Title: S^2-Transformer for Mask-Aware Hyperspectral Image Reconstruction. Abstract: The technology of hyperspectral imaging (HSI) records the visual information upon long-range-distributed spectral wavelengths. A representative hyperspectral image acquisition procedure conducts a 3D-to-2D encoding by the coded aperture snapshot spectral imager (CASSI), and requires a software decoder for the 3D signal reconstruction. By observing this physical encoding procedure, two major challenges may stand in the way of a high-fidelity reconstruction: (i) To obtain 2D measurements, CASSI dislocates multiple channels by disperser-titling and squeezes them onto the same spatial region, yielding an entangled data loss. (ii) The physical coded aperture (mask) will lead to a masked data loss by selectively blocking the pixel-wise light exposure. To tackle these challenges, we propose a spatial-spectral (S2-) transformer architecture with a mask-aware learning strat- egy. Firstly, we simultaneously leverage spatial and spectral attention modelings to disentangle the blended information in the 2D measurement along both two dimensions. A series of Transformer structures across spatial & spectral clues are systematically designed, which considers the information inter-dependency between the two-fold cues. Secondly, the masked pixels will induce higher predic- tion difficulty and should be treated differently from unmasked ones. Thereby, we adaptively prioritize the loss penalty attributing to the mask structure by inferring the difficulty-level upon the mask-aware prediction. Our proposed method not only sets a new state-of-the-art quantitatively, but also yields a better perceptual quality upon structured areas. Code and pre-trained models are available at https://anonymous.4open.science/r/S2-transformer-HSI-FEBF.