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LLM-based digital twins can be made 50-90% more accurate with a lightweight calibration framework inspired by causal inference.
Second-order uncertainty representations matter: set-based and distribution-based methods, often considered incomparable, can be rigorously compared, revealing how representation choices impact uncertainty-aware performance.
