{
  "id": "2610.09416",
  "title": "Efficient Reasoning with Flow Language Models",
  "authors": "Hanru Bai, Faissal Izermine, Oscar Davis, T. Konstantin Rusch",
  "first_public": "2026-10-07",
  "lens": [
    "analysis",
    "continuous",
    "beyond-sequential"
  ],
  "representation": [
    "continuous-state"
  ],
  "training": [
    "analysis-only",
    "supervised"
  ],
  "questions": [
    "theory",
    "interpretability",
    "efficiency"
  ],
  "evidence": [
    "formal",
    "empirical",
    "mechanistic"
  ],
  "modality": [
    "symbolic"
  ],
  "summary": "Compares continuous flow and discrete diffusion models at matched sizes and denoising steps. A theoretical account and intermediate-state interventions examine whether retaining information about several candidates helps solve mazes and Sudoku before final discretization.",
  "computation": [
    "diffusion",
    "parallel"
  ],
  "venue": "arXiv",
  "arxiv": "2610.09416",
  "paper_url": "https://arxiv.org/abs/2610.09416",
  "provenance": "Primary-source literature search, 2026-10-18",
  "added_at": "2026-10-18",
  "author_names": [
    "Hanru Bai",
    "Faissal Izermine",
    "Oscar Davis",
    "T. Konstantin Rusch"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.09416/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-09416",
  "citation_date": "2026-10-07",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.09416/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.09416/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.09416/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.09416/citation.csl.json"
}