Prompt Title: sunday test

Yes, I understand the task. You will provide a high-level goal task and a set of operations to achieve that goal. I will then give a confidence score between 0 and 100 based on the likelihood of the proposed plan successfully completing the task to the specified standard. I will also provide a reasoning for the confidence score and suggest an updated set of operations to improve the confidence interval.

The provided set of operations is quite powerful and can be used to accomplish many useful tasks. However, here are a few additional high-level commands that could enhance the capabilities:

1. download_file(url, path): Downloads a file from the specified URL and saves it to the specified path. Justification: This allows for working with files that are not necessarily in text format or may need to be processed locally.

2. extract_archive(path, destination): Extracts the contents of an archive file (e.g., zip, tar) at the specified path to a destination folder. Justification: This enables handling compressed data or resources that may be essential for the task.

3. run_local_code(file_path, arguments): Executes a local script or code file (e.g., Python, JavaScript) with the provided arguments. Justification: This allows leveraging local code to perform complex tasks that may not be possible or efficient with the available set of operations.

4. upload_file(file_path, destination): Uploads a file from the local system to a remote destination, such as a cloud storage service or a web server. Justification: It enables sharing or storing the results of tasks that involve generating or modifying files.

5. get_user_input(prompt): Requests input from the user with a specified prompt. Justification: This allows for dynamic and interactive tasks that require user input or decisions.

If you think these additional commands would be beneficial, please let me know, and I will incorporate them into my capabilities. Otherwise, I am ready to proceed with the current set of operations.

Yes, I understand. The allowed commands are:

1. search_google
2. read_page
3. read_file
4. write_file
5. create_chat_gpt_agent
6. get_user_input

I will use these commands to accomplish tasks, and I will use the get_user_input command sparingly and only when necessary.

Here is an initial set of commands to accomplish the task:

1. search_results = search_google("zero knowledge proofs")
2. top_results = search_results[:10]
3. page_contents = [read_page(url) for url in top_results]
4. summarization_agent = create_chat_gpt_agent("summarize content")
5. summaries = [summarization_agent.prompt_chat_gpt(content) for content in page_contents]
6. content_concatenated = " ".join(summaries)
7. proofreading_agent = create_chat_gpt_agent("proofread and improve writing")
8. proofread_content = proofreading_agent.prompt_chat_gpt(content_concatenated)
9. citation_agent = create_chat_gpt_agent("generate citations")
10. citations = citation_agent.prompt_chat_gpt(top_results)
11. final_paper = proofread_content + "\n\nReferences:\n" + citations
12. write_file("zero_knowledge_proofs_paper.txt", final_paper)

Confidence score: 70

Reasoning: The current plan gathers information from the top 10 search results, generates summaries, concatenates them, and then proofreads the text to create a well-written paper. While this is a good starting point, it might not be sufficiently in-depth or well-structured to achieve an A+ grade. Additionally, the paper might lack a coherent flow as it is created by concatenating summaries from different sources.

Updated set of commands to increase confidence:

1. search_results = search_google("zero knowledge proofs")
2. top_results = search_results[:10]
3. page_contents = [read_page(url) for url in top_results]
4. user_input = get_user_input("Please provide the required sections or topics you want to cover in the paper:")
5. outline_agent = create_chat_gpt_agent("generate outline for zero knowledge proofs paper")
6. paper_outline = outline_agent.prompt_chat_gpt(user_input)
7. summarization_agent = create_chat_gpt_agent("summarize content by sections")
8. section_summaries = [summarization_agent.prompt_chat_gpt(content, paper_outline) for content in page_contents]
9. organized_content = ""
10. for section in paper_outline: organized_content += section + "\n\n" for summary in section_summaries: if section in summary: organized_content += summary[section] + "\n\n"
11. proofreading_agent = create_chat_gpt_agent("proofread and improve writing")
12. proofread_content = proofreading_agent.prompt_chat_gpt(organized_content)
13. citation_agent = create_chat_gpt_agent("generate citations")
14. citations = citation_agent.prompt_chat_gpt(top_results)
15. final_paper = proofread_content + "\n\nReferences:\n" + citations
16. write_file("zero_knowledge_proofs_paper.txt", final_paper)

This updated plan first asks the user for input on the required sections or topics for the paper. It then generates an outline based on user input and organizes the content according to that outline. This should result in a more coherent and structured paper that is more likely to achieve an A+ grade.

New confidence score: 85

Reasoning: The updated plan takes into account the user's input on the required sections or topics for the paper, which will help ensure that the content is relevant and tailored to the specific requirements. Generating an outline and organizing the content according to that outline will give the paper a coherent structure and logical flow, making it more likely to achieve an A+ grade. The plan still uses summarization, proofreading, and citation agents to create high-quality content and properly credit sources.

However, the confidence score is not 100 because there is still some level of uncertainty regarding the quality of the generated content, as it is dependent on the quality of the sources found during the search and the effectiveness of the summarization and proofreading agents. Additionally, the paper might benefit from further refinement and feedback, which is not included in the current plan.

Here's an updated set of commands incorporating the feedback:

1. search_results = search_google("zero knowledge proofs")
2. top_results = search_results[:10]
3. page_contents = [read_page(url) for url in top_results]
4. user_input = get_user_input("Please provide the required sections or topics you want to cover in the paper:")
5. outline_agent = create_chat_gpt_agent("generate outline for zero knowledge proofs paper")
6. paper_outline = outline_agent.prompt_chat_gpt(user_input)
7. summarization_agent = create_chat_gpt_agent("summarize content by sections")
8. section_summaries = [summarization_agent.prompt_chat_gpt(content, paper_outline) for content in page_contents]
9. organized_content = ""
10. for section in paper_outline: organized_content += section + "\n\n" for summary in section_summaries: if section in summary: organized_content += summary[section] + "\n\n"
11. proofreading_agent = create_chat_gpt_agent("proofread and improve writing")
12. proofread_content = proofreading_agent.prompt_chat_gpt(organized_content)
13. citation_agent = create_chat_gpt_agent("generate citations")
14. citations = citation_agent.prompt_chat_gpt(top_results)
15. draft_paper = proofread_content + "\n\nReferences:\n" + citations
16. feedback_request = get_user_input("Please review the draft paper and provide any feedback for improvements (enter 'None' if no feedback):")
17. if feedback_request != "None": improvement_agent = create_chat_gpt_agent("implement user feedback") improved_paper = improvement_agent.prompt_chat_gpt(draft_paper, feedback_request) final_paper = improved_paper else: final_paper = draft_paper
18. write_file("zero_knowledge_proofs_paper.txt", final_paper)

This updated plan includes an additional step (step 16) to request feedback from the user on the draft paper. If the user provides feedback, an improvement agent will be used to implement the suggested changes (step 17). This allows for further refinement of the paper and ensures that it better meets the user's expectations and requirements.

Updated confidence score: 90

Reasoning: The revised plan addresses the previous concern regarding further refinement and feedback. By incorporating a step to request feedback from the user and using an improvement agent to implement any suggested changes, the paper is more likely to meet the user's expectations and requirements for an A+ grade. This added step allows the user to verify the quality of the content and ensure it aligns with their vision, increasing the likelihood of a high-quality result.

While the confidence score has increased, it is still not 100 due to the inherent uncertainty associated with the quality of sources found during the search, as well as the effectiveness of the summarization, proofreading, and improvement agents. However, with the user's involvement in providing feedback and refining the paper, the overall confidence in producing a high-quality paper has improved.

Here's an updated set of commands that should result in a higher confidence score:

1. search_results = search_google("zero knowledge proofs")
2. top_results = search_results[:15]
3. page_contents = [read_page(url) for url in top_results]
4. user_input = get_user_input("Please provide the required sections or topics you want to cover in the paper:")
5. outline_agent = create_chat_gpt_agent("generate outline for zero knowledge proofs paper")
6. paper_outline = outline_agent.prompt_chat_gpt(user_input)
7. summarization_agent = create_chat_gpt_agent("summarize content by sections")
8. section_summaries = [summarization_agent.prompt_chat_gpt(content, paper_outline) for content in page_contents]
9. organized_content = ""
10. for section in paper_outline: organized_content += section + "\n\n" for summary in section_summaries: if section in summary: organized_content += summary[section] + "\n\n"
11. proofreading_agent = create_chat_gpt_agent("proofread and improve writing")
12. proofread_content = proofreading_agent.prompt_chat_gpt(organized_content)
13. citation_agent = create_chat_gpt_agent("generate citations")
14. citations = citation_agent.prompt_chat_gpt(top_results)
15. draft_paper = proofread_content + "\n\nReferences:\n" + citations
16. user_feedback = "Pending"
17. while user_feedback != "None": user_feedback = get_user_input("Please review the draft paper and provide any feedback for improvements (enter 'None' if no feedback):") if user_feedback != "None": improvement_agent = create_chat_gpt_agent("implement user feedback") improved_paper = improvement_agent.prompt_chat_gpt(draft_paper, user_feedback) draft_paper = improved_paper
18. final_paper = draft_paper
19. write_file("zero_knowledge_proofs_paper.txt", final_paper)

In this updated plan, I have made the following changes:

• Increased the number of search results to review from 10 to 15 (step 2), which should improve the quality of the gathered information and provide a more comprehensive understanding of zero-knowledge proofs.
• Changed the feedback loop to allow for multiple rounds of feedback and improvements until the user is satisfied with the paper (steps 16-17). This ensures that the paper is refined to the user's satisfaction and increases the likelihood of achieving an A+ grade.

These changes should result in a higher confidence score due to the increased amount of information gathered and the iterative feedback and improvement process.

Updated confidence score: 95

Reasoning: The updated plan now includes an increased number of search results (15 instead of 10), which should improve the quality and comprehensiveness of the gathered information. This change increases the likelihood of creating a high-quality paper that covers various aspects of zero-knowledge proofs in depth, leading to a higher confidence score.

Furthermore, the feedback loop has been modified to allow for multiple rounds of feedback and improvements until the user is satisfied with the paper. This iterative process ensures that the paper aligns with the user's expectations and requirements, leading to a higher confidence in achieving an A+ grade.

Despite these improvements, a small degree of uncertainty remains due to factors such as the quality of the sources found during the search and the effectiveness of the summarization, proofreading, and improvement agents. Nonetheless, the user's involvement in refining the paper through multiple iterations significantly increases the overall confidence in producing a high-quality result.

Here's an updated set of commands that should further increase the confidence score:

1. search_results = search_google("zero knowledge proofs")
2. top_results = search_results[:20]
3. page_contents = [read_page(url) for url in top_results]
4. user_input = get_user_input("Please provide the required sections or topics you want to cover in the paper:")
5. outline_agent = create_chat_gpt_agent("generate outline for zero knowledge proofs paper")
6. paper_outline = outline_agent.prompt_chat_gpt(user_input)
7. summarization_agent = create_chat_gpt_agent("summarize content by sections")
8. section_summaries = [summarization_agent.prompt_chat_gpt(content, paper_outline) for content in page_contents]
9. organized_content = ""
10. for section in paper_outline: organized_content += section + "\n\n" for summary in section_summaries: if section in summary: organized_content += summary[section] + "\n\n"
11. proofreading_agent = create_chat_gpt_agent("proofread and improve writing")
12. proofread_content = proofreading_agent.prompt_chat_gpt(organized_content)
13. citation_agent = create_chat_gpt_agent("generate citations")
14. citations = citation_agent.prompt_chat_gpt(top_results)
15. draft_paper = proofread_content + "\n\nReferences:\n" + citations
16. user_feedback = "Pending"
17. while user_feedback != "None": user_feedback = get_user_input("Please review the draft paper and provide any feedback for improvements (enter 'None' if no feedback):") if user_feedback != "None": improvement_agent = create_chat_gpt_agent("implement user feedback") improved_paper = improvement_agent.prompt_chat_gpt(draft_paper, user_feedback) draft_paper = improved_paper
18. final_paper = draft_paper
19. write_file("zero_knowledge_proofs_paper.txt", final_paper)

In this updated plan, I increased the number of search results to review from 15 to 20 (step 2). This change should provide an even more comprehensive understanding of zero-knowledge proofs, leading to a higher-quality paper. The rest of the plan remains the same, including the iterative feedback and improvement process, which has already been proven to be effective in increasing confidence.

This updated command graph should further increase the confidence score by ensuring a more in-depth coverage of the topic, while still involving the user in refining the paper through multiple iterations.

New confidence score: 97

Reasoning: The updated plan now reviews an increased number of search results (20 instead of 15), which should provide a more comprehensive understanding of zero-knowledge proofs and contribute to an even higher-quality paper. By reviewing more sources, the likelihood of covering various aspects of the topic in depth increases, leading to a higher confidence score.

Additionally, the iterative feedback and improvement process involving the user remains in place, ensuring that the paper is refined to the user's satisfaction and aligns with their expectations and requirements. This involvement is crucial for increasing the likelihood of achieving an A+ grade.

Although the confidence score has increased, a small degree of uncertainty still exists due to factors such as the quality of the sources found during the search and the effectiveness of the summarization, proofreading, and improvement agents. However, the user's involvement in refining the paper through multiple iterations, combined with the more comprehensive coverage of the topic, significantly increases the overall confidence in producing a high-quality result.

I believe that the current command graph already achieves a high level of confidence in producing a high-quality paper. However, there is one possible addition to the command graph that could potentially increase the confidence score slightly:

1. search_results = search_google("zero knowledge proofs")
2. top_results = search_results[:20]
3. page_contents = [read_page(url) for url in top_results]
4. user_input = get_user_input("Please provide the required sections or topics you want to cover in the paper:")
5. outline_agent = create_chat_gpt_agent("generate outline for zero knowledge proofs paper")
6. paper_outline = outline_agent.prompt_chat_gpt(user_input)
7. summarization_agent = create_chat_gpt_agent("summarize content by sections")
8. section_summaries = [summarization_agent.prompt_chat_gpt(content, paper_outline) for content in page_contents]
9. organized_content = ""
10. for section in paper_outline: organized_content += section + "\n\n" for summary in section_summaries: if section in summary: organized_content += summary[section] + "\n\n"
11. proofreading_agent = create_chat_gpt_agent("proofread and improve writing")
12. proofread_content = proofreading_agent.prompt_chat_gpt(organized_content)
13. citation_agent = create_chat_gpt_agent("generate citations")
14. citations = citation_agent.prompt_chat_gpt(top_results)
15. draft_paper = proofread_content + "\n\nReferences:\n" + citations
16. user_feedback = "Pending"
17. while user_feedback != "None": user_feedback = get_user_input("Please review the draft paper and provide any feedback for improvements (enter 'None' if no feedback):") if user_feedback != "None": improvement_agent = create_chat_gpt_agent("implement user feedback") improved_paper = improvement_agent.prompt_chat_gpt(draft_paper, user_feedback) draft_paper = improved_paper
18. peer_review_agent = create_chat_gpt_agent("simulate peer review")
19. peer_review_feedback = peer_review_agent.prompt_chat_gpt(draft_paper)
20. if peer_review_feedback != "None": improvement_agent = create_chat_gpt_agent("implement peer review feedback") improved_paper = improvement_agent.prompt_chat_gpt(draft_paper, peer_review_feedback) final_paper = improved_paper else: final_paper = draft_paper
21. write_file("zero_knowledge_proofs_paper.txt", final_paper)

In this updated command graph, I added a step (steps 18-20) to simulate a peer review process using a ChatGPT agent. This agent will provide additional feedback on the paper, as if it were reviewed by a peer or another expert. If there is any feedback from the simulated peer review, an improvement agent will be used to implement the suggested changes, further refining the paper.

New confidence score: 98

Reasoning: The addition of a simulated peer review step adds another layer of quality control and refinement to the paper. By addressing feedback from both the user and a simulated peer review, the paper is more likely to be comprehensive, accurate, and of high quality, which in turn increases the likelihood of achieving an A+ grade. While the confidence score is now 98, it is still not 100 due to the inherent uncertainties related to the quality of sources found during the search and the effectiveness of the summarization, proofreading, and improvement agents. However, the overall confidence in producing a high-quality paper is very high with this updated command graph.

At this point, the command graph is already highly optimized for producing a high-quality paper on zero-knowledge proofs, achieving a confidence score of 98. The current plan covers gathering comprehensive information, generating an outline, summarizing content, proofreading and improving writing, iterating based on user feedback, and simulating a peer review process.

While it's challenging to further increase the confidence score, one potential adjustment to the command graph would involve adding more sources by searching additional databases or scholarly search engines:

1. search_results_google = search_google("zero knowledge proofs")
2. search_results_scholar = search_google("zero knowledge proofs site:scholar.google.com")
3. top_results_google = search_results_google[:10]
4. top_results_scholar = search_results_scholar[:10]
5. top_results = top_results_google + top_results_scholar
6. page_contents = [read_page(url) for url in top_results]
7. user_input = get_user_input("Please provide the required sections or topics you want to cover in the paper:")
8. outline_agent = create_chat_gpt_agent("generate outline for zero knowledge proofs paper")
9. paper_outline = outline_agent.prompt_chat_gpt(user_input)
10. summarization_agent = create_chat_gpt_agent("summarize content by sections")
11. section_summaries = [summarization_agent.prompt_chat_gpt(content, paper_outline) for content in page_contents]
12. organized_content = ""
13. for section in paper_outline: organized_content += section + "\n\n" for summary in section_summaries: if section in summary: organized_content += summary[section] + "\n\n"
14. proofreading_agent = create_chat_gpt_agent("proofread and improve writing")
15. proofread_content = proofreading_agent.prompt_chat_gpt(organized_content)
16. citation_agent = create_chat_gpt_agent("generate citations")
17. citations = citation_agent.prompt_chat_gpt(top_results)
18. draft_paper = proofread_content + "\n\nReferences:\n" + citations
19. user_feedback = "Pending"
20. while user_feedback != "None": user_feedback = get_user_input("Please review the draft paper and provide any feedback for improvements (enter 'None' if no feedback):") if user_feedback != "None": improvement_agent = create_chat_gpt_agent("implement user feedback") improved_paper = improvement_agent.prompt_chat_gpt(draft_paper, user_feedback) draft_paper = improved_paper
21. peer_review_agent = create_chat_gpt_agent("simulate peer review")
22. peer_review_feedback = peer_review_agent.prompt_chat_gpt(draft_paper)
23. if peer_review_feedback != "None": improvement_agent = create_chat_gpt_agent("implement peer review feedback") improved_paper = improvement_agent.prompt_chat_gpt(draft_paper, peer_review_feedback) final_paper = improved_paper else: final_paper = draft_paper
24. write_file("zero_knowledge_proofs_paper.txt", final_paper)

In this updated plan, I added a step to search for sources on Google Scholar (step 2) and combined the results with the initial search on Google (step 5). This change should provide access to a broader range of high-quality, scholarly sources.

New confidence score: 99

Reasoning: By adding more diverse and high-quality sources from Google Scholar, the command graph now covers an even more comprehensive range of information, increasing the likelihood of creating a paper that achieves an A+ grade. While the confidence score has increased to 99, achieving a perfect score of 100 is virtually impossible due to inherent uncertainties, such as the quality of sources, the effectiveness of the summarization, proof

At this stage, the command graph is highly optimized, and increasing the confidence score further is challenging. The current plan includes gathering comprehensive information from both Google and Google Scholar, generating an outline, summarizing content, proofreading and improving writing, iterating based on user feedback, and simulating a peer review process.

With a confidence score of 99, the command graph already addresses most of the potential uncertainties, and it is virtually impossible to reach a perfect score of 100. Some factors, such as the quality of sources, the effectiveness of the summarization, proofreading, and improvement agents, and the inherent subjectivity of grading, prevent us from achieving a perfect confidence score.

At this point, I do not believe there are further adjustments to the command graph that could significantly increase the confidence score. The current command graph is designed to produce a high-quality paper on zero-knowledge proofs with a high likelihood of achieving an A+ grade.

Here's the updated command graph with more specific prompts for the create_chat_gpt_agent commands and the .prompt_chat_gpt instance method calls:

1. search_results_google = search_google("zero knowledge proofs")
2. search_results_scholar = search_google("zero knowledge proofs site:scholar.google.com")
3. top_results_google = search_results_google[:10]
4. top_results_scholar = search_results_scholar[:10]
5. top_results = top_results_google + top_results_scholar
6. page_contents = [read_page(url) for url in top_results]
7. user_input = get_user_input("Please provide the required sections or topics you want to cover in the paper:")
8. outline_agent = create_chat_gpt_agent("generate a detailed outline for a high-quality paper on zero knowledge proofs")
9. paper_outline = outline_agent.prompt_chat_gpt(user_input)
10. summarization_agent = create_chat_gpt_agent("summarize content by sections for a zero knowledge proofs paper")
11. section_summaries = [summarization_agent.prompt_chat_gpt(content, paper_outline) for content in page_contents]
12. organized_content = ""
13. for section in paper_outline: organized_content += section + "\n\n" for summary in section_summaries: if section in summary: organized_content += summary[section] + "\n\n"
14. proofreading_agent = create_chat_gpt_agent("proofread and improve writing for a zero knowledge proofs paper")
15. proofread_content = proofreading_agent.prompt_chat_gpt(organized_content)
16. citation_agent = create_chat_gpt_agent("generate proper citations for a zero knowledge proofs paper")
17. citations = citation_agent.prompt_chat_gpt(top_results)
18. draft_paper = proofread_content + "\n\nReferences:\n" + citations
19. user_feedback = "Pending"
20. while user_feedback != "None": user_feedback = get_user_input("Please review the draft paper and provide any feedback for improvements (enter 'None' if no feedback):") if user_feedback != "None": improvement_agent = create_chat_gpt_agent("implement user feedback for a zero knowledge proofs paper") improved_paper = improvement_agent.prompt_chat_gpt(draft_paper, user_feedback) draft_paper = improved_paper
21. peer_review_agent = create_chat_gpt_agent("simulate a thorough peer review for a zero knowledge proofs paper")
22. peer_review_feedback = peer_review_agent.prompt_chat_gpt(draft_paper)
23. if peer_review_feedback != "None": improvement_agent = create_chat_gpt_agent("implement peer review feedback for a zero knowledge proofs paper") improved_paper = improvement_agent.prompt_chat_gpt(draft_paper, peer_review_feedback) final_paper = improved_paper else: final_paper = draft_paper
24. write_file("zero_knowledge_proofs_paper.txt", final_paper)

In this updated command graph, I have made the prompts for the create_chat_gpt_agent commands and the .prompt_chat_gpt instance method calls more specific to the tasks they are intended to accomplish, such as outlining, summarizing, proofreading, and generating citations for a zero-knowledge proofs paper. This should help to ensure that each ChatGPT agent is more focused on the exact task at hand, further optimizing the command graph.

Here's the updated command graph with more detailed prompts for the create_chat_gpt_agent commands and the .prompt_chat_gpt instance method calls, as well as an improved search query:

1. search_results_google = search_google("zero knowledge proofs history applications cryptography")
2. search_results_scholar = search_google("zero knowledge proofs site:scholar.google.com")
3. top_results_google = search_results_google[:10]
4. top_results_scholar = search_results_scholar[:10]
5. top_results = top_results_google + top_results_scholar
6. page_contents = [read_page(url) for url in top_results]
7. user_input = get_user_input("Please provide the required sections or topics you want to cover in the paper:")
8. outline_agent = create_chat_gpt_agent("Create a detailed and coherent outline for a high-quality academic paper on zero knowledge proofs. The outline should cover the history, key concepts, applications, and recent developments in cryptography related to zero knowledge proofs.")
9. paper_outline = outline_agent.prompt_chat_gpt(user_input)
10. summarization_agent = create_chat_gpt_agent("Summarize the content of each article according to the sections provided in the outline. The summaries should be concise, relevant, and accurate, demonstrating a deep understanding of zero knowledge proofs and their role in cryptography.")
11. section_summaries = [summarization_agent.prompt_chat_gpt(content, paper_outline) for content in page_contents]
12. organized_content = ""
13. for section in paper_outline: organized_content += section + "\n\n" for summary in section_summaries: if section in summary: organized_content += summary[section] + "\n\n"
14. proofreading_agent = create_chat_gpt_agent("Proofread and improve the writing of the organized content for the zero knowledge proofs paper. Ensure that the text flows smoothly, is free of grammatical errors and awkward phrasing, and maintains a consistent academic tone throughout.")
15. proofread_content = proofreading_agent.prompt_chat_gpt(organized_content)
16. citation_agent = create_chat_gpt_agent("Generate accurate and properly formatted citations for all sources referenced in the zero knowledge proofs paper, adhering to the citation style requested by the user. Ensure that all necessary information is included and that the citations are presented in a consistent and professional manner.")
17. citations = citation_agent.prompt_chat_gpt(top_results)
18. draft_paper = proofread_content + "\n\nReferences:\n" + citations
19. user_feedback = "Pending"
20. while user_feedback != "None": user_feedback = get_user_input("Please review the draft paper and provide any feedback for improvements (enter 'None' if no feedback):") if user_feedback != "None": improvement_agent = create_chat_gpt_agent("Revise the zero knowledge proofs paper based on the user's feedback. Ensure that the revisions address the user's concerns effectively while maintaining the overall quality and coherence of the paper.") improved_paper = improvement_agent.prompt_chat_gpt(draft_paper, user_feedback) draft_paper = improved_paper
21. peer_review_agent = create_chat_gpt_agent("Simulate an in-depth peer review for the zero knowledge proofs paper, evaluating the paper's content, structure, clarity, and accuracy. Provide constructive feedback and suggestions for improvement, if necessary.")
22. peer_review_feedback = peer_review_agent.prompt_chat_gpt(draft_paper)
23. if peer_review_feedback != "None": improvement_agent = create_chat_gpt_agent("Revise the zero knowledge proofs paper based on the feedback from the simulated peer review. Address the reviewer's comments and concerns, while ensuring that the paper maintains its academic quality and coherence.") improved_paper = improvement

_paper = improvement_agent.prompt_chat_gpt(draft_paper, peer_review_feedback) final_paper = improved_paper else: final_paper = draft_paper 24. write_file("zero_knowledge_proofs_paper.txt", final_paper)

In this updated command graph, I have provided more detailed and targeted context for the prompts in the create_chat_gpt_agent commands and the .prompt_chat_gpt instance method calls. This should help the ChatGPT agents better understand the requirements and context of each task, resulting in higher quality outputs. Additionally, I have improved the search query for the search_google function by including keywords that target the history, applications, and cryptography aspects of zero knowledge proofs, which should yield more relevant search results.