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The VEX-Bench is introduced, a unified benchmark for evaluating the verification complexity of LLM-generated misinformation, as perceived during screening, across models and generation methods, and shows that no single method dominates all dimensions, underscoring the need for multi-dimensional evaluation.
This survey examines the protective paradigm that has grown around this intervention point, and finds that most protections are still validated mainly against static or weakly adaptive adversaries, while evidence beyond controlled benchmarks remains scarce.
Theoretical analysis shows that under this anisotropic structure, adversarial attacks increase the expected geometric separation between clean and adversarial examples (AEs), and proposes GeoDetect, which leverages these off-manifold deviations via geometric scores to identify AEs.
VisER is proposed, a training-free two-sided metric for object-level hallucination detection that improves AUROC and AUPR over a range of baselines and measures whether object-context compatibility is backed by object-specific evidence from image tokens.
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