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FraudBench is a multimodal benchmark for detecting AI-generated fraudulent refund evidence and shows that current MLLMs often recognize real-damaged evidence but fail on many fake-damaged subsets, with fake-damage detection rates far below the 50\% baseline on most generator subsets.
Conditional co-ablation (CoAx) is introduced, which ranks candidates by growth in ablation effect after primary-set removal, and coAx completions outperform matched random completions on all 8 non-GPT-2 models spanning 6 architecture families.
CoCurve (Cross-Module Co-Pruning Curvature), a calibration-only, fine-tuning-free method that prunes attention and FFN units jointly, and reduces to one budgeted quadratic program, solved in a single shot under a shared attention--FFN budget, with no labels, fine-tuning, or recovery.
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