The field of string repetitiveness and ordinal classification is witnessing significant developments, with a focus on improving the accuracy and efficiency of measures and models. Researchers are exploring new approaches to quantify the repetitiveness of strings and the uncertainty associated with ordinal classification problems. Notably, there is a growing interest in developing measures and models that can effectively capture the ordinal nature of classification problems, leading to more accurate and reliable predictions.

In particular, the concept of additive sensitivity is being investigated in the context of string compressors and attractors, leading to a better understanding of the limitations and potential of these measures. Additionally, novel metrics and models are being proposed to address the challenges associated with ordinal classification, such as class imbalance and the need for more effective evaluation metrics.

Some noteworthy papers in this area include:

• A study on the worst-case additive sensitivity of string repetitiveness measures, which presents tight upper and lower bounds for several measures.
• A paper introducing a novel class of measures of aleatoric and epistemic uncertainty in ordinal classification, which demonstrates competitive performance on various benchmark datasets.
• A work proposing a new model and evaluation metric for discrete-level question difficulty estimation, which addresses the limitations of existing approaches and provides a principled foundation for future research.