1. The paper combines EvoPrompt and Mid Vision Feedback (MVF), but does not explain the principles and detailed processes of the two in the intrduction or related work section. In addition, the method section is a bit casual, without strict mathematical definitions and rigorous process expressions, making the method not specific and clear enough.
2. The paper does not have sufficient experimental demonstration of the contribution points. There is only an experimental comparison between ELF (the author's method) and the baseline without Mid Vision Feedback (MVF), but no comparison with the image classification result of Mid Vision Feedback (MVF). This does not prove that the schema searched by ELF (the author's method) is better than the schema in Mid Vision Feedback (MVF).
3. The description of the experimental section is not rigorous enough (potentially, it may lead to an imprecise experimental setting). For example, in the comparison of Stage1 and ELF in Table 1, the total training generations of the two do not seem to be consistent. Whether Stage1 has reached sufficient convergence may need to be explained. In lines #346-347, the author mentions using a 32x32 input size neural network for CIFAR100 experiments, but in lines #383-384, the experiment continues on ImageNet, switching to a larger ViT-B/16 and ResNet50, and the resolution setting is not explained at this time.
4. The analysis in the experimental part is not sufficient. The authors can show the difference between the schema optimized by EvoPrompt and the original schema (and MVF), and explain clearly and more deeply the growth points brought by using EvoPrompt to optimize the schema.
2. The paper does not have sufficient experimental demonstration of the contribution points. There is only an experimental comparison between ELF (the author's method) and the baseline without Mid Vision Feedback (MVF), but no comparison with the image classification result of Mid Vision Feedback (MVF). This does not prove that the schema searched by ELF (the author's method) is better than the schema in Mid Vision Feedback (MVF).