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Leading MLLMs still struggle to articulate and localize physical changes across revisited scenes, exposing a fundamental blind spot in multi-image spatial grounding that targeted synthetic data can overcome.
Skip the labeled data: TEGU uses LLM-derived textual information to achieve state-of-the-art zero-shot temporal action localization.