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Proposes a Task-Conditioned Synthetic Data Generation (TCSDG) algorithm combining a Bayesian Network generator with a transformer-based tabular foundation model to improve ML performance in agricultural prediction tasks, showing consistent improvements over benchmarks.
This paper introduces xAARA, an uncertainty-aware multi-expert fusion engine that augments clinical assessment of stroke rehabilitation by providing calibrated uncertainty and interpretable explanations, achieving high accuracy and reducing predictive uncertainty in movement quality evaluation.
This paper introduces PersuasionTrace, a framework for studying multi-turn persuasion in human-LLM interaction, using a Bayesian-network simulated target that models belief updates. The framework reveals that LLMs are persuasive across topics and modalities, and that the Bayesian target better matches human belief dynamics than vanilla LLM simulators.