AI RESEARCH
Latent Anchor-Driven Test Generation for Deep Neural Networks
arXiv CS.LG
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ArXi:2606.04310v1 Announce Type: new Deep Neural Networks (DNNs) are increasingly being deployed in security-critical and safety-sensitive applications, which makes rigorous testing essential to identify and mitigate model weaknesses. Existing DNN testing approaches explore either the input space or a learned latent space. While latent-space generation can better maintain plausibility than direct input-space mutation, current methods still face a trade-off among exploration controllability, failure diversity, and seed-relative semantic drift.