Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation
Multimodal Large Language Models (MLLMs) are increasingly deployed for nuanced content safety and moderation tasks, yet they remain vulnerable to adversarial attacks and out-of-distribution edge cases. Traditional active learning and manual annotation fail to scale against the complexity and volume of novel multimodal threats. In this paper, we propose an automated, agentic red-teaming framework that systematically synthesizes difficult examples using an iterative strategy that proposes novel hypotheses as well as mutating on past attempts. Leveraging a multi-agent architecture that consists of a high-reasoning Architect agent, an advanced image generator, and a multi-level verification c...
| Authors | Genglin Liu, Muye Zhang, Krishnamurthy Viswanathan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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