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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.
