State-of-the-art image restoration methods currently face challenges in terms of computational requirements and performance, making them impractical for deployment on edge devices such as phones and resource-limited devices.

In particular, LLMs are capable of "lying", knowingly outputting false statements.

Experimental results corroborate our theoretical findings.

Do machines and humans process language in similar ways?

Further, IPRM's internal computations can be visualized across reasoning steps, aiding interpretability and diagnosis of its errors.

The inner workings of neural networks can be better understood if we can fully decipher the information encoded in neural activations. In this paper, we argue that this information is embodied by the subset of inputs that give rise to similar activations.

Data assimilation is a vital component in modern global medium-range weather forecasting systems to obtain the best estimation of the atmospheric state by combining the short-term forecast and observations. Recently, AI-based data assimilation approaches have attracted increasing attention for their significant advantages over traditional techniques in terms of computational consumption.