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:2311.01092 (2023)
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
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.
GazeX uses radiologist gaze trajectories as a behavioral prior during pretraining to generate more accurate and expert-consistent results in chest X-ray report generation, disease grounding, and visual question answering.
citing papers explorer
-
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.
-
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.
-
Seeing Through Experts Eyes A Foundational Vision Language Model Trained on Radiologists Gaze and Reasoning
GazeX uses radiologist gaze trajectories as a behavioral prior during pretraining to generate more accurate and expert-consistent results in chest X-ray report generation, disease grounding, and visual question answering.