This paper proposes WASI (Weight-Activation Subspace Iteration), which leverages the observation that parameter subspaces remain stable during fine-tuning to simultaneously compress both the weights ...

This paper proposes Self-Aug, a training-free decoding strategy that employs Self-Augmentation Selection (SAS) Prompting to enable LVLMs to leverage their own parametric knowledge for dynamically ...