paper_id,Summary,Questions,Limitations,Ethical Concerns,Soundness,Presentation,Contribution,Overall,Confidence,Strengths,Weaknesses,Originality,Quality,Clarity,Significance,Decision
adaptive_dual_scale_denoising,"The paper introduces an adaptive dual-scale denoising approach for low-dimensional diffusion models, aiming to balance global structure and local details in generated samples. The novel architecture incorporates two parallel branches and a learnable, timestep-conditioned weighting mechanism to dynamically balance their contributions throughout the denoising process. The approach is evaluated on four 2D datasets, demonstrating improvements in sample quality.","['Can you provide a more detailed theoretical justification for the dual-scale architecture?', ""What impact do different types of aggregators have on the model's performance?"", 'How does the model perform on more complex, real-world low-dimensional datasets?', 'Can the computational cost be reduced without sacrificing performance?']","['The paper should address the high computational cost and explore ways to optimize it.', 'The limited diversity of datasets and lack of detailed theoretical backing for the proposed architecture are notable limitations.']",False,3,3,3,5,4,"['Novel approach to balancing global and local features in diffusion models for low-dimensional data.', 'Comprehensive empirical evaluation on multiple 2D datasets.', 'Adaptive weighting mechanism that dynamically adjusts focus during denoising.']","['Lacks detailed theoretical justification for the dual-scale architecture.', 'Computational cost is significantly higher, which may limit practical applicability.', 'Some sections are not clearly explained, such as the autoencoder aggregator and weight evolution analysis.', 'Limited diversity in the datasets used for evaluation. More complex, real-world datasets could strengthen claims.', 'Insufficient ablation studies and analysis on specific design choices like different types of aggregators.']",4,3,3,3,Reject
layerwise_lr_grokking,"The paper proposes a novel layer-wise learning rate strategy to accelerate and enhance the grokking phenomenon in Transformer models. The approach involves assigning different learning rates to the embedding layers, lower Transformer layers, and higher Transformer layers. The method is empirically validated on algorithmic tasks such as modular arithmetic and permutations, showing significant improvements in convergence speed and final performance.","['Can the authors provide more detailed explanations of the hyperparameter tuning process and the exact implementation of the layer-wise learning rates?', 'How do the authors ensure that the proposed method generalizes to tasks beyond the algorithmic ones tested in the paper?', 'Can the authors compare their approach with other related methods in more detail?', 'Can you provide more theoretical insights into why layer-wise learning rates specifically facilitate grokking?', 'How were the specific learning rates chosen for embedding, lower, and higher layers?', 'Can you discuss the potential for overfitting and how it was mitigated?', 'Have you tested the robustness of your method across different datasets and larger model sizes?', 'What is the impact of different learning rate configurations on the results?', 'Can the authors discuss potential strategies for mitigating the need for careful tuning of learning rates to avoid instability?']","['The methodology lacks detailed clarity, and the authors do not provide sufficient information on the hyperparameter tuning process.', 'The scope of tasks is limited to algorithmic ones, and the generalizability of the findings is unclear.', 'The paper requires more theoretical backing for the proposed method.', 'The choice of specific learning rates and potential overfitting issues need to be addressed in more detail.', 'The scalability of the approach to larger models and more complex tasks is not thoroughly addressed.', 'Ethical concerns related to the potential misuse of accelerated learning techniques are not addressed.']",False,2,2,3,4,4,"['The paper addresses an important problem in deep learning: the grokking phenomenon.', 'The proposed layer-wise learning rate strategy is novel and shows significant improvements in experimental results.', 'Experiments demonstrate substantial improvements in both convergence speed and final performance.']","['The paper lacks detailed methodological clarity, particularly regarding the exact implementation of the layer-wise learning rates and hyperparameter tuning.', 'The theoretical explanation for why layer-wise learning rates work is insufficient.', 'The scope of tasks is limited to algorithmic ones, making it unclear how well the findings generalize to other domains.', 'The choice of learning rates seems arbitrary and lacks justification.', 'More comprehensive ablation studies and comparisons with other related methods would strengthen the paper.', 'Certain sections, such as the experimental setup and ablation studies, could be more detailed and clearer.']",3,2,3,3,Reject
multi_style_adapter,"The paper introduces the Multi-Style Adapter, which enhances style awareness and consistency in character-level language models by integrating learnable style embeddings, a style classification head, and a StyleAdapter module into the GPT architecture. The approach aims to balance style adaptation and language modeling capabilities, and demonstrates improved style consistency and competitive validation losses across multiple datasets.","['How does the model handle unseen styles during inference?', 'Can the authors provide more details on the training process and hyperparameter tuning?', ""What are the potential impacts of overfitting on the model's ability to generate diverse text within each style?"", 'Can the authors provide more detailed ablation studies, especially focusing on the impact of different components in the Multi-Style Adapter?', 'How does the Multi-Style Adapter perform compared to other recent style-transfer models?', 'Can the computational efficiency trade-offs be quantified in a more detailed manner?', ""Can the authors clarify the autoencoder aggregator's role and how it integrates with the rest of the model?"", 'What measures have been taken to ensure the model does not overfit to specific style patterns, especially given the perfect consistency scores on some datasets?', 'Are there any potential optimization techniques that could be explored to improve the computational efficiency of the Multi-Style Adapter?', 'How does the model handle cases where the input sequence contains mixed styles?', 'Could you provide more qualitative examples of generated text to demonstrate the style consistency?', 'What is the impact of reducing the number of gating parameters in the modulation function?']","[""The reduced inference speed and potential overfitting to specific style patterns are significant limitations. Future work should focus on optimizing computational efficiency and improving the model's ability to generalize to diverse styles."", 'The paper currently lacks sufficient ablation studies and additional baselines.', ""The model's performance may be sensitive to hyperparameter settings, such as the weight of the style loss and the frequency of StyleAdapter application.""]",False,3,3,3,5,4,"['The paper presents a novel approach to style-aware language modeling, addressing a critical need for fine-grained stylistic control.', 'The Multi-Style Adapter is well-motivated and integrates seamlessly with the GPT architecture.', 'Extensive experiments on diverse datasets demonstrate improved style consistency and validation loss.', 'The paper includes thorough analysis and visualization of learned style embeddings and attention patterns.']","['The model achieves perfect style consistency scores on some datasets, which may indicate overfitting to specific style patterns.', 'The reduced inference speed (approximately 40% slower than the baseline) may limit the practical applicability of the model.', 'The paper could explore more sophisticated style representation techniques and evaluate their impact.', 'Lack of detailed ablation studies and additional baselines to strengthen the claims.', 'Clarity of the autoencoder aggregator mechanism could be enhanced.']",3,3,3,3,Reject
rl_lr_adaptation,"The paper explores the application of Q-learning to dynamically adjust the learning rate during transformer model training, aiming to enhance training efficiency and model performance. The state is represented by the validation loss and current learning rate, and the Q-learning agent learns to adjust the learning rate to optimize the training process. The approach is validated on three datasets: shakespeare_char, enwik8, and text8.","['Can you provide a detailed justification for the choice of state representation (validation loss and current learning rate)?', 'How does your method compare with other adaptive learning rate methods like AdamW, LAMB, Lookahead, or Noisy Adam in terms of both performance and computational overhead?', 'Can you clarify the reward signal used in your Q-learning approach?', 'Why were other RL approaches not considered or compared with Q-learning?', 'Can the authors provide more details on the hyperparameter tuning process?', 'Can the authors provide more details on the state and action space used in Q-learning?', 'How sensitive is the approach to the choice of hyperparameters for Q-learning?', 'Can the authors provide a more in-depth analysis of why Q-learning leads to better performance?', 'Can you provide more details on the implementation of the Q-learning agent and its interaction with the training process?', 'What specific benefits does Q-learning offer over other RL-based hyperparameter optimization methods?', 'Can you elaborate on the marginal improvements in validation loss? Why are the differences so small?', 'How does the proposed method generalize to other types of neural network architectures or other hyperparameters?', 'Can the authors provide more insights into the robustness and generality of the proposed Q-learning based approach?', 'How does the method perform on other types of neural network architectures apart from transformers?', 'Can the authors discuss potential limitations and ethical concerns in more detail?']","[""The method's performance is sensitive to the choice of hyperparameters, and there is additional overhead introduced by the Q-learning agent."", 'The experimental results do not convincingly demonstrate significant improvements over baseline methods.', 'The approach may not generalize well to other types of neural network architectures without further tuning.', 'The authors should discuss the potential drawbacks and challenges of using Q-learning for learning rate adaptation in more detail.', 'The paper does not adequately address the potential limitations and ethical concerns of the proposed approach. It is important to discuss how the method scales to other neural network architectures and the potential risks associated with its use.']",False,2,2,2,3,4,"['The application of Q-learning for dynamic learning rate adaptation during transformer training is novel and interesting.', 'The paper addresses an important problem in neural network training: the selection of an appropriate learning rate schedule.', 'Comprehensive experimental setup on multiple datasets.']","['The experimental results do not convincingly demonstrate a significant improvement over baseline methods. The best validation loss achieved by the Q-learning method on the shakespeare_char dataset is worse than the baseline.', 'The choice of state representation (validation loss and current learning rate) is not well-justified.', 'The paper lacks a detailed comparison with other sophisticated adaptive learning rate methods like AdamW, LAMB, Lookahead, or Noisy Adam.', 'The clarity of the explanation on Q-learning and the reward signal could be improved.', 'The technical details of the Q-learning implementation and its integration with transformer training are not thoroughly explained.', 'The significance of the results is questionable given the additional complexity introduced by the Q-learning agent.', 'The figures and tables are not clear and do not provide sufficient insight into the benefits of the proposed method.', 'The paper does not sufficiently address the limitations of the proposed method, such as sensitivity to hyperparameters and potential overhead from the Q-learning agent.', 'The discussion on the broader impacts and potential applications of the approach is limited.']",2,2,2,2,Reject
weight_initialization_grokking,"The paper investigates the impact of weight initialization strategies on the grokking phenomenon in Transformer models, focusing on arithmetic tasks in finite fields. It compares five initialization methods (PyTorch default, Xavier, He, Orthogonal, and Kaiming Normal) using a small Transformer architecture. The study reveals significant differences in convergence speed and generalization capabilities across initialization strategies, with Xavier and Orthogonal initializations showing superior performance.","['Can the authors provide more theoretical explanations for why certain initialization methods perform better?', 'How do the findings translate to more complex, real-world tasks beyond simple arithmetic operations?', 'Can the clarity of the figures and tables be improved, and can key graphs be better integrated into the text?', 'What are the potential negative societal impacts of the findings?']","['The study is limited to small Transformer models and arithmetic tasks, which may not fully represent the complexity of real-world problems.', 'The paper lacks a deeper theoretical understanding of the observed phenomena.', 'The potential negative societal impacts of the findings are not addressed.']",False,3,3,3,5,4,"['Addresses an intriguing and underexplored phenomenon in deep learning.', 'Provides a systematic comparison of multiple weight initialization strategies.', 'Includes rigorous empirical analysis and statistical validation.', 'Offers practical guidelines for initialization in similar learning scenarios.']","['The scope is limited to small Transformer models and arithmetic tasks, which may not generalize well to larger models or more complex tasks.', 'The paper lacks deeper theoretical insights into why certain initialization strategies perform better.', 'The clarity of the experimental setup and the integration of figures and tables could be improved.', 'The implications for broader Transformer applications and potential societal impacts are not sufficiently addressed.']",3,3,3,3,Reject
gan_diffusion,"The paper proposes integrating a Generative Adversarial Network (GAN) framework into diffusion models to improve sample quality and diversity. The approach includes a simple discriminator network, an adversarial loss term, and a gradient penalty to the adversarial loss. Extensive experiments on multiple 2D datasets are conducted to validate the approach, comparing results in terms of training time, evaluation loss, KL divergence, and sample quality.","['Can you provide more details on the architecture of the discriminator network?', ""How do the hyperparameters λadv and λgp affect the model's performance?"", 'Can you explain why the improvements are inconsistent across different datasets?', 'Can the authors provide more detailed descriptions of the denoiser and discriminator networks?', 'Have the authors considered using more comprehensive evaluation metrics like FID?', 'Can the authors provide more ablation studies to isolate the contributions of the gradient penalty and adversarial loss?', 'How would the proposed method perform on more complex and higher-dimensional datasets?']","['The paper acknowledges the increased training time and dataset dependency of the improvements. However, it could benefit from a more thorough exploration of different architectures and higher-dimensional datasets.', ""The empirical results show mixed improvements, indicating that the model's performance may be dataset-dependent."", 'The paper does not explore the limitations of the proposed approach in depth, particularly in terms of scalability to higher-dimensional data.']",False,2,2,2,3,4,"['The integration of GAN framework with diffusion models is a novel approach to improve sample quality and diversity.', 'The introduction of a gradient penalty to improve training stability is a thoughtful addition.', 'The paper provides a comprehensive evaluation on multiple 2D datasets, using various metrics such as training time, evaluation loss, KL divergence, and sample quality.']","['The methodology section lacks detailed explanations for certain components, such as the exact architecture of the discriminator network and the choice of hyperparameters.', ""The improvements in evaluation loss and KL divergence are not consistent across all datasets, indicating that the model's performance may be dataset-dependent."", ""The experimental scope is limited to 2D datasets. Further research is needed to evaluate the model's performance on higher-dimensional data."", 'The paper lacks sufficient ablation studies to isolate the contributions of different components of the proposed method.', 'The evaluation metrics are somewhat limited; including metrics like FID could strengthen the evaluation.', 'The paper does not sufficiently address the limitations of the approach, particularly its dataset dependency and scalability to higher-dimensional data.', 'There is no discussion on potential negative societal impacts or ethical concerns related to the work.']",3,2,2,2,Reject
layerwise_learning_rates,"The paper investigates the impact of learning rate schedules on language model training, specifically focusing on linear and exponential decay schedules. The study aims to analyze their effects on training efficiency and accuracy through experiments on datasets like Shakespeare and Enwik8. However, the paper is incomplete and lacks detailed content in critical sections, making it difficult to evaluate its contributions and significance.","[""Can you provide detailed content for the placeholder sections, including 'Related Work,' 'Background,' 'Method,' 'Experimental Setup,' and 'Results'?"", 'Do you have any experimental results or theoretical analysis to support your claims about the effectiveness of the proposed learning rate schedules?', 'Can you provide more detailed explanations of the methodology used?', 'What are the specific experimental setups and results?', 'How does this work compare with other related work in the field?', 'Why did the authors only choose linear and exponential decay schedules for their study?', 'Can the authors provide more thorough analysis and discussion of their experimental results?', 'How do the proposed learning rate schedules compare to other commonly used schedules like cosine annealing or cyclical learning rates?', 'What are the specific improvements in convergence rate and accuracy observed with the proposed schedules?']","['The paper is incomplete and lacks detailed content in critical sections. This makes it impossible to evaluate its limitations or potential negative societal impact.', 'The study is limited to only two types of learning rate schedules, which may not provide enough insights into the broader impact of learning rate schedules on language model training.']",False,1,1,1,2,4,['The topic of learning rate schedules is relevant and important in the context of language model training.'],"['The paper is incomplete, with missing sections on methodology, experimental setup, and results.', 'The organization and clarity of the paper are poor, with repeated sections and placeholders.', 'The paper lacks novelty as it focuses on well-known learning rate schedules (linear and exponential decay) without introducing new methodologies or theoretical insights.', 'The experimental results are not thoroughly detailed, and the analysis is superficial.', 'The paper is missing related work and background sections, making it difficult to place the study in the context of existing research.', 'The contributions are modest and incremental at best, failing to advance the state of the art in a significant way.']",1,1,1,1,Reject
grid_based_noise_adaptation,"The paper introduces a multi-scale grid-based noise adaptation mechanism for diffusion models to improve their performance on low-dimensional datasets. It employs a combination of coarse (5x5) and fine (20x20) grids to dynamically adjust noise levels during the diffusion process, with L1 regularization encouraging sparsity in fine-grained adjustments. The approach is evaluated on four 2D datasets: circle, dino, line, and moons, showing improvements in sample quality and distribution matching.","['Can the authors provide more detailed explanations of the multi-scale grid-based noise adaptation mechanism?', 'How does the performance of the proposed method compare to other state-of-the-art methods for low-dimensional data generation?', 'Can the authors discuss the potential societal impact and limitations of their work in more detail?', 'Can the authors provide more detailed ablation studies to isolate the impact of coarse and fine grids, as well as L1 regularization?', 'How does the proposed method perform on higher-dimensional datasets, and what are the challenges anticipated in such scenarios?', 'Can the authors elaborate on the choice of the specific grid sizes (5x5 and 20x20)? Have alternative configurations been tested?', 'Can the authors provide more visualizations for the generated samples, particularly for the dino and moons datasets?', 'Can you provide a detailed explanation of the L1 regularization term and its impact on the results?']","[""The paper does not discuss the potential societal impact and limitations of the proposed method in sufficient detail. It would be beneficial to address these aspects to provide a more comprehensive understanding of the work's implications."", 'The paper does not address the potential computational overhead and increased training time associated with the proposed method.', 'There is limited discussion on the generalizability of the approach to higher-dimensional datasets or other types of data.', 'The paper does not thoroughly address potential limitations of the proposed method, such as increased computational complexity and dataset-specific tuning requirements.', ""The method's effectiveness on higher-dimensional datasets remains unexplored."", 'Increased computational costs for training and inference.']",False,2,2,2,4,4,"['The paper addresses a relevant problem in the application of diffusion models to low-dimensional data.', 'The proposed multi-scale grid-based noise adaptation mechanism is novel and shows potential.', 'The experimental results demonstrate improvements in sample quality and distribution matching on several 2D datasets.']","['The paper lacks clarity in some sections, especially regarding the detailed implementation of the proposed method.', 'The experiments, while showing improvements, lack comprehensive analyses and more ablation studies.', 'The potential societal impact and limitations of the proposed method are not adequately discussed.', 'The paper does not compare the proposed method with a wide range of existing methods, limiting the context of its contributions.', 'There are some formatting issues, such as missing figure captions (e.g., Figure 2).', 'The choice of datasets, while diverse, needs better justification in terms of their relevance and representativeness for broader applications.', 'The computational overhead and training time increases are significant and need more discussion regarding their practical implications.']",3,2,2,3,Reject
data_augmentation_grokking,"The paper investigates the impact of data augmentation on the grokking phenomenon in neural networks learning modular arithmetic operations. Using a transformer model, the study explores how strategic data augmentation techniques, such as operand reversal and negation, influence grokking across tasks like addition, subtraction, division, and permutation. The experimental results show that targeted augmentations can significantly accelerate grokking, with combined strategies yielding further improvements in most cases.","['Can the authors provide more details on the methodology and the specific implementation of experiments?', 'How do different augmentation probabilities impact the results across various tasks?', 'Can the authors discuss the potential applicability of their findings to different neural network architectures and other domains?', 'Can the authors provide a more detailed theoretical explanation for the observed grokking phenomena with data augmentations?', 'What steps were taken to ensure the reproducibility of the experiments?', 'Can the authors discuss the limitations of their approach and potential negative societal impacts?', 'Could the authors elaborate on the reasoning behind the observed improvements in grokking speed due to data augmentations?', 'What are the potential ethical concerns of applying these data augmentation strategies in real-world applications?', 'Can the authors include more ablation studies to dissect the individual contributions of each augmentation technique in greater detail?', 'How do the results generalize to other neural network architectures or more complex tasks beyond modular arithmetic?']","[""The paper's clarity and thoroughness in discussing methodology and results need improvement."", 'The generalizability of the findings to other domains and architectures requires further exploration.', 'The study acknowledges the sensitivity of results to hyperparameters and task specificity. However, it should also consider the broader applicability and potential limitations in real-world scenarios.', 'Potential negative societal impacts are not discussed, which is important for a comprehensive evaluation of the work.']",False,3,3,3,5,4,"['Addresses a novel and relevant topic in deep learning, focusing on the grokking phenomenon.', 'Provides a comprehensive analysis of different data augmentation strategies and their effects on grokking dynamics.', 'Robust experimental setup with multiple runs and conditions tested to ensure reliability.', 'Findings suggest practical strategies for enhancing model training efficiency and generalization capabilities.']","['Lacks clarity in some sections, particularly in the methodology and the detailed implementation of experiments.', 'Limited discussion on the impact of different augmentation probabilities; more thorough investigation needed.', 'Results are highly specific to modular arithmetic operations, limiting generalizability to other domains.', 'Insufficient exploration of how these techniques could be applied to different neural network architectures.', 'Theoretical justifications for the observed effects are lacking.', 'Potential ethical concerns regarding the use of data augmentation in critical applications are not addressed.']",3,3,3,3,Reject
mdl_grokking_correlation,"This paper investigates the phenomenon of grokking in neural networks through the lens of Minimal Description Length (MDL), offering an information-theoretic perspective on sudden generalization. The authors propose a method to estimate and track MDL during training using weight pruning techniques. Experiments on modular arithmetic and permutation tasks reveal a strong connection between MDL transitions and grokking points, with varying dynamics across different tasks.","['Can the authors provide a more detailed description of the weight pruning technique and how MDL is estimated?', 'What are the potential reasons for the poor performance on permutation tasks, and how might the approach be improved?', 'Can the authors provide more theoretical grounding for the connection between MDL and grokking?', 'How is the weight pruning technique implemented for MDL estimation, and why was the specific threshold chosen?', 'Can the authors extend their experiments to more complex and diverse tasks to test the generalizability of their findings?', 'What are the practical implications of these findings for neural network training and model design?']","['The paper needs to address the clarity of the description of methods, particularly weight pruning and MDL estimation.', 'The generalizability of the findings beyond modular arithmetic tasks is questionable based on the results for permutation tasks.', 'The potential negative societal impacts of this work are not discussed, although the focus on theoretical and empirical analysis may have minimal direct societal consequences.']",False,2,2,2,3,4,"['The paper addresses a significant and poorly understood phenomenon in neural networks, grokking.', 'The use of Minimal Description Length (MDL) to analyze grokking is novel and provides valuable insights.', 'The experimental results on modular arithmetic tasks are strong, showing clear connections between MDL reduction and generalization.', 'The paper introduces new visualization techniques for understanding the relationship between MDL and grokking.']","['The description of the weight pruning technique and how MDL is estimated lacks clarity and detail.', 'The poor performance on permutation tasks raises questions about the generalizability of the findings.', 'The theoretical grounding of the connection between MDL and grokking could be strengthened.', 'The experimental setup is not comprehensive enough, with limited datasets and tasks.', 'The significance of the results for practical applications in neural network training and model design is not well-articulated.']",3,2,2,3,Reject
dual_expert_denoiser,"The paper 'DualDiff: Enhancing Mode Capture in Low-Dimensional Diffusion Models via Dual-Expert Denoising' introduces a dual-expert denoising architecture aimed at enhancing diffusion models' performance on low-dimensional datasets. The method uses a gating mechanism to combine two specialized expert networks dynamically, which helps in capturing multiple modes in low-dimensional data distributions. The paper demonstrates substantial improvements in terms of mode capture and sample diversity, validated through various experiments on 2D datasets like 'circle', 'dino', 'line', and 'moons'.","['Could you provide more detailed analysis on how the gating mechanism adapts during training?', 'How would the model perform on higher-dimensional datasets or more complex low-dimensional datasets?', 'Is the choice of the diversity loss weight (λ) empirically validated? Could different values lead to significantly different results?', 'Can the authors provide more details on the gating mechanism and how it determines the weight for each expert network?', 'How does the performance vary with different configurations of the gating network?', 'Can the authors explain the choice of hyperparameters, particularly the value of lambda in the diversity loss term?', 'Can the authors provide more detailed ablation studies to quantify the impact of each component (e.g., gating mechanism, diversity loss)?', 'How does the model perform with different types of aggregators for the expert networks?', 'Can more qualitative examples and visualizations be provided to substantiate the claims of improved mode capture?', 'Can you provide more details on the architecture of the expert networks and the gating mechanism?', 'How does the diversity loss term impact the final performance, and what are the trade-offs?', 'Can you include more comprehensive ablation studies to evaluate the impact of each component of the proposed method?', 'What are the computational costs associated with the dual-expert architecture, and how do they compare to the baseline?']","['The increased computational cost and the focus on low-dimensional datasets are the primary limitations of the proposed approach.', 'The generalizability to higher-dimensional settings remains unclear.', 'Potential negative societal impacts and limitations are not adequately addressed.']",False,3,3,3,5,4,"['The paper addresses a relevant and challenging problem in the field of generative modeling.', 'The dual-expert architecture and dynamic gating mechanism are novel and well-formulated.', ""Extensive experiments provide strong evidence of the approach's effectiveness."", 'The introduction of a diversity loss term to encourage multiple mode capture is a valuable contribution.']","['The novelty of combining two expert networks with a gating mechanism is somewhat incremental.', 'The choice of datasets is limited to simple 2D shapes, which might not fully demonstrate the generalizability of the approach.', 'The evaluation of gating mechanism behavior is not sufficiently detailed.', 'The increased training and inference times are a significant drawback that may limit practical applicability.', 'The diversity loss term is weighted arbitrarily without thorough justification for the chosen value.', 'The paper lacks detailed ablation studies to isolate the impact of different components (e.g., gating mechanism, diversity loss).', 'Potential limitations and negative societal impacts are not adequately addressed.']",3,3,3,3,Reject