{
  "id": 7855410,
  "title": "scACORN: Context-engineered agent orchestration of specialized small language models for single-cell transcriptomic interpretation",
  "url": "https://urgent.news/2026/09/16/scacorn-context-engineered-agent-orchestration-of-specialized-small",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-16T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750801v1?rss=1"
  },
  "original_language": "en",
  "account": "Single-cell atlases have surpassed 66 million cells, yet converting expression profiles and biological queries into accurate, evidence-based responses remains unresolved. Scaling a single model proves inadequate because single-cell interpretation encompasses diverse tasks, each with answers contingent on tissue, cohort, perturbation, and annotation resolution. Introducing scACORN, an alternative to monolithic single-cell language models that employs agentic orchestration of specialized small language models. Each expert undergoes two stages: domain-aligned contrastive adaptation aligns a pretrained cell-to-text backbone with the transcriptomic geometry of a specific dataset, followed by geometry-preserving specialization that learns question-conditioned biological completions without losing that geometry. A language model agent orchestrates the selection and combination of experts based on a natural-language playbook, which is optimized using textual feedback without modifying the orchestrator itself. Across 10 Tabula Sapiens tissues, domain alignment increased transfer macro-F1 from 0.36 to 0.64 and Recall@5 from 0.87 to 0.97; specialized experts achieved 0.89 mean exact-match annotation accuracy; and playbook optimization lowered unsupported gene citations from 14.5% to 3.5%. The results underscore the complementary roles of specialization and orchestration in addressing the heterogeneity and evidentiary challenges of single-cell analysis.",
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}