{
  "id": "2509.12875",
  "title": "LTA-thinker: Latent Thought-Augmented Training Framework for Large Language Models on Complex Reasoning",
  "first_public": "2025-09-16",
  "paper_url": "https://arxiv.org/abs/2509.12875",
  "authors": "Jiaqi Wang, Binquan Ji, Haibo Luo, Yiyang Qi, Ruiting Li, Huiyan Wang, Yuantao Han, Cangyi Yang, Jiaxu Zhang, Feiliang Ren",
  "arxiv": "2509.12875",
  "added_at": "2026-11-09",
  "venue": "arXiv",
  "provenance": "Primary-source literature review, 2026-11-09",
  "html_url": "https://arxiv.org/html/2509.12875v3",
  "lens": [
    "continuous"
  ],
  "representation": [
    "continuous-state",
    "text-latent-hybrid"
  ],
  "computation": [
    "token-wise"
  ],
  "training": [
    "supervised"
  ],
  "questions": [
    "capability",
    "efficiency"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "language"
  ],
  "summary": "Trains a small transformer module to produce continuous thought vectors for a frozen language model. Aligns those vectors with the question and important solution steps; the language model then generates a written explanation and answer.",
  "author_names": [
    "Jiaqi Wang",
    "Binquan Ji",
    "Haibo Luo",
    "Yiyang Qi",
    "Ruiting Li",
    "Huiyan Wang",
    "Yuantao Han",
    "Cangyi Yang",
    "Jiaxu Zhang",
    "Feiliang Ren"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2509.12875/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2509-12875",
  "citation_date": "2025-09-16",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2509.12875/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2509.12875/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2509.12875/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2509.12875/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2509.12875"
}