A minimal model analytically separates shortcut attraction during training from the switch to a shortcut rule and from cross-family out-of-distribution failure.
arXiv preprint arXiv:2010.05761 , year =
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Agentic AI systems are required to overcome the parameter coverage ceiling that prevents foundation models from handling certain out-of-distribution cases.
MaskGen improves domain generalization for biomedical image segmentation by using source intensities plus domain-stable foundation model representations with minimal added complexity.
citing papers explorer
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Separating Shortcut Transition from Cross-Family OOD Failure in a Minimal Model
A minimal model analytically separates shortcut attraction during training from the switch to a shortcut rule and from cross-family out-of-distribution failure.
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Agentic AIs Are the Missing Paradigm for Out-of-Distribution Generalization in Foundation Models
Agentic AI systems are required to overcome the parameter coverage ceiling that prevents foundation models from handling certain out-of-distribution cases.
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Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It
MaskGen improves domain generalization for biomedical image segmentation by using source intensities plus domain-stable foundation model representations with minimal added complexity.