reviews: 
Comments: This paper addresses identifying the neuron path within Vision Transformers (ViT) that comprises the most impactful neurons. Two methods are utilized to identify a neuron pathway. The proposed joint attribution score assesses the influence of the neuron pathway on the network's inference process. Another method referred to as layer-progressive neuron locating identifies the most significant neuron pathway for predicting the target image. The proposed method demonstrates its effectiveness in identifying neuron pathways. This paper presents a range of applications that leverage the proposed method.

Thank you for the authors' response. I think they answered my question adequately. Thus, I keep my score as a weak accept.
Strengths: This paper presents innovative concepts for identifying influential neuron pathways. The proposed approach is capable of identifying the trajectory of neurons by analyzing the gradient magnitude. This method has the potential for application across diverse architectural frameworks.

This paper demonstrates the applicability of the proposed method across a range of scenarios. Initially, Figure 4 visualizes the pathways of neurons categorized by class. The visualization validates that various classes utilize unique neural pathways. Subsequently, Figure 5 demonstrates the class relationships derived from the discovered neuron pathways. This approach has the potential to facilitate the assessment of similarity between classes in forthcoming research.
Weaknesses: This paper does not adequately present a thorough review of the existing literature regarding the proposed method. The reviewer, upon examining the current manuscript, encounters difficulty in clarifying the significance of the proposed method in relation to recent studies. The discussion should not merely enumerate related works; it should also engage in a comparative analysis of the proposed method to facilitate a clear understanding of its significance for the readers.

The manuscript requires enhancements in coherence for better clarity and flow. The abstract states, “showcasing that the found neuron paths have already preserved the model capability on downstream tasks.” However, there is a lack of research focused on downstream tasks.

The absence of crucial ablation studies for the proposed joint attribution score is a significant oversight in this paper. The proposed joint attribution score represents a sophisticated approach that necessitates multiple iterations of back-propagation. To establish its efficacy, it is essential to conduct a comparison with a more straightforward baseline method and demonstrate its superiority over that baseline. Nonetheless, Table 1 presents a simultaneous variation of search methods and scores, which complicates the identification of the specific component contributing to performance improvement. An appropriate ablation study is essential to address the increase in search complexity associated with the proposed method.

This paper does not include comparisons with the baseline method in Figures 5 and 6. The analysis studies are commendable; however, it is essential that they also incorporate quantitative metrics in relation to the baseline for a more comprehensive evaluation. The sole quantitative metric presented in this paper is presented in Table 1. The author needs to incorporate more extensive assessments to demonstrate the advantages of the proposed method.
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 3