{
  "id": "2610.22075",
  "title": "Retrofitting Recurrent Vision-Language Models from Pretrained Backbones",
  "authors": "Madhav Iyengar, Andreas Geiger, Hilde Kühne",
  "first_public": "2026-10-15",
  "added_at": "2026-10-26",
  "venue": "arXiv",
  "arxiv": "2610.22075",
  "paper_url": "https://arxiv.org/abs/2610.22075",
  "html_url": "https://arxiv.org/html/2610.22075v1",
  "lens": [
    "recurrent"
  ],
  "representation": [
    "hidden-state",
    "multimodal-latent"
  ],
  "computation": [
    "recurrent",
    "depth-wise"
  ],
  "training": [
    "pretraining",
    "supervised",
    "distillation"
  ],
  "questions": [
    "architecture",
    "capability",
    "efficiency"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "multimodal",
    "vision",
    "language"
  ],
  "summary": "Prunes layers from a pretrained vision-language model, then loops the remaining middle block with a learned adapter. Uses text training, projector realignment, and visual instruction tuning to recover performance; on TinyLLaVA, the resulting model improves reasoning benchmarks with fewer language-model parameters.",
  "provenance": "Primary-source literature review, 2026-10-26",
  "author_names": [
    "Madhav Iyengar",
    "Andreas Geiger",
    "Hilde Kühne"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.22075/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-22075",
  "citation_date": "2026-10-15",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.22075/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.22075/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.22075/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.22075/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.22075"
}