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-valued payoff computation, toxicity measurement, and governance intervention. SWARM implements a modular governance engine with configurable levers (transaction taxes, circuit breakers, reputation deca...
| Authors | Aizierjiang Aiersilan, Raeli Savitt |
|---|---|
| Primary category | cs.MA |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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