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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.
Depth integration in audio-visual segmentation leads to over 10% performance gains, revealing a critical yet overlooked modality in multimodal perception.
Visual-Seeker outperforms proprietary models by actively engaging with visual details, redefining multimodal search capabilities.
Jointly training MTP and RL doesn't have to hurt: a simple coefficient calibration scheme unlocks performance gains on mathematical reasoning tasks.
