We sincerely appreciate the time and the efforts the reviewers invested in reading our paper and providing valuable1 feedback. We illustrated the usefulness of our framework on3 the sampling problem and obtained a significantly better result than numerous previous results without making any4 extra assumptions. We believe that there are many other applications of our framework and that our paper is among5 thetopacceptedpapers.6 To Reviewer 1: Thanks for the citations and the correction you provided. To Reviewer 2: The problem studied in our paper, sampling from log-concave distributions, is an essential tool for12 Bayesian inference. It also has many other applications such as volume computation and bandit optimization.

Encoding models predict brain responses to a set of given stimuli. Recently, deep neural networks have been used as encoding models to predict brain activity as recorded by functional MRI (fMRI) [1, 2, 3, 4, 5, 6]. These studies investigate whether representations in deep neural networks correspond to those in the human brain. This relationship is often assessed using linear models, with successful prediction taken as evidence of shared representational structure. Studies have investigated representations from both unimodal and multimodal deep neural networks, including large language models (LLMs) [2, 4, 7, 8], vision models [9, 10], audio models [1, 11], and video-language models (VLMs) [12], to predict brain activity. However, existing studies face challenges in generalizability and comparability. Differences in stimulus modality, quantity, and content, as well as in preprocessing and scoring, make cross-study comparisons difficult. The Algonauts 2025 Challenge [13] provides a framework to address these issues, offering an openly available, preprocessed dataset with a large amount of data per subject and aligned stimuli across modalities, including video, audio, and transcripts, along with a standardized evaluation procedure. The challenge places particular emphasis on generalizability, including both in-distribution and out-of-distribution test sets to rigorously evaluate how well models transfer to new stimuli. 1

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper studies a planted partition model for random m-uniform hypergraphs, and proves the consistency of a natural generalization of spectral clustering. The hypergraph adjacency tensor is (mode-1) flattened to a matrix, from which a normalized Laplacian matrix is formed and the standard spectral partitioning is then applied. The striking feature of the analysis is that the rate of convergence improves as m increases, provided that the number of partitions is small. Some experiments on both synthetic and application derived data are reported, and the proposed method is shown to be relatively effective, especially given its simplicity. The model is well-motivated by applications in computer vision and likely elsewhere.

We would like to emphasize again the main contribution of our paper. To Reviewer 1: Thanks for the citations and the correction you provided. In our final submission, we will cite [4] We will also correct all the items mentioned in SPECIFIC REMARKS/TYPOS. It also has many other applications such as volume computation and bandit optimization. The preliminary results are attached.