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Achieving 94.52% of state-of-the-art performance with just 25% of the training data reveals a groundbreaking efficiency in visual instruction tuning.
DiffPDE achieves faster and more accurate PDE code repair by focusing only on localized errors, challenging the inefficiencies of existing autoregressive methods.
