arXiv Machine Learning June 2, 2026 By Subhadip Mitra

Quality-Diversity Evolution for Discovering Diverse Vulnerabilities in LLM Safety

Read the original on arXiv Machine Learning →

arXiv:2606. 00801v1 Announce Type: cross Abstract: Current approaches to LLM adversarial testing suffer from coverage gaps: manual red-teaming does not scale, LLM-as-attacker methods exhibit mode collapse, and gradient-based approaches produce uninterpretable gibberish.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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