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MOSS-VL achieves a staggering 66.0 average score in proactive alerting, far surpassing the best baseline by nearly 30 points.
MLLMs excel at reasoning over diagrams but falter in parsing and editing them, revealing a significant gap that needs addressing.
Confidence miscalibration in medical AI can be mitigated, leading to both higher diagnostic accuracy and improved trust in clinical applications.
