Adversarial Scenario Generation

The field studies how to construct difficult but meaningful situations that reveal where AI systems fail, from autonomous vehicles and vision models to language and tool-using agents. Methods include simulator-based search, reinforcement learning, diffusion and other generative models, controlled perturbations, and LLM-driven red teaming with iterative feedback. Researchers evaluate generated cases using measures such as collision rates, attack success, realism, feasibility, diversity, and whether failures are avoidable. A central challenge is making scenarios adversarial enough to expose weaknesses while keeping them realistic, interpretable, and useful for training or safety evaluation.