text: This paper proposes a recurrent neural network for visual question answering.  The recurrent neural network is equipped with a carefully designed recurrent unit called MAC (Memory, Attention and Control) cell, which encourages sequential reasoning by restraining interaction between inputs and its hidden states.  The proposed model shows the state-of-the-art performance on CLEVR and CLEVR-Humans dataset, which are standard benchmarks for visual reasoning problem.  Additional experiments with limited training data shows the data efficiency of the model, which supports its strong generalization ability. The proposed model in this paper is designed with reasonable motivations and shows strong experimental results in terms of overall accuracy and the data efficiency.  However, an issue in the writing, usage of external component and lack of experimental justification of the design choices hinder the clear understanding of the proposed model. An issue in the writing Overall, the paper is well written and easy to understand,  but Section 3.2.3 (The Write Unit) has contradictory statements about their implementation.  Specifically, they proposed three different ways to update the memory (simple update, self attention and memory gate),  but it is not clear which method is used in the end. Usage of external component The proposed model uses pretrained word vectors called GloVE, which has boosted the performance on visual question answering.  This experimental setting makes fair comparison with the previous works difficult  as the pre-trained word vectors are not used for the previous works.  To isolate the strength of the proposed reasoning module, I ask to provide experiments without pretrained word vectors. Lack of experimental justification of the design choices The proposed recurrent unit contains various design choices such as separation of three different units (control unit, read unit and memory unit), attention based input processing and different memory updates stem from different motivations.  However, these design choices are not justified well  because there is neither ablation study nor visualization of internal states.  Any analysis or empirical study on these design choices is necessary to understand the characteristics of the model.  Here, I suggest to provide few visualizations of attention weights and ablation study that could support indispensability of the design choices.