{
  "abstract": "Biological discovery requires using molecular knowledge and new measurements to infer responses beyond the conditions already observed. We introduce CellCanon, the first agentic cellular world model to unify single-cell expression prediction, evidence-grounded explanation, and observation-driven updating. Its predictive core reuses condition-level biological evidence across gene-specific numerical readouts to guide single-cell expression generation. A shared world state links predictions, acquired measurements, and source-linked claims, supporting explanation and revision. On 339 held-out SciPlex3 conditions across three cell lines, CellCanon improves differential-expression AUPRC and Macro-F1 over the best baseline for each metric by 63.3% and 17.3%, respectively, and direction Macro-F1 by 7.2%. Relative to gene-wise reasoning on matched drug-cell cohorts, measured language-model token use falls by 99.77% for Predict and 98.41% for Explain. On the source-grounded benchmark, Explain correctly answers all answerable questions with supporting proofs and identifies all insufficient-evidence cases. In simulated experimental feedback, Update uses one measured source context per drug to exceed the best fine-tuned baselines in differential-expression AUPRC and Macro-F1 without changing model weights. Together, these capabilities support a cumulative account of cellular responses, with predictions and biological judgments that can be revised as evidence accumulates.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.05890",
  "author_total": 4,
  "authors": [
    {
      "name": "Zhaokang Liang"
    },
    {
      "name": "Shuyang Zhuang"
    },
    {
      "name": "Xiaoran Jiao"
    },
    {
      "name": "Hao Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6cfe6j8qvjt0g.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.05890",
  "fulltext_url": null,
  "id": "6cfe6j8qvjt0g",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.05890",
  "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.AI"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.05890",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6cfe6j8qvjt0g.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T15:21:01Z",
      "native_id": "2611.05890",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.05890"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "CellCanon: An Agentic Cellular World Model for Prediction, Explanation, and Observation-Driven Updating",
  "title_text": "CellCanon: An Agentic Cellular World Model for Prediction, Explanation, and Observation-Driven Updating",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:31Z",
  "url": "https://research.draconitic.com/paper/6cfe6j8qvjt0g",
  "venue": "arXiv",
  "versions": []
}