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A systematic, cross-domain characterisation of data processing practices for the offline evaluation of recommender systems is provided, identifying substantial heterogeneity in how auxiliary information is prepared and represented, as well as inconsistencies in the specification of data splitting protocols.
PUMA, a sparse autoencoder recipe that maps universal multimodal embeddings to compact sparse codes without retraining the backbone, reduces vector storage by 8-16x (FP32) and is up to 25x faster than exact dense scoring on larger candidate pools, enabling efficient multimodal retrieval.
StyleFlow instantiates the task with StyleFlow, a Rectified Flow Matching model that jointly conditions on the seed image and instruction within a single multimodal transformer, and consistently produces instruction-aligned and stylistically coherent garments while reducing architectural complexity and inference cost r...
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