Auditing Cascading Risks in Multi-Agent Systems via Semantic-Geometric Co-evolution
Large Language model (LLM)-based Multi-Agent Systems (MAS) are prone to cascading risks, where early-stage interactions remain semantically fluent and policy-compliant, yet the underlying interaction dynamics begin to distort in ways that amplify latent instability or misalignment. Traditional auditing methods that focus on per-message semantic content are inherently reactive and lagging, failing to capture these early structural precursors. In this paper, we propose a principled framework for cascading-risk detection grounded in semantic--geometric co-evolution. We model MAS interactions as dynamic graphs and introduce Ollivier--Ricci Curvature (ORC) -- a discrete geometric measure -- to...
| Authors | Zixun Luo, Yuhang Fan, Hengyu Lin et al. |
|---|---|
| Primary category | cs.MA |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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