{
  "id": "2611.02857",
  "title": "Solv3R: 3D Looped Transformer as a Multi-view Solver",
  "authors": "Jeong-gi Kwak, Andrea Tagliasacchi, Sho Kagami, Yuki Ono, Kwang Moo Yi",
  "first_public": "2026-11-02",
  "added_at": "2026-11-03",
  "venue": "arXiv",
  "arxiv": "2611.02857",
  "paper_url": "https://arxiv.org/abs/2611.02857",
  "html_url": "https://arxiv.org/html/2611.02857v1",
  "lens": [
    "recurrent"
  ],
  "representation": [
    "hidden-state"
  ],
  "computation": [
    "recurrent",
    "stateful"
  ],
  "training": [
    "supervised",
    "distillation"
  ],
  "questions": [
    "architecture",
    "capability",
    "efficiency"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "vision"
  ],
  "summary": "Reuses one transformer block to refine a multi-view geometric state. Trains stable, corrective updates so reconstruction can continue when new images arrive, with stopping based on changes in the hidden state.",
  "provenance": "Primary-source literature review, 2026-11-03",
  "author_names": [
    "Jeong-gi Kwak",
    "Andrea Tagliasacchi",
    "Sho Kagami",
    "Yuki Ono",
    "Kwang Moo Yi"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2611.02857/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2611-02857",
  "citation_date": "2026-11-02",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2611.02857/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2611.02857/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2611.02857/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2611.02857/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2611.02857"
}