{
  "id": 10917,
  "url": "https://arxiv.org/abs/2607.13608v1",
  "title": "Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System",
  "summary": "Automatic scientific discovery has long been a goal of computational scholars - a machine that can discover nature's secrets on its own, moving computational systems beyond data-fitting tools toward the generation and refinement of mechanistic models of the universe. Recent advances in symbolic regression (SR) and large-language-model (LLM)-based agents suggest that such systems can recover equations from data, incorporate domain priors, and automate parts of the research workflow. However, most",
  "authors": "David Krongauz, Arad Zulti, Eran Segal, Teddy Lazebnik",
  "category": "research",
  "topics": "agents-autonomy,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-15T08:56:56.000Z",
  "fetched_at": "2026-07-16T05:10:56.605Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/10917",
  "original_url": "https://arxiv.org/abs/2607.13608v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}