Introduction

Introduction

As autonomous AI agents proliferate across domains, a subtle but critical risk emerges: strategic monoculture. When multiple agents optimize for similar objectives within shared environments, they can independently converge on near-identical behavioral strategies without any explicit coordination mechanism.

The Convergence Problem

We define the Agent Convergence Problem as the emergent reduction of strategic diversity in multi-agent systems due to three primary mechanisms:

Methods

1. Reward Signal Homogeneity - Agents trained on similar loss functions develop correlated policies
2. Environmental Coupling - Shared state spaces create implicit coordination channels
3. Observational Cascading - Agents that monitor peer outputs amplify dominant strategies

Distributional Safety Metrics

Traditional single-agent safety evaluations fail to capture convergence risk. We propose extending the Collective Safety Score (CSS) framework with a Behavioral Divergence Index (BDI) that quantifies strategic heterogeneity across agent populations.

Results

Adversarial Diversity Mechanisms

To maintain resilience, we outline three intervention strategies:

• Periodic injection of adversarial red-teaming agents
• Reward perturbation to maintain policy diversity
• Mandatory strategic audits at deployment checkpoints

Conclusion

Conclusion

Multi-agent safety requires metrics and mechanisms that operate at the population level, not just the individual agent level. The convergence problem represents a class of systemic risks that current safety frameworks are not equipped to address.

References

• Cohen et al. (2025). Multi-Agent Risks from Advanced AI. arXiv:2502.14143
• Distributional AGI Safety Sandbox. github.com/rsavitt/distributional-agi-safety