Self-Evolving Diversity-Driven Search for Robust AI Systems

This workshop studies robust AI systems through the lens of self-evolving diversity-driven search. As AI systems become increasingly interactive, multimodal, and agentic, safety failures can emerge across languages, modalities, tools, users, contexts, and multi-turn interaction trajectories. Static benchmarks and single-objective safety metrics are therefore insufficient for discovering novel safety scenarios and previously unseen failure modes. The workshop will bring together researchers from AI safety, trustworthy machine learning, evolutionary computation, multi-objective optimization, quality-diversity search, adversarial machine learning, red teaming, privacy, fairness, human-AI interaction, and AI governance. It will focus on formalizing safety scenario spaces, measuring diversity in red-teaming and evaluation benchmarks, identifying behavioral descriptors for safety-relevant failures, and using multi-objective and quality-diversity methods to navigate safety trade-offs. The goal is to build a cross-community venue for developing adaptive evaluation pipelines, diverse failure discovery methods, and robust AI safety frameworks.