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• PFN-based tabular in-context learning (TICL) models are sample-efficient in low-data regimes but degrade under naive distance-based retrieval as heterogeneity and imbalance increase.result 90%
• AWARE, a task-aligned retrieval framework using supervised embedding learning and lightweight adapters, improves AUPRC by up to 12.2% under extreme imbalance.result 90%
• Retrieval quality and retrieval-inference alignment are key bottlenecks for deploying tabular in-context learning in clinical prediction.technical 85%
• Clinical prediction from structured EHRs is challenging due to high dimensionality, heterogeneity, class imbalance, and distribution shift.technical 90%
• The behavior of tabular in-context learning and retrieval-augmented methods in clinical settings remains unclear despite their good performance on generic benchmarks.limitation 85%
• AWARE's performance gains increase with data complexity.result 80%
• The paper presents a multi-cohort EHR benchmark comparing classical, deep tabular, and TICL models across varying data scale, feature dimensionality, outcome rarity, and cross-cohort generalization.method 90%

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