{
  "id": 9704117,
  "title": "Cell-Lens: Advancing LLM Reasoning at Single-Cell Resolution",
  "url": "https://urgent.news/2026/09/24/cell-lens-advancing-llm-reasoning-at-single-cell-resolution",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-24T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.19.752842v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Recent work applies large language models (LLMs) to single-cell data, but the cell usually reaches the model as a short ranked list of genes. This list drops much of what defines a cell, so the model reasons from a partial view. Yet the information is not lost in the measurement, only in the text. Here, we introduce Cell-Lens, a training-free structured representation of the measured cell. It…",
  "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."
}