Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation

• URL: http://arxiv.org/abs/2607.14256v1
• Date: Wed, 15 Jul 2026 18:13:05 GMT
• Title: Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation
• Abstract summary: Multimodal Large Language Models (MLLMs) are increasingly deployed for nuanced content safety and moderation tasks.<n>Traditional active learning and manual annotation fail to scale against the complexity and volume of novel multimodal threats.<n>We propose an automated, agentic red-teaming framework that systematically synthesizes difficult examples.
• Score: 6.991910357381941
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 committee of LLM raters, our system autonomously uncovers boundary-pushing violations and ambiguous policy edge cases without any human intervention. By employing these carefully synthesized adversarial examples as in-context demonstrations via test-time Retrieval, we substantially improve the target model's robustness, reducing the False Negative Rate (FNR) from 41.2% to 24.5% in a public image safety benchmark without relying on any human labeling.

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