{
  "abstract": "Single-cell atlases now exceed 66 million cells, but turning a ranked expression profile and a free-form biological question into a reliable, evidence-grounded answer remains unsolved. Scaling a single model does not resolve this, because single-cell interpretation is a heterogeneous family of tasks whose correct answer depends on tissue, cohort, perturbation and annotation resolution. Here we present scACORN, an agentic alternative to monolithic single-cell language models that combines specialized small language models with context-engineered agent orchestration for their selection and composition at inference time. Each expert is built in two stages: domain-aligned contrastive adaptation fits a pretrained cell-to-text backbone to the transcriptomic geometry of a target dataset, and geometry-preserving specialization learns question-conditioned biological completions without eroding that geometry. A fixed orchestrating language model agent then selects and combines experts under a natural-language playbook that is itself optimized from textual feedback, with no gradient updates to the orchestrator. Across 10 Tabula Sapiens tissues, domain alignment raised transfer macro-F1 from 0.36 to 0.64 and Recall@5 from 0.87 to 0.97; specialized experts reached 0.89 mean exact-match annotation accuracy; and playbook optimization reduced unsupported gene citations from 14.5% to 3.5%. Our findings support specialization and orchestration as complementary responses to the heterogeneity and evidentiary demands of single-cell analysis.",
  "abstract_shown": true,
  "affiliations": [
    "York University; Vector Institute, Toronto, Ontario, Canada"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "A. Rasti-Meymandi"
    },
    {
      "name": "S. Nahali"
    },
    {
      "name": "E. Paramithiotis"
    },
    {
      "name": "A. M. Cheung"
    },
    {
      "affs": [
        0
      ],
      "name": "E. Dolatabadi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8acbaypgh4b5q.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.10.750801",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750801v1.full",
  "id": "8acbaypgh4b5q",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750801v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7213399239",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750801v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8acbaypgh4b5q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:41Z",
      "native_id": "10.64898/2026.09.10.750801",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750801v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "scACORN: Context-engineered agent orchestration of specialized small language models for single-cell transcriptomic interpretation",
  "title_text": "scACORN: Context-engineered agent orchestration of specialized small language models for single-cell transcriptomic interpretation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:41Z",
  "url": "https://research.draconitic.com/paper/8acbaypgh4b5q",
  "venue": "bioRxiv",
  "versions": []
}