XrayClaw deploys cooperative-competitive multi-agent alignment and Competitive Preference Optimization to raise diagnostic accuracy, reasoning fidelity, and generalization on chest X-ray benchmarks.
arXiv preprint arXiv:2409.19684 (2024)
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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.
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.
A unified transformer performs four clinical tasks on chest X-rays and generates reports rated comparable to human ones in 66% of cases by radiologists.
citing papers explorer
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XrayClaw: Cooperative-Competitive Multi-Agent Alignment for Trustworthy Chest X-ray Diagnosis
XrayClaw deploys cooperative-competitive multi-agent alignment and Competitive Preference Optimization to raise diagnostic accuracy, reasoning fidelity, and generalization on chest X-ray benchmarks.
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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.
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A unified multi-task framework enables interpretable chest radiograph analysis
A unified transformer performs four clinical tasks on chest X-rays and generates reports rated comparable to human ones in 66% of cases by radiologists.