{
  "id": 7039,
  "url": "https://arxiv.org/abs/2604.19752v1",
  "title": "Soft-Label Governance for Distributional Safety in Multi-Agent Systems",
  "summary": "Multi-agent AI systems exhibit emergent risks that no single agent produces in isolation. Existing safety frameworks rely on binary classifications of agent behavior, discarding the uncertainty inherent in proxy-based evaluation. We introduce SWARM (\\textbf{S}ystem-\\textbf{W}ide \\textbf{A}ssessment of \\textbf{R}isk in \\textbf{M}ulti-agent systems), a simulation framework that replaces binary good/bad labels with \\emph{soft probabilistic labels} $p = P(v{=}+1) \\in [0,1]$, enabling continuous-valu",
  "authors": "Aizierjiang Aiersilan, Raeli Savitt",
  "category": "research",
  "topics": "regulation,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-19T00:56:09.000Z",
  "fetched_at": "2026-07-14T16:32:54.536Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7039",
  "original_url": "https://arxiv.org/abs/2604.19752v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}