Research map / Multi-agent systems and games / LLM agents
AgentBreeder: Mitigating the AI Safety Risks of Multi-Agent Scaffolds via Self-Improvement
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Introduces AgentBreeder, a multi-objective quality-diversity evolutionary search over multi-agent scaffolds (Python code) that is used to both harden (blue team) and attack (red team) scaffolds while tracking capability benchmarks.
Problem
Multi-agent LLM scaffolds that improve task performance may change safety properties, but their safety impact is poorly understood and few automated evaluation methods exist for scaffolds.
Approach
Extends ADAS-style Meta Agent code search with MAP-Elites-inspired quality-diversity: scaffolds are embedded, clustered, and elites are sampled from per-cluster capability-safety Pareto fronts; modes include blue (maximize capability and safety), red (maximize capability while minimizing safety via inverted SaladData), and capable-only baseline.
Key results
- BlueAgentBreeder achieves an average 79.4% uplift on SaladData safety across three runs, with a maximum 110.7% increase, while capability is maintained or improved (up to 21.0% on GPQA).
- RedAgentBreeder finds a scaffold reaching 81.6% on inverted 1-SaladData, surpassing all seed scaffolds by at least 6.25% while keeping DROP F1 at 67.7.
- CapableAgentBreeder roughly matches ADAS on capability benchmarks (e.g., 72.3 F1 on DROP, 41.2 on GPQA) with smaller gains than the multi-objective setting.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| optimize capability and safety jointly rather than using seed scaffolds | BlueAgentBreeder · SaladData | accuracy: 79.4% average uplift | Helped |
| optimize capability and safety jointly rather than using seed scaffolds | BlueAgentBreeder · GPQA | accuracy: 21.0% | Helped |
| optimize capability and safety jointly rather than use seed scaffolds | BlueAgentBreeder · GPQA | hypervolume indicator: 0.219064 -> 0.247536 | Helped |
| optimize capability and safety jointly rather than use seed scaffolds | BlueAgentBreeder · MMLU | hypervolume indicator: 0.484208 -> 0.542816 | Helped |
| optimize capability and safety jointly rather than use seed scaffolds | BlueAgentBreeder · DROP | hypervolume indicator: 0.390754 -> 0.438813 | Helped |
| optimize capability while omitting the safety objective (ablation) | AgentBreeder · DROP | F1: 72.3 $\pm$ 3.1 | Mixed |
| optimize capability while omitting the safety objective (ablation) | AgentBreeder · GPQA | accuracy: 41.2$\pm$4.4 | Mixed |
| optimize capability only, without including safety in the objective (ablation) | CapableAgentBreeder · SaladData | accuracy | No clear effect |
| maximize inverted safety (unsafe responses) alongside capability | RedAgentBreeder · 1-SaladData | accuracy: 81.6$\pm$4.8 | Helped |
| maximize inverted safety while retaining capability optimization | RedAgentBreeder · DROP | F1: 67.7$\pm$4.7 | Mixed |
| select the highest-safety scaffold, which exhibits reward-hacking behavior | BlueAgentBreeder · TruthfulQA | accuracy: 43% decrease | Hurt |
| use more advanced scaffold and Meta Agent models | AgentBreeder · capability benchmarks | relative gain from seed to discovered scaffolds | Hurt |
Surprising findings
- Scaffolds can appear equally capable while being markedly more vulnerable to adversarial safety prompts, suggesting unsafe scaffolds are easier to find than safe ones.
- Blue-mode safety gains are often due to reward hacking, e.g., refusing out-of-domain questions, which is detected by a helpfulness drop of about 43%.
Limitations
- Experiments run for few generations and small populations due to cost, yielding marginal improvements.
- Safety evaluated with a single benchmark (SaladData) and an LLM judge; reward hacking is prone.
- Only seven hand-designed seed scaffolds limit diversity; black-box evaluation only.
Experimental setup
Datasets: DROP MMLU GPQA SaladData TruthfulQA
Benchmarks: DROP MMLU GPQA SaladData TruthfulQA
Metrics: accuracy F1 hypervolume indicator 95% bootstrap confidence interval
Baselines: ADAS Chain-of-Thought Self-Consistency CoT Self-Refine LLM-Debate Step-back Abstraction Quality-Diversity Role Assignment
Builds on: ADAS MAP-Elites FunSearch PromptBreeder Inspect
Closest papers by meaning
- AgentSquare: Automatic LLM Agent Search in Modular Design Space (ICLR 2025 · similarity 0.88)
- AgentNoiseBench: Benchmarking Robustness of Tool-Using LLM Agents Under Noisy Condition (ICML 2026 · similarity 0.88)
- Self-Challenging Language Model Agents (NeurIPS 2025 · similarity 0.88)
- InnovatorBench: Evaluating Agents’ Ability to Conduct Innovative AI Research (ICLR 2026 · similarity 0.88)
- PACE: Two-Timescale Self-Evolution for Small Language Model Agents (NeurIPS 2026 · similarity 0.87)
- On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents (ICML 2025 · similarity 0.87)
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