Auditing Cascading Risks in Multi-Agent Systems via Semantic-Geometric Co-evolution

2026-03-17 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Cybersecurity & Data Privacy · Depth: Advanced, extended

Summary

Researchers from Huazhong University of Science and Technology, Lingnan University, Tsinghua University, and the Chinese Academy of Sciences have developed a framework called Semantic–Curvature Co-evolutionary Auditing Loop (SCCAL) to proactively detect cascading risks in Large Language Model (LLM)-based Multi-Agent Systems (MAS). Unlike traditional methods that react to explicit semantic violations, SCCAL models MAS interactions as dynamic graphs and uses Ollivier–Ricci Curvature (ORC) to quantify information redundancy and bottlenecks in communication. By coupling semantic flow signals with graph geometry, the framework learns normal co-evolutionary dynamics and identifies deviations as early-warning signs. Experiments across various cascading-risk scenarios demonstrate that SCCAL detects anomalies several interaction turns before semantic violations, offering proactive intervention and root-cause attribution by pinpointing specific agents or links responsible for system instability.

Key takeaway

For research scientists developing or deploying LLM-based Multi-Agent Systems, you should integrate proactive structural auditing to anticipate cascading failures. Relying solely on semantic content for safety is reactive; instead, monitor the co-evolution of semantic flow and interaction geometry using metrics like Ollivier–Ricci Curvature. This approach provides early warnings and helps pinpoint the specific agents or links causing system instability, enabling targeted interventions before critical semantic violations occur.

Key insights

Coupling semantic flow with interaction geometry enables proactive detection of cascading risks in LLM-based multi-agent systems.

Principles

• Cascading risks are trajectory stability problems on a semantic–geometric manifold.
• Semantic signals are lagging indicators; geometric deviations are leading indicators.
• System stability requires coherence between semantic intent and structural evolution.

Method

SCCAL models MAS interactions as dynamic graphs, using Ollivier–Ricci Curvature (ORC) to profile geometry. It couples semantic flow with ORC to learn normal co-evolution and flags deviations as risk, enabling early detection and root-cause attribution.

In practice

• Use ORC to identify structural bottlenecks or redundancies in MAS communication.
• Monitor semantic-geometric consistency to detect risks before explicit violations.
• Localize curvature distortions to attribute root causes of system failure.

Topics

• Multi-Agent Systems
• Cascading Risks
• Ollivier–Ricci Curvature
• Semantic-Geometric Co-evolution
• LLM Safety Auditing

Best for: Research Scientist, AI Researcher, AI Scientist, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.