{
  "id": "2609.35694",
  "title": "Reasoning with Continuous Latent Diffusion",
  "authors": "Xiang Cheng",
  "first_public": "2026-09-28",
  "lens": [
    "continuous",
    "beyond-sequential"
  ],
  "representation": [
    "continuous-state"
  ],
  "training": [
    "supervised",
    "distillation",
    "reinforcement-learning",
    "curriculum"
  ],
  "questions": [
    "capability",
    "efficiency"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "CEDR learns compact representations from several teacher layers, denoises them at different rates, and trains a smaller prompt encoder to replace the teacher at inference. It generates complete mathematical and coding solutions through continuous refinement before text decoding.",
  "computation": [
    "diffusion",
    "parallel"
  ],
  "venue": "arXiv",
  "arxiv": "2609.35694",
  "paper_url": "https://arxiv.org/abs/2609.35694",
  "provenance": "Primary-source literature search, 2026-10-18",
  "added_at": "2026-10-18",
  "author_names": [
    "Xiang Cheng"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2609.35694/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-35694",
  "citation_date": "2026-09-28",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2609.35694/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.35694/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.35694/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.35694/citation.csl.json"
}