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ZooWork-ShopRanker, a family of e-commerce rerankers aligned to judge-labeled shopping preference, and ShopRank-Bench, a contamination-limited benchmark of ~10,000 private-traffic preference pairs in both text formats, tiered by how many judge families committed to each label.
Outperforming larger models and existing methods, ZooClaw-FashionSigLIP2 sets a new standard for fashion retrieval while addressing critical biases in benchmark evaluations.
Developer-style keyword searches completely nullify the advantage of even the best code embedding models, highlighting a critical gap in current code search techniques.
Forget scaling model size – QuitoBench reveals that simply scaling training data delivers bigger gains for time series forecasting, across both deep learning and foundation models.
