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University of Science and Technology of China
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SSR not only resolves destructive parameter collisions in LoRA merging but also guarantees mathematical optimality, setting a new standard for efficiency in diffusion model training.
Current video MLLMs struggle to grasp fleeting visual events, with top models barely surpassing 39% accuracy on critical momentary tasks.
NITP achieves a remarkable 5.7% performance boost on MMLU-Pro by transforming how LLMs are trained, moving beyond sparse supervision to dense semantic predictions.
