{
  "id": "2611.05255",
  "title": "Going Deeper With Memories",
  "authors": "Afshin Khadangi",
  "first_public": "2026-11-03",
  "added_at": "2026-11-09",
  "venue": "arXiv",
  "arxiv": "2611.05255",
  "paper_url": "https://arxiv.org/abs/2611.05255",
  "html_url": "https://arxiv.org/html/2611.05255v1",
  "lens": [
    "recurrent"
  ],
  "representation": [
    "hidden-state",
    "cache-memory"
  ],
  "computation": [
    "recurrent",
    "hierarchical",
    "stateful"
  ],
  "training": [
    "pretraining"
  ],
  "questions": [
    "architecture",
    "efficiency"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "language"
  ],
  "summary": "Compresses byte sequences into a hierarchy while preserving exact byte information in a separate stream. A shared memory module refines the deepest hidden representation before a causal decoder restores byte-level predictions.",
  "provenance": "Primary-source literature review, 2026-11-09",
  "author_names": [
    "Afshin Khadangi"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2611.05255/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2611-05255",
  "citation_date": "2026-11-03",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2611.05255/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2611.05255/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2611.05255/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2611.05255/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2611.05255"
}