review_ai_scientist/grokking/llama3.1-runs_ratings.csv

paper_id,Summary,Questions,Limitations,Ethical Concerns,Soundness,Presentation,Contribution,Overall,Confidence,Strengths,Weaknesses,Originality,Quality,Clarity,Significance,Decision
permutation_equivariance_grokking,The paper proposes a novel approach to incorporating permutation equivariance into neural networks to improve their performance on algorithmic tasks such as arithmetic operations and sorting. The authors claim that their approach combines mathematical insights and architectural innovations to achieve permutation equivariance.,"['Please provide the missing sections: methodology, experimental setup, results, and related work.', 'Can you elaborate on the specific architectural innovations and mathematical insights used to achieve permutation equivariance?', 'How does the proposed approach compare to existing methods in the literature?', 'What are the detailed experimental setups and results to validate the proposed approach?', 'Can you provide more citations and a comprehensive review of related work?']","[""Due to the paper's incompleteness, it is difficult to identify specific limitations or potential negative societal impacts."", 'The paper needs to clearly present any limitations associated with the proposed approach and discuss potential negative societal impacts.']",False,1,1,2,2,5,"['Addresses a significant issue in neural network architectures related to permutation equivariance.', 'The topic is relevant and important for improving the performance and generalization of neural networks on algorithmic tasks.']","['The paper is incomplete, with critical sections such as the methodology, experimental setup, results, and related work missing.', 'Lack of experimental validation and details makes it impossible to assess the efficacy of the proposed approach.', 'There are placeholders and repeated sections indicating that the paper is not in its final form.', 'The abstract and introduction are repetitive and vague.', 'The related work section lacks citations and a comprehensive review of existing methods.']",2,1,1,2,Reject
math_operation_depth_grokking,"The paper investigates the impact of mathematical operation depth on the phenomenon of grokking in neural networks, particularly focusing on small algorithmic datasets. It aims to understand how increasing the depth of mathematical operations can affect a model's ability to learn and generalize mathematical concepts.","['Can the authors provide a detailed explanation of their experimental setup and methodologies?', 'What are the specific results and findings from the experiments?', 'How do these results compare to previous work in the field?', 'Can the authors elaborate on the mathematical operation depth and how it was systematically varied in their experiments?']","['The current submission lacks sufficient detail to assess the limitations accurately. The authors need to fully develop their methodology and results sections.', 'Potential negative societal impacts are not addressed.']",False,1,1,2,2,4,"['The research question is novel and relevant to the field of machine learning.', 'The idea of exploring mathematical operation depth in the context of grokking is innovative.']","['The paper is incomplete, with significant sections like experimental setup, results, and conclusions missing or left as placeholders.', 'Lacks detailed methodology or explanation of how experiments were conducted.', 'No actual results or findings are presented, making it impossible to evaluate the validity of the claims.', 'Citations and related work sections are not adequately developed, lacking depth and context.', 'The background section is repetitive and does not add value.']",2,1,1,2,Reject
hierarchical_meta_pattern_grokking,"The paper proposes a novel approach to learning group operations from data using transformer networks. The authors modify the transformer architecture to handle the unique properties of group operations and claim to achieve high accuracy, outperforming traditional machine learning models. The proposed method has potential applications in fields such as cryptography, coding theory, and computer vision.","['Please provide detailed experimental setup and results.', 'How does your method compare with other existing methods for learning group operations?', 'Can you provide more details on the modifications made to the transformer architecture for learning group operations?', 'What are the exact datasets and experimental setups used to evaluate the model?', 'How does the proposed model compare to other approaches in terms of computational efficiency and scalability?', 'Can the authors provide practical examples or applications where their model has been successfully used?']","['The paper does not adequately address the limitations and potential negative societal impacts of their work.', 'The computational complexity of transformer networks and the scalability of the approach are not discussed.']",False,1,1,2,2,4,"['Addresses an interesting and fundamental problem in mathematics and computer science.', 'The use of transformer networks for learning group operations is a novel idea.', 'Potential implications for various fields, including cryptography and coding theory.']","['The experimental setup and results sections are missing, making it impossible to verify the claims.', 'The related work and background sections are poorly developed, with missing references and incomplete information.', 'The paper heavily relies on existing transformer architectures and does not provide a comprehensive comparison with other methods.', 'The paper is poorly written and organized, with several sections missing or incomplete.', 'Lacks detailed explanation of the modifications made to the transformer architecture for group operations.', 'No discussion of limitations or potential negative societal impacts is provided.']",2,1,2,2,Reject
structured_irrelevant_info_grokking,"The paper proposes a transformer-based architecture for algorithmic reasoning tasks, claiming to achieve high accuracy and generalization. The approach leverages attention mechanisms and a novel training procedure combining supervised and self-supervised learning. However, the paper is incomplete, with missing sections such as the method, experimental setup, results, and conclusions.","['How does the proposed method compare to existing approaches in terms of performance and computational efficiency?', 'Can the authors provide more details on the novel training procedure and the transformer-based architecture?', 'What datasets were used in the experiments, and what are the experimental results?', 'How does your approach compare to existing methods in terms of performance and computational efficiency?', 'What are the limitations of your approach, and how can they be addressed in future work?', 'Can the authors provide a detailed explanation of the proposed transformer-based architecture?', 'What is the novel training procedure mentioned in the abstract?', 'Can the authors complete the sections on Related Work, Method, Experimental Setup, and Results for a comprehensive review?', 'How do the results compare with existing methods in more detail?']","['The paper does not discuss any limitations or potential negative societal impacts of the proposed method. A thorough evaluation of these aspects is necessary for a complete assessment.', 'The incomplete sections make it challenging to evaluate the limitations and potential negative societal impacts comprehensively.', 'The paper does not adequately address the limitations of the proposed approach, particularly in terms of computational requirements and scalability to larger datasets.']",False,1,1,1,1,5,"['The idea of using transformers for algorithmic reasoning tasks is novel and interesting.', 'The paper addresses an important problem in algorithmic reasoning, which has applications in various domains.']","['The paper is incomplete, with missing sections like the method, experimental setup, results, and conclusions.', 'The claims are not well-supported due to the lack of detailed descriptions and experimental results.', 'The paper is poorly organized and lacks clarity. Essential sections are missing, and the provided sections are not well-explained.', 'The abstract and introduction are repetitive and do not offer a clear explanation of the contributions.', 'The paper does not discuss the limitations of the proposed method or its potential negative societal impact.', 'The paper lacks detailed comparisons with existing approaches and does not adequately cite related work.']",2,1,1,2,Reject
controlled_concept_drift_grokking,"The paper proposes a neural network architecture leveraging attention mechanisms and layer normalization to improve the ability to learn and generalize mathematical concepts. The model is tested on mathematical problems, including arithmetic and algebraic equations, and claims to outperform existing state-of-the-art methods.","['Can the authors provide more technical details about the proposed neural network architecture?', 'What are the specific contributions of the proposed approach compared to existing state-of-the-art methods?', 'Can the authors present concrete experimental results and analysis to validate their claims?', 'How does the proposed approach handle different types of mathematical problems?', 'What are the potential limitations and ethical concerns related to the proposed approach?', 'Can you provide more details on the modifications made to the Transformer model for mathematical problem-solving?', 'What are the exact datasets used in the experiments, and how were they generated?', 'Can you include a comprehensive comparison with existing state-of-the-art methods?', 'What are the limitations of your approach, and how might it impact society negatively?', 'Can the authors provide more details on how the proposed model specifically addresses the challenges of mathematical reasoning compared to a standard Transformer model?', 'What are the specific contributions of the proposed method beyond using attention mechanisms and layer normalization?', 'Can the authors include more detailed experimental results and comparisons with other state-of-the-art methods in mathematical problem-solving?', 'Can the authors provide detailed experimental results, including accuracy and loss metrics, and comparisons to a comprehensive set of baseline models?', 'How does the proposed model specifically leverage the attention mechanism to handle the abstract nature of mathematical concepts differently from existing models?', 'Can the authors clarify the training process, including how the novel loss function is designed and its impact on learning mathematical relationships?', 'What are the potential limitations of the proposed approach, and how do the authors plan to address them in future work?', 'How were the datasets generated, and were there any preprocessing steps?']","[""The paper does not address the potential limitations and ethical concerns of the proposed approach. For instance, the impact of the model's decisions in practical applications is not discussed."", 'The paper does not discuss the limitations of the proposed approach or its potential negative societal impacts. Further elaboration on these aspects is needed.', 'The authors have not adequately discussed the limitations of their work. It is important to address the potential challenges and pitfalls of the proposed approach, especially concerning its scalability and applicability to real-world mathematical problems.', ""The methodology's reliance on large datasets may be a limitation in scenarios where such data is not available.""]",False,2,2,2,3,4,"['The paper addresses a significant challenge in artificial intelligence, namely algorithmic comprehension.', 'The use of attention mechanisms and layer normalization is a reasonable approach for improving deep learning models.', 'The problem of algorithmic comprehension is significant and relevant to various applications requiring mathematical reasoning.']","['The paper lacks sufficient technical detail and clarity in the description of the proposed methodology.', 'The experimental validation is incomplete, with no concrete results or analysis presented in the results section.', 'There is no clear comparison with existing state-of-the-art methods, making it difficult to assess the contributions of the paper.', 'The proposed approach does not seem to provide a significant advancement over existing methods, as the novelty is not well-demonstrated.', 'The paper does not address potential limitations or ethical concerns related to the proposed approach.', 'The related work section is missing, which makes it hard to place this work in the context of existing research.', 'Several sections of the paper are incomplete, including the results and conclusion sections, making it difficult to assess the overall contribution and effectiveness of the proposed method.', 'The clarity and organization of the paper need significant improvement to ensure the ideas are communicated effectively.', 'The implementation details are vague, and reproducibility is not well-supported. There is no discussion on how the datasets were generated or any preprocessing steps.']",2,2,2,2,Reject
hierarchical_math_operations,"The paper explores the concept of grokking in deep neural networks, proposing an approach that leverages the hierarchical structure of mathematical operations to improve generalization on small algorithmic datasets. Using a transformer-based architecture, the paper aims to demonstrate enhanced generalization capabilities across various algorithmic tasks.","['Can you provide detailed experimental results that support your claims?', 'How does your approach significantly advance the state of the art compared to previous work?', 'Can you explain the steps of your method in more detail?', 'What are the limitations of your approach and potential negative societal impacts?', 'Can you provide a more detailed description of the dataset and the specific mathematical operations used?', 'How was the dataset generated, and what are the characteristics of the tasks included?', 'Can you provide a complete analysis of the results, including both qualitative and quantitative metrics?', 'Can the authors provide more detailed comparisons to existing work in the related work section?', 'Could the authors elaborate on the specific design choices made in their transformer-based architecture?', 'What are the key differences between this work and previous contributions in the field of grokking and generalization?']","['The paper does not adequately address the limitations of the proposed approach.', 'There is no discussion on potential negative societal impacts or ethical considerations.']",False,1,1,2,2,4,"['Addresses an interesting and relevant problem in deep learning: generalization beyond overfitting on small algorithmic datasets.', 'The idea of leveraging the hierarchical structure of mathematical operations is novel and has potential.']","['The paper lacks detailed experimental results and evidence supporting the claims.', 'Sections of the paper are poorly organized and incomplete, with placeholders and missing captions.', 'The method section is high-level and lacks detailed explanation of the algorithms.', 'Fails to address the limitations of the approach or potential negative societal impacts.', 'The related work section is incomplete, making it difficult to place its contributions within the existing research landscape.', 'The experimental setup is not adequately described, lacking details about the dataset, the specific mathematical operations used, and the generation process.', 'The results section is incomplete and lacks both qualitative and quantitative analysis.', 'The novelty of the approach is questionable, as it heavily relies on existing techniques like transformers and AdamW optimization without significant modifications.']",2,1,2,2,Reject
operation_invariance,"The paper investigates the phenomenon of grokking, where neural networks generalize to unseen data despite being trained on a limited set of examples. It aims to understand the underlying mechanisms that enable grokking, focusing on the role of neural network architecture and training data. However, the paper lacks significant details in key sections, including related work, methodology, experimental setup, and results.","['Can the authors provide a thorough review of related work?', 'What specific neural network architectures and training data were analyzed?', 'How were the experiments conducted, and what were the key findings?', 'Can the authors present detailed experimental results and their analysis?']","['The paper does not address limitations or potential negative societal impacts, which is an important aspect of responsible AI research.']",False,1,1,1,1,4,"['The topic is highly relevant and interesting, addressing a crucial aspect of neural network generalization.', 'The idea of systematically analyzing the impact of neural network architecture and training data on grokking is valuable.']","['The paper is incomplete, with major sections such as related work, background, methodology, experimental setup, and results missing or serving as placeholders.', 'There is a lack of specific contributions and detailed explanations of the experiments conducted.', 'The paper does not provide any experimental results or analysis, making it impossible to evaluate the claims made.', 'The conclusion section is empty, offering no insights or future directions.']",2,1,1,2,Reject
missing_operand_grokking,"The paper addresses the problem of missing operands in mathematical operations using attention mechanisms in transformer models. It proposes generating datasets with missing operands and training models to predict results when operands are missing. The approach is tested on addition, subtraction, and division, showing improved performance over baselines.","['Can you provide a detailed description of how the datasets with missing operands are generated?', 'How are the models trained and evaluated? Are there specific hyperparameters or training techniques used?', 'Can you include more comprehensive ablation studies to analyze the contribution of each component?', 'What are the potential limitations of the approach, and how can they be addressed in future work?', 'What challenges did you encounter when training models on datasets with missing operands, and how were these addressed?', 'Can you include a more comprehensive discussion on the limitations and potential societal impact of your approach?']","['The approach requires a large amount of training data, which may not always be available.', 'There is a potential for overfitting, especially with small datasets.', 'The proposed approach may not generalize to more complex mathematical operations without further investigation.']",False,2,2,2,3,4,"['Addresses a relevant problem in handling incomplete data.', 'Proposes a novel approach using attention mechanisms in transformer models.', 'Demonstrates improved performance on mathematical operations with missing operands.']","['The methodology lacks detail, particularly in data generation and model training.', 'Experimental results lack comprehensive analysis and ablation studies.', 'Several typos and formatting issues affect readability.', 'The 1.4% improvement in accuracy may not justify the novelty.', 'The related work section is not thorough and does not position the contribution well.', 'The results section lacks thorough analysis and discussion.', 'No comprehensive discussion on limitations and potential societal impact.']",2,2,2,2,Reject
arithmetic_decomposition,"The paper proposes a novel approach to learning group operations using transformer-based architectures. However, the submission lacks substantive content in key sections, including the introduction, related work, background, method, experimental setup, results, and conclusions, making it difficult to assess the work's contributions and impact.","['Can the authors provide detailed methodologies and experimental results to support their claims?', 'What specific transformer-based architecture was used, and how was it adapted for learning group operations?', 'How does your method compare to existing approaches in terms of accuracy and efficiency?', 'What are the specific group operations learned by the model, and how are they evaluated?', 'Can the authors include more background and related work to contextualize their contributions?']","[""The paper's primary limitation is its incomplete nature, which prevents a thorough evaluation of its contributions and impact."", 'The limitations and potential negative societal impacts of the work are not addressed.']",False,1,1,1,2,4,['The idea of using transformer-based models to learn and represent group operations is novel and potentially impactful.'],"['The paper is incomplete, with placeholders in crucial sections such as the introduction, related work, background, method, experimental setup, results, and conclusions.', 'Lack of experimental results or methodologies makes it impossible to evaluate the technical soundness and quality of the work.', 'The absence of detailed explanations and content hinders the ability to assess the originality and significance of the paper.']",2,1,1,1,Reject
numbering_system_grokking,"The paper proposes a transformer-based approach to learn modular arithmetic and permutation groups, leveraging the architecture's capability to model complex relationships. The authors claim high accuracy and efficiency in computing these operations, suggesting applications in cryptography, coding theory, and computer networks.","['How does the proposed method differentiate from existing transformer-based approaches?', 'Can the authors provide more detailed experimental setups and comparisons with other machine learning methods?', 'Please clarify the ablation studies and explore the impact of different hyperparameters and configurations.', 'Can you provide more details on the encoding of input sequences and handling of permutation functions?', 'Why were no other machine learning methods included in the comparison?', 'How does the model generalize to unseen inputs, and what are its limitations?', 'Can you discuss any potential negative societal impacts or ethical concerns of this work?', 'Can the authors provide a more detailed theoretical grounding for the use of transformers in mathematical operations?', 'How does the proposed method compare with other machine learning approaches for mathematical operations?', 'Can the authors provide a more detailed description of the experimental setup and methodology to ensure reproducibility?', 'What are the potential limitations and ethical implications of the proposed approach?', 'Can the authors provide more detailed explanations of the model architecture and training process?', 'How does this approach compare to other neural network architectures applied to mathematical tasks?', 'Can the authors clarify the inconsistencies in the reported results and provide more comprehensive experimental analyses?', 'Can the authors provide more details on the datasets used for the experiments?', 'Why were other modern machine learning techniques not considered for comparison?', 'How can the approach be generalized to more complex mathematical operations?']","['The paper does not adequately discuss the limitations and potential negative societal impacts of the work. The choice of hyperparameters is noted as affecting fairness but is not explored in detail.', 'The transformer architecture may not be suitable for all types of mathematical operations.', 'The method may not generalize well to unseen inputs.']",False,2,2,2,3,4,"['Explores an interesting application of transformers in mathematical operations.', 'Potential for impact in fields such as cryptography and coding theory.']","['The novelty of the approach is questionable as it does not clearly differentiate from existing transformer-based methods.', 'The experimental setup lacks detail and robustness, with minimal comparison to other machine learning methods.', 'The ablation study is inadequate and does not explore critical aspects of the model.', 'The paper is poorly written, with repetitive content and lack of coherence in the narrative.', 'Figures and tables are poorly presented and described, reducing the clarity of the results.', 'Fails to adequately address the potential significance and real-world applications of the proposed method.', 'The theoretical foundation for using transformers in mathematical operations is not well established.', 'The results section is shallow and lacks a robust analysis of the findings.', 'No thorough theoretical justification for why transformers are suitable for these tasks.', 'Insufficient information for reproducibility, such as dataset generation and hyperparameters.', 'No discussion of potential ethical concerns or societal impacts.']",2,2,2,2,Reject
modular_to_nonmodular_grokking,"The paper investigates the transition from modular to non-modular arithmetic using transformer models. It introduces new datasets and models to learn and generalize this transition, focusing on mathematical operations like addition, subtraction, and division. The approach is validated through experiments that show the efficacy and efficiency of the proposed methods.","['Can the authors provide a detailed description of the transformer model used in their experiments?', 'How does this work compare to existing methods for transitioning between modular and non-modular arithmetic?', 'Can the authors provide more details on the dataset generation process?', 'What are the specific contributions of this work compared to previous approaches?', 'Can you provide a detailed related work section to contextualize your research?', 'Could you elaborate on the architecture of your transformer model and provide a diagram?', 'What specific mathematical operations were evaluated, and what were their respective performance metrics?', 'How exactly does your model achieve the transition from modular to non-modular arithmetic?', 'Can you compare your results with state-of-the-art methods?', 'Please explain the figures in more detail and provide proper captions.']","['The authors should address the lack of related work and provide a clearer description of their methodology. Additionally, more detailed experimental results and analysis are needed to support the claims made in the paper.', 'The paper does not adequately address the limitations of the proposed approach. The need for large datasets is mentioned as a limitation, but no solutions or alternatives are provided.', 'Potential negative societal impacts of the work are not considered.']",False,1,1,2,3,4,"['The problem addressed is important and has significant applications in fields such as cryptography, coding theory, and computer networks.', 'The use of transformer models for bridging the gap between modular and non-modular arithmetic is novel.', 'The paper introduces new datasets specifically designed for this transition, which is a valuable contribution.']","['The paper lacks novelty as it mainly applies existing techniques (transformer architecture) to a new problem without introducing significant new insights or methods.', 'The related work section is missing, making it difficult to understand how this work compares to existing literature.', 'The methodology is not clearly described, particularly the details of the transformer model and the dataset generation process.', 'Experimental results are presented without sufficient detail or analysis, making it hard to evaluate the significance of the findings.', 'Several sections, including the conclusion and figure captions, are incomplete or missing, indicating a lack of thoroughness.', 'The results section lacks a thorough comparison with state-of-the-art methods and does not provide enough insight into the performance of the model.', 'There are several typographical errors and missing captions in figures.']",2,1,2,2,Reject
cognitive_demand_generalization,"The paper explores the relationship between cognitive demand and generalization in mathematical operations using neural networks. It proposes a novel approach to improve generalization by considering cognitive demand in training. However, the paper is incomplete and lacks crucial details in the methodology, experimental setup, and results sections.","['Can the authors provide a detailed explanation of the proposed method and how it incorporates cognitive demand into the training process?', 'What specific experiments were conducted to validate the proposed approach? Please provide detailed results and analysis.', 'How does the proposed approach compare to existing methods in the literature? Can the authors provide a comparative analysis?', 'What neural network architectures and datasets were used in the experiments?', 'How was cognitive demand measured and incorporated into the training process?', 'Can you elaborate on the results and provide more quantitative metrics to support your claims?']","['The paper does not adequately address the limitations of the proposed approach or its potential negative societal impact.', 'The incomplete nature of the paper raises concerns about the thoroughness and reliability of the research.']",False,1,1,1,2,4,"['The paper addresses an interesting and practical problem in the field of AI and neural networks.', 'The idea of incorporating cognitive demand into the training process to improve generalization is novel and has potential.']","['Several sections of the paper, including the introduction, method, experimental setup, and results, are incomplete or lack detail.', 'The methodology is not clearly explained, making it difficult to understand the proposed approach and how it was implemented.', 'There is a lack of detailed experimental setup and results, which are crucial to validate the claims made in the paper.', 'The paper does not adequately cite related work and differentiate its contributions from previous research.', 'The clarity and organization of the paper are poor, making it difficult to follow the arguments and understand the contributions.']",2,1,1,2,Reject
math_operation_representation_comparison,The paper proposes a deep learning approach to algorithmic comprehension using a novel neural network architecture that leverages attention mechanisms and layer normalization. The goal is to improve comprehension and generalization capabilities on algorithmic tasks such as modular arithmetic and permutation-based problems.,"['Can the authors provide a detailed description of the proposed neural network architecture?', 'What are the specific algorithmic tasks used in the experiments?', 'How does the proposed model compare with existing approaches in terms of performance metrics?', 'Can the authors provide more details on the experimental setup and the datasets used?', 'Please provide the missing sections, including Related Work, Method, Experimental Setup, Results, and Conclusions.', 'Can you elaborate on the theoretical foundation and detailed methodology of your proposed architecture?', 'Where are the experimental results to substantiate your claims of improved comprehension and generalization?', 'What are the baselines used for comparison, and how is the performance measured?', 'What are the limitations of the proposed approach, and how do the authors address potential negative societal impacts?']","['The paper does not discuss the limitations of the proposed approach or its potential negative societal impact, which is crucial for a thorough evaluation.']",False,1,1,1,2,5,"['Addresses a fundamental and challenging problem in AI—algorithmic comprehension.', 'Proposes the use of attention mechanisms and layer normalization, which have shown success in other domains like NLP.']","['The paper is incomplete and lacks crucial sections such as the detailed methodology, experimental setup, and results.', 'No empirical validation or experimental data is provided to support the claims.', 'The contributions are stated in very general terms without detailed technical insights.', 'Related work and background are not adequately covered.', 'The paper lacks clarity and organization, making it difficult to follow and understand.']",2,1,1,2,Reject
operation_compositionality_grokking,"The paper discusses the concept of grokking, which refers to a model's ability to generalize beyond overfitting on small algorithmic datasets. The authors propose a novel approach to improve model generalization capabilities and claim state-of-the-art performance on multiple tasks. However, the paper lacks detailed methodology, experimental setup, and results, making it difficult to evaluate the validity of these claims.","['Can the authors provide the missing methodology section?', 'What is the experimental setup used to validate your approach?', 'Can you present the results of your experiments to substantiate your claims?', 'How does the proposed approach specifically improve generalization beyond overfitting?', 'What specific datasets and tasks were used to claim state-of-the-art performance?', 'How does the proposed method differ from existing approaches addressing model generalization from limited data?', 'What are the potential limitations and ethical concerns of this work?', 'Can you provide more details on the theoretical underpinnings of your approach?']","['The paper does not adequately address its own limitations due to its incomplete nature.', 'The major limitation is the incomplete nature of the paper, which makes it difficult to assess its scientific contributions.', 'The lack of empirical evidence makes it hard to validate the claims made in the paper.']",False,1,1,1,1,5,"['Addresses an important and interesting topic in machine learning: generalization beyond overfitting on small datasets.', 'The concept of grokking is intriguing and relevant to few-shot learning and generalization in neural networks.']","['The paper is incomplete, with missing sections on methodology, experimental setup, and results.', 'The claims of state-of-the-art performance are unsubstantiated due to the lack of empirical evidence.', 'The related work section is empty, and there are insufficient citations to place the work within the context of existing literature.', 'The abstract and introduction are vague and do not clearly explain the novel approach or its novelty compared to existing methods.', 'The paper does not address any limitations or potential negative societal impacts.']",2,1,1,2,Reject
identity_element_grokking,"The paper explores the impact of incorporating identity elements into the training data on the ability of neural networks to generalize compositionally, a phenomenon known as 'grokking.' The authors claim that this approach improves the learning and generalization capabilities of neural networks across various tasks.","['Can the authors provide a detailed explanation of the methodology, including how identity elements are incorporated into the training data?', 'What is the specific experimental setup used to evaluate the proposed approach?', 'Can the authors provide quantitative results and analysis to demonstrate the effectiveness of incorporating identity elements for grokking?', 'How does the proposed approach compare to existing methods in terms of performance and generalization capabilities?']","[""The paper does not discuss the limitations of the proposed approach or its potential negative societal impact. Addressing these aspects would improve the paper's comprehensiveness and transparency.""]",False,1,1,1,2,4,"['The idea of using identity elements to enhance the compositional generalization of neural networks is novel and could be impactful.', 'The topic of grokking is relevant and timely, given the interest in improving the robustness and generalizability of neural networks.']","['The paper is incomplete, with several sections missing, such as the method, experimental setup, results, and conclusions.', 'There is a lack of detailed explanation and clarity on how identity elements are incorporated into the training data and how they enhance grokking.', 'The paper does not provide any experimental evidence or results to support the claims made.', 'The related work section is missing, leading to inadequate contextualization of the proposed approach relative to prior work.', 'The references section is sparse, and key related works are not adequately cited.']",2,1,1,2,Reject
graph_structure_grokking,"The paper aims to explore the impact of graph structure on the grokking phenomenon in deep learning models. It suggests that input data connectivity affects a model's ability to learn and generalize, with implications for the design of neural networks. The study utilizes synthetic and real-world datasets to evaluate the performance of different neural network architectures.","['Can the authors provide a detailed explanation of the methodology used to analyze the impact of graph structure on grokking?', 'What specific datasets and neural network architectures were used in the experiments?', 'What are the key findings and insights from the study?', 'What specific graph structures were evaluated, and how were they implemented in the neural network models?', 'What metrics were used to evaluate the impact of graph structure on the grokking phenomenon?', 'Can you provide concrete results and data to support your claims?']","['The paper lacks completeness and thoroughness in its content, making it difficult to assess its contributions and significance.', ""The absence of detailed explanations and evaluations hinders the understanding of the work's novelty and impact."", 'The paper does not address the limitations and potential negative societal impacts of the work. The authors should discuss these aspects to provide a more comprehensive view of their research.']",False,1,1,1,2,4,"['The topic is intriguing and relevant to the field of deep learning, especially in understanding the grokking phenomenon.', 'The focus on graph structure and its impact on model performance is a novel and valuable direction of research.']","['The paper is incomplete, with several sections missing (introduction, related work, background, method, experimental setup, results, and conclusions).', 'The abstract lacks specific details about the methodology and findings, making it difficult to assess the contributions and significance of the work.', 'There is no detailed explanation or evaluation of the experiments conducted, if any.', ""The clarity and organization of the paper are poor, making it difficult to follow the authors' reasoning and conclusions."", 'The paper does not adequately situate itself within the existing literature, missing key references and failing to clearly articulate its contributions relative to prior work.']",2,1,1,2,Reject
distracting_patterns_grokking,"The paper proposes a Transformer-based model designed to handle mathematical operations like addition, subtraction, and division in the presence of distracting patterns. The authors argue that the model's self-attention mechanism helps it focus on relevant information and filter out noise, achieving high accuracy and robustness.","['Can the authors provide more details on the model architecture and experimental setup?', 'How does the proposed model compare to existing approaches in terms of performance and robustness?', 'Are there any ablation studies or additional metrics to support the findings?', 'What are the specific distracting patterns considered in the experiments?', 'Can the authors provide detailed experimental results, including ablation studies and comparisons with baselines?']",['The paper does not address potential limitations or negative societal impacts. More detail and discussion are needed.'],False,1,1,1,2,4,"['Addressing mathematical operations with distracting patterns is an interesting problem, especially for real-world applications where data can be noisy.', 'Using a Transformer-based model is a strong choice given its success in various domains, owing to its self-attention mechanism.']","['The paper lacks sufficient detail on the model architecture and experimental setup. Key sections like Method, Experimental Setup, and Results are placeholders.', 'The paper does not adequately differentiate its approach from existing methods. It appears to apply a standard Transformer model without significant modifications or innovations tailored to the specific problem.', 'The evaluation seems limited to a single metric (mean validation accuracy) and lacks comprehensive analysis, such as comparisons with baseline models or ablation studies.', 'The abstract and introduction are vague and do not provide enough context or motivation for the work.', 'The overall clarity and completeness of the paper are severely lacking.']",2,1,1,2,Reject
few_shot_grokking,"The paper investigates few-shot learning for mathematical operations using transformer-based architectures and attention mechanisms. The goal is to enable models to learn new operations from limited examples and outperform baseline models in low-data regimes. However, the paper lacks detailed content in crucial sections such as Introduction, Related Work, Background, Method, Experimental Setup, and Results, making it impossible to assess its contributions properly.","['Please provide detailed content for the introduction, related work, background, method, experimental setup, results, and conclusions sections.', 'How does the proposed method compare to existing approaches for few-shot learning in terms of performance and computational efficiency?', 'What specific transformer-based architectures and attention mechanisms are used, and how are they adapted for mathematical operations?', 'What datasets and baseline models were used in the experiments?', 'Can the authors share the experimental results and analysis?', 'What are the limitations and potential negative societal impacts of this work?']","['The paper needs to address the limitations and potential challenges of applying few-shot learning to mathematical operations. Additionally, discussing any potential negative societal impacts of this work would be beneficial.', 'The lack of content in critical sections makes it difficult to identify specific limitations of the proposed approach.']",False,1,1,1,2,5,"['The topic of few-shot learning for mathematical operations is relevant and addresses a practical problem in machine learning.', 'Leveraging transformer-based architectures and attention mechanisms is a promising and modern approach for few-shot learning tasks.']","['The paper lacks substantive content in critical sections such as the introduction, related work, background, method, experimental setup, results, and conclusions.', 'Without detailed descriptions and actual content, it is impossible to evaluate the originality, technical soundness, and significance of the proposed approach.', 'There are no theoretical or experimental results provided to support the claims made in the abstract.', 'The clarity of the paper is poor, with placeholders instead of actual content.', 'No discussion on limitations or potential negative societal impacts.']",2,1,1,2,Reject
operation_representation_networks,The paper aims to analyze the graph structure of learned representations of mathematical operations in neural networks. The goal is to understand how these networks represent and generalize mathematical concepts by examining graph structures using concepts from graph theory and network science.,"['Can the authors provide a detailed description of the methodology used to analyze the graph structures?', 'What are the specific mathematical operations considered in the experiments?', 'How is the effectiveness of the proposed approach evaluated?', 'Can the authors provide empirical results to support their claims?', 'What datasets were used for training and evaluating the neural networks?', 'What specific techniques are used for clustering, centrality, and community detection in the graph structure?', 'What evaluation metrics were used to assess the effectiveness of the proposed approach?']","['The paper does not address any potential limitations or negative societal impacts of the work.', 'The lack of detailed methodology and results makes it difficult to assess the limitations and potential negative societal impact of the work.']",False,1,1,1,2,5,"['The paper addresses an important aspect of neural networks related to representation and generalization of mathematical concepts.', 'The approach of using graph theory to analyze neural network representations is novel and potentially insightful.']","['The paper is incomplete, with missing sections for introduction, methodology, experimental setup, and results.', 'There is insufficient detail to evaluate the soundness and reproducibility of the proposed method.', 'The clarity is severely lacking due to missing content, making it difficult to understand the proposed approach and its effectiveness.', 'The paper does not provide any empirical results or discussions that validate the proposed method.', 'The related work section is very brief and does not adequately cover existing literature or how the proposed work differs.']",2,1,1,2,Reject
nested_operations,"The paper proposes a deep learning approach for performing mathematical operations with nested levels of abstraction using attention mechanisms and recursive neural networks. The goal is to generalize mathematical operations such as division, subtraction, addition, and permutation. However, the paper lacks critical details in methodology, related work, experimental setup, and results sections, which are essential for evaluating its contributions.","['Can the authors provide detailed information on the proposed methodology?', 'What are the specific contributions of this work compared to existing approaches?', 'Can the authors include a comprehensive related work section?', 'Where is the experimental setup and results to validate the claims?', 'How does the proposed approach compare to existing methods in terms of performance?', 'Can the authors elaborate on the implementation details of the proposed model?', 'Can you elaborate on the dataset creation and its specific properties?', 'What are the limitations of your approach?', 'Are there any potential negative societal impacts of this work?']","['The paper does not address its limitations or potential negative societal impacts, which is a significant oversight.']",False,1,1,1,2,4,"['Addresses an important and challenging problem in mathematical reasoning.', 'Proposes a novel combination of attention mechanisms and recursive neural networks.']","['The related work section is missing, which is essential for understanding the context and positioning of the current work.', 'The methodology section is absent, providing no information on how the proposed model operates.', 'The experimental setup and results sections are placeholders, providing no evidence to support the claims made.', 'The paper lacks clarity and detail, making it difficult to evaluate the technical soundness of the approach.', 'The contributions are not well-supported by theoretical analysis or experimental results.', 'The paper is not well-organized and contains placeholders, indicating it is not ready for publication.', 'No discussion on potential negative societal impacts or ethical considerations.']",2,1,1,2,Reject
isomorphic_grokking,"The paper proposes a transformer-based model for learning mathematical group operations from data. The aim is to enable efficient computation of group elements' combinations, leveraging the strengths of transformers in identifying complex patterns in data. The proposed approach targets applications such as modular arithmetic and permutation groups.","['Can the authors provide a detailed description of the transformer-based model architecture and the training process?', 'How does the proposed model compare with existing methods for computing group operations?', 'Can the authors include a comprehensive related work section to position their work within the existing literature?', 'Where are the experimental results that demonstrate the effectiveness of the proposed model?']",['The paper is currently too incomplete to provide a thorough assessment of its limitations and potential negative societal impact. The authors should address the missing sections and provide detailed discussions on these aspects.'],False,1,1,1,2,4,"['The topic of learning mathematical group operations is interesting and has applications in various fields including computer science and cryptography.', 'Using a transformer-based model for this purpose is a novel idea that leverages the strengths of transformers in identifying complex patterns in data.']","['The paper is incomplete, with several sections (related work, experimental setup, results, conclusions) missing or left as placeholders.', 'There is a lack of technical detail in the methods section, making it difficult to understand how the proposed model operates.', 'No experimental results are provided, which makes it impossible to evaluate the performance and claims made by the authors.', 'The paper lacks a detailed discussion on the significance and novelty of the proposed approach in the context of existing work.', 'The clarity and organization of the paper need significant improvement.']",2,1,2,2,Reject
model_capacity_grokking,"The paper proposes the application of transformer models to algorithmic tasks, focusing on mathematical reasoning such as modular arithmetic and permutation tasks. It aims to develop a transformer-based approach to improve AI models' performance in mathematical reasoning.","['Can the authors provide detailed descriptions of the proposed methodology and experimental setup?', 'Are there any preliminary results or experiments that can validate the effectiveness of the proposed approach?', 'How does the proposed mathematical reasoning module integrate with the transformer model, and what are its unique contributions?', 'What specific mathematical tasks were used for evaluation, and what metrics were employed to assess performance?', 'How does the proposed approach compare with existing methods in the literature?']","['The paper fails to provide sufficient details on the proposed method and its evaluation, making it difficult to assess its limitations or potential negative impacts.', 'The absence of empirical data makes it difficult to assess the effectiveness and significance of the proposed approach.']",False,1,1,1,2,4,"['Addresses a challenging and relevant problem in the field of AI, which is mathematical reasoning.', 'Leveraging transformers for sequential data and complex pattern learning is a promising direction.']","['The paper is incomplete, with several sections marked as placeholders, such as the methodology, experimental setup, and results.', 'The novelty and justification of the proposed approach are not adequately explained or supported.', 'Insufficient details on experiments and results make it impossible to evaluate the effectiveness of the proposed method.', 'The clarity and structure of the paper are significantly compromised due to the incomplete sections.', 'The related work section is missing, which is crucial for understanding how this work fits into the current state of research.']",2,1,1,2,Reject
rotational_symmetry_grokking,"The paper aims to explore the impact of rotational symmetry on the grokking phenomenon in neural networks. It suggests that rotational symmetry can significantly affect the model's ability to generalize and learn underlying patterns. However, the paper is incomplete, lacking critical sections such as related work, background, methodology, experimental setup, results, and conclusions.","['Can the authors provide a complete draft with detailed methodology, experimental setup, and results?', 'What specific tasks or datasets were used to evaluate the impact of rotational symmetry on grokking?', 'How was rotational symmetry quantified in the datasets?', 'What are the underlying mechanisms that drive the effect of rotational symmetry on grokking?', 'Can you provide more details on the methodology used in the experiments?', 'What metrics are used to evaluate the generalization performance of the models?', 'How does your work compare to existing literature on grokking and symmetry in neural networks?', 'Can the authors provide a more thorough discussion of related work and how their approach compares to existing methods?']","['The paper does not address any limitations or potential negative societal impacts, likely because it is incomplete.', 'The paper does not discuss potential limitations or broader implications of the work.']",False,1,1,1,1,5,"['The topic is interesting and has potential implications for understanding neural network generalization.', ""The idea of examining rotational symmetry's effect on neural network performance is novel and potentially impactful.""]","['The paper is incomplete, with all major sections missing content.', 'No experimental setup or results are provided to validate the claims.', 'The methodology is not described, making it impossible to assess the technical merits of the work.', 'The abstract and introduction are too vague and do not provide a clear outline of the work.', 'The paper lacks clarity and organization due to the missing sections.', 'There is no discussion on potential limitations or ethical concerns.']",1,1,1,2,Reject
fractal_operations_grokking,"The paper explores the application of deep learning techniques to algorithmic problems, focusing on the generalization of neural networks. The authors propose a framework for learning algorithmic concepts and claim to demonstrate the effectiveness of this approach through experiments on arithmetic operations and permutation tasks.","['Can the authors provide detailed descriptions of the methodologies used in their experiments?', 'What are the specific architectures and training methods evaluated in the study?', 'How do the results compare to existing approaches for similar tasks?', 'Can the authors provide empirical results and analysis to support their claims?', 'How does this work compare to existing literature in the field?', 'What specific algorithmic tasks were considered, and how were they evaluated?']","['The paper does not discuss the limitations and potential negative societal impact of the work. Given the gaps in the methodology and results sections, it is challenging to identify specific limitations.', 'The clarity of the paper is a major concern due to missing sections. The lack of detailed methodology and experimental results makes it difficult to assess the validity of the claims.']",False,1,1,1,1,5,"['The paper addresses an interesting and novel application of deep learning to algorithmic problems, which is relatively unexplored in the literature.', 'The topic of applying deep learning to algorithmic problems and focusing on generalization is interesting and relevant.']","['The paper lacks significant detail in crucial sections such as the methodology and experimental setup. These sections are placeholders and do not provide sufficient information to understand or reproduce the study.', 'The related work and background sections are missing, making it difficult to situate this work within the existing body of literature and evaluate its originality and contributions.', 'The results section is incomplete, with no actual results provided to substantiate the claims made in the paper.', ""The overall clarity and organization of the paper are poor, making it difficult to follow the authors' reasoning and contributions."", 'The conclusions and future work sections are placeholders and do not provide any meaningful insights.', 'Key terms and concepts are not well defined, and the paper lacks a coherent flow.']",1,1,1,1,Reject
abstractness_grokking,"The paper proposes a transformer-based model to learn and generalize group operations such as modular addition, multiplication, and permutation groups. The authors claim that the model leverages self-attention and layer normalization to capture the structures of these operations.","['Can the authors provide detailed experimental results to validate their claims?', 'What is the theoretical basis for choosing transformers for learning group operations?', 'How does your approach differ from existing works that apply transformers to mathematical operations?', 'Can you provide specific examples or case studies where your model demonstrates high accuracy?', 'What specific datasets and evaluation metrics were used in the experiments?', 'How does the proposed model compare to existing approaches quantitatively and qualitatively?', 'Can the authors provide detailed descriptions of the transformer architecture used, including any modifications made for this specific application?', 'How were the datasets for modular addition, multiplication, and permutation groups constructed?', ""What specific metrics were used to evaluate the model's performance?"", 'Can the authors provide a comparison with baseline methods?']","['The primary limitation is the incomplete nature of the paper, which prevents a thorough evaluation.', 'The paper does not discuss potential limitations or challenges in applying transformers to abstract algebra. Discussing such limitations would provide a more balanced perspective.', 'Potential negative societal impacts are not addressed, though they may be minimal for this specific domain.']",False,1,1,1,2,4,"['The application of transformers to learning group operations is novel.', 'The proposed method has potential applications in various fields, including computer science, physics, and engineering.', 'The use of self-attention and layer normalization to capture group structures is creative.']","['The paper is incomplete, missing critical sections such as related work, background, methods, experimental setup, and results.', ""Without experimental details and results, it is impossible to assess the model's effectiveness and generalizability."", 'The theoretical justification for using transformers for group operations is insufficient.', 'The paper lacks adequate citations of related work.', 'The clarity and organization of the paper are severely impacted by the missing sections.']",2,1,2,2,Reject
operation_complexity_generalization,"The paper proposes a transformer-based architecture for solving mathematical operations such as modular arithmetic and permutation. The model leverages the self-attention mechanism of transformers to capture underlying patterns and relationships within these operations, enabling it to generalize and solve unseen problems. The authors present experimental results demonstrating high accuracy on test sets, including an accuracy of 1.0 on the division operation.","['Can the authors provide a more detailed justification for the choice of hyperparameters and architectural details?', 'How does the proposed approach compare to existing methods in terms of performance and generalization capabilities?', ""Can the authors provide additional experiments to demonstrate the model's effectiveness on a broader range of mathematical operations?"", 'How does the proposed approach differ from existing works like Power et al. (2022) and Lample & Charton (2019)?', 'Can the authors provide more detailed explanations of the experimental setup and the specific configurations used?', 'Can the authors provide more details about the specific architecture and training procedure used in the transformer model?', 'What are the specific contributions of this work compared to prior works like Power et al. (2022)?']","['The paper does not adequately address the limitations of the proposed approach, such as the need for large amounts of training data and potential generalization issues.', 'The potential negative societal impacts of the work are not discussed.']",False,2,2,2,3,4,"['The application of transformers to mathematical problem-solving is an interesting direction.', 'The paper demonstrates high accuracy on test sets, indicating the potential effectiveness of the approach.']","['The novelty of the approach is questionable, as it primarily leverages existing transformer architectures without significant modifications or innovations.', 'The methodology is not well-justified, and the rationale for choosing specific hyperparameters and architectural details is not provided.', 'The experimental setup lacks comprehensiveness, with limited comparisons to existing methods and insufficient exploration of different mathematical operations.', 'The paper lacks clarity and organization in several sections, making it difficult to follow the methodology and results.', ""The discussion of results is superficial, and the paper does not provide insights into the model's generalization capabilities or limitations."", 'The related work section is inadequate, lacking a comprehensive review of existing methods.', 'The paper does not adequately address potential negative societal impacts or provide an ethical review.']",2,2,2,2,Reject
abstract_algebra_grokking,"The paper proposes a transformer-based approach for learning basic mathematical operations such as addition, subtraction, multiplication, and division from examples. The authors claim that their novel architecture, which combines transformer layers and embedding schemes, achieves high accuracy and generalizes well to unseen inputs. They conduct extensive experiments to demonstrate the effectiveness of their approach.","['Can the authors provide more details on the transformer architecture used in their experiments?', 'How does the proposed method compare with existing methods for learning mathematical operations?', 'What datasets were used in the experiments, and what are the evaluation metrics?', 'Can the authors provide a more in-depth analysis of the results, including error analysis and comparison with baselines?', 'What is the added value of using transformers for mathematical operations compared to traditional deterministic methods?', 'How does the proposed method handle edge cases and potential numerical errors in mathematical operations?', 'What are the potential limitations and negative societal impacts of this approach?']",['The paper does not discuss the limitations or potential negative societal impacts of their work. It is important for the authors to address these aspects to provide a balanced view of their contributions.'],False,1,1,1,2,4,"['The idea of using transformer networks for learning mathematical operations is novel and interesting.', 'The paper addresses a fundamental problem that is applicable across various fields such as science, engineering, and finance.']","['The paper lacks detailed descriptions of the method and experimental setup sections, making it difficult to understand the exact architecture and training procedures.', 'There are no comparisons with existing methods or baselines, which makes it hard to assess the novelty and effectiveness of the proposed approach.', 'The results section is very brief and lacks in-depth analysis. The paper does not provide sufficient information on the datasets used, evaluation metrics, or error analysis.', 'The paper does not discuss the limitations or potential negative societal impacts of their work.', 'The related work section is incomplete and does not provide a comprehensive overview of existing methods in this area.']",2,1,1,2,Reject