Abstract
<title>Abstract</title> <p>Large-scale multi-agent AI systems can exhibit nonlinear cascade instabilities that emerge through network interactions and recursive amplification. Most existing AI-safety approaches, however, primarily focus on isolated model alignment rather than the network-level dynamics through which such instabilities propagate. This study introduces the Ethical Field Theory (EFT), a computational framework for investigating cascade instability and adaptive stabilization dynamics within large-scale multi-agent AI systems. Each agent is modeled through three coupled recursive dynamical components: constructive activation (nP), regulatory awareness (n0), and destabilizing activation (nN). Large-scale simulations were performed across random, scale-free, and small-world network topologies, using N = 5000 interacting agents, T = 200 timesteps, and 20 independent realizations per configuration. Three stabilization policies were investigated: no intervention, weak uniform regulation, and adaptive targeted stabilizer intervention. The simulations reveal pronounced topology-dependent instability and consistently demonstrate the effectiveness of targeted stabilization across all tested network architectures. Under no-intervention conditions, recursive destabilization produced severe large-scale collapse, with final collapse ratios of 0.994 in random networks, 0.828 in small-world systems, and 0.504 in scale-free architectures. In contrast, adaptive targeted stabilization suppressed recursive cascade propagation across all tested topologies under the baseline parameterization, reducing collapse ratios to near-zero levels while preserving substantially higher network stability than weak globally distributed regulation. The results further reveal metastable transition behavior, recursive amplification effects, topology-sensitive cascade propagation, and nonlinear instability thresholds. Although exploratory in scope, these findings suggest that future AI safety may increasingly require topology-aware stabilization, recursive oversight, distributed verifier systems, and network-level dynamical regulation beyond isolated model alignment. The framework is not intended as a predictive theory of machine cognition or consciousness. Rather, it provides a computational systems perspective for studying recursive instability propagation and adaptive stabilization in large-scale interacting AI ecosystems.</p>