{
  "id": "2610.35425",
  "title": "From Denoising Dynamics to Endpoint Uncertainty in Diffusion Language Models",
  "authors": "Julio C. Amador Díaz López",
  "first_public": "2026-10-26",
  "added_at": "2026-11-02",
  "venue": "arXiv",
  "arxiv": "2610.35425",
  "paper_url": "https://arxiv.org/abs/2610.35425",
  "html_url": "https://arxiv.org/html/2610.35425v1",
  "lens": [
    "beyond-sequential",
    "analysis"
  ],
  "representation": [
    "discrete-token"
  ],
  "computation": [
    "diffusion",
    "parallel"
  ],
  "training": [
    "analysis-only"
  ],
  "questions": [
    "evaluation",
    "interpretability"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "Measures how quickly tokens settle during diffusion decoding and relates this to variation across repeated answers. Tests whether features from one denoising run predict agreement among independent runs, separating output stability from correctness.",
  "provenance": "Primary-source literature review, 2026-11-02",
  "author_names": [
    "Julio C. Amador Díaz López"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.35425/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-35425",
  "citation_date": "2026-10-26",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.35425/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.35425/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.35425/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.35425/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.35425"
}