reviews: 
Comments: In this work, the authors propose a technique for generating complete human genomes. Motivated by the challenges of obtaining individual genomes due to sequencing costs and data privacy concerns, they argue that generating synthetic genomes will improve the performance of predictive models. To address the long-context problem faced by machine learning models processing full genomes, they employ a previously introduced technique to embed genomes in a low-dimensional latent space. A diffusion model is then trained within this space. The authors train their model using classifier guidance on two datasets: one for generating genomes with or without ALS, and another for generating genomes representing 26 populations from the 1000 Genomes Project. To validate their diffusion model, they try to demonstrate (1) that classifiers trained on the generated data achieve comparable accuracy to those trained on real data, and (2) that the generated data distribution is both diverse and sufficiently distinct from the real data distribution.
Strengths: Most current deep learning models in genomics operate at the sequence level, preventing them from processing complete genomes due to their immense length. Additionally, most models rely on the reference genome, hindering their ability to accurately capture intra-individual variants. These limitations, well-documented in the literature, are likely central to the challenges faced in improving these models. Therefore, I appreciate this paper's focus on working at the complete genome level to represent full individuals. Employing an embedding technique to represent full genomes in a low-dimensional space and utilizing latent diffusion within this space is a promising approach, and I am happy to see research in this area. 

Furthermore, the choice of datasets for training the model is well-justified.
Weaknesses: However, the paper presents several limitations:

- The most compelling aspect, in my view, is the embedding of complete genomes into a low-dimensional space and subsequent model training. However, the employed technique is not a novel contribution and lacks sufficient discussion. The authors also fail to adequately address the extent to which complete genomes can be reconstructed from this latent space and the impact on the final sequence. While reconstruction is briefly mentioned, a detailed explanation of the process and precise reconstruction metrics at the sequence level are missing.

- The literature review is incomplete. The authors should include references to D3 [1] and DNADiffusion [2]. While these works have slightly different motivations and are not directly comparable, they warrant mention. Given the discussion of long-range limitations in current models, referencing Enformer [3] and Borzoi [4] would also be beneficial as they represent significant advancements in addressing this issue.

- The paper lacks crucial technical details, hindering a comprehensive understanding of the proposed method. Certain sections require rewriting for improved clarity. Specifically, a dedicated section should detail genome processing and embedding, and another should explain the diffusion model training process, including duration. The model architecture section is difficult to follow, and the ablations lack proper introductions. The section introducing the classifier accuracy recovery metric is overly long and imprecise; it could be condensed into a few concise sentences. Section 3.4.2, while critical, is poorly detailed. The introduced metrics lack explanations, particularly the notation 'd' (presumably representing distance), which is not defined, nor is its measurement space specified. Additionally, the mathematical notations in the equations are incorrect. Measuring the quality and diversity of generated data is crucial in generative modeling and has been a key research area in computer vision. I strongly encourage the authors to refine this aspect, provide thorough introductions to their metrics, and better reference existing literature.

- Employing a convolution-based architecture might be suitable, but the authors should reference existing architectures, use them as a starting point, and discuss necessary modifications for their specific context. Inspiration can be drawn from architectures used in genomics, such as those in D3, Borzoi, BPNet, and Enformer. The 1D U-NET architecture from SegmentNT [5] could also be relevant. Additionally, a vast body of literature exists on 1D convolution and 1D U-NET in signal processing.

- The figures are of poor quality. The cartoons are unclear and lack information, and the font sizes are too small. The evolution plots are difficult to read, and using screenshots from run monitoring software is unacceptable. The authors should regenerate these plots using a plotting library like matplotlib.

- The paper's structure is unconventional. The authors underutilize the allotted space and employ numerous small sub-paragraphs. A denser presentation is expected for a conference like ICLR. The authors should utilize the available space to provide a detailed description of the method and experimental setup.

- The experimental section is also unconvincing. The experimental setup lacks a proper introduction; for instance, the classifier's input and architecture are not specified. As previously mentioned, the privacy metrics are inadequately introduced, making it difficult to assess the reported performance. Lastly, the experiment investigating the use of synthetic data for improving predictors in data scarcity regimes is flawed. When training the classifier on k% of the dataset, the diffusion model should also be trained on k% to accurately reflect the intended use case. In reality, with only k% of the data available, training a diffusion model on 100% is not feasible.

Overall, this paper falls short of the expected quality and contribution standards for ICLR, and I confidently recommend strong rejection.

[1] https://www.biorxiv.org/content/10.1101/2024.05.23.595630v1.abstract

[2] https://www.biorxiv.org/content/10.1101/2024.02.01.578352v1

[3] https://www.nature.com/articles/s41592-021-01252-x

[4] https://www.biorxiv.org/content/10.1101/2023.08.30.555582v1

[5] https://www.biorxiv.org/content/10.1101/2024.03.14.584712v1.full.pdf
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 1