Soft-Label Governance for Distributional Safety in Multi-Agent Systems
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
Record details
Published: 19 March 2026
Source: arXiv
Category: Research
Topics: Regulation · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (19 March 2026), “Soft-Label Governance for Distributional Safety in Multi-Agent Systems,” evidence record 7039, https://ethics.ai/record/7039 (originally published by arXiv).
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