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AgentBreeder: Mitigating the AI Safety Impact of Multi-Agent Scaffolds via Self-Improvement

Created by
  • Haebom

Author

J Rosser, Jakob Nicolaus Foerster

Outline

This paper points out that integrating large-scale language models (LLMs) into multi-agent systems improves the performance of complex tasks, but the safety implications of such approaches have not been fully explored. The researchers introduce AgentBreeder, a multi-objective self-improving evolutionary search framework for scaffolds. They evaluate the scaffolds discovered on well-known inference, mathematics, and safety benchmarks and compare them to popular baselines. In 'blue' mode, the safety benchmark performance is improved by an average of 79.4% while maintaining or improving the feature score. In 'red' mode, they find that scaffolds emerge as adversarially vulnerable while simultaneously optimizing the features. This study demonstrates the risks of multi-agent scaffolding and provides a framework to mitigate them. The code is available at https://github.com/J-Rosser-UK/AgentBreeder .

Takeaways, Limitations

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Takeaways: We highlight the importance of LLM scaffolding safety in multi-agent systems and present a novel framework, AgentBreeder, to evaluate and mitigate it. The 'Blue' mode results show that safety and performance can be improved simultaneously. The 'Red' mode results warn that safety risks may arise simultaneously with improved capabilities.
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Limitations: Further research is needed on the generality and applicability of the AgentBreeder framework. Extensive experiments on different types of LLMs and multi-agent systems are needed, and more clarity on the criteria and definitions of 'blue' and 'red' modes may be needed. In addition, further consideration is needed on the limitations of safety benchmarks and their real-world applicability.
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