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This work reformulate WSI token pruning as a sequential selection process, enabling the model to autonomously learn an optimal routing strategy rather than relying on static heuristics.
A new evaluation framework that decouples MLLMs'capabilities to faithfully assess VTC quality is introduced, and the ZeroSense Benchmark is introduced to ensure low semantic correlation of testing samples.
LiteMamba-Synth, an architecturally streamlined state space framework designed for high-fidelity MRI translation with minimal resource requirements, is proposed and provides a practical and scalable solution for deploying advanced MRI synthesis in resource-constrained clinical environments.
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