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A single model can now seamlessly handle over 10 diverse audio-visual tasks without the need for task-specific architectures.
SAOT achieves up to 15% accuracy gains in continual graph learning by preserving global relational structures that traditional methods overlook.
All tested coding agents fail within 5-6 turns, but providing feedback can boost their performance by up to 12x, revealing critical insights into agent design.
Quantizing user preferences into discrete tokens unlocks personalized multimodal content generation with improved consistency between modalities.
