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Existing detection systems fail to reliably identify synthetic credibility, with MLLMs achieving only a 10.5% true positive rate under stringent conditions.
Existing video world models struggle with long-term memory retention, and MBench exposes their critical limitations while providing a structured path for future improvements.
