Weaknesses: The main weakness that I see with the paper is the mismatch between the theoretical analysis and the algorithm used in the experiments. The proposed estimator uses samples from the true "positive distribution" which consists of images from the same class. This is of course infeasible in a self-supervised setting where labels are unavailable. As a result, the authors approximate this distribution with the usual "positive distribution" which consists of random transformations of a single image. I understand that this two-step procedure is necessary to have an tractable analysis (using the true "positive distribution") and an experimental approach which is comparable to other self-supervised approaches (which use the approximate "positive distribution"), but the approximation of the "true positive" distribution by the other should be made explicit and discussed.

This paper aims to improve the performance of large language models by addressing the variable computational demands in inference steps, where some tokens require more computational resources than others. We present HARP, a simple modification to "off-the-shelf" Transformer forward pass. Drawing from hesitation and the framing effect in decision-making, HARP selectively applies additional computation when the model encounters uncertainty during token generation. Our method mimics human cognitive processes by pausing at difficult decision points and reframing inputs for a different perspective. Unlike other approaches, HARP is model-agnostic, training-free, and easy to implement. We thoroughly evaluate our method across various downstream tasks and model sizes, demonstrating performance improvements up to +5.16%. Notably, HARP achieves these gains while maintaining inference times twice faster than beam search. Simple and yet with significant gains, HARP offers a practical solution for enhancing the performance of Transformer-based language models with minimal computational impact.