reviews: 
Comments: This work presents PIBD (Prototypical Information Bottlenecking and Disentangling) for multimodal survival analysis using pathology and genomics, which extends previous progress thus far on co-attention-based early-based fusion [1] and learning information bottleneck [2] in computational pathology via two mechanisms: 1) a Prototypical Information Bottleneck (PIB) module for intra-modal redundancy and 2) a Prototypical Information Disentanglement (PID) module for inter-modal redundancy. Experimentation is performed on the same splits for disease-specific survival analysis in Jaume et al. [1], with comparisons against other relevant works (both unimodal and multimodal) and ablation experiments that assess PIB and PID independently.


References
1. Jaume, G., Vaidya, A., Chen, R., Williamson, D., Liang, P. and Mahmood, F., 2023. Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction. arXiv preprint arXiv:2304.06819.
2. Li, H., Zhu, C., Zhang, Y., Sun, Y., Shui, Z., Kuang, W., Zheng, S. and Yang, L., 2023. Task-specific fine-tuning via variational information bottleneck for weakly-supervised pathology whole slide image classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7454-7463).
Strengths: - As summarized above, extensive experimentation regarding comparisons, ablation studies, and assessment of multiple cancer types is performed. In particular, this work compares with many state-of-the-art multimodal methods such as MCAT (both concatenation and Kronecker Product), SurvPath, MOTCat, and other relevant information bottleneck-related works. Beyond ABMIL and TransMIL, other strong unimodal methods such as the task-specific finetuning variant of CLAM is also considered. Background information and literature is also extensive, with many important studies related to PIBD cited in a comprehensive manner.
- The proposed PID and PIB methodology, though largely adapted and inspired by other IB applications in computational pathology such as that of Li et al. 2023, is (on balance) appropriate for its extension into survival analysis w.r.t. addressing sparsity of patch features in MIL via prototypes. Figure 1 is informative and communciates how PID/PIB are used.
Weaknesses: - **Limited findings w.r.t. clinical application / multimodal interpretability**: PID/PIB is intuitive for solving multimodal integration problems of WSIs and gene sets, but the demonstrated findings of this work appears limited to only improvement of c-Index performance. Methodologies such as MOTCat and SurvPath, though not approaching survival analysis from an information bottleneck perspective, similarly resolve issues regarding redundancy of patch features in MIL and reach similar findings already established in prior works. The interpretability experiments, shown as attention heatmaps in the supplement, are difficult to interpret and draw conclusions from. As the improvement in c-Index is minor, it would be valuable to investigate the interpretability of PIBD, for example: (1) performing local and global interpretability to assess individual and cohort-level image-omic drivers of disease survival, or (2) experiment+showcase unique clinical applications that would arise from PIBD that cannot be performed in other methods, which would produce new scientific findings and expand this study's significance. In particular, I found the prototypical aspect of this work to be intriguing, with concurrent works also realizing (and finding new applications) of using prototypical patterns for pathology [5].
- **Ablation Experiments for PIB/PID**: Some baselines do not seem fair in the PID/PIB ablation experiments.
- - In ablating PID, the baseline model considered was average pooling the prototypical features - a more appropriate baseline would be to use a non-disentangled Transformer like TransMIL on top of the prototypical features. 
- - In ablating PIB, the reviewer is not certain what the baseline looks like (using PID without PIB), but would think that this comparison would be similar to having each modality being processed by a TransMIL-like encoder + PID (or SurvPath + PID). 
- **Using CTransPath for TCGA evaluation**: A limitation of this study is the usage of CTransPath (pretrained on TCGA) for training and evaluating MIL models for survival analysis in TCGA. Understandably, access to powerful pretrained encoders for pathology is challenging, due to: (1) lack of computing power, and (2) lack of diverse and independent data outside of TCGA, with other studies in computational pathology such as Jaume et al. [1] and Filliot et al. [2] having also conducted survival analyses via TCGA-pretrained encoders. This limitation could remain as "a limitation of this study" (fitted in the conclusion) as the novelties of this work are with respect to the MIL encoder (all comparisons are performed using the same features and evaluated in the same way), in combination with the aforementioned challenges in computational pathology. However, as the broader pathology and machine learning community is becoming more aware of potential biases that emerge from data contamination of self-supervised models that are also trained using the evaluation data (as seen in LLMs and other studies [3-7]), it is important for advances going forward to recognize and resolve this issue and to develop better standardization for developing and evaluating MIL methods. 

References
1. Jaume, G., Vaidya, A., Chen, R., Williamson, D., Liang, P. and Mahmood, F., 2023. Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction. arXiv preprint arXiv:2304.06819.
2. Filiot, A., Ghermi, R., Olivier, A., Jacob, P., Fidon, L., Mac Kain, A., Saillard, C. and Schiratti, J.B., 2023. Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling. medRxiv, pp.2023-07.
3. Guo, C., Bordes, F., Vincent, P. and Chaudhuri, K., 2023. Do SSL Models Have D\'ej\a Vu? A Case of Unintended Memorization in Self-supervised Learning. arXiv preprint arXiv:2304.13850.
4. Xiang, J. and Zhang, J., 2022, September. Exploring low-rank property in multiple instance learning for whole slide image classification. In The Eleventh International Conference on Learning Representations.
5. Chen, R.J., Ding, T., Lu, M.Y., Williamson, D.F., Jaume, G., Chen, B., Zhang, A., Shao, D., Song, A.H., Shaban, M. and Williams, M., 2023. A General-Purpose Self-Supervised Model for Computational Pathology. arXiv preprint arXiv:2308.15474.
6. Jacovi, A., Caciularu, A., Goldman, O. and Goldberg, Y., 2023. Stop uploading test data in plain text: Practical strategies for mitigating data contamination by evaluation benchmarks. arXiv preprint arXiv:2305.10160.
7. Kapoor, S. and Narayanan, A., 2023. Leakage and the reproducibility crisis in machine-learning-based science. Patterns, 4(9).
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 3 good
Clarity: 2 fair