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This paper introduces a contrastive modeling framework for multimodal in-context learning to improve reasoning path alignment, enhancing performance on tasks like visual question answering.
The paper introduces CXR-MAX, a large-scale benchmark for evaluating reasoning alignment in non-stationary environments using X-ray data from multiple MLLMs.