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-33360",
  "citation_date": "2026-10-23",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.33360/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.33360/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.33360/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.33360/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.33360"
}