{
  "id": "2610.25835",
  "title": "Diffusion Language Models Change Speed Across Tasks and Languages",
  "authors": "Mohsen Fayyaz, Ali Modarressi, Daniel Israel, Lucas Bandarkar, Nanyun Peng",
  "first_public": "2026-10-19",
  "added_at": "2026-10-22",
  "venue": "arXiv",
  "arxiv": "2610.25835",
  "paper_url": "https://arxiv.org/abs/2610.25835",
  "html_url": "https://arxiv.org/html/2610.25835v1",
  "lens": [
    "beyond-sequential",
    "analysis"
  ],
  "representation": [
    "discrete-token"
  ],
  "computation": [
    "diffusion",
    "parallel"
  ],
  "training": [
    "analysis-only",
    "inference-only"
  ],
  "questions": [
    "efficiency",
    "evaluation",
    "limitations"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "Measures how many forward passes diffusion language models spend per output token across tasks and languages. Relates this cost to output copying and novelty, and tests whether answer-decoding speed can improve voting over sampled math answers.",
  "provenance": "Primary-source literature review, 2026-10-22",
  "author_names": [
    "Mohsen Fayyaz",
    "Ali Modarressi",
    "Daniel Israel",
    "Lucas Bandarkar",
    "Nanyun Peng"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.25835/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-25835",
  "citation_date": "2026-10-19",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.25835/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.25835/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.25835/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.25835/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.25835"
}