{
  "id": "2610.25723",
  "title": "Learning to Couple: Context-Adaptive Joint Denoising for Diffusion Language Models",
  "authors": "Zilei Shao, Ian Li, Heng Zhao, Kewei Lian, Guy Van den Broeck, Zhe Zeng, Anji Liu",
  "first_public": "2026-10-19",
  "added_at": "2026-10-22",
  "venue": "arXiv",
  "arxiv": "2610.25723",
  "paper_url": "https://arxiv.org/abs/2610.25723",
  "html_url": "https://arxiv.org/html/2610.25723v1",
  "lens": [
    "beyond-sequential"
  ],
  "representation": [
    "discrete-token"
  ],
  "computation": [
    "diffusion",
    "parallel"
  ],
  "training": [
    "supervised"
  ],
  "questions": [
    "architecture",
    "capability",
    "efficiency"
  ],
  "evidence": [
    "empirical",
    "formal"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "Combines a diffusion language model with a probabilistic circuit that models dependencies among tokens proposed together. Learns context-dependent token coupling and uses it to decide which discrete tokens to commit in parallel on math and code tasks.",
  "provenance": "Primary-source literature review, 2026-10-22",
  "author_names": [
    "Zilei Shao",
    "Ian Li",
    "Heng Zhao",
    "Kewei Lian",
    "Guy Van den Broeck",
    "Zhe Zeng",
    "Anji Liu"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.25723/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-25723",
  "citation_date": "2026-10-19",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.25723/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.25723/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.25723/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.25723/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.25723"
}