AI RESEARCH
Auto-ART: Structured Literature Synthesis and Automated Adversarial Robustness Testing
arXiv CS.LG
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ArXi:2604.20704v1 Announce Type: cross Adversarial robustness evaluation underpins every claim of trustworthy ML deployment, yet the field suffers from fragmented protocols and undetected gradient masking. We make two contributions. (1) Structured synthesis. We analyze nine peer-reviewed corpus sources (2020--2026) through seven complementary protocols, producing the first end-to-end structured analysis of the field's consensus and unresolved challenges. (2) Auto-ART framework. We