ensembling

The paper introduces D^CF5, a diagnostic to predict regionwise gains in dynamic ensembling for regression tasks under distribution shift, validated with high correlation across datasets.

This paper proposes HomoEnsNER, a homogeneous ensemble of five GujaratiBERT models for Gujarati named entity recognition, and shows it outperforms heterogeneous alternatives that rely on architectural diversity, achieving state-of-the-art F1 on the Naamapadam test split.

This paper proposes TIE, a knowledge fusion framework for masked diffusion language models that tracks confidence dynamics to identify reliable decoding trajectories and iteratively transfers partially denoised sequences between models, improving generation quality on reasoning tasks.