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cs.LG2026
Distilling Foundation Models for Agentic What-If Reasoning:Cost, Latency, and Governance in a Hybrid LLM+SLM Architecture
Sourish Dey, Aditya Kumar
Tabular foundation models deliver strong zero-training predictive performance via in-context learning, but their high inference latency makes them impractical as hot-path decision…
cs.LG2026
Temporal Fusion Nexus: A task-agnostic multi-modal embedding model for clinical narratives and irregular time series in post-kidney transplant care
Aditya Kumar, Simon Rauch, Mario Cypko +10
We introduce Temporal Fusion Nexus (TFN), a multi-modal and task-agnostic embedding model to integrate irregular time series and unstructured clinical narratives. We analysed TFN i…
cs.LG2026
Learning temporal embeddings from electronic health records of chronic kidney disease patients
Aditya Kumar, Mario A. Cypko, Oliver Amft
We investigate whether temporal embedding models trained on longitudinal electronic health records can learn clinically meaningful representations without compromising predictive p…