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 hy

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ethics.ai (15 July 2026), “Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation,” evidence record 11621, https://ethics.ai/record/11621 (originally published by arXiv red teaming query).

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