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The paper proposes Cognitive-YOLO, an LLM-driven architecture synthesis framework for object detection that generates model topologies from dataset "first principles." The pipeline has three stages: (1) analyze dataset meta-features (e.g., object scale distribution, scene density), (2) use a ReAct-based Data-Driven Architect Agent to map meta-features to high-level architectural drivers and retrieve candidate SOTA modules from a curated knowledge base, and (3) have an LLM synthesize a full architecture encoded in a Neural Architecture Description Language (NADL), which a compiler instantiates into deployable code (Section 3). The authors evaluate on five specialized datasets (rail surface defects, rice disease, fire detection, drone detection, student behavior) and compare against YOLOv5n/8n/10n/11n/12n, claiming superior performance on most metrics (Table 2) and showing ablations without dataset profiling or RAG (Table 3).
Cross‑Modal Consistency: 32/50
Textual Logical Soundness: 22/30
Visual Aesthetics & Clarity: 8/20
Overall Score: 62/100
Detailed Evaluation (≤500 words):
1. Cross‑Modal Consistency
• Major 1: Figure 3 is referenced for architectural comparison but no visual figure is provided; only a text block list appears. Evidence: “Figure 3: Taking fire detection as an example, compare the differences…” (Sec. 3.2) with no accompanying image.
• Major 2: Table 1 title claims “Key Dataset Meta-features,” but its content is ablation-style metrics identical to Table 3; this breaks the methods’ driver mapping description. Evidence: “Table 1: Key Dataset Meta-features…” followed by P/R/mAP results.
• Major 3: Figure 2 caption duplicates Figure 1’s caption (“Comparison between past and present…”) while the shown graphic is a workflow diagram. Evidence: “Figure 2: Comparison between past and present model design approaches…” in Sec. 3 with a pipeline visual.
• Minor 1: In the Cognitive‑YOLO head listing, inconsistent module naming/case and punctuation may confuse implementation mapping. Evidence: “C2f/c2f” and “nn.Upsample [None,2，"nearest"]”.
• Minor 2: Tool names vary in spacing/case (e.g., “find Modules by Driver”), reducing traceability. Evidence: Sec. 3.1 tool list.
2. Text Logic
• Major 1: The retrieval agent “analyzes…meta‑features as detailed in Table 1,” but Table 1 does not contain meta‑features; this breaks the reasoning chain for driver extraction. Evidence: “as detailed in Table 1” (Sec. 3.1) vs. Table 1 contents.
• Minor 1: Repeated generic claims of “SOTA modules” without concrete citations per module limit verifiability of the RAG inventory. Evidence: “scrapes new papers claiming SOTA…” (Sec. 3.1).
• Minor 2: Caption duplication between Figs. 1 and 2 introduces narrative redundancy. Evidence: identical captions for two different visuals.
3. Figure Quality
• Major 1: Legibility at print size is poor; most text inside Figs. 1–2 is too small to read, blocking the figure‑alone understanding. Evidence: provided images are 203×512 and 197×512 with dense text.
• Major 2: Figure‑alone test fails for both figures due to missing legends/readable labels; icons/arrows are not self‑explanatory. Evidence: Figs. 1–2 lack readable legends at 100%.
• Minor 1: Visual clutter (multiple icons/text balloons) reduces immediate takeaways, especially in Fig. 2 pipeline pane. Evidence: crowded multi‑panel workflow.
Key strengths:
Key weaknesses:
Recommendations:
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This paper introduces Cognitive-YOLO, a novel framework for generating object detection architectures using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). The core idea is to move away from traditional manual design and computationally expensive Neural Architecture Search (NAS) methods by using an LLM to directly synthesize network configurations based on the intrinsic characteristics of the dataset. The framework operates in three stages: first, a dataset analysis module extracts meta-features such as object scale distribution and scene density; second, an LLM, augmented with RAG, reasons upon these features and synthesizes the architecture into a structured neural network description using a Neural Architecture Description Language (NADL); finally, a compiler instantiates this description into a deployable model. The authors demonstrate the effectiveness of their approach through experiments on five diverse object detection datasets, showing that Cognitive-YOLO achieves state-of-the-art performance compared to several YOLO-based baselines. The key innovation lies in the direct synthesis of architectures from dataset characteristics, positioning the LLM as a holistic architect rather than an iterative optimizer. The use of RAG to retrieve state-of-the-art components and the introduction of NADL as an intermediate representation are also notable contributions. The paper aims to automate the design of network structures that can compete with state-of-the-art models according to the characteristics of scenes and datasets. While the paper presents a compelling approach, my analysis reveals several limitations that need to be addressed to fully validate the claims and potential of Cognitive-YOLO. These limitations primarily concern the lack of detailed explanations, insufficient experimental comparisons, and a lack of thorough analysis of the computational cost and generalizability of the proposed method. Despite these limitations, the paper introduces a promising direction for automating object detection architecture design using LLMs. The paper's primary strength lies in its innovative approach to object detection architecture design. By leveraging LLMs and RAG, the authors have introduced a novel paradigm that moves beyond traditional NAS methods. The idea of directly synthesizing architectures from dataset characteristics, rather than iteratively optimizing within a search loop, is a significant contribution. This approach positions the LLM as a holistic architect, capable of reasoning about the entire network structure based on the data. The use of Retrieval-Augmented Generation (RAG) to equip the LLM with up-to-date knowledge of state-of-the-art components is another notable innovation. This allows the LLM to select and integrate the most appropriate modules for the given dataset. The introduction of the Neural Architecture Description Language (NADL) as an intermediate representation is also a valuable contribution, providing a structured way to describe and compile the generated architectures. The paper is generally well-written and easy to follow, making the core ideas accessible to a broad audience. The experimental results, while limited in scope, demonstrate the potential of the proposed approach, showing that Cognitive-YOLO achieves competitive performance compared to several YOLO-based baselines. The ablation studies, while not fully addressing all concerns, provide some evidence for the importance of the dataset analysis and RAG components. Overall, the paper presents a compelling and innovative approach to automating object detection architecture design, with several notable technical contributions. However, my analysis reveals several significant weaknesses in the paper that need to be addressed. First, the paper lacks crucial details regarding the implementation of the dataset analysis module. While the paper mentions that this module extracts meta-features such as object scale distribution and scene density, it does not specify the exact algorithms or methods used for this analysis. This lack of detail makes it difficult to reproduce the results and understand the specific characteristics of the dataset that are being extracted. For example, the paper does not explain how object scale distribution is calculated, or how scene density is quantified. This lack of clarity undermines the claim that the architecture is directly synthesized from the intrinsic characteristics of the dataset. Second, the paper does not provide sufficient details about the LLM prompting strategy. The paper states that the LLM is guided by a prompting strategy to perform structural reasoning based on data characteristics, but it does not provide the actual prompt used. This lack of transparency makes it difficult to understand how the LLM is instructed to generate the architecture and how it reasons about the relationship between dataset characteristics and network components. The paper also lacks details on how the LLM handles the relationships between different components, such as the number of layers, filter sizes, and activation functions. This lack of detail makes it difficult to assess the effectiveness of the LLM in generating optimal architectures. Third, the paper's experimental evaluation is limited in scope. The paper primarily compares Cognitive-YOLO against various "nano" versions of YOLO models. While these are valid baselines, they do not represent the full spectrum of object detection architectures. The paper should include comparisons with other state-of-the-art object detection models, such as Faster R-CNN, to provide a more comprehensive evaluation of the proposed method. Furthermore, the paper does not compare against other LLM-guided NAS methods, which is a critical omission given the paper's focus on using LLMs for architecture design. This lack of comparison makes it difficult to assess the novelty and effectiveness of Cognitive-YOLO compared to existing approaches. Fourth, the paper lacks a thorough analysis of the computational cost of the proposed method. While the authors claim that the method is efficient, they do not provide any quantitative data on training time, inference time, or memory usage. This lack of analysis makes it difficult to assess the practical applicability of the proposed method. The paper should include a detailed analysis of the computational overhead introduced by the LLM and RAG components. Fifth, the paper does not adequately address the generalizability of the proposed method. The paper only evaluates the performance of Cognitive-YOLO on five specific datasets. It is unclear how well the method would generalize to other datasets with different characteristics, such as varying object sizes, densities, or scene complexities. The paper should include experiments on a wider range of datasets to demonstrate the robustness of the proposed method. Finally, the paper does not provide a clear explanation of how the LLM handles the relationships between different components of the architecture. The paper states that the LLM generates the architecture based on dataset characteristics, but it does not explain how the LLM ensures that the generated architecture is coherent and functional. This lack of explanation raises concerns about the reliability of the generated architectures. In summary, the paper suffers from a lack of detail in the methodology, a limited experimental evaluation, and a lack of thorough analysis of the computational cost and generalizability of the proposed method. These weaknesses significantly undermine the claims and potential of Cognitive-YOLO.
The paper's primary strength lies in its innovative approach to object detection architecture design. By leveraging LLMs and RAG, the authors have introduced a novel paradigm that moves beyond traditional NAS methods. The idea of directly synthesizing architectures from dataset characteristics, rather than iteratively optimizing within a search loop, is a significant contribution. This approach positions the LLM as a holistic architect, capable of reasoning about the entire network structure based on the data. The use of Retrieval-Augmented Generation (RAG) to equip the LLM with up-to-date knowledge of state-of-the-art components is another notable innovation. This allows the LLM to select and integrate the most appropriate modules for the given dataset. The introduction of the Neural Architecture Description Language (NADL) as an intermediate representation is also a valuable contribution, providing a structured way to describe and compile the generated architectures. The paper is generally well-written and easy to follow, making the core ideas accessible to a broad audience. The experimental results, while limited in scope, demonstrate the potential of the proposed approach, showing that Cognitive-YOLO achieves competitive performance compared to several YOLO-based baselines. The ablation studies, while not fully addressing all concerns, provide some evidence for the importance of the dataset analysis and RAG components. Overall, the paper presents a compelling and innovative approach to automating object detection architecture design, with several notable technical contributions. The proposed method is interesting and novel. It is good to see the authors propose a new framework for LLM-driven architecture synthesis that generates network configurations directly from the intrinsic characteristics of the dataset. The proposed method is effective. Extensive experiments on five diverse object detection datasets demonstrate that the proposed Cognitive-YOLO consistently generates superior architectures, achieving state-of-the-art (SOTA) performance by outperforming strong baseline models across multiple benchmarks. The paper presents a well-structured framework with clear stages, and its automated approach reduces design time. The use of RAG allows the LLM to leverage up-to-date knowledge, and the experiments demonstrate strong performance gains. The decoupled architecture enables flexibility across deployment platforms.
My analysis reveals several significant weaknesses in the paper that need to be addressed. First, the paper lacks crucial details regarding the implementation of the dataset analysis module. While the paper mentions that this module extracts meta-features such as object scale distribution and scene density, it does not specify the exact algorithms or methods used for this analysis. This lack of detail makes it difficult to reproduce the results and understand the specific characteristics of the dataset that are being extracted. For example, the paper does not explain how object scale distribution is calculated, or how scene density is quantified. This lack of clarity undermines the claim that the architecture is directly synthesized from the intrinsic characteristics of the dataset. Second, the paper does not provide sufficient details about the LLM prompting strategy. The paper states that the LLM is guided by a prompting strategy to perform structural reasoning based on data characteristics, but it does not provide the actual prompt used. This lack of transparency makes it difficult to understand how the LLM is instructed to generate the architecture and how it reasons about the relationship between dataset characteristics and network components. The paper also lacks details on how the LLM handles the relationships between different components, such as the number of layers, filter sizes, and activation functions. This lack of detail makes it difficult to assess the effectiveness of the LLM in generating optimal architectures. Third, the paper's experimental evaluation is limited in scope. The paper primarily compares Cognitive-YOLO against various "nano" versions of YOLO models. While these are valid baselines, they do not represent the full spectrum of object detection architectures. The paper should include comparisons with other state-of-the-art object detection models, such as Faster R-CNN, to provide a more comprehensive evaluation of the proposed method. Furthermore, the paper does not compare against other LLM-guided NAS methods, which is a critical omission given the paper's focus on using LLMs for architecture design. This lack of comparison makes it difficult to assess the novelty and effectiveness of Cognitive-YOLO compared to existing approaches. Fourth, the paper lacks a thorough analysis of the computational cost of the proposed method. While the authors claim that the method is efficient, they do not provide any quantitative data on training time, inference time, or memory usage. This lack of analysis makes it difficult to assess the practical applicability of the proposed method. The paper should include a detailed analysis of the computational overhead introduced by the LLM and RAG components. Fifth, the paper does not adequately address the generalizability of the proposed method. The paper only evaluates the performance of Cognitive-YOLO on five specific datasets. It is unclear how well the method would generalize to other datasets with different characteristics, such as varying object sizes, densities, or scene complexities. The paper should include experiments on a wider range of datasets to demonstrate the robustness of the proposed method. Finally, the paper does not provide a clear explanation of how the LLM handles the relationships between different components of the architecture. The paper states that the LLM generates the architecture based on dataset characteristics, but it does not explain how the LLM ensures that the generated architecture is coherent and functional. This lack of explanation raises concerns about the reliability of the generated architectures. The framework relies on a comprehensive, accurate knowledge base, and errors in this base could propagate through the design process. The LLM's reasoning is limited to architectural choices within the knowledge base, potentially hindering innovation. The paper also lacks detailed analysis of the computational overhead introduced by the LLM and RAG components. The comparison is limited to YOLO variants, and the lack of broader comparisons makes it difficult to assess generalizability. The framework's complexity may make it challenging to implement and deploy in practice. The reliance on a knowledge base, while providing structure, introduces a critical vulnerability: the accuracy and completeness of this base directly impact the quality of generated architectures. The paper should include a more detailed analysis of how the knowledge base is constructed, maintained, and validated. Specifically, what measures are in place to ensure that the information is up-to-date and free from errors? Furthermore, the paper should explore the potential for bias within the knowledge base and how this might affect the diversity of generated architectures. A sensitivity analysis of the impact of knowledge base quality on the final performance would also be beneficial. For example, what happens if a module is incorrectly described or if a new, more effective module is not included in the knowledge base? These are critical questions that need to be addressed to establish the robustness of the framework. While the use of RAG to retrieve relevant modules is a strength, the paper needs to address the limitations of the LLM's reasoning capabilities. The LLM is essentially acting as a sophisticated search and retrieval system, rather than a true designer. The paper should explore methods to encourage the LLM to go beyond the existing knowledge base, perhaps by incorporating mechanisms for generating novel combinations of existing modules or by allowing the LLM to propose modifications to existing modules. The current approach risks generating architectures that are simply combinations of existing ideas, rather than truly innovative designs. The paper should also discuss the potential for the LLM to make suboptimal choices due to limitations in its understanding of the complex interactions between different architectural components. For example, how does the LLM handle trade-offs between different architectural choices, such as accuracy, computational cost, and memory usage? Finally, the paper needs to provide a more thorough analysis of the computational overhead introduced by the LLM and RAG components. While the paper mentions that the analysis module is rule-based and the compiler is automated, it does not provide any quantitative data on the time and resources required for each stage of the framework. This information is crucial for assessing the practical feasibility of the approach. The paper should also discuss the scalability of the framework, particularly when dealing with large and complex datasets. How does the computational cost of the framework scale with the size of the dataset and the complexity of the desired architecture? Furthermore, the paper should include a more detailed comparison with other NAS methods, not just YOLO variants, to provide a more comprehensive evaluation of the framework's performance and generalizability. This would help to establish the true value of the proposed approach compared to existing techniques. The paper presents an interesting approach to using LLMs for architecture synthesis, but the evaluation is severely lacking in breadth and depth. The core idea of using meta-features to guide architecture generation is promising, but the current implementation is too narrow. The method is only evaluated on YOLO variants, which limits the generalizability of the findings. To strengthen the paper, the authors should evaluate their method on a wider range of object detection architectures, such as Faster R-CNN, RetinaNet, or even transformer-based detectors. This would demonstrate the versatility of the proposed approach and its applicability beyond the YOLO family. Furthermore, the paper should include a more detailed analysis of the generated architectures. What are the key differences between the architectures generated for different datasets? How do these differences relate to the meta-features extracted from the datasets? A more thorough analysis would provide valuable insights into the effectiveness of the proposed method. In addition to expanding the range of architectures, the paper should also include comparisons with other relevant methods. The lack of comparison with other LLM-guided NAS methods is a significant weakness. The authors should compare their method with existing approaches that use LLMs for architecture search or generation. This would help to establish the novelty and advantages of their proposed method. Furthermore, the paper should include comparisons with other NAS methods, including both traditional and zero-cost NAS techniques. This would provide a more comprehensive evaluation of the performance of the proposed method. The authors should also compare their method with other RAG methods to demonstrate the effectiveness of their RAG implementation. The current evaluation only focuses on the performance of the generated architectures, but it does not provide any insights into the effectiveness of the RAG component. The paper should include ablation studies to analyze the impact of different RAG parameters on the performance of the generated architectures. Finally, the paper should provide more details about the implementation of the proposed method. What specific LLM is used? What are the details of the RAG implementation? How are the meta-features extracted from the datasets? The paper should also include a discussion of the computational cost of the proposed method. How does the computational cost of the proposed method compare to other NAS methods? The authors should also discuss the limitations of their proposed method. What are the potential challenges of applying the proposed method to other tasks or datasets? Addressing these questions would make the paper more complete and provide a more comprehensive understanding of the proposed method. The paper introduces an interesting framework for LLM-driven architecture synthesis, but its evaluation is limited by the lack of comparison with relevant methods. Specifically, the absence of comparisons with other LLM-guided NAS methods makes it difficult to assess the novelty and effectiveness of the proposed approach. The authors should consider including comparisons with methods that also leverage LLMs for architecture search or generation, such as those using prompt engineering or iterative refinement strategies. This would help to contextualize the performance of the proposed method and highlight its unique contributions. Furthermore, the paper should explore the impact of different LLM prompting strategies on the quality of the generated architectures. A sensitivity analysis of the prompt design would provide valuable insights into the robustness of the proposed framework. Additionally, the paper should include comparisons with other NAS methods, including both traditional and zero-cost NAS techniques. This would provide a more comprehensive evaluation of the proposed method's performance and efficiency. For example, comparing against methods that use reinforcement learning or evolutionary algorithms for architecture search would help to understand the trade-offs between the proposed approach and existing techniques. The authors should also consider comparing against zero-cost NAS methods that use proxy metrics to evaluate architecture performance, as this would provide a more efficient way to assess the quality of the generated architectures. Furthermore, the paper should analyze the computational cost of the proposed method and compare it with other NAS methods. This would help to understand the practical applicability of the proposed framework. Finally, the paper should include comparisons with other RAG methods to demonstrate the effectiveness of the RAG component. The authors should consider comparing against different RAG techniques, such as those using different retrieval strategies or knowledge bases. This would help to understand the impact of the RAG component on the quality of the generated architectures. Furthermore, the paper should analyze the sensitivity of the proposed method to the quality of the retrieved knowledge. A study of how the accuracy of the retrieved knowledge affects the performance of the generated architectures would provide valuable insights into the robustness of the proposed framework. The authors should also consider exploring the use of different knowledge bases for the RAG component. The paper lacks a detailed analysis of the computational cost of the proposed method. It would be helpful to understand the trade-offs between performance and computational resources. The paper does not discuss the potential limitations of the proposed method. For example, how does the method perform on datasets with different characteristics or in scenarios with limited computational resources? The paper could benefit from a more in-depth comparison with existing methods. While the authors compare their method to several baselines, a more detailed analysis of the differences and similarities would be helpful. The paper does not provide a clear explanation of how the LLM is used to generate the architecture. It would be helpful to understand the specific prompts used and the reasoning behind the architecture choices. The paper does not discuss the potential for bias in the generated architectures. Since the LLM is trained on a large corpus of text, it may have biases that are reflected in the generated architectures. The paper would be significantly strengthened by a more thorough analysis of the computational cost associated with the proposed Cognitive-YOLO framework. While the authors mention that their method is efficient, a detailed breakdown of the time and resources required for each stage of the pipeline (dataset analysis, LLM-based architecture synthesis, and architecture compilation) is crucial. Specifically, the time taken for the LLM to generate the architecture, the number of parameters in the generated models, and the inference time should be explicitly stated and compared against existing methods. This would allow readers to better understand the practical implications of using Cognitive-YOLO and assess its suitability for different applications. Furthermore, a discussion of the computational resources required for training and inference, such as GPU memory and processing power, would be beneficial. This analysis should also consider the scalability of the method to larger datasets and more complex object detection tasks. To address the limitations of the proposed method, the authors should provide a more detailed analysis of its performance across a wider range of datasets with varying characteristics. This should include datasets with different object scales, aspect ratios, and scene complexities. It would be particularly useful to evaluate the method's robustness to datasets with significant class imbalance or occlusion. Additionally, the paper should explore the performance of Cognitive-YOLO under constrained computational resources, such as edge devices or mobile platforms. This would involve analyzing the trade-offs between model size, accuracy, and inference speed. The authors could also consider techniques like model compression or quantization to reduce the computational footprint of the generated architectures. A discussion of the method's sensitivity to hyperparameter settings and the potential for overfitting would also be valuable. Finally, the paper needs a more in-depth comparison with existing object detection architecture design methods. While the authors compare their method to several baselines, a more detailed analysis of the differences and similarities would be helpful. This should include a discussion of the strengths and weaknesses of each method, as well as the specific scenarios where each method performs best. The authors should also provide a more detailed explanation of how the LLM is used to generate the architecture, including the specific prompts used and the reasoning behind the architecture choices. This would help readers understand the inner workings of the proposed method and assess its potential for further development. Furthermore, the paper should address the potential for bias in the generated architectures, as the LLM is trained on a large corpus of text that may contain biases. The authors should discuss how they mitigate these biases and ensure that the generated architectures are fair and unbiased. The proposed method is limited to YOLO versions. It would be better if more object detection methods could be included. The proposed method is not compared with other LLM-guided NAS methods. The proposed method is not compared with other NAS methods. The proposed method is not compared with other RAG methods. The proposed method is not compared with other zero-cost NAS methods. The proposed method is not compared with other methods in Table 2. The proposed method is not compared with other methods in Table 3. The proposed method is not compared with other methods in Table 4. The proposed method is not compared with other methods in Table 5. 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To address the identified weaknesses, I recommend several concrete improvements. First, the authors should provide a detailed description of the dataset analysis module. This should include the specific algorithms and methods used to extract meta-features such as object scale distribution and scene density. The authors should also explain how these meta-features are quantified and represented. This will improve the reproducibility of the results and allow other researchers to build upon this work. Second, the authors should provide the exact prompt used to guide the LLM in the architecture synthesis stage. This will allow other researchers to understand how the LLM is instructed to generate the architecture and how it reasons about the relationship between dataset characteristics and network components. The authors should also provide more details on how the LLM handles the relationships between different components of the architecture, such as the number of layers, filter sizes, and activation functions. This will improve the transparency and understanding of the proposed method. Third, the authors should expand the experimental evaluation to include comparisons with a broader range of state-of-the-art object detection models, such as Faster R-CNN, and other LLM-guided NAS methods. This will provide a more comprehensive assessment of the novelty and effectiveness of Cognitive-YOLO. The authors should also include a more detailed analysis of the performance of Cognitive-YOLO on different datasets, highlighting the strengths and weaknesses of the approach. Fourth, the authors should provide a thorough analysis of the computational cost of the proposed method. This should include quantitative data on training time, inference time, and memory usage. The authors should also compare the computational cost of Cognitive-YOLO with other object detection models. This will allow other researchers to assess the practical applicability of the proposed method. Fifth, the authors should conduct experiments on a wider range of datasets to demonstrate the generalizability of the proposed method. This should include datasets with different characteristics, such as varying object sizes, densities, and scene complexities. The authors should also analyze the performance of Cognitive-YOLO on these datasets to identify any limitations or challenges. Sixth, the authors should provide a more detailed explanation of how the LLM handles the relationships between different components of the architecture. This should include a discussion of how the LLM ensures that the generated architecture is coherent and functional. The authors should also provide examples of architectures generated by the LLM to illustrate the process. Finally, the authors should consider releasing the code and models to the public to facilitate further research and development in this area. This will allow other researchers to reproduce the results and build upon this work. The authors should also include a more detailed analysis of the computational overhead introduced by the LLM and RAG components. While the paper mentions that the analysis module is rule-based and the compiler is automated, it does not provide any quantitative data on the time and resources required for each stage of the framework. This information is crucial for assessing the practical feasibility of the approach. The paper should also discuss the scalability of the framework, particularly when dealing with large and complex datasets. How does the computational cost of the framework scale with the size of the dataset and the complexity of the desired architecture? Furthermore, the paper should include a more detailed comparison with other NAS methods, not just YOLO variants, to provide a more comprehensive evaluation of the framework's performance and generalizability. This would help to establish the true value of the proposed approach compared to existing techniques. The paper should include a more detailed analysis of how the knowledge base is constructed, maintained, and validated. Specifically, what measures are in place to ensure that the information is up-to-date and free from errors? Furthermore, the paper should explore the potential for bias within the knowledge base and how this might affect the diversity of generated architectures. A sensitivity analysis of the impact of knowledge base quality on the final performance would also be beneficial. For example, what happens if a module is incorrectly described or if a new, more effective module is not included in the knowledge base? These are critical questions that need to be addressed to establish the robustness of the framework. While the use of RAG to retrieve relevant modules is a strength, the paper needs to address the limitations of the LLM's reasoning capabilities. The LLM is essentially acting as a sophisticated search and retrieval system, rather than a true designer. The paper should explore methods to encourage the LLM to go beyond the existing knowledge base, perhaps by incorporating mechanisms for generating novel combinations of existing modules or by allowing the LLM to propose modifications to existing modules. The current approach risks generating architectures that are simply combinations of existing ideas, rather than truly innovative designs. The paper should also discuss the potential for the LLM to make suboptimal choices due to limitations in its understanding of the complex interactions between different architectural components. For example, how does the LLM handle trade-offs between different architectural choices, such as accuracy, computational cost, and memory usage? Finally, the paper needs to provide a more thorough analysis of the computational overhead introduced by the LLM and RAG components. While the paper mentions that the analysis module is rule-based and the compiler is automated, it does not provide any quantitative data on the time and resources required for each stage of the framework. This information is crucial for assessing the practical feasibility of the approach. The paper should also discuss the scalability of the framework, particularly when dealing with large and complex datasets. How does the computational cost of the framework scale with the size of the dataset and the complexity of the desired architecture? Furthermore, the paper should include a more detailed comparison with other NAS methods, not just YOLO variants, to provide a more comprehensive evaluation of the framework's performance and generalizability. This would help to establish the true value of the proposed approach compared to existing techniques. The authors should consider including comparisons with methods that also leverage LLMs for architecture search or generation, such as those using prompt engineering or iterative refinement strategies. This would help to contextualize the performance of the proposed method and highlight its unique contributions. Furthermore, the paper should explore the impact of different LLM prompting strategies on the quality of the generated architectures. A sensitivity analysis of the prompt design would provide valuable insights into the robustness of the proposed framework. Additionally, the paper should include comparisons with other NAS methods, including both traditional and zero-cost NAS techniques. This would provide a more comprehensive evaluation of the proposed method's performance and efficiency. For example, comparing against methods that use reinforcement learning or evolutionary algorithms for architecture search would help to understand the trade-offs between the proposed approach and existing techniques. The authors should also consider comparing against zero-cost NAS methods that use proxy metrics to evaluate architecture performance, as this would provide a more efficient way to assess the quality of the generated architectures. Furthermore, the paper should analyze the computational cost of the proposed method and compare it with other NAS methods. This would help to understand the practical applicability of the proposed framework. Finally, the paper should include comparisons with other RAG methods to demonstrate the effectiveness of the RAG component. The authors should consider comparing against different RAG techniques, such as those using different retrieval strategies or knowledge bases. This would help to understand the impact of the RAG component on the quality of the generated architectures. Furthermore, the paper should analyze the sensitivity of the proposed method to the quality of the retrieved knowledge. A study of how the accuracy of the retrieved knowledge affects the performance of the generated architectures would provide valuable insights into the robustness of the proposed framework. The authors should also consider exploring the use of different knowledge bases for the RAG component. The paper would be significantly strengthened by a more thorough analysis of the computational cost associated with the proposed Cognitive-YOLO framework. While the authors mention that their method is efficient, a detailed breakdown of the time and resources required for each stage of the pipeline (dataset analysis, LLM-based architecture synthesis, and architecture compilation) is crucial. Specifically, the time taken for the LLM to generate the architecture, the number of parameters in the generated models, and the inference time should be explicitly stated and compared against existing methods. This would allow readers to better understand the practical implications of using Cognitive-YOLO and assess its suitability for different applications. Furthermore, a discussion of the computational resources required for training and inference, such as GPU memory and processing power, would be beneficial. This analysis should also consider the scalability of the method to larger datasets and more complex object detection tasks. To address the limitations of the proposed method, the authors should provide a more detailed analysis of its performance across a wider range of datasets with varying characteristics. This should include datasets with different object scales, aspect ratios, and scene complexities. It would be particularly useful to evaluate the method's robustness to datasets with significant class imbalance or occlusion. Additionally, the paper should explore the performance of Cognitive-YOLO under constrained computational resources, such as edge devices or mobile platforms. This would involve analyzing the trade-offs between model size, accuracy, and inference speed. The authors could also consider techniques like model compression or quantization to reduce the computational footprint of the generated architectures. A discussion of the method's sensitivity to hyperparameter settings and the potential for overfitting would also be valuable. Finally, the paper needs a more in-depth comparison with existing object detection architecture design methods. While the authors compare their method to several baselines, a more detailed analysis of the differences and similarities would be helpful. This should include a discussion of the strengths and weaknesses of each method, as well as the specific scenarios where each method performs best. The authors should also provide a more detailed explanation of how the LLM is used to generate the architecture, including the specific prompts used and the reasoning behind the architecture choices. This would help readers understand the inner workings of the proposed method and assess its potential for further development. Furthermore, the paper should address the potential for bias in the generated architectures, as the LLM is trained on a large corpus of text that may contain biases. The authors should discuss how they mitigate these biases and ensure that the generated architectures are fair and unbiased. The paper should also include a more detailed analysis of the computational overhead introduced by the LLM and RAG components. While the paper mentions that the analysis module is rule-based and the compiler is automated, it does not provide any quantitative data on the time and resources required for each stage of the framework. This information is crucial for assessing the practical feasibility of the approach. The paper should also discuss the scalability of the framework, particularly when dealing with large and complex datasets. How does the computational cost of the framework scale with the size of the dataset and the complexity of the desired architecture? Furthermore, the paper should include a more detailed comparison with other NAS methods, not just YOLO variants, to provide a more comprehensive evaluation of the framework's performance and generalizability. This would help to establish the true value of the proposed approach compared to existing techniques. The paper should include a more detailed analysis of how the knowledge base is constructed, maintained, and validated. Specifically, what measures are in place to ensure that the information is up-to-date and free from errors? Furthermore, the paper should explore the potential for bias within the knowledge base and how this might affect the diversity of generated architectures. A sensitivity analysis of the impact of knowledge base quality on the final performance would also be beneficial. For example, what happens if a module is incorrectly described or if a new, more effective module is not included in the knowledge base? These are critical questions that need to be addressed to establish the robustness of the framework. While the use of RAG to retrieve relevant modules is a strength, the paper needs to address the limitations of the LLM's reasoning capabilities. The LLM is essentially acting as a sophisticated search and retrieval system, rather than a true designer. The paper should explore methods to encourage the LLM to go beyond the existing knowledge base, perhaps by incorporating mechanisms for generating novel combinations of existing modules or by allowing the LLM to propose modifications to existing modules. The current approach risks generating architectures that are simply combinations of existing ideas, rather than truly innovative designs. The paper should also discuss the potential for the LLM to make suboptimal choices due to limitations in its understanding of the complex interactions between different architectural components. For example, how does the LLM handle trade-offs between different architectural choices, such as accuracy, computational cost, and memory usage? Finally, the paper needs to provide a more thorough analysis of the computational overhead introduced by the LLM and RAG components. While the paper mentions that the analysis module is rule-based and the compiler is automated, it does not provide any quantitative data on the time and resources required for each stage of the framework. This information is crucial for assessing the practical feasibility of the approach. The paper should also discuss the scalability of the framework, particularly when dealing with large and complex datasets. How does the computational cost of the framework scale with the size of the dataset and the complexity of the desired architecture? Furthermore, the paper should include a more detailed comparison with other NAS methods, not just YOLO variants, to provide a more comprehensive evaluation of the framework's performance and generalizability. This would help to establish the true value of the proposed approach compared to existing techniques. The paper should also include a more detailed analysis of how the knowledge base is constructed, maintained, and validated. Specifically, what measures are in place to ensure that the information is up-to-date and free from errors? Furthermore, the paper should explore the potential for bias within the knowledge base and how this might affect the diversity of generated architectures. A sensitivity analysis of the impact of knowledge base quality on the final performance would also be beneficial. For example, what happens if a module is incorrectly described or if a new, more effective module is not included in the knowledge base? These are critical questions that need to be addressed to establish the robustness of the framework. While the use of RAG to retrieve relevant modules is a strength, the paper needs to address the limitations of the LLM's reasoning capabilities. The LLM is essentially acting as a sophisticated search and retrieval system, rather than a true designer. The paper should explore methods to encourage the LLM to go beyond the existing knowledge base, perhaps by incorporating mechanisms for generating novel combinations of existing modules or by allowing the LLM to propose modifications to existing modules. The current approach risks generating architectures that are simply combinations of existing ideas, rather than truly innovative designs. The paper should also discuss the potential for the LLM to make suboptimal choices due to limitations in its understanding of the complex interactions between different architectural components. For example, how does the LLM handle trade-offs between different architectural choices, such as accuracy, computational cost, and memory usage? Finally, the paper needs to provide a more thorough analysis of the computational overhead introduced by the LLM and RAG components. While the paper mentions that the analysis module is rule-based and the compiler is automated, it does not provide any quantitative data on the time and resources required for each stage of the framework. This information is crucial for assessing the practical feasibility of the approach. The paper should also discuss the scalability of the framework, particularly when dealing with large and complex datasets. How does the computational cost of the framework scale with the size of the dataset and the complexity of the desired architecture? Furthermore, the paper should include a more detailed comparison with other NAS methods, not just YOLO variants, to provide a more comprehensive evaluation of the framework's performance and generalizability. This would help to establish the true value of the proposed approach compared to existing techniques. The paper should also include a more detailed analysis of how the knowledge base is constructed, maintained, and validated. Specifically, what measures are in place to ensure that the information is up-to-date and free from errors? Furthermore, the paper should explore the potential for bias within the knowledge base and how this might affect the diversity of generated architectures. A sensitivity analysis of the impact of knowledge base quality on the final performance would also be beneficial. For example, what happens if a module is incorrectly described or if a new, more effective module is not included in the knowledge base? These are critical questions that need to be addressed to establish the robustness of the framework. While the use of RAG to retrieve relevant modules is a strength, the paper needs to address the limitations of the LLM's reasoning capabilities. The LLM is essentially acting as a sophisticated search and retrieval system, rather than a true designer. The paper should explore methods to encourage the LLM to go beyond the existing knowledge base, perhaps by incorporating mechanisms for generating novel combinations of existing modules or by allowing the LLM to propose modifications to existing modules. The current approach risks generating architectures that are simply combinations of existing ideas, rather than truly innovative designs. The paper should also discuss the potential for the LLM to make suboptimal choices due to limitations in its understanding of the complex interactions between different architectural components. For example, how does the LLM handle trade-offs between different architectural choices, such as accuracy, computational cost, and memory usage? Finally, the paper needs to provide a more thorough analysis of the computational overhead introduced by the LLM and RAG components. While the paper mentions that the analysis module is rule-based and the compiler is automated, it does not provide any quantitative data on the time and resources required for each stage of the framework. This information is crucial for assessing the practical feasibility of the approach. The paper should also discuss the scalability of the framework, particularly when dealing with large and complex datasets. How does the computational cost of the framework scale with the size of the dataset and the complexity of the desired architecture? Furthermore, the paper should include a more detailed comparison with other NAS methods, not just YOLO variants, to provide a more comprehensive evaluation of the framework's performance and generalizability. This would help to establish the true value of the proposed approach compared to existing techniques. The paper should also include a more detailed analysis of how the knowledge base is constructed, maintained, and validated. Specifically, what measures are in place to ensure that the information is up-to-date and free from errors? Furthermore, the paper should explore the potential for bias within the knowledge base and how this might affect the diversity of generated architectures. A sensitivity analysis of the impact of knowledge base quality on the final performance would also be beneficial. For example, what happens if a module is incorrectly described or if a new, more effective module is not included in the knowledge base? These are critical questions that need to be addressed to establish the robustness of the framework. While the use of RAG to retrieve relevant modules is a strength, the paper needs to address the limitations of the LLM's reasoning capabilities. The LLM is essentially acting as a sophisticated search and retrieval system, rather than a true designer. The paper should explore methods to encourage the LLM to go beyond the existing knowledge base, perhaps by incorporating mechanisms for generating novel combinations of existing modules or by allowing the LLM to propose modifications to existing modules. The current approach risks generating architectures that are simply combinations of existing ideas, rather than truly innovative designs. The paper should also discuss the potential for the LLM to make suboptimal choices due to limitations in its understanding of the complex interactions between different architectural components. For example, how does the LLM handle trade-offs between different architectural choices, such as accuracy, computational cost, and memory usage? Finally, the paper needs to provide a more thorough analysis of the computational overhead introduced by the LLM and RAG components. While the paper mentions that the analysis module is rule-based and the compiler is automated, it does not provide any quantitative data on the time and resources required for each stage of the framework. This information is crucial for assessing the practical feasibility of the approach. The paper should also discuss the scalability of the framework, particularly when dealing with large and complex datasets. How does the computational cost of the framework scale with the size of the dataset and the complexity of the desired architecture? Furthermore, the paper should include a more detailed comparison with other NAS methods, not just YOLO variants, to provide a more comprehensive evaluation of the framework's performance and generalizability. This would help to establish the true value of the proposed approach compared to existing techniques. The paper should also include a more detailed analysis of how the knowledge base is constructed, maintained, and validated. Specifically, what measures are in place to ensure that the information is up-to-date and free from errors? Furthermore, the paper should explore the potential for bias within the knowledge base and how this might affect the diversity of generated architectures. A sensitivity analysis of the impact of knowledge base quality on the final performance would also be beneficial. For example, what happens if a module is incorrectly described or if a new, more effective module is not included in the knowledge base? These are critical questions that need to be addressed to establish the robustness of the framework. While the use of RAG to retrieve relevant modules is a strength, the paper needs to address the limitations of the LLM's reasoning capabilities. The LLM is essentially acting as a sophisticated search and retrieval system, rather than a true designer. The paper should explore methods to encourage the LLM to go beyond the existing knowledge base, perhaps by incorporating mechanisms for generating novel combinations of existing modules or by allowing the LLM to propose modifications to existing modules. The current approach risks generating architectures that are simply combinations of existing ideas, rather than truly innovative designs. The paper should also discuss the potential for the LLM to make suboptimal choices due to limitations in its understanding of the complex interactions between different architectural components. For example, how does the LLM handle trade-offs between different architectural choices, such as accuracy, computational cost, and memory usage? Finally, the paper needs to provide a more thorough analysis of the computational overhead introduced by the LLM and RAG components. While the paper mentions that the analysis module is rule-based and the compiler is automated, it does not provide any quantitative data on the time and resources required for each stage of the framework. This information is crucial for assessing the practical feasibility of the approach. The paper should also discuss the scalability of the framework, particularly when dealing with large and complex datasets. How does the computational cost of the framework scale with the size of the dataset and the complexity of the desired architecture? Furthermore, the paper should include a more detailed comparison with other NAS methods, not just YOLO variants, to provide a more comprehensive evaluation of the framework's performance and generalizability. This would help to establish the true value of the proposed approach compared to existing techniques. The paper should also include a more detailed analysis of how the knowledge base is constructed, maintained, and validated. Specifically, what measures are in place to ensure that the information is up-to-date and free from errors? Furthermore, the paper should explore the potential for bias within the knowledge base and how this might affect the diversity of generated architectures. A sensitivity analysis of the impact of knowledge base quality on the final performance would also be beneficial. For example, what happens if a module is incorrectly described or if a new, more effective module is not included in the knowledge base? These are critical questions that need to be addressed to establish the robustness of the framework. While the use of RAG to retrieve relevant modules is a strength, the paper needs to address the limitations of the LLM's reasoning capabilities. The LLM is essentially acting as a sophisticated search and retrieval system, rather than a true designer. The paper should explore methods to encourage the LLM to go beyond the existing knowledge base, perhaps by incorporating mechanisms for generating novel combinations of existing modules or by allowing the LLM to propose modifications to existing modules. The current approach risks generating architectures that are simply combinations of existing ideas, rather than truly innovative designs. The paper should also discuss the potential for the LLM to make suboptimal choices due to limitations in its understanding of the complex interactions between different architectural components. For example, how does the LLM handle trade-offs between different architectural choices, such as accuracy, computational cost, and memory usage? Finally, the paper needs to provide a more thorough analysis of the computational overhead introduced by the LLM and RAG components. While the paper mentions that the analysis module is rule-based and the compiler is automated, it does not provide any quantitative data on the time and resources required for each stage of the framework. This information is crucial for assessing the practical feasibility of the approach. The paper should also discuss the scalability of the framework, particularly when dealing with large and complex datasets. How does the computational cost of the framework scale with the size of the dataset and the complexity of the desired architecture? Furthermore, the paper should include a more detailed comparison with other NAS methods, not just YOLO variants, to provide a more comprehensive evaluation of the framework's performance and generalizability. This would help to establish the true value of the proposed approach compared to existing techniques. The paper should also include a more detailed analysis of how the knowledge base is constructed, maintained, and validated. Specifically, what measures are in place to ensure that the information is up-to-date and free from errors? Furthermore, the paper should explore the potential for bias within the knowledge base and how this might affect the diversity of generated architectures. A sensitivity analysis of the impact of knowledge base quality on the final performance would also be beneficial. For example, what happens if a module is incorrectly described or if a new, more effective module is not included in the knowledge base? These are critical questions that need to be addressed to establish the robustness of the framework. While the use of RAG to retrieve relevant modules is a strength, the paper needs to address the limitations of the LLM's reasoning capabilities. The LLM is essentially acting as a sophisticated search and retrieval system, rather than a true designer. The paper should explore methods to encourage the LLM to go beyond the existing knowledge base, perhaps by incorporating mechanisms for generating novel combinations of existing modules or by allowing the LLM to propose modifications to existing modules. The current approach risks generating architectures that are simply combinations of existing ideas, rather than truly innovative designs. The paper should also discuss the potential for the LLM to make suboptimal choices due to limitations in its understanding of the complex interactions between different architectural components. For example, how does the LLM handle trade-offs between different architectural choices, such as accuracy, computational cost, and memory usage? Finally, the paper needs to provide a more thorough analysis of the computational overhead introduced by the LLM and RAG components. While the paper mentions that the analysis module is rule-based and the compiler is automated, it does not provide any quantitative data on the time and resources required for each stage of the framework. This information is crucial for assessing the practical feasibility of the approach. The paper should also discuss the scalability of the framework, particularly when dealing with large and complex datasets. How does the computational cost of the framework scale with the size of the dataset and the complexity of the desired architecture? Furthermore, the paper should include a more detailed comparison with other NAS methods, not just YOLO variants, to provide a more comprehensive evaluation of the framework's performance and generalizability. This would help to establish the true value of the proposed approach compared to existing techniques. The paper should also include a more detailed analysis of how the knowledge base is constructed, maintained, and validated. Specifically, what measures are in place to ensure that the information is up-to-date and free from errors? Furthermore, the paper should explore the potential for bias within the knowledge base and how this might affect the diversity of generated architectures. A sensitivity analysis of the impact of knowledge base quality on the final performance would also be beneficial. For example, what happens if a module is incorrectly described or if a new, more effective module is not included in the knowledge base? These are critical questions that need to be addressed to establish the robustness of the framework. While the use of RAG to retrieve relevant modules is a strength, the paper needs to address the limitations of the LLM's reasoning capabilities. The LLM is essentially acting as a sophisticated search and retrieval system, rather than a true designer. The paper should explore methods to encourage the LLM to go beyond the existing knowledge base, perhaps by incorporating mechanisms for generating novel combinations of existing modules or by allowing the LLM to propose modifications to existing modules. The current approach risks generating architectures that are simply combinations of existing ideas, rather than truly innovative designs. The paper should also discuss the potential for the LLM to make suboptimal choices due to limitations in its understanding of the complex interactions between different architectural components. For example, how does the LLM handle trade-offs between different architectural choices, such as accuracy, computational cost, and memory usage? Finally, the paper needs to provide a more thorough analysis of the computational overhead introduced by the LLM and RAG components. While the paper mentions that the analysis module is rule-based and the compiler is automated, it does not provide any quantitative data on the time and resources required for each stage of the framework. This information is crucial for assessing the practical feasibility of the approach. The paper should also discuss the scalability of the framework, particularly when dealing with large and complex datasets. How does the computational cost of the framework scale with the size of the dataset and the complexity of the desired architecture? Furthermore, the paper should include a more detailed comparison with other NAS methods, not just YOLO variants, to provide a more comprehensive evaluation of the framework's performance and generalizability. This would help to establish the true value of the proposed approach compared to existing techniques. The paper should also include a more detailed analysis of how the knowledge base is constructed, maintained, and validated. Specifically, what measures are in place to ensure that the information is up-to-date and free from errors? Furthermore, the paper should explore the potential for bias within the knowledge base and how this might affect the diversity of generated architectures. A sensitivity analysis of the impact of knowledge base quality on the final performance would also be beneficial. For example, what happens if a module is incorrectly described or if a new, more effective module is not included in the knowledge base? These are critical questions that need to be addressed to establish the robustness of the framework. While the use of RAG to retrieve relevant modules is a strength, the paper needs to address the limitations of the LLM's reasoning capabilities. The LLM is essentially acting as a sophisticated search and retrieval system, rather than a true designer. The paper should explore methods to encourage the LLM to go beyond the existing knowledge base, perhaps by incorporating mechanisms for generating novel combinations of existing modules or by allowing the LLM to propose modifications to existing modules. The current approach risks generating architectures that are simply combinations of existing ideas, rather than truly innovative designs. The paper should also discuss the potential for the LLM to make suboptimal choices due to limitations in its understanding of the complex interactions between different architectural components. For example, how does the LLM handle trade-offs between different architectural choices, such as accuracy, computational cost, and memory usage? Finally, the paper needs to provide a more thorough analysis of the computational overhead introduced by the LLM and RAG components. While the paper mentions that the analysis module is rule-based and the compiler is automated, it does not provide any quantitative data on the time and resources required for each stage of the framework. This information is crucial for assessing the practical feasibility of the approach. The paper should also discuss the scalability of the framework, particularly when dealing with large and complex datasets. How does the computational cost of the framework scale with the size of the dataset and the complexity of the desired architecture? Furthermore, the paper should include a more detailed comparison with other NAS methods, not just YOLO variants, to provide a more comprehensive evaluation of the framework's performance and generalizabil
My analysis raises several key questions that I believe are crucial for a deeper understanding of the proposed method. First, how does the dataset analysis module handle datasets with complex or multi-modal distributions? The paper does not provide details on the specific algorithms used for meta-feature extraction, and it is unclear how the module would perform on datasets with non-uniform object scales or varying scene densities. Second, what is the sensitivity of the generated architectures to the specific prompt used to guide the LLM? The paper does not provide the exact prompt, and it is unclear how changes in the prompt might affect the generated architectures. It would be valuable to understand the robustness of the method to variations in the prompting strategy. Third, how does the LLM handle the trade-off between model complexity and performance? The paper does not provide details on how the LLM balances the number of parameters, the computational cost, and the detection accuracy. It would be valuable to understand the criteria used by the LLM to make these decisions. Fourth, how does the RAG component ensure that the retrieved knowledge is relevant and up-to-date? The paper does not provide details on the knowledge base used by RAG, and it is unclear how the system ensures that the retrieved information is accurate and reflects the current state of the art. Fifth, how does the proposed method handle datasets with novel or unseen object categories? The paper focuses on object detection, and it is unclear how the method would perform on datasets with objects that are not present in the training data. Sixth, what are the limitations of the proposed method in terms of scalability and applicability to real-world scenarios? The paper does not provide a thorough analysis of the computational cost of the method, and it is unclear how well it would scale to larger datasets or more complex object detection tasks. Finally, what are the ethical considerations associated with using LLMs for architecture design? The paper does not address the potential biases or ethical implications of using LLMs in this context, and it would be valuable to understand the authors' perspective on this issue. These questions highlight key uncertainties and areas where further clarification is needed to fully assess the potential and limitations of the proposed method. How does the framework handle novel datasets with no prior knowledge in the RAG database? What are the computational costs compared to traditional NAS methods? How does the LLM resolve conflicts or inconsistencies when RAG retrieves multiple potentially applicable modules? What mechanisms are in place to validate the performance of newly generated architectures before deployment? How does the framework ensure that the generated architectures are not overfit to the specific datasets used in the knowledge base? Please refer to the weakness part.