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College of Computing and Data Science, School of Computer Science and Engineering, Nanyang Technological University, The Chinese University of Hong Kong
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Traditional interpretability fails to predict task-critical mechanisms before training, but this new framework bridges that gap, enabling more effective fine-tuning strategies.
Pruning 77.8% of visual tokens without losing performance could revolutionize the efficiency of multimodal large language models.
The fragmented field of world modeling can now be unified under a "levels x laws" taxonomy, revealing critical gaps in autonomous model revision and decision-centric evaluation.
