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Retaining even a single structural relative slashes post-deletion retraining loss by $8\times$, revealing that target accuracy after unlearning is often driven by intrinsic dataset redundancy rather than unlearning failure.
A model-free benchmark reveals that vision-language models often misread rather than misreason, exposing a performance gap that a supervised vision model can surpass.