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This work proposes Control-based Motion Customization (CMC), a principled training framework that is structurally robust to content leakage and effectively mitigates content leakage and achieves competitive motion fidelity while preserving the diversity of the base model across diverse scenarios.
MIOH provides a controlled framework for analyzing multi-image object hallucination and serves as a critical evaluation tool for developing more reliable multimodal AI systems.
This work identifies batch prompting as a distinct safety failure mode, not reducible to known vulnerabilities such as in-context learning or long-context effects, and analyzes its causes from two complementary perspectives: alignment signal weakening and refusal signal dilution.
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