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Spherical interpolation between old and new model embeddings recovers backward-compatibility for vision-language retrieval, outperforming orthogonal alignment by finding optimal query directions in the interstitial space.
DistMoE enables MLLMs to adapt to diverse visual-language domains without centralized data, achieving competitive performance while preserving client-specific knowledge.
