{
  "id": "2508.00574",
  "title": "SynAdapt: Learning Adaptive Reasoning in Large Language Models via Synthetic Continuous Chain-of-Thought",
  "authors": "Jianwei Wang, Ziming Wu, Fuming Lai, Shaobing Lian, Ziqian Zeng",
  "first_public": "2025-08-01",
  "venue": "arXiv",
  "arxiv": "2508.00574",
  "paper_url": "https://arxiv.org/abs/2508.00574",
  "lens": [
    "continuous"
  ],
  "representation": [
    "continuous-state"
  ],
  "computation": [
    "token-wise"
  ],
  "training": [
    "supervised"
  ],
  "questions": [
    "capability",
    "efficiency"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "language"
  ],
  "summary": "Uses synthetic continuous reasoning trajectories to train language models to allocate internal reasoning adaptively.",
  "provenance": "Primary-source literature search, 2026-09-04",
  "author_names": [
    "Jianwei Wang",
    "Ziming Wu",
    "Fuming Lai",
    "Shaobing Lian",
    "Ziqian Zeng"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2508.00574/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2508-00574",
  "citation_date": "2025-08-01",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2508.00574/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2508.00574/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2508.00574/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2508.00574/citation.csl.json"
}