{
  "id": 6347,
  "url": "https://arxiv.org/abs/2606.15579v1",
  "title": "Your Agent Has a Genome: Sequence-Level Behavioral Analysis and Runtime Governance of LLM-Powered Autonomous Agents",
  "summary": "We propose Base Sequence Analysis, a framework that encodes the runtime behavior of LLM-powered autonomous agents into compact symbolic sequences using a four-letter alphabet: X (Explore), E (Execute), P (Plan), and V (Verify). Drawing an analogy to genomic sequence analysis, we apply n-gram pattern mining, Markov transition matrices, and point-biserial correlation to 347 real-world execution traces collected from a production ReAct agent system over 8 days. Our analysis reveals that (1) the tri",
  "authors": "Sidi Deng",
  "category": "research",
  "topics": "regulation,agents-autonomy,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-05T14:56:56.000Z",
  "fetched_at": "2026-07-14T16:32:24.292Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6347",
  "original_url": "https://arxiv.org/abs/2606.15579v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}