{
  "abstract": "Generative models are transforming Bayesian computational imaging, yet the field still lacks physics-aware foundation models. Current practice falls into two camps. Large foundation image models are deployed as plug-and-play priors with zero-shot approximate likelihood guidance, which introduces significant bias and computational cost. Physics-aware generative models avoid this bias, but each is tied to a specific dataset, task and instrument. We introduce BAM (Bayesian Anything Model), a lightweight foundation model for few-step, physics-aware posterior sampling that generalises robustly to unseen data and tasks, zero-shot or with minimal finetuning. BAM upgrades the operator-conditioned Reconstruct Anything Model (RAM) backbone (Terris et al.) into a conditional flow map, so instrument physics is specified at inference time rather than fixed during training. BAM has just 36M parameters and is pre-trained jointly on large image corpora and libraries of forward operators. A single network then draws posterior samples in a few steps, with no likelihood approximation and no guidance weights to tune. Across linear inverse problems on FFHQ, AFHQ, LSUN, DIV2K and the Kohler camera-shake benchmark, BAM outperforms in just 3 steps both specialised models and leading zero-shot methods in sample quality, at a fraction of their computational cost. BAM gives the community an accessible entry point to generative computational imaging, lowers the economic and environmental cost of training imaging models, and opens a new path for research on physics-aware Bayesian computational imaging. Official page: https://bayesian-anything-model.github.io/",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.39660",
  "author_total": 5,
  "authors": [
    {
      "name": "Alessio Spagnoletti"
    },
    {
      "name": "Charlesquin Kemajou Mbakam"
    },
    {
      "name": "Jonathan Spence"
    },
    {
      "name": "Andrés Almansa"
    },
    {
      "name": "Marcelo Pereyra"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fnscs1qe8pw7p.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.39660",
  "fulltext_url": null,
  "id": "fnscs1qe8pw7p",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.39660",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1707|Generative Adversarial Networks and Image Synthesis"
    ]
  ],
  "openalex": "W7215089256",
  "pdf_url": "https://arxiv.org/pdf/2609.39660",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fnscs1qe8pw7p.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:58Z",
      "native_id": "2609.39660",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.39660"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "BAM! Bayesian Anything Model: a foundation model for generative computational imaging",
  "title_text": "BAM! Bayesian Anything Model: a foundation model for generative computational imaging",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:58Z",
  "url": "https://research.draconitic.com/paper/fnscs1qe8pw7p",
  "venue": "arXiv",
  "versions": []
}