{
  "abstract": "Immune therapies act across cell-intrinsic programs, tissue ecosystems, and patient-specific immune states, yet most predictors address these scales separately. We used a governed evolutionary AI Scientist to construct the Immune World Model, an action-conditioned model that learns how interventions move immune states across cellular, tissue, and individual levels. The Immune World Model--building Scientist searched candidate architectures and workflows, and the resulting world model was frozen before independent confirmation. The frozen model generalized to unseen interventions and biological contexts, recovered intervention-specific cellular programs, integrated cell and tissue information to improve ecosystem and patient-response prediction, and forecast unseen perturbation combinations. Immune World Model--guided analysis then combined measured perturbations with cross-axis inference to nominate IL-36$γ$ plus SIRP$α$ inhibition as a complementary-axis therapeutic hypothesis, whereas a governed self-correction audit rejected every screened cytokine pair. The Immune World Model provides a framework for multiscale immune simulation that connects AI Scientist-driven model construction, intervention forecasting, and the generation of prospectively testable therapeutic hypotheses.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.14709",
  "author_total": 14,
  "authors": [
    {
      "name": "Taoyong Cui"
    },
    {
      "name": "Xi Wang"
    },
    {
      "name": "Zonghang Li"
    },
    {
      "name": "Jinchao Ding"
    },
    {
      "name": "Lingsen You"
    },
    {
      "name": "Yuzhi Xu"
    },
    {
      "name": "Wanghan Xu"
    },
    {
      "name": "Fang Wu"
    },
    {
      "name": "Kejun Ying"
    },
    {
      "name": "Wanli Ouyang"
    },
    {
      "name": "Pheng Ann Heng"
    },
    {
      "name": "Ling Yang"
    },
    {
      "name": "Zhenfei Yin"
    },
    {
      "name": "Yingcheng Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9edtfymgg6n9s.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.14709",
  "fulltext_url": null,
  "id": "9edtfymgg6n9s",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.14709",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "2204|Artificial Immune Systems Applications"
    ]
  ],
  "openalex": "W7213258591",
  "pdf_url": "https://arxiv.org/pdf/2609.14709",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9edtfymgg6n9s.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:30Z",
      "native_id": "2609.14709",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.14709"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "An immune world model for multiscale forecasting and therapeutic hypothesis generation",
  "title_text": "An immune world model for multiscale forecasting and therapeutic hypothesis generation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:30Z",
  "url": "https://research.draconitic.com/paper/9edtfymgg6n9s",
  "venue": "arXiv",
  "versions": []
}