BMI applications, ranging from speech production to finger control.

Previous work showed that despite claims of interpretability, humans are unable to use formal specifications presented in a variety of ways to validate even simple robot behaviors.

Hence, the following research questions arise: What is the optimal regret lower bound in contextual MNL bandits?

Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been applied in various kinds of areas such as science, finance and software engineering.

First, counterintuitively, we observe that pretrain-ing on more data shows no significant improvement in the model's capability to

This raises a challenging research question: How can we keep improving the systems when their capabilities have surpassed the levels of humans?