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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.
DistMoE enables MLLMs to adapt to diverse visual-language domains without centralized data, achieving competitive performance while preserving client-specific knowledge.
