Learnable sparsification framework compresses WSI visual tokens to 32 (0.78% of original) via SparseLearn, achieving 73.32% accuracy on SlideBench (TCGA) and outperforming baselines.
Multimodal model for computational pathology: Representation learning and image compression.arXiv preprint arXiv:2603.18660, 2026
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DiffPrune reformulates visual token pruning as continuous control of token information using an Information Throttler with importance-conditioned variance-preserving noise, enabling fully differentiable learning of scores that are hard-thresholded at inference.
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Learnable Token Sparsification for Efficient Gigapixel Whole Slide Image Reasoning
Learnable sparsification framework compresses WSI visual tokens to 32 (0.78% of original) via SparseLearn, achieving 73.32% accuracy on SlideBench (TCGA) and outperforming baselines.
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Beyond Surrogate Gradients: Fully Differentiable Token Pruning for Vision-Language Models
DiffPrune reformulates visual token pruning as continuous control of token information using an Information Throttler with importance-conditioned variance-preserving noise, enabling fully differentiable learning of scores that are hard-thresholded at inference.