{
  "id": "2610.39372",
  "title": "Cord: Confidence-Anchored Order Residual Decoding for Masked Diffusion Language Models",
  "authors": "Chengwei Zhou, Zhaoyan Jia, Massoud Pedram, Gourav Datta",
  "first_public": "2026-10-29",
  "added_at": "2026-11-02",
  "venue": "arXiv",
  "arxiv": "2610.39372",
  "paper_url": "https://arxiv.org/abs/2610.39372",
  "html_url": "https://arxiv.org/html/2610.39372v1",
  "lens": [
    "beyond-sequential"
  ],
  "representation": [
    "discrete-token"
  ],
  "computation": [
    "diffusion"
  ],
  "training": [
    "reinforcement-learning"
  ],
  "questions": [
    "controllability",
    "capability",
    "efficiency"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "Learns which position a diffusion language model should reveal next while keeping the model fixed. A small policy adjusts confidence-based decoding to favor local continuation, distant starting points, or filling gaps, using whole-answer rewards from reasoning and code tasks.",
  "provenance": "Primary-source literature review, 2026-11-02",
  "author_names": [
    "Chengwei Zhou",
    "Zhaoyan Jia",
    "Massoud Pedram",
    "Gourav Datta"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.39372/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-39372",
  "citation_date": "2026-10-29",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.39372/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.39372/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.39372/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.39372/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.39372"
}