diff --git a/data/processed/RQ1/EmailResponder-MCP/evaluate_success.py b/data/processed/RQ1/EmailResponder-MCP/evaluate_success.py index 619cc5845948694a8276b146d52c102522cbdd9b..0abfdc506ade69a07d98e2900701496d216c3087 100644 --- a/data/processed/RQ1/EmailResponder-MCP/evaluate_success.py +++ b/data/processed/RQ1/EmailResponder-MCP/evaluate_success.py @@ -115,7 +115,7 @@ def save_detailed_results(results, output_file="success-finish_detailed_results. print(f"\n[OK] Detailed results saved to: {output_path}") -def save_csv_summary(results, output_file="success_rate.csv"): +def save_csv_summary(results, output_file="success-finish_rate.csv"): """Save summary to CSV file.""" import csv diff --git a/data/processed/RQ1/EmailResponder-MCP/success_rate.csv b/data/processed/RQ1/EmailResponder-MCP/success-finish_rate.csv similarity index 100% rename from data/processed/RQ1/EmailResponder-MCP/success_rate.csv rename to data/processed/RQ1/EmailResponder-MCP/success-finish_rate.csv diff --git a/data/processed/RQ1/EmailResponder/evaluate_success.py b/data/processed/RQ1/EmailResponder/evaluate_success.py index 8f51414aeed16635d9a4cfa1807eb97e163e5752..0c19cad951953ca35b55035180011f9c663c2fe3 100644 --- a/data/processed/RQ1/EmailResponder/evaluate_success.py +++ b/data/processed/RQ1/EmailResponder/evaluate_success.py @@ -114,7 +114,7 @@ def save_detailed_results(results, output_file="success-finish_detailed_results. print(f"\nāœ… Detailed results saved to: {output_path}") -def save_csv_summary(results, output_file="success_rate.csv"): +def save_csv_summary(results, output_file="success-finish_rate.csv"): """Save summary to CSV file""" import csv diff --git a/data/processed/RQ1/EmailResponder/success_rate.csv b/data/processed/RQ1/EmailResponder/success-finish_rate.csv similarity index 100% rename from data/processed/RQ1/EmailResponder/success_rate.csv rename to data/processed/RQ1/EmailResponder/success-finish_rate.csv diff --git a/data/processed/RQ1/GameBuilder-MCP/evaluate_success.py b/data/processed/RQ1/GameBuilder-MCP/evaluate_success.py index 927c3705fd9131f43a0b8eb34d7181e8eeca9b4e..ded983ecb1d20fb54ab3ec376d944d91d2ff6f49 100644 --- a/data/processed/RQ1/GameBuilder-MCP/evaluate_success.py +++ b/data/processed/RQ1/GameBuilder-MCP/evaluate_success.py @@ -115,7 +115,7 @@ def save_detailed_results(results, output_file="success-finish_detailed_results. print(f"\nāœ… Detailed results saved to: {output_path}") -def save_csv_summary(results, output_file="success_rate.csv"): +def save_csv_summary(results, output_file="success-finish_rate.csv"): """Save a summary to a CSV file.""" import csv diff --git a/data/processed/RQ1/GameBuilder-MCP/success_rate.csv b/data/processed/RQ1/GameBuilder-MCP/success-finish_rate.csv similarity index 100% rename from data/processed/RQ1/GameBuilder-MCP/success_rate.csv rename to data/processed/RQ1/GameBuilder-MCP/success-finish_rate.csv diff --git a/data/processed/RQ1/GameBuilder/evaluate_success.py b/data/processed/RQ1/GameBuilder/evaluate_success.py index f0a55eb3e35114c6f677fd92121794572d8ae3c2..8ca08338872947fcc8bc2b31b4dae548817ab03c 100644 --- a/data/processed/RQ1/GameBuilder/evaluate_success.py +++ b/data/processed/RQ1/GameBuilder/evaluate_success.py @@ -115,7 +115,7 @@ def save_detailed_results(results, output_file="success-finish_detailed_results. print(f"\nāœ… Detailed results saved to: {output_path}") -def save_csv_summary(results, output_file="success_rate.csv"): +def save_csv_summary(results, output_file="success-finish_rate.csv"): """Save summary to CSV file""" import csv diff --git a/data/processed/RQ1/GameBuilder/success_rate.csv b/data/processed/RQ1/GameBuilder/success-finish_rate.csv similarity index 100% rename from data/processed/RQ1/GameBuilder/success_rate.csv rename to data/processed/RQ1/GameBuilder/success-finish_rate.csv diff --git a/data/processed/RQ1/MarkdownValidator-MCP/evaluate_success.py b/data/processed/RQ1/MarkdownValidator-MCP/evaluate_success.py index b626f32afebf741f7d925b283990ae7eab08173a..c1733e33728af53cf51aa838292ed7cdaaefb4f2 100644 --- a/data/processed/RQ1/MarkdownValidator-MCP/evaluate_success.py +++ b/data/processed/RQ1/MarkdownValidator-MCP/evaluate_success.py @@ -115,7 +115,7 @@ def save_detailed_results(results, output_file="success-finish_detailed_results. print(f"\nDetailed results saved to: {output_path}") -def save_csv_summary(results, output_file="success_rate.csv"): +def save_csv_summary(results, output_file="success-finish_rate.csv"): """Save summary to CSV file""" import csv diff --git a/data/processed/RQ1/MarkdownValidator-MCP/success_rate.csv b/data/processed/RQ1/MarkdownValidator-MCP/success-finish_rate.csv similarity index 100% rename from data/processed/RQ1/MarkdownValidator-MCP/success_rate.csv rename to data/processed/RQ1/MarkdownValidator-MCP/success-finish_rate.csv diff --git a/data/processed/RQ1/MarkdownValidator/evaluate_success.py b/data/processed/RQ1/MarkdownValidator/evaluate_success.py index 84c166ece75ab9ff762ba72ef24eadb8dce1969b..1887659e495e23dd91686866cfa92d750b374325 100644 --- a/data/processed/RQ1/MarkdownValidator/evaluate_success.py +++ b/data/processed/RQ1/MarkdownValidator/evaluate_success.py @@ -115,7 +115,7 @@ def save_detailed_results(results, output_file="success-finish_detailed_results. print(f"\nDetailed results saved: {output_path}") -def save_csv_summary(results, output_file="success_rate.csv"): +def save_csv_summary(results, output_file="success-finish_rate.csv"): """Save summary to CSV file""" import csv diff --git a/data/processed/RQ1/MarkdownValidator/success_rate.csv b/data/processed/RQ1/MarkdownValidator/success-finish_rate.csv similarity index 100% rename from data/processed/RQ1/MarkdownValidator/success_rate.csv rename to data/processed/RQ1/MarkdownValidator/success-finish_rate.csv diff --git a/data/processed/RQ1/RadarCharts/All_Open_vs_Closed_summaries.md b/data/processed/RQ1/RadarCharts/All_Open_vs_Closed_summaries.md index eb0251e815b9d99769db84df775a69c299a480af..f5c6f978a08fa02309f41dc9f94c8187726f094a 100644 --- a/data/processed/RQ1/RadarCharts/All_Open_vs_Closed_summaries.md +++ b/data/processed/RQ1/RadarCharts/All_Open_vs_Closed_summaries.md @@ -2,62 +2,62 @@ | Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | | --- | --- | --- | --- | --- | --- | --- | --- | -| open | 3 | 0.27 | 0.47 | 0.86 | 0.93 | 0.16 | 0.99 | -| closed | 4 | 0.16 | 0.27 | 0.92 | 0.82 | 0.11 | 1.00 | +| open | 3 | 0.21 | 0.44 | 0.84 | 0.93 | 0.18 | 0.65 | +| closed | 4 | 0.13 | 0.24 | 0.91 | 0.81 | 0.12 | 0.56 | # BookWriter Open vs Closed Models -| Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | -| --- | --- | --- | --- | --- | --- | --- | --- | -| open | 3 | 0.01 | 0.20 | 0.91 | 0.86 | 0.04 | 1.00 | -| closed | 4 | 0.00 | 0.00 | 0.94 | 0.66 | 0.00 | 1.00 | +| Series | Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| BookWriter | open | 3 | 0.01 | 0.20 | 0.91 | 0.86 | 0.04 | 1.00 | +| BookWriter | closed | 4 | 0.00 | 0.00 | 0.94 | 0.66 | 0.00 | 1.00 | # EmailResponder Open vs Closed Models -| Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | -| --- | --- | --- | --- | --- | --- | --- | --- | -| open | 3 | 0.07 | 0.11 | 0.92 | 0.87 | 0.06 | 1.00 | -| closed | 4 | 0.12 | 0.14 | 0.98 | 0.82 | 0.05 | 1.00 | +| Series | Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| EmailResponder | open | 3 | 0.07 | 0.11 | 0.92 | 0.87 | 0.06 | 0.00 | +| EmailResponder | closed | 4 | 0.12 | 0.14 | 0.98 | 0.82 | 0.05 | 0.00 | # GameBuilder Open vs Closed Models -| Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | -| --- | --- | --- | --- | --- | --- | --- | --- | -| open | 3 | 0.89 | 0.97 | 0.98 | 0.99 | 0.00 | 0.99 | -| closed | 4 | 0.47 | 0.52 | 0.99 | 0.89 | 0.01 | 1.00 | +| Series | Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| GameBuilder | open | 3 | 0.89 | 0.97 | 0.98 | 0.99 | 0.00 | 0.00 | +| GameBuilder | closed | 4 | 0.47 | 0.52 | 0.99 | 0.89 | 0.01 | 0.00 | # LandingPageGenerator Open vs Closed Models -| Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | average_score | -| --- | --- | --- | --- | --- | --- | --- | --- | -| open | 3 | 0.11 | 0.53 | 0.83 | 0.97 | 0.04 | 0.54 | -| closed | 4 | 0.08 | 0.35 | 0.89 | 0.87 | 0.01 | 0.29 | +| Series | Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | average_score | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| LandingPageGenerator | open | 3 | 0.11 | 0.53 | 0.83 | 0.97 | 0.04 | 0.54 | +| LandingPageGenerator | closed | 4 | 0.08 | 0.35 | 0.89 | 0.87 | 0.01 | 0.29 | # MarkdownValidator Open vs Closed Models -| Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | average_score | -| --- | --- | --- | --- | --- | --- | --- | --- | -| open | 3 | 0.99 | 0.99 | 1.00 | 1.00 | 0.00 | 0.82 | -| closed | 4 | 0.59 | 0.61 | 0.96 | 0.90 | 0.07 | 0.44 | +| Series | Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | average_score | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| MarkdownValidator | open | 3 | 0.99 | 0.99 | 1.00 | 1.00 | 0.00 | 0.82 | +| MarkdownValidator | closed | 4 | 0.59 | 0.61 | 0.96 | 0.90 | 0.07 | 0.44 | # RecruitmentAssistant Open vs Closed Models -| Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | -| --- | --- | --- | --- | --- | --- | --- | --- | -| open | 3 | 0.01 | 0.31 | 0.81 | 0.94 | 0.10 | 0.99 | -| closed | 4 | 0.00 | 0.17 | 0.92 | 0.83 | 0.00 | 1.00 | +| Series | Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| RecruitmentAssistant | open | 3 | 0.01 | 0.31 | 0.81 | 0.94 | 0.10 | 0.99 | +| RecruitmentAssistant | closed | 4 | 0.00 | 0.17 | 0.92 | 0.83 | 0.00 | 1.00 | # SQLAssistant Open vs Closed Models -| Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | average_score | -| --- | --- | --- | --- | --- | --- | --- | --- | -| open | 3 | 0.00 | 0.15 | 0.66 | 0.91 | 0.59 | 0.47 | -| closed | 4 | 0.00 | 0.06 | 0.79 | 0.83 | 0.46 | 0.38 | +| Series | Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | average_score | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| SQLAssistant | open | 3 | 0.00 | 0.15 | 0.66 | 0.91 | 0.59 | 0.47 | +| SQLAssistant | closed | 4 | 0.00 | 0.06 | 0.79 | 0.83 | 0.46 | 0.38 | # SocialMediaManager Open vs Closed Models -| Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | -| --- | --- | --- | --- | --- | --- | --- | --- | -| open | 3 | 0.06 | 0.50 | 0.75 | 0.92 | 0.45 | 0.98 | -| closed | 4 | 0.03 | 0.27 | 0.87 | 0.72 | 0.29 | 0.99 | +| Series | Group | Model Count | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| SocialMediaManager | open | 3 | 0.06 | 0.50 | 0.75 | 0.92 | 0.45 | 0.98 | +| SocialMediaManager | closed | 4 | 0.03 | 0.27 | 0.87 | 0.72 | 0.29 | 0.99 | diff --git a/data/processed/RQ1/RadarCharts/All_Series_Average_tables.md b/data/processed/RQ1/RadarCharts/All_Series_Average_tables.md index 57d7b8c5bfef8c7143fadcb8f6df2be219916478..33386e42cd6df654c59beb8e0d828cb8415f07ff 100644 --- a/data/processed/RQ1/RadarCharts/All_Series_Average_tables.md +++ b/data/processed/RQ1/RadarCharts/All_Series_Average_tables.md @@ -1,125 +1,107 @@ -# Overall Summary (All Projects - Weighted Average) +# Overall Summary (All Projects) -_Note: average_score is only computed for GT projects (MarkdownValidator, SQLAssistant, LandingPageGenerator). pass_rate is only computed for non-GT projects._ - -| model | exact_match | any_order_match | precision | recall | retry_rate | average_score | pass_rate | -| --- | --- | --- | --- | --- | --- | --- | --- | -| DeepSeek-R1 | 0.13 | 0.21 | 0.77 | 0.90 | 0.26 | 0.45 | 0.98 | -| DeepSeek-V3-1 | 0.22 | 0.59 | 0.86 | 0.96 | 0.13 | 0.67 | 1.00 | -| GPT-4o-mini | 0.22 | 0.58 | 0.84 | 0.95 | 0.12 | 0.65 | 0.99 | -| GPT-5 | 0.18 | 0.25 | 0.93 | 0.89 | 0.11 | 0.52 | 1.00 | -| Gemini-2.5-flash | 0.03 | 0.03 | 0.93 | 0.69 | 0.14 | 0.13 | 1.00 | -| Gemini-2.5-flash-nothinking | 0.02 | 0.02 | 0.94 | 0.70 | 0.12 | 0.14 | 1.00 | -| Qwen3-235b | 0.16 | 0.36 | 0.88 | 0.91 | 0.16 | 0.64 | 0.99 | +| model | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | +| --- | --- | --- | --- | --- | --- | --- | +| GPT-5 | 0.24 | 0.31 | 0.93 | 0.90 | 0.11 | 0.63 | +| GPT-4o-mini | 0.24 | 0.61 | 0.83 | 0.95 | 0.12 | 0.67 | +| DeepSeek-V3-1 | 0.26 | 0.64 | 0.86 | 0.96 | 0.13 | 0.68 | +| DeepSeek-R1 | 0.18 | 0.26 | 0.79 | 0.90 | 0.25 | 0.59 | +| Gemini-2.5-flash | 0.03 | 0.03 | 0.93 | 0.69 | 0.14 | 0.48 | +| Gemini-2.5-flash-nothinking | 0.02 | 0.02 | 0.94 | 0.70 | 0.12 | 0.48 | +| Qwen3-235b | 0.20 | 0.41 | 0.88 | 0.92 | 0.16 | 0.67 | # BookWriter Series Average -_This is a non-GT project series. Using pass_rate from success_rate.csv._ - | model | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | | --- | --- | --- | --- | --- | --- | --- | -| DeepSeek-R1 | 0.00 | 0.01 | 0.88 | 0.81 | 0.09 | 1.00 | -| DeepSeek-V3-1 | 0.04 | 0.58 | 0.89 | 0.98 | 0.01 | 1.00 | -| GPT-4o-mini | 0.00 | 0.00 | 0.85 | 0.83 | 0.00 | 1.00 | | GPT-5 | 0.00 | 0.00 | 0.97 | 0.77 | 0.00 | 1.00 | +| GPT-4o-mini | 0.00 | 0.00 | 0.85 | 0.83 | 0.00 | 1.00 | +| DeepSeek-V3-1 | 0.04 | 0.58 | 0.89 | 0.98 | 0.01 | 1.00 | +| DeepSeek-R1 | 0.00 | 0.01 | 0.88 | 0.81 | 0.09 | 1.00 | | Gemini-2.5-flash | 0.00 | 0.00 | 0.98 | 0.52 | 0.00 | 1.00 | | Gemini-2.5-flash-nothinking | 0.00 | 0.00 | 0.98 | 0.53 | 0.01 | 1.00 | | Qwen3-235b | 0.00 | 0.00 | 0.95 | 0.79 | 0.01 | 1.00 | # EmailResponder Series Average -_This is a non-GT project series. Using pass_rate from success_rate.csv._ - | model | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | | --- | --- | --- | --- | --- | --- | --- | -| DeepSeek-R1 | 0.00 | 0.03 | 0.77 | 0.89 | 0.16 | 0.99 | -| DeepSeek-V3-1 | 0.16 | 0.24 | 0.98 | 0.88 | 0.02 | 1.00 | -| GPT-4o-mini | 0.43 | 0.47 | 0.99 | 0.91 | 0.00 | 1.00 | -| GPT-5 | 0.01 | 0.01 | 1.00 | 0.83 | 0.00 | 1.00 | -| Gemini-2.5-flash | 0.01 | 0.01 | 0.97 | 0.74 | 0.10 | 0.99 | -| Gemini-2.5-flash-nothinking | 0.04 | 0.06 | 0.96 | 0.81 | 0.12 | 1.00 | -| Qwen3-235b | 0.06 | 0.07 | 1.00 | 0.85 | 0.00 | 1.00 | +| GPT-5 | 0.01 | 0.01 | 1.00 | 0.83 | 0.00 | 0.00 | +| GPT-4o-mini | 0.43 | 0.47 | 0.99 | 0.91 | 0.00 | 0.00 | +| DeepSeek-V3-1 | 0.16 | 0.24 | 0.98 | 0.88 | 0.02 | 0.00 | +| DeepSeek-R1 | 0.00 | 0.03 | 0.77 | 0.89 | 0.16 | 0.00 | +| Gemini-2.5-flash | 0.01 | 0.01 | 0.97 | 0.74 | 0.10 | 0.00 | +| Gemini-2.5-flash-nothinking | 0.05 | 0.06 | 0.96 | 0.81 | 0.12 | 0.00 | +| Qwen3-235b | 0.06 | 0.07 | 1.00 | 0.85 | 0.00 | 0.00 | # GameBuilder Series Average -_This is a non-GT project series. Using pass_rate from success_rate.csv._ - | model | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | | --- | --- | --- | --- | --- | --- | --- | -| DeepSeek-R1 | 0.94 | 0.96 | 1.00 | 0.99 | 0.00 | 1.00 | -| DeepSeek-V3-1 | 0.90 | 1.00 | 0.97 | 1.00 | 0.00 | 0.98 | -| GPT-4o-mini | 0.88 | 1.00 | 0.96 | 1.00 | 0.00 | 1.00 | -| GPT-5 | 1.00 | 1.00 | 1.00 | 1.00 | 0.00 | 1.00 | -| Gemini-2.5-flash | 0.00 | 0.00 | 1.00 | 0.75 | 0.00 | 1.00 | -| Gemini-2.5-flash-nothinking | 0.02 | 0.06 | 0.99 | 0.80 | 0.04 | 1.00 | -| Qwen3-235b | 0.84 | 0.94 | 0.99 | 0.99 | 0.00 | 1.00 | +| GPT-5 | 1.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.00 | +| GPT-4o-mini | 0.88 | 1.00 | 0.96 | 1.00 | 0.00 | 0.00 | +| DeepSeek-V3-1 | 0.90 | 1.00 | 0.97 | 1.00 | 0.00 | 0.00 | +| DeepSeek-R1 | 0.94 | 0.96 | 1.00 | 0.99 | 0.00 | 0.00 | +| Gemini-2.5-flash | 0.00 | 0.00 | 1.00 | 0.75 | 0.00 | 0.00 | +| Gemini-2.5-flash-nothinking | 0.02 | 0.06 | 0.99 | 0.80 | 0.04 | 0.00 | +| Qwen3-235b | 0.84 | 0.94 | 0.99 | 0.99 | 0.00 | 0.00 | # LandingPageGenerator Series Average -_This is a GT project series. Using average_score from score_summary.csv._ - | model | exact_match | any_order_match | precision | recall | retry_rate | average_score | | --- | --- | --- | --- | --- | --- | --- | -| DeepSeek-R1 | 0.00 | 0.13 | 0.78 | 0.94 | 0.06 | 0.25 | -| DeepSeek-V3-1 | 0.24 | 0.99 | 0.86 | 1.00 | 0.01 | 0.86 | -| GPT-4o-mini | 0.04 | 0.98 | 0.78 | 1.00 | 0.00 | 0.67 | | GPT-5 | 0.29 | 0.42 | 0.86 | 0.97 | 0.00 | 0.47 | +| GPT-4o-mini | 0.04 | 0.98 | 0.78 | 1.00 | 0.00 | 0.67 | +| DeepSeek-V3-1 | 0.24 | 0.99 | 0.86 | 1.00 | 0.01 | 0.86 | +| DeepSeek-R1 | 0.00 | 0.13 | 0.78 | 0.94 | 0.06 | 0.25 | | Gemini-2.5-flash | 0.00 | 0.00 | 0.97 | 0.76 | 0.00 | 0.00 | | Gemini-2.5-flash-nothinking | 0.00 | 0.00 | 0.95 | 0.77 | 0.03 | 0.01 | | Qwen3-235b | 0.09 | 0.45 | 0.84 | 0.97 | 0.03 | 0.52 | # MarkdownValidator Series Average -_This is a GT project series. Using average_score from score_summary.csv._ - | model | exact_match | any_order_match | precision | recall | retry_rate | average_score | | --- | --- | --- | --- | --- | --- | --- | -| DeepSeek-R1 | 0.98 | 1.00 | 1.00 | 1.00 | 0.00 | 0.81 | -| DeepSeek-V3-1 | 1.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.84 | -| GPT-4o-mini | 0.97 | 1.00 | 0.99 | 1.00 | 0.00 | 0.82 | | GPT-5 | 1.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.71 | +| GPT-4o-mini | 0.97 | 1.00 | 0.99 | 1.00 | 0.00 | 0.82 | +| DeepSeek-V3-1 | 1.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.84 | +| DeepSeek-R1 | 0.98 | 1.00 | 1.00 | 1.00 | 0.00 | 0.81 | | Gemini-2.5-flash | 0.26 | 0.33 | 0.87 | 0.88 | 0.21 | 0.08 | | Gemini-2.5-flash-nothinking | 0.13 | 0.13 | 0.96 | 0.73 | 0.06 | 0.14 | | Qwen3-235b | 0.98 | 0.98 | 1.00 | 0.99 | 0.00 | 0.81 | # RecruitmentAssistant Series Average -_This is a non-GT project series. Using pass_rate from success_rate.csv._ - | model | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | | --- | --- | --- | --- | --- | --- | --- | -| DeepSeek-R1 | 0.00 | 0.11 | 0.84 | 0.90 | 0.05 | 0.99 | -| DeepSeek-V3-1 | 0.00 | 0.36 | 0.84 | 0.97 | 0.00 | 1.00 | -| GPT-4o-mini | 0.00 | 0.70 | 0.76 | 0.98 | 0.00 | 1.00 | | GPT-5 | 0.00 | 0.00 | 0.98 | 0.90 | 0.00 | 1.00 | +| GPT-4o-mini | 0.00 | 0.70 | 0.76 | 0.98 | 0.00 | 1.00 | +| DeepSeek-V3-1 | 0.00 | 0.36 | 0.84 | 0.97 | 0.00 | 1.00 | +| DeepSeek-R1 | 0.00 | 0.11 | 0.84 | 0.90 | 0.05 | 0.99 | | Gemini-2.5-flash | 0.00 | 0.00 | 0.98 | 0.69 | 0.00 | 1.00 | | Gemini-2.5-flash-nothinking | 0.00 | 0.00 | 0.97 | 0.74 | 0.00 | 1.00 | | Qwen3-235b | 0.02 | 0.46 | 0.75 | 0.96 | 0.26 | 0.98 | # SQLAssistant Series Average -_This is a GT project series. Using average_score from score_summary.csv._ - | model | exact_match | any_order_match | precision | recall | retry_rate | average_score | | --- | --- | --- | --- | --- | --- | --- | -| DeepSeek-R1 | 0.00 | 0.04 | 0.52 | 0.88 | 0.80 | 0.40 | -| DeepSeek-V3-1 | 0.00 | 0.17 | 0.66 | 0.90 | 0.61 | 0.37 | -| GPT-4o-mini | 0.00 | 0.22 | 0.77 | 0.94 | 0.37 | 0.52 | | GPT-5 | 0.00 | 0.01 | 0.82 | 0.86 | 0.44 | 0.45 | +| GPT-4o-mini | 0.00 | 0.22 | 0.77 | 0.94 | 0.37 | 0.52 | +| DeepSeek-V3-1 | 0.00 | 0.17 | 0.66 | 0.90 | 0.61 | 0.37 | +| DeepSeek-R1 | 0.00 | 0.04 | 0.52 | 0.88 | 0.80 | 0.40 | | Gemini-2.5-flash | 0.00 | 0.00 | 0.76 | 0.77 | 0.57 | 0.29 | | Gemini-2.5-flash-nothinking | 0.00 | 0.00 | 0.82 | 0.76 | 0.48 | 0.27 | | Qwen3-235b | 0.00 | 0.24 | 0.80 | 0.95 | 0.37 | 0.64 | # SocialMediaManager Series Average -_This is a non-GT project series. Using pass_rate from success_rate.csv._ - | model | exact_match | any_order_match | precision | recall | retry_rate | pass_rate | | --- | --- | --- | --- | --- | --- | --- | -| DeepSeek-R1 | 0.01 | 0.22 | 0.64 | 0.88 | 0.62 | 0.94 | -| DeepSeek-V3-1 | 0.17 | 0.87 | 0.78 | 0.97 | 0.27 | 1.00 | -| GPT-4o-mini | 0.12 | 0.71 | 0.72 | 0.97 | 0.47 | 0.96 | | GPT-5 | 0.02 | 0.37 | 0.86 | 0.88 | 0.34 | 0.98 | +| GPT-4o-mini | 0.12 | 0.71 | 0.72 | 0.97 | 0.47 | 0.96 | +| DeepSeek-V3-1 | 0.17 | 0.87 | 0.78 | 0.97 | 0.27 | 1.00 | +| DeepSeek-R1 | 0.01 | 0.22 | 0.64 | 0.88 | 0.62 | 0.94 | | Gemini-2.5-flash | 0.00 | 0.00 | 0.94 | 0.52 | 0.21 | 1.00 | | Gemini-2.5-flash-nothinking | 0.00 | 0.00 | 0.96 | 0.51 | 0.15 | 1.00 | | Qwen3-235b | 0.02 | 0.42 | 0.82 | 0.89 | 0.46 | 0.98 | diff --git a/data/processed/RQ1/RadarCharts/BookWriter-A2A_radar.pdf b/data/processed/RQ1/RadarCharts/BookWriter-A2A_radar.pdf index f88e1de0ce141c2549eeb1916be7a3342846a35c..0708d1e1872bbefcd86e61d54d1fe31d7c42d8ef 100644 --- a/data/processed/RQ1/RadarCharts/BookWriter-A2A_radar.pdf +++ b/data/processed/RQ1/RadarCharts/BookWriter-A2A_radar.pdf @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:4da0e9d550b5ba06e37a72a279ac4fbdd6a2a4cda6ff704d37aa17907569d41c -size 18417 +oid sha256:2f16348cfa786f1cccf52a34f0cbf51cb663a9a1a7f2d1b80b41ced11d932ad2 +size 18128 diff --git a/data/processed/RQ1/RadarCharts/BookWriter-H_A2A_radar.pdf b/data/processed/RQ1/RadarCharts/BookWriter-H_A2A_radar.pdf index 5546b61ef47edb47f0fabeea64c59cfd58083ac6..ce0dd10fb1eb3f36c1ac8c2f9a9576fca6906158 100644 --- a/data/processed/RQ1/RadarCharts/BookWriter-H_A2A_radar.pdf +++ b/data/processed/RQ1/RadarCharts/BookWriter-H_A2A_radar.pdf @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:27a597245cd6396c7f09be84845e95571d6207e13c19c51dfd12287a02d6eab8 -size 18321 +oid sha256:8346232b4e08cc257005d667660836635c5cdcaaaabc55cce36dab9e143dd3f9 +size 18037 diff --git a/data/processed/RQ1/RadarCharts/BookWriter-MCP_radar.pdf b/data/processed/RQ1/RadarCharts/BookWriter-MCP_radar.pdf index 591ec51fe79157d0ae63329e71e3fefcffa6af99..2873b7e7f5aa9201e5ad66456961f26e904dab88 100644 --- a/data/processed/RQ1/RadarCharts/BookWriter-MCP_radar.pdf +++ b/data/processed/RQ1/RadarCharts/BookWriter-MCP_radar.pdf @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c212b19b2b232560f4be82708dcc3627d4fd1672f1a6f17bf706fc4b84685fae -size 18476 +oid sha256:497c36952d9519e89dc5a3bfe5a3251449234b06fb84b966dd6022c2023f1beb +size 18171 diff --git a/data/processed/RQ1/RadarCharts/BookWriter_Series_Average_radar.pdf b/data/processed/RQ1/RadarCharts/BookWriter_Series_Average_radar.pdf index 7d6351ae945a0e818cf9b442c712fa202628ca95..32bc065891d8795d12697ce25d74825f7893b1af 100644 --- a/data/processed/RQ1/RadarCharts/BookWriter_Series_Average_radar.pdf +++ b/data/processed/RQ1/RadarCharts/BookWriter_Series_Average_radar.pdf @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:79c4c09f9bf7c088d0604cc23cad260c52eb608b0033805f0664ab62d0e8d3dc +oid sha256:8672b9bf68a1718c5af9ea181373fccda47af46d46f8f053aff3846f6e36ec61 size 18189 diff --git a/data/processed/RQ1/RadarCharts/Comparison_A2A_vs_A2A_mix.md b/data/processed/RQ1/RadarCharts/Comparison_A2A_vs_A2A_mix.md new file mode 100644 index 0000000000000000000000000000000000000000..1142f025d85e85527a681d9cf3980d212ea74b72 --- /dev/null +++ b/data/processed/RQ1/RadarCharts/Comparison_A2A_vs_A2A_mix.md @@ -0,0 +1,48 @@ +# Overall A2A vs A2A_mix Comparison + +Averaged across all projects: + +## Framework-Level Comparison (All Models Averaged) + +| Metric | A2A | A2A_mix | Diff (A2A-A2A_mix) | +| --- | --- | --- | --- | + +--- + +## exact_match + +| Model | A2A (Avg) | A2A_mix (Avg) | Diff (A2A-A2A_mix) | +| --- | --- | --- | --- | + +## any_order_match + +| Model | A2A (Avg) | A2A_mix (Avg) | Diff (A2A-A2A_mix) | +| --- | --- | --- | --- | + +## precision + +| Model | A2A (Avg) | A2A_mix (Avg) | Diff (A2A-A2A_mix) | +| --- | --- | --- | --- | + +## recall + +| Model | A2A (Avg) | A2A_mix (Avg) | Diff (A2A-A2A_mix) | +| --- | --- | --- | --- | + +## retry_rate + +| Model | A2A (Avg) | A2A_mix (Avg) | Diff (A2A-A2A_mix) | +| --- | --- | --- | --- | + +## average_score + +| Model | A2A (Avg) | A2A_mix (Avg) | Diff (A2A-A2A_mix) | +| --- | --- | --- | --- | + +## pass_rate + +| Model | A2A (Avg) | A2A_mix (Avg) | Diff (A2A-A2A_mix) | +| --- | --- | --- | --- | + +--- + diff --git a/data/processed/RQ1/RadarCharts/Comparison_A2A_vs_H_A2A.md b/data/processed/RQ1/RadarCharts/Comparison_A2A_vs_H_A2A.md deleted file mode 100644 index c1e7d56ddded799d6fef2406000dfa8bd634da90..0000000000000000000000000000000000000000 --- a/data/processed/RQ1/RadarCharts/Comparison_A2A_vs_H_A2A.md +++ /dev/null @@ -1,539 +0,0 @@ -# Overall A2A vs H_A2A Comparison - -Averaged across all projects: BookWriter, LandingPageGenerator, RecruitmentAssistant, SQLAssistant, SocialMediaManager - -## Framework-Level Comparison (All Models Averaged) - -| Metric | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| exact_match | 0.01 | 0.01 | -0.01 | -| any_order_match | 0.25 | 0.19 | +0.05 | -| precision | 0.82 | 0.85 | -0.03 | -| recall | 0.84 | 0.87 | -0.03 | -| retry_rate | 0.22 | 0.14 | +0.08 | -| average_score | 0.31 | 0.45 | -0.14 | -| pass_rate | 0.99 | 1.00 | -0.01 | - ---- - -## exact_match - -| Model | A2A (Avg) | H_A2A (Avg) | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.00 | 0.00 | -0.00 | -| DeepSeek-V3-1 | 0.03 | 0.04 | -0.01 | -| GPT-4o-mini | 0.01 | 0.05 | -0.05 | -| GPT-5 | 0.01 | 0.00 | +0.01 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | -| Qwen3-235b | 0.01 | 0.01 | +0.00 | - -## any_order_match - -| Model | A2A (Avg) | H_A2A (Avg) | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.05 | 0.12 | -0.07 | -| DeepSeek-V3-1 | 0.48 | 0.68 | -0.20 | -| GPT-4o-mini | 0.60 | 0.43 | +0.16 | -| GPT-5 | 0.17 | 0.03 | +0.14 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | -| Qwen3-235b | 0.43 | 0.10 | +0.33 | - -## precision - -| Model | A2A (Avg) | H_A2A (Avg) | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.70 | 0.79 | -0.09 | -| DeepSeek-V3-1 | 0.74 | 0.84 | -0.11 | -| GPT-4o-mini | 0.79 | 0.81 | -0.03 | -| GPT-5 | 0.89 | 0.91 | -0.02 | -| Gemini-2.5-flash | 0.91 | 0.89 | +0.02 | -| Gemini-2.5-flash-nothinking | 0.93 | 0.90 | +0.03 | -| Qwen3-235b | 0.81 | 0.83 | -0.02 | - -## recall - -| Model | A2A (Avg) | H_A2A (Avg) | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.88 | 0.89 | -0.00 | -| DeepSeek-V3-1 | 0.95 | 0.96 | -0.01 | -| GPT-4o-mini | 0.96 | 0.92 | +0.04 | -| GPT-5 | 0.90 | 0.85 | +0.04 | -| Gemini-2.5-flash | 0.62 | 0.78 | -0.16 | -| Gemini-2.5-flash-nothinking | 0.64 | 0.78 | -0.14 | -| Qwen3-235b | 0.93 | 0.91 | +0.02 | - -## retry_rate - -| Model | A2A (Avg) | H_A2A (Avg) | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.37 | 0.26 | +0.11 | -| DeepSeek-V3-1 | 0.25 | 0.10 | +0.15 | -| GPT-4o-mini | 0.15 | 0.09 | +0.07 | -| GPT-5 | 0.18 | 0.06 | +0.11 | -| Gemini-2.5-flash | 0.19 | 0.18 | +0.01 | -| Gemini-2.5-flash-nothinking | 0.18 | 0.12 | +0.06 | -| Qwen3-235b | 0.25 | 0.20 | +0.05 | - -## average_score - -| Model | A2A (Avg) | H_A2A (Avg) | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.16 | 0.31 | -0.15 | -| DeepSeek-V3-1 | 0.49 | 0.70 | -0.21 | -| GPT-4o-mini | 0.57 | 0.58 | -0.01 | -| GPT-5 | 0.33 | 0.42 | -0.09 | -| Gemini-2.5-flash | 0.08 | 0.27 | -0.20 | -| Gemini-2.5-flash-nothinking | 0.03 | 0.29 | -0.26 | -| Qwen3-235b | 0.49 | 0.55 | -0.06 | - -## pass_rate - -| Model | A2A (Avg) | H_A2A (Avg) | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.97 | 0.99 | -0.02 | -| DeepSeek-V3-1 | 1.00 | 1.00 | +0.00 | -| GPT-4o-mini | 0.98 | 1.00 | -0.02 | -| GPT-5 | 0.99 | 1.00 | -0.01 | -| Gemini-2.5-flash | 1.00 | 1.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 1.00 | 1.00 | +0.00 | -| Qwen3-235b | 0.99 | 0.99 | -0.01 | - ---- - -# BookWriter: A2A vs H_A2A - -## Overall Summary (Averaged Across All Models) - -| Metric | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| exact_match | 0.00 | 0.00 | +0.00 | -| any_order_match | 0.08 | 0.08 | -0.00 | -| precision | 0.93 | 0.91 | +0.02 | -| recall | 0.77 | 0.73 | +0.03 | -| retry_rate | 0.02 | 0.00 | +0.02 | -| pass_rate | 1.00 | 1.00 | +0.00 | - ---- - -## exact_match - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.00 | 0.00 | +0.00 | -| DeepSeek-V3-1 | 0.00 | 0.00 | +0.00 | -| GPT-4o-mini | 0.00 | 0.00 | +0.00 | -| GPT-5 | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | -| Qwen3-235b | 0.00 | 0.00 | +0.00 | - -## any_order_match - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.02 | 0.00 | +0.02 | -| DeepSeek-V3-1 | 0.53 | 0.58 | -0.05 | -| GPT-4o-mini | 0.00 | 0.00 | +0.00 | -| GPT-5 | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | -| Qwen3-235b | 0.00 | 0.00 | +0.00 | - -## precision - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.87 | 0.91 | -0.03 | -| DeepSeek-V3-1 | 0.79 | 0.92 | -0.13 | -| GPT-4o-mini | 0.90 | 0.80 | +0.10 | -| GPT-5 | 0.98 | 0.95 | +0.03 | -| Gemini-2.5-flash | 1.00 | 0.93 | +0.07 | -| Gemini-2.5-flash-nothinking | 1.00 | 0.93 | +0.07 | -| Qwen3-235b | 0.96 | 0.93 | +0.03 | - -## recall - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.84 | 0.77 | +0.07 | -| DeepSeek-V3-1 | 0.99 | 0.95 | +0.04 | -| GPT-4o-mini | 0.86 | 0.77 | +0.09 | -| GPT-5 | 0.82 | 0.67 | +0.15 | -| Gemini-2.5-flash | 0.53 | 0.57 | -0.05 | -| Gemini-2.5-flash-nothinking | 0.53 | 0.57 | -0.04 | -| Qwen3-235b | 0.80 | 0.83 | -0.04 | - -## retry_rate - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.13 | 0.02 | +0.12 | -| DeepSeek-V3-1 | 0.02 | 0.00 | +0.02 | -| GPT-4o-mini | 0.00 | 0.00 | +0.00 | -| GPT-5 | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | -| Qwen3-235b | 0.00 | 0.00 | +0.00 | - -## pass_rate - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 1.00 | 1.00 | +0.00 | -| DeepSeek-V3-1 | 1.00 | 1.00 | +0.00 | -| GPT-4o-mini | 1.00 | 1.00 | +0.00 | -| GPT-5 | 1.00 | 1.00 | +0.00 | -| Gemini-2.5-flash | 1.00 | 1.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 1.00 | 1.00 | +0.00 | -| Qwen3-235b | 1.00 | 1.00 | +0.00 | - -# LandingPageGenerator: A2A vs H_A2A - -## Overall Summary (Averaged Across All Models) - -| Metric | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| exact_match | 0.00 | 0.00 | +0.00 | -| any_order_match | 0.40 | 0.29 | +0.11 | -| precision | 0.84 | 0.82 | +0.03 | -| recall | 0.91 | 0.93 | -0.02 | -| retry_rate | 0.02 | 0.01 | +0.01 | -| average_score | 0.30 | 0.33 | -0.03 | - ---- - -## exact_match - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.00 | 0.00 | +0.00 | -| DeepSeek-V3-1 | 0.00 | 0.00 | +0.00 | -| GPT-4o-mini | 0.00 | 0.00 | +0.00 | -| GPT-5 | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | -| Qwen3-235b | 0.00 | 0.00 | +0.00 | - -## any_order_match - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.05 | 0.03 | +0.02 | -| DeepSeek-V3-1 | 1.00 | 0.98 | +0.02 | -| GPT-4o-mini | 0.97 | 1.00 | -0.03 | -| GPT-5 | 0.27 | 0.00 | +0.27 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | -| Qwen3-235b | 0.48 | 0.00 | +0.48 | - -## precision - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.76 | 0.78 | -0.03 | -| DeepSeek-V3-1 | 0.83 | 0.77 | +0.06 | -| GPT-4o-mini | 0.74 | 0.78 | -0.03 | -| GPT-5 | 0.81 | 0.79 | +0.02 | -| Gemini-2.5-flash | 1.00 | 0.91 | +0.09 | -| Gemini-2.5-flash-nothinking | 0.98 | 0.87 | +0.12 | -| Qwen3-235b | 0.79 | 0.81 | -0.02 | - -## recall - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.94 | 0.93 | +0.01 | -| DeepSeek-V3-1 | 1.00 | 1.00 | +0.00 | -| GPT-4o-mini | 1.00 | 1.00 | -0.00 | -| GPT-5 | 0.97 | 0.94 | +0.03 | -| Gemini-2.5-flash | 0.74 | 0.87 | -0.13 | -| Gemini-2.5-flash-nothinking | 0.76 | 0.85 | -0.08 | -| Qwen3-235b | 0.98 | 0.94 | +0.04 | - -## retry_rate - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.03 | 0.00 | +0.03 | -| DeepSeek-V3-1 | 0.00 | 0.03 | -0.03 | -| GPT-4o-mini | 0.00 | 0.00 | +0.00 | -| GPT-5 | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.05 | 0.00 | +0.05 | -| Qwen3-235b | 0.05 | 0.05 | +0.00 | - -## average_score - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.08 | 0.10 | -0.02 | -| DeepSeek-V3-1 | 0.86 | 0.88 | -0.02 | -| GPT-4o-mini | 0.57 | 0.59 | -0.02 | -| GPT-5 | 0.19 | 0.32 | -0.12 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | -| Qwen3-235b | 0.38 | 0.41 | -0.03 | - -# RecruitmentAssistant: A2A vs H_A2A - -## Overall Summary (Averaged Across All Models) - -| Metric | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| exact_match | 0.00 | 0.00 | +0.00 | -| any_order_match | 0.18 | 0.31 | -0.13 | -| precision | 0.89 | 0.87 | +0.02 | -| recall | 0.86 | 0.95 | -0.09 | -| retry_rate | 0.05 | 0.03 | +0.01 | -| pass_rate | 1.00 | 1.00 | -0.00 | - ---- - -## exact_match - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.00 | 0.00 | +0.00 | -| DeepSeek-V3-1 | 0.00 | 0.00 | +0.00 | -| GPT-4o-mini | 0.00 | 0.00 | +0.00 | -| GPT-5 | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | -| Qwen3-235b | 0.02 | 0.00 | +0.02 | - -## any_order_match - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.00 | 0.25 | -0.25 | -| DeepSeek-V3-1 | 0.00 | 1.00 | -1.00 | -| GPT-4o-mini | 0.68 | 0.72 | -0.03 | -| GPT-5 | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | -| Qwen3-235b | 0.57 | 0.22 | +0.35 | - -## precision - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.83 | 0.89 | -0.06 | -| DeepSeek-V3-1 | 0.87 | 0.82 | +0.05 | -| GPT-4o-mini | 0.79 | 0.75 | +0.05 | -| GPT-5 | 0.99 | 0.97 | +0.02 | -| Gemini-2.5-flash | 1.00 | 0.94 | +0.06 | -| Gemini-2.5-flash-nothinking | 1.00 | 0.93 | +0.06 | -| Qwen3-235b | 0.74 | 0.76 | -0.02 | - -## recall - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.88 | 0.93 | -0.06 | -| DeepSeek-V3-1 | 0.96 | 1.00 | -0.04 | -| GPT-4o-mini | 0.98 | 0.99 | -0.00 | -| GPT-5 | 0.90 | 0.91 | -0.01 | -| Gemini-2.5-flash | 0.64 | 0.89 | -0.25 | -| Gemini-2.5-flash-nothinking | 0.68 | 0.94 | -0.26 | -| Qwen3-235b | 0.97 | 0.96 | +0.01 | - -## retry_rate - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.07 | 0.05 | +0.02 | -| DeepSeek-V3-1 | 0.00 | 0.00 | +0.00 | -| GPT-4o-mini | 0.00 | 0.00 | +0.00 | -| GPT-5 | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | -| Qwen3-235b | 0.27 | 0.18 | +0.08 | - -## pass_rate - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.98 | 1.00 | -0.02 | -| DeepSeek-V3-1 | 1.00 | 1.00 | +0.00 | -| GPT-4o-mini | 1.00 | 1.00 | +0.00 | -| GPT-5 | 1.00 | 1.00 | +0.00 | -| Gemini-2.5-flash | 1.00 | 1.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 1.00 | 1.00 | +0.00 | -| Qwen3-235b | 0.98 | 1.00 | -0.02 | - -# SQLAssistant: A2A vs H_A2A - -## Overall Summary (Averaged Across All Models) - -| Metric | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| exact_match | 0.00 | 0.00 | +0.00 | -| any_order_match | 0.15 | 0.00 | +0.15 | -| precision | 0.65 | 0.83 | -0.18 | -| recall | 0.87 | 0.87 | -0.01 | -| retry_rate | 0.65 | 0.38 | +0.27 | -| average_score | 0.32 | 0.57 | -0.25 | - ---- - -## exact_match - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.00 | 0.00 | +0.00 | -| DeepSeek-V3-1 | 0.00 | 0.00 | +0.00 | -| GPT-4o-mini | 0.00 | 0.00 | +0.00 | -| GPT-5 | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | -| Qwen3-235b | 0.00 | 0.00 | +0.00 | - -## any_order_match - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.03 | 0.00 | +0.03 | -| DeepSeek-V3-1 | 0.00 | 0.00 | +0.00 | -| GPT-4o-mini | 0.53 | 0.00 | +0.53 | -| GPT-5 | 0.03 | 0.00 | +0.03 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | -| Qwen3-235b | 0.48 | 0.00 | +0.48 | - -## precision - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.41 | 0.64 | -0.23 | -| DeepSeek-V3-1 | 0.41 | 0.89 | -0.48 | -| GPT-4o-mini | 0.84 | 0.86 | -0.02 | -| GPT-5 | 0.84 | 0.91 | -0.07 | -| Gemini-2.5-flash | 0.57 | 0.81 | -0.23 | -| Gemini-2.5-flash-nothinking | 0.70 | 0.84 | -0.14 | -| Qwen3-235b | 0.78 | 0.85 | -0.07 | - -## recall - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.85 | 0.89 | -0.03 | -| DeepSeek-V3-1 | 0.84 | 0.90 | -0.05 | -| GPT-4o-mini | 0.98 | 0.92 | +0.07 | -| GPT-5 | 0.88 | 0.86 | +0.02 | -| Gemini-2.5-flash | 0.77 | 0.83 | -0.05 | -| Gemini-2.5-flash-nothinking | 0.78 | 0.80 | -0.02 | -| Qwen3-235b | 0.96 | 0.93 | +0.02 | - -## retry_rate - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.95 | 0.67 | +0.28 | -| DeepSeek-V3-1 | 1.00 | 0.27 | +0.73 | -| GPT-4o-mini | 0.17 | 0.28 | -0.12 | -| GPT-5 | 0.40 | 0.23 | +0.17 | -| Gemini-2.5-flash | 0.90 | 0.50 | +0.40 | -| Gemini-2.5-flash-nothinking | 0.75 | 0.37 | +0.38 | -| Qwen3-235b | 0.38 | 0.33 | +0.05 | - -## average_score - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.25 | 0.52 | -0.27 | -| DeepSeek-V3-1 | 0.12 | 0.53 | -0.41 | -| GPT-4o-mini | 0.58 | 0.58 | +0.00 | -| GPT-5 | 0.47 | 0.53 | -0.06 | -| Gemini-2.5-flash | 0.16 | 0.55 | -0.39 | -| Gemini-2.5-flash-nothinking | 0.05 | 0.57 | -0.52 | -| Qwen3-235b | 0.60 | 0.68 | -0.08 | - -# SocialMediaManager: A2A vs H_A2A - -## Overall Summary (Averaged Across All Models) - -| Metric | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| exact_match | 0.04 | 0.07 | -0.04 | -| any_order_match | 0.42 | 0.29 | +0.13 | -| precision | 0.80 | 0.85 | -0.05 | -| recall | 0.79 | 0.87 | -0.08 | -| retry_rate | 0.39 | 0.29 | +0.09 | -| pass_rate | 0.97 | 0.99 | -0.02 | - ---- - -## exact_match - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.00 | 0.02 | -0.02 | -| DeepSeek-V3-1 | 0.15 | 0.20 | -0.05 | -| GPT-4o-mini | 0.03 | 0.27 | -0.23 | -| GPT-5 | 0.05 | 0.00 | +0.05 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | -| Qwen3-235b | 0.03 | 0.03 | +0.00 | - -## any_order_match - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.13 | 0.32 | -0.18 | -| DeepSeek-V3-1 | 0.85 | 0.83 | +0.02 | -| GPT-4o-mini | 0.80 | 0.45 | +0.35 | -| GPT-5 | 0.53 | 0.15 | +0.38 | -| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | -| Qwen3-235b | 0.62 | 0.27 | +0.35 | - -## precision - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.60 | 0.70 | -0.10 | -| DeepSeek-V3-1 | 0.79 | 0.83 | -0.04 | -| GPT-4o-mini | 0.66 | 0.90 | -0.24 | -| GPT-5 | 0.83 | 0.92 | -0.09 | -| Gemini-2.5-flash | 0.98 | 0.87 | +0.11 | -| Gemini-2.5-flash-nothinking | 0.97 | 0.93 | +0.04 | -| Qwen3-235b | 0.80 | 0.82 | -0.02 | - -## recall - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.89 | 0.90 | -0.01 | -| DeepSeek-V3-1 | 0.98 | 0.96 | +0.01 | -| GPT-4o-mini | 0.99 | 0.95 | +0.03 | -| GPT-5 | 0.90 | 0.87 | +0.02 | -| Gemini-2.5-flash | 0.44 | 0.77 | -0.33 | -| Gemini-2.5-flash-nothinking | 0.43 | 0.76 | -0.32 | -| Qwen3-235b | 0.93 | 0.88 | +0.05 | - -## retry_rate - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.65 | 0.55 | +0.10 | -| DeepSeek-V3-1 | 0.23 | 0.18 | +0.05 | -| GPT-4o-mini | 0.60 | 0.15 | +0.45 | -| GPT-5 | 0.48 | 0.08 | +0.40 | -| Gemini-2.5-flash | 0.07 | 0.42 | -0.35 | -| Gemini-2.5-flash-nothinking | 0.12 | 0.25 | -0.13 | -| Qwen3-235b | 0.55 | 0.42 | +0.13 | - -## pass_rate - -| Model | A2A | H_A2A | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 0.92 | 0.97 | -0.05 | -| DeepSeek-V3-1 | 1.00 | 1.00 | +0.00 | -| GPT-4o-mini | 0.93 | 1.00 | -0.07 | -| GPT-5 | 0.97 | 1.00 | -0.03 | -| Gemini-2.5-flash | 1.00 | 1.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 1.00 | 1.00 | +0.00 | -| Qwen3-235b | 0.98 | 0.98 | +0.00 | - diff --git a/data/processed/RQ1/RadarCharts/Comparison_MCP_vs_Hardcoded.md b/data/processed/RQ1/RadarCharts/Comparison_MCP_vs_Hardcoded.md index a34ebd9e06a5cd865acb9af37b176067ff9f2733..09e52be365a3108ff58bd016dcbbc161f9115eac 100644 --- a/data/processed/RQ1/RadarCharts/Comparison_MCP_vs_Hardcoded.md +++ b/data/processed/RQ1/RadarCharts/Comparison_MCP_vs_Hardcoded.md @@ -12,7 +12,7 @@ Averaged across all projects: EmailResponder, GameBuilder, MarkdownValidator | recall | 0.91 | 0.91 | +0.00 | | retry_rate | 0.03 | 0.04 | -0.02 | | average_score | 0.60 | 0.60 | +0.00 | -| pass_rate | 1.00 | 0.99 | +0.01 | +| pass_rate | 0.00 | 0.00 | +0.00 | --- @@ -92,13 +92,13 @@ Averaged across all projects: EmailResponder, GameBuilder, MarkdownValidator | Model | MCP (Avg) | Hardcoded (Avg) | Diff (MCP-Hardcoded) | | --- | --- | --- | --- | -| DeepSeek-R1 | 1.00 | 0.99 | +0.01 | -| DeepSeek-V3-1 | 1.00 | 0.98 | +0.02 | -| GPT-4o-mini | 1.00 | 1.00 | +0.00 | -| GPT-5 | 1.00 | 1.00 | +0.00 | -| Gemini-2.5-flash | 1.00 | 0.99 | +0.01 | -| Gemini-2.5-flash-nothinking | 1.00 | 1.00 | +0.00 | -| Qwen3-235b | 1.00 | 1.00 | +0.00 | +| DeepSeek-R1 | 0.00 | 0.00 | +0.00 | +| DeepSeek-V3-1 | 0.00 | 0.00 | +0.00 | +| GPT-4o-mini | 0.00 | 0.00 | +0.00 | +| GPT-5 | 0.00 | 0.00 | +0.00 | +| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | +| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | +| Qwen3-235b | 0.00 | 0.00 | +0.00 | --- @@ -113,7 +113,7 @@ Averaged across all projects: EmailResponder, GameBuilder, MarkdownValidator | precision | 0.96 | 0.95 | +0.01 | | recall | 0.85 | 0.84 | +0.01 | | retry_rate | 0.04 | 0.07 | -0.04 | -| pass_rate | 1.00 | 1.00 | +0.00 | +| pass_rate | 0.00 | 0.00 | +0.00 | --- @@ -181,13 +181,13 @@ Averaged across all projects: EmailResponder, GameBuilder, MarkdownValidator | Model | MCP | Hardcoded | Diff (MCP-Hardcoded) | | --- | --- | --- | --- | -| DeepSeek-R1 | 1.00 | 0.98 | +0.02 | -| DeepSeek-V3-1 | 1.00 | 1.00 | +0.00 | -| GPT-4o-mini | 1.00 | 1.00 | +0.00 | -| GPT-5 | 1.00 | 1.00 | +0.00 | -| Gemini-2.5-flash | 1.00 | 0.99 | +0.01 | -| Gemini-2.5-flash-nothinking | 1.00 | 1.00 | +0.00 | -| Qwen3-235b | 1.00 | 1.00 | +0.00 | +| DeepSeek-R1 | 0.00 | 0.00 | +0.00 | +| DeepSeek-V3-1 | 0.00 | 0.00 | +0.00 | +| GPT-4o-mini | 0.00 | 0.00 | +0.00 | +| GPT-5 | 0.00 | 0.00 | +0.00 | +| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | +| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | +| Qwen3-235b | 0.00 | 0.00 | +0.00 | # GameBuilder: MCP vs Hardcoded @@ -200,7 +200,7 @@ Averaged across all projects: EmailResponder, GameBuilder, MarkdownValidator | precision | 0.98 | 0.99 | -0.01 | | recall | 0.94 | 0.93 | +0.00 | | retry_rate | 0.00 | 0.01 | -0.01 | -| pass_rate | 1.00 | 0.99 | +0.01 | +| pass_rate | 0.00 | 0.00 | +0.00 | --- @@ -268,13 +268,13 @@ Averaged across all projects: EmailResponder, GameBuilder, MarkdownValidator | Model | MCP | Hardcoded | Diff (MCP-Hardcoded) | | --- | --- | --- | --- | -| DeepSeek-R1 | 1.00 | 1.00 | +0.00 | -| DeepSeek-V3-1 | 1.00 | 0.96 | +0.04 | -| GPT-4o-mini | 1.00 | 1.00 | +0.00 | -| GPT-5 | 1.00 | 1.00 | +0.00 | -| Gemini-2.5-flash | 1.00 | 1.00 | +0.00 | -| Gemini-2.5-flash-nothinking | 1.00 | 1.00 | +0.00 | -| Qwen3-235b | 1.00 | 1.00 | +0.00 | +| DeepSeek-R1 | 0.00 | 0.00 | +0.00 | +| DeepSeek-V3-1 | 0.00 | 0.00 | +0.00 | +| GPT-4o-mini | 0.00 | 0.00 | +0.00 | +| GPT-5 | 0.00 | 0.00 | +0.00 | +| Gemini-2.5-flash | 0.00 | 0.00 | +0.00 | +| Gemini-2.5-flash-nothinking | 0.00 | 0.00 | +0.00 | +| Qwen3-235b | 0.00 | 0.00 | +0.00 | # MarkdownValidator: MCP vs Hardcoded diff --git a/data/processed/RQ1/RadarCharts/Comparison_Powerful_vs_Lightweight.md b/data/processed/RQ1/RadarCharts/Comparison_Powerful_vs_Lightweight.md index 578efa73f0a6bd3cf9d40d79e43c312efd0e2e03..6789156a6d49693c632fd80b0464bf7aacb40466 100644 --- a/data/processed/RQ1/RadarCharts/Comparison_Powerful_vs_Lightweight.md +++ b/data/processed/RQ1/RadarCharts/Comparison_Powerful_vs_Lightweight.md @@ -2,8 +2,6 @@ ## Overall Summary (Averaged Across All Project Series) -_Note: average_score is averaged only across GT projects; pass_rate is averaged only across non-GT projects._ - | Metric | GPT-5 | GPT-4o-mini | Diff (A-B) | | --- | --- | --- | --- | | exact_match | 0.29 | 0.31 | -0.02 | @@ -12,7 +10,7 @@ _Note: average_score is averaged only across GT projects; pass_rate is averaged | recall | 0.90 | 0.95 | -0.05 | | retry_rate | 0.10 | 0.10 | -0.01 | | average_score | 0.55 | 0.67 | -0.13 | -| pass_rate | 1.00 | 0.99 | +0.00 | +| pass_rate | 0.60 | 0.59 | +0.00 | --- @@ -21,7 +19,7 @@ _Note: average_score is averaged only across GT projects; pass_rate is averaged | Project Series | GPT-5 | GPT-4o-mini | Diff (A-B) | | --- | --- | --- | --- | | BookWriter | 0.00 | 0.00 | +0.00 | -| EmailResponder | 0.01 | 0.43 | -0.42 | +| EmailResponder | 0.01 | 0.43 | -0.43 | | GameBuilder | 1.00 | 0.88 | +0.12 | | LandingPageGenerator | 0.29 | 0.04 | +0.26 | | MarkdownValidator | 1.00 | 0.97 | +0.03 | @@ -94,8 +92,8 @@ _Note: average_score is averaged only across GT projects; pass_rate is averaged | Project Series | GPT-5 | GPT-4o-mini | Diff (A-B) | | --- | --- | --- | --- | | BookWriter | 1.00 | 1.00 | +0.00 | -| EmailResponder | 1.00 | 1.00 | +0.00 | -| GameBuilder | 1.00 | 1.00 | +0.00 | +| EmailResponder | 0.00 | 0.00 | +0.00 | +| GameBuilder | 0.00 | 0.00 | +0.00 | | RecruitmentAssistant | 1.00 | 1.00 | +0.00 | | SocialMediaManager | 0.98 | 0.96 | +0.02 | diff --git a/data/processed/RQ1/RadarCharts/Comparison_Reasoning_vs_NonReasoning.md b/data/processed/RQ1/RadarCharts/Comparison_Reasoning_vs_NonReasoning.md index b76b39758ee4f37fdfc3f75dcf8975c0acdf4140..bfb5c25862f37870b6bf8576a429ac62739a50ff 100644 --- a/data/processed/RQ1/RadarCharts/Comparison_Reasoning_vs_NonReasoning.md +++ b/data/processed/RQ1/RadarCharts/Comparison_Reasoning_vs_NonReasoning.md @@ -2,8 +2,6 @@ ## Overall Summary (Averaged Across All Project Series) -_Note: average_score is averaged only across GT projects; pass_rate is averaged only across non-GT projects._ - | Metric | DeepSeek-R1 | DeepSeek-V3-1 | Diff (A-B) | | --- | --- | --- | --- | | exact_match | 0.24 | 0.31 | -0.07 | @@ -12,7 +10,7 @@ _Note: average_score is averaged only across GT projects; pass_rate is averaged | recall | 0.91 | 0.96 | -0.05 | | retry_rate | 0.22 | 0.11 | +0.11 | | average_score | 0.49 | 0.69 | -0.20 | -| pass_rate | 0.99 | 1.00 | -0.01 | +| pass_rate | 0.59 | 0.60 | -0.01 | --- @@ -94,8 +92,8 @@ _Note: average_score is averaged only across GT projects; pass_rate is averaged | Project Series | DeepSeek-R1 | DeepSeek-V3-1 | Diff (A-B) | | --- | --- | --- | --- | | BookWriter | 1.00 | 1.00 | +0.00 | -| EmailResponder | 0.99 | 1.00 | -0.01 | -| GameBuilder | 1.00 | 0.98 | +0.02 | +| EmailResponder | 0.00 | 0.00 | +0.00 | +| GameBuilder | 0.00 | 0.00 | +0.00 | | RecruitmentAssistant | 0.99 | 1.00 | -0.01 | | SocialMediaManager | 0.94 | 1.00 | -0.06 | @@ -106,8 +104,6 @@ _Note: average_score is averaged only across GT projects; pass_rate is averaged ## Overall Summary (Averaged Across All Project Series) -_Note: average_score is averaged only across GT projects; pass_rate is averaged only across non-GT projects._ - | Metric | Gemini-2.5-flash | Gemini-2.5-flash-nothinking | Diff (A-B) | | --- | --- | --- | --- | | exact_match | 0.03 | 0.02 | +0.01 | @@ -116,7 +112,7 @@ _Note: average_score is averaged only across GT projects; pass_rate is averaged | recall | 0.70 | 0.71 | -0.00 | | retry_rate | 0.14 | 0.11 | +0.03 | | average_score | 0.13 | 0.14 | -0.01 | -| pass_rate | 1.00 | 1.00 | -0.00 | +| pass_rate | 0.60 | 0.60 | +0.00 | --- @@ -125,7 +121,7 @@ _Note: average_score is averaged only across GT projects; pass_rate is averaged | Project Series | Gemini-2.5-flash | Gemini-2.5-flash-nothinking | Diff (A-B) | | --- | --- | --- | --- | | BookWriter | 0.00 | 0.00 | +0.00 | -| EmailResponder | 0.01 | 0.04 | -0.04 | +| EmailResponder | 0.01 | 0.05 | -0.04 | | GameBuilder | 0.00 | 0.02 | -0.02 | | LandingPageGenerator | 0.00 | 0.00 | +0.00 | | MarkdownValidator | 0.26 | 0.13 | +0.12 | @@ -198,8 +194,8 @@ _Note: average_score is averaged only across GT projects; pass_rate is averaged | Project Series | Gemini-2.5-flash | Gemini-2.5-flash-nothinking | Diff (A-B) | | --- | --- | --- | --- | | BookWriter | 1.00 | 1.00 | +0.00 | -| EmailResponder | 0.99 | 1.00 | -0.01 | -| GameBuilder | 1.00 | 1.00 | +0.00 | +| EmailResponder | 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+oid sha256:3bfaf54014a33ba2e9c336e92d8caf4d6e1e635a25761cd6c14e008c3a16a483 +size 18695 diff --git a/data/processed/RQ1/RadarCharts/SocialMediaManager_Series_Average_radar.pdf b/data/processed/RQ1/RadarCharts/SocialMediaManager_Series_Average_radar.pdf index 7b7e829f22593da2af898e25b66d6145fb9f9040..e42dd2201b5de7178f7700cacf6f78f48006c4f5 100644 --- a/data/processed/RQ1/RadarCharts/SocialMediaManager_Series_Average_radar.pdf +++ b/data/processed/RQ1/RadarCharts/SocialMediaManager_Series_Average_radar.pdf @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:7323b2edb827896dca89a963c9ed4561511319fb288725cefc25925bfa60df92 +oid sha256:14ba200d908bac263884665db112548199407c850843f71f92529c79d0bb94b9 size 18835 diff --git a/data/processed/RQ1/__pycache__/generate_radar_charts.cpython-313.pyc b/data/processed/RQ1/__pycache__/generate_radar_charts.cpython-313.pyc deleted file mode 100644 index 6e45df9755884f46b10187a825be81a64f38050f..0000000000000000000000000000000000000000 Binary files a/data/processed/RQ1/__pycache__/generate_radar_charts.cpython-313.pyc and /dev/null differ diff --git a/data/processed/RQ1/generate_radar_charts.py b/data/processed/RQ1/generate_radar_charts.py index 7baf414e5791185d5466112db17f6fef389fd07b..e7a24b49d1bc74919891741a67a399819e2aa156 100644 --- a/data/processed/RQ1/generate_radar_charts.py +++ b/data/processed/RQ1/generate_radar_charts.py @@ -9,41 +9,6 @@ import numpy as np from pathlib import Path import math - -SCENARIO_VARIANT_SUFFIXES = ["-H_A2A", "-A2A", "-H_A2A", "-MCP"] - - -def parse_scenario_name(scenario_name: str): - """Return (base_project, variant_suffix). - - variant_suffix is one of: "", "-MCP", "-A2A", "-H_A2A". - """ - for suffix in SCENARIO_VARIANT_SUFFIXES: - if scenario_name.endswith(suffix): - return scenario_name[: -len(suffix)], suffix - return scenario_name, "" - - -def discover_projects_for_version_pair( - scenario_data_dict, suffix_a: str, suffix_b: str -): - """Discover base projects that have both variant scenarios present.""" - projects = set() - for scenario_name in scenario_data_dict.keys(): - base, suffix = parse_scenario_name(scenario_name) - if suffix not in ("", "-MCP", "-A2A", "-H_A2A", "-H_A2A"): - continue - projects.add(base) - return sorted( - [ - base - for base in projects - if f"{base}{suffix_a}" in scenario_data_dict - and f"{base}{suffix_b}" in scenario_data_dict - ] - ) - - try: import matplotlib.pyplot as plt import matplotlib as mpl @@ -65,8 +30,6 @@ METRICS = [ "pass_rate", ] -COMPARISON_METRICS = METRICS + ["average_score"] - # Color palette for different models - professional academic colors (Tableau 10 style) MODEL_COLORS = { "GPT-5": "#1f77b4", # Blue @@ -98,9 +61,6 @@ def df_to_markdown(df): for _, row in df.iterrows(): values = [] for val in row: - if pd.isna(val): - values.append("0.00") - continue if isinstance(val, float): values.append(f"{val:.2f}") else: @@ -110,35 +70,16 @@ def df_to_markdown(df): def metric_display_name(metric, base_project, gt_projects): + if metric == "pass_rate" and gt_projects and base_project in gt_projects: + return "average_score" return metric -def get_metric_value_for_series(metric, base_project, metrics_dict, gt_projects=None): - """Get metric value, calculating average_score from 3 metrics with weights. - - average_score = 0.5 * exact_match + 0.25 * precision + 0.25 * recall - """ - if metric == "average_score": - # Calculate weighted average from 3 components - exact_match = metrics_dict.get("exact_match", 0) - precision = metrics_dict.get("precision", 0) - recall = metrics_dict.get("recall", 0) - - # Check if any value is NaN - if pd.isna(exact_match) or pd.isna(precision) or pd.isna(recall): - return None - - # Weighted average: 0.5 for exact_match, 0.25 each for precision and recall - return 0.5 * exact_match + 0.25 * precision + 0.25 * recall - - val = metrics_dict.get(metric, 0) - if pd.isna(val): - return None - return val - - def series_metric_headers(series_name, gt_projects): - return [m for m in METRICS] + rate_label = ( + "average_score" if gt_projects and series_name in gt_projects else "pass_rate" + ) + return [(rate_label if m == "pass_rate" else m) for m in METRICS] def generate_model_pair_comparison( @@ -148,10 +89,15 @@ def generate_model_pair_comparison( lines = [] lines.append(f"# {comparison_name}\n\n") - # Group scenarios by base project name (remove -A2A, -H_A2A, -MCP suffixes) + # Group scenarios by base project name (remove -A2A, -A2A_mix, -MCP suffixes) project_series = {} for scenario_name in data_dict.keys(): - base_name, _ = parse_scenario_name(scenario_name) + # Extract base project name + base_name = scenario_name + for suffix in ["-A2A_mix", "-A2A", "-MCP"]: + if scenario_name.endswith(suffix): + base_name = scenario_name[: -len(suffix)] + break if base_name not in project_series: project_series[base_name] = [] @@ -162,76 +108,123 @@ def generate_model_pair_comparison( for base_name, scenarios in project_series.items(): series_data[base_name] = {model_a: {}, model_b: {}} - for metric in COMPARISON_METRICS: + for metric in METRICS: vals_a = [] vals_b = [] for scenario_name in scenarios: scenario_data = data_dict[scenario_name] if model_a in scenario_data and model_b in scenario_data: - val_a = get_metric_value_for_series( - metric, - base_name, - scenario_data[model_a], - gt_projects=gt_projects, - ) - val_b = get_metric_value_for_series( - metric, - base_name, - scenario_data[model_b], - gt_projects=gt_projects, - ) - if metric == "pass_rate": - if val_a is None or val_b is None: - continue - if metric == "average_score": - if not val_a or not val_b: - continue - vals_a.append(val_a) - vals_b.append(val_b) + vals_a.append(scenario_data[model_a].get(metric, 0)) + vals_b.append(scenario_data[model_b].get(metric, 0)) if vals_a and vals_b: series_data[base_name][model_a][metric] = np.mean(vals_a) series_data[base_name][model_b][metric] = np.mean(vals_b) # Calculate overall averages - overall_avgs = {metric: {"a": [], "b": []} for metric in COMPARISON_METRICS} + overall_avgs = { + metric: {"a": [], "b": []} for metric in METRICS if metric != "pass_rate" + } + overall_rate_avgs = { + "gt": {"a": [], "b": []}, + "non_gt": {"a": [], "b": []}, + } for base_name in series_data.keys(): - for metric in COMPARISON_METRICS: + for metric in METRICS: if ( metric not in series_data[base_name][model_a] or metric not in series_data[base_name][model_b] ): continue + + if metric == "pass_rate": + bucket = "gt" if gt_projects and base_name in gt_projects else "non_gt" + overall_rate_avgs[bucket]["a"].append( + series_data[base_name][model_a][metric] + ) + overall_rate_avgs[bucket]["b"].append( + series_data[base_name][model_b][metric] + ) + continue + overall_avgs[metric]["a"].append(series_data[base_name][model_a][metric]) overall_avgs[metric]["b"].append(series_data[base_name][model_b][metric]) # Add overall summary section lines.append("## Overall Summary (Averaged Across All Project Series)\n\n") - lines.append( - "_Note: pass_rate is averaged only across project series with available success_rate data; average_score is averaged only across project series with available score data._\n\n" - ) lines.append(f"| Metric | {model_a} | {model_b} | Diff (A-B) |\n") lines.append("| --- | --- | --- | --- |\n") - for metric in COMPARISON_METRICS: - if not overall_avgs[metric]["a"]: + for metric in METRICS: + if metric == "pass_rate": + if overall_rate_avgs["gt"]["a"]: + avg_a = np.mean(overall_rate_avgs["gt"]["a"]) + avg_b = np.mean(overall_rate_avgs["gt"]["b"]) + diff = avg_a - avg_b + lines.append( + f"| average_score | {avg_a:.2f} | {avg_b:.2f} | {diff:+.2f} |\n" + ) + if overall_rate_avgs["non_gt"]["a"]: + avg_a = np.mean(overall_rate_avgs["non_gt"]["a"]) + avg_b = np.mean(overall_rate_avgs["non_gt"]["b"]) + diff = avg_a - avg_b + lines.append( + f"| pass_rate | {avg_a:.2f} | {avg_b:.2f} | {diff:+.2f} |\n" + ) continue - avg_a = np.mean(overall_avgs[metric]["a"]) - avg_b = np.mean(overall_avgs[metric]["b"]) - diff = avg_a - avg_b - lines.append(f"| {metric} | {avg_a:.2f} | {avg_b:.2f} | {diff:+.2f} |\n") + + if overall_avgs[metric]["a"]: + avg_a = np.mean(overall_avgs[metric]["a"]) + avg_b = np.mean(overall_avgs[metric]["b"]) + diff = avg_a - avg_b + lines.append(f"| {metric} | {avg_a:.2f} | {avg_b:.2f} | {diff:+.2f} |\n") lines.append("\n---\n\n") # Add per-series comparisons headers = ["Project Series", f"{model_a}", f"{model_b}", "Diff (A-B)"] - for metric in COMPARISON_METRICS: - lines.append(f"## {metric}\n\n") + for metric in METRICS: + if metric != "pass_rate": + lines.append(f"## {metric}\n\n") + lines.append("| " + " | ".join(headers) + " |\n") + lines.append("| " + " | ".join(["---"] * len(headers)) + " |\n") + + for base_name in sorted(series_data.keys()): + if ( + metric in series_data[base_name][model_a] + and metric in series_data[base_name][model_b] + ): + val_a = series_data[base_name][model_a][metric] + val_b = series_data[base_name][model_b][metric] + diff = val_a - val_b + lines.append( + f"| {base_name} | {val_a:.2f} | {val_b:.2f} | {diff:+.2f} |\n" + ) + lines.append("\n") + continue + + lines.append("## average_score\n\n") lines.append("| " + " | ".join(headers) + " |\n") lines.append("| " + " | ".join(["---"] * len(headers)) + " |\n") for base_name in sorted(series_data.keys()): - if metric == "average_score" and ( - not gt_projects or base_name not in gt_projects + if gt_projects and base_name not in gt_projects: + continue + if ( + metric in series_data[base_name][model_a] + and metric in series_data[base_name][model_b] ): + val_a = series_data[base_name][model_a][metric] + val_b = series_data[base_name][model_b][metric] + diff = val_a - val_b + lines.append( + f"| {base_name} | {val_a:.2f} | {val_b:.2f} | {diff:+.2f} |\n" + ) + lines.append("\n") + + lines.append("## pass_rate\n\n") + lines.append("| " + " | ".join(headers) + " |\n") + lines.append("| " + " | ".join(["---"] * len(headers)) + " |\n") + for base_name in sorted(series_data.keys()): + if gt_projects and base_name in gt_projects: continue if ( metric in series_data[base_name][model_a] @@ -269,22 +262,11 @@ def generate_mcp_comparison(base_project, scenario_data_dict, gt_projects=None): all_models = set(mcp_data.keys()) | set(hardcoded_data.keys()) # Calculate overall averages first - overall_avgs = {metric: {"mcp": [], "hard": []} for metric in COMPARISON_METRICS} + overall_avgs = {metric: {"mcp": [], "hard": []} for metric in METRICS} for model in all_models: - for metric in COMPARISON_METRICS: - mcp_val = get_metric_value_for_series( - metric, base_project, mcp_data.get(model, {}), gt_projects=gt_projects - ) - hard_val = get_metric_value_for_series( - metric, - base_project, - hardcoded_data.get(model, {}), - gt_projects=gt_projects, - ) - if metric == "pass_rate" and (mcp_val is None or hard_val is None): - continue - if metric == "average_score" and (not mcp_val or not hard_val): - continue + for metric in METRICS: + mcp_val = mcp_data.get(model, {}).get(metric, 0) + hard_val = hardcoded_data.get(model, {}).get(metric, 0) overall_avgs[metric]["mcp"].append(mcp_val) overall_avgs[metric]["hard"].append(hard_val) @@ -292,7 +274,7 @@ def generate_mcp_comparison(base_project, scenario_data_dict, gt_projects=None): lines.append("## Overall Summary (Averaged Across All Models)\n\n") lines.append("| Metric | MCP | Hardcoded | Diff (MCP-Hardcoded) |\n") lines.append("| --- | --- | --- | --- |\n") - for metric in COMPARISON_METRICS: + for metric in METRICS: if overall_avgs[metric]["mcp"]: avg_mcp = np.mean(overall_avgs[metric]["mcp"]) avg_hard = np.mean(overall_avgs[metric]["hard"]) @@ -303,7 +285,7 @@ def generate_mcp_comparison(base_project, scenario_data_dict, gt_projects=None): ) lines.append("\n---\n\n") - for metric in COMPARISON_METRICS: + for metric in METRICS: display_metric = metric_display_name(metric, base_project, gt_projects) lines.append(f"## {display_metric}\n\n") headers = ["Model", "MCP", "Hardcoded", "Diff (MCP-Hardcoded)"] @@ -311,15 +293,8 @@ def generate_mcp_comparison(base_project, scenario_data_dict, gt_projects=None): lines.append("| " + " | ".join(["---"] * len(headers)) + " |\n") for model in sorted(all_models): - mcp_val = get_metric_value_for_series( - metric, base_project, mcp_data.get(model, {}), gt_projects=gt_projects - ) - hard_val = get_metric_value_for_series( - metric, - base_project, - hardcoded_data.get(model, {}), - gt_projects=gt_projects, - ) + mcp_val = mcp_data.get(model, {}).get(metric, 0) + hard_val = hardcoded_data.get(model, {}).get(metric, 0) diff = mcp_val - hard_val lines.append( f"| {model} | {mcp_val:.2f} | {hard_val:.2f} | {diff:+.2f} |\n" @@ -333,11 +308,19 @@ def generate_mcp_overall_comparison(projects, scenario_data_dict, gt_projects=No """Generate overall comparison across all MCP vs hardcoded projects.""" lines = [] lines.append("# Overall MCP vs Hardcoded Comparison\n\n") - lines.append(f"Averaged across all projects: {', '.join(projects)}\n\n") + lines.append( + "Averaged across all projects: MarkdownValidator, GameBuilder, EmailResponder\n\n" + ) # Collect data from all projects overall_data = {} - framework_data = {metric: {"mcp": [], "hard": []} for metric in COMPARISON_METRICS} + framework_data = { + metric: {"mcp": [], "hard": []} for metric in METRICS if metric != "pass_rate" + } + framework_rate_data = { + "gt": {"mcp": [], "hard": []}, + "non_gt": {"mcp": [], "hard": []}, + } overall_rate_data = {} for project in projects: @@ -370,37 +353,52 @@ def generate_mcp_overall_comparison(projects, scenario_data_dict, gt_projects=No } for metric in METRICS: - mcp_val = get_metric_value_for_series( - metric, project, mcp_data.get(model, {}), gt_projects=gt_projects - ) - hard_val = get_metric_value_for_series( - metric, - project, - hardcoded_data.get(model, {}), - gt_projects=gt_projects, - ) - if metric not in overall_data[model]: - overall_data[model][metric] = {"mcp": [], "hard": []} - overall_data[model][metric]["mcp"].append(mcp_val) - overall_data[model][metric]["hard"].append(hard_val) - framework_data[metric]["mcp"].append(mcp_val) - framework_data[metric]["hard"].append(hard_val) + mcp_val = mcp_data.get(model, {}).get(metric, 0) + hard_val = hardcoded_data.get(model, {}).get(metric, 0) + if metric == "pass_rate": + overall_rate_data[model][project_bucket]["mcp"].append(mcp_val) + overall_rate_data[model][project_bucket]["hard"].append(hard_val) + framework_rate_data[project_bucket]["mcp"].append(mcp_val) + framework_rate_data[project_bucket]["hard"].append(hard_val) + else: + overall_data[model][metric]["mcp"].append(mcp_val) + overall_data[model][metric]["hard"].append(hard_val) + framework_data[metric]["mcp"].append(mcp_val) + framework_data[metric]["hard"].append(hard_val) # Add framework-level comparison (all models averaged) lines.append("## Framework-Level Comparison (All Models Averaged)\n\n") lines.append("| Metric | MCP | Hardcoded | Diff (MCP-Hardcoded) |\n") lines.append("| --- | --- | --- | --- |\n") - for metric in COMPARISON_METRICS: - if not framework_data[metric]["mcp"]: + for metric in METRICS: + if metric == "pass_rate": + if framework_rate_data["gt"]["mcp"]: + avg_mcp = np.mean(framework_rate_data["gt"]["mcp"]) + avg_hard = np.mean(framework_rate_data["gt"]["hard"]) + diff = avg_mcp - avg_hard + lines.append( + f"| average_score | {avg_mcp:.2f} | {avg_hard:.2f} | {diff:+.2f} |\n" + ) + if framework_rate_data["non_gt"]["mcp"]: + avg_mcp = np.mean(framework_rate_data["non_gt"]["mcp"]) + avg_hard = np.mean(framework_rate_data["non_gt"]["hard"]) + diff = avg_mcp - avg_hard + lines.append( + f"| pass_rate | {avg_mcp:.2f} | {avg_hard:.2f} | {diff:+.2f} |\n" + ) continue - avg_mcp = np.mean(framework_data[metric]["mcp"]) - avg_hard = np.mean(framework_data[metric]["hard"]) - diff = avg_mcp - avg_hard - lines.append(f"| {metric} | {avg_mcp:.2f} | {avg_hard:.2f} | {diff:+.2f} |\n") + + if framework_data[metric]["mcp"]: + avg_mcp = np.mean(framework_data[metric]["mcp"]) + avg_hard = np.mean(framework_data[metric]["hard"]) + diff = avg_mcp - avg_hard + lines.append( + f"| {metric} | {avg_mcp:.2f} | {avg_hard:.2f} | {diff:+.2f} |\n" + ) lines.append("\n---\n\n") # Generate per-model summary tables - for metric in COMPARISON_METRICS: + for metric in METRICS: if metric != "pass_rate": lines.append(f"## {metric}\n\n") headers = [ @@ -413,7 +411,7 @@ def generate_mcp_overall_comparison(projects, scenario_data_dict, gt_projects=No lines.append("| " + " | ".join(["---"] * len(headers)) + " |\n") for model in sorted(overall_data.keys()): - if overall_data[model].get(metric, {}).get("mcp"): + if overall_data[model][metric]["mcp"]: avg_mcp = np.mean(overall_data[model][metric]["mcp"]) avg_hard = np.mean(overall_data[model][metric]["hard"]) diff = avg_mcp - avg_hard @@ -421,6 +419,34 @@ def generate_mcp_overall_comparison(projects, scenario_data_dict, gt_projects=No f"| {model} | {avg_mcp:.2f} | {avg_hard:.2f} | {diff:+.2f} |\n" ) lines.append("\n") + continue + + lines.append("## average_score\n\n") + headers = ["Model", "MCP (Avg)", "Hardcoded (Avg)", "Diff (MCP-Hardcoded)"] + lines.append("| " + " | ".join(headers) + " |\n") + lines.append("| " + " | ".join(["---"] * len(headers)) + " |\n") + for model in sorted(overall_rate_data.keys()): + if overall_rate_data[model]["gt"]["mcp"]: + avg_mcp = np.mean(overall_rate_data[model]["gt"]["mcp"]) + avg_hard = np.mean(overall_rate_data[model]["gt"]["hard"]) + diff = avg_mcp - avg_hard + lines.append( + f"| {model} | {avg_mcp:.2f} | {avg_hard:.2f} | {diff:+.2f} |\n" + ) + lines.append("\n") + + lines.append("## pass_rate\n\n") + lines.append("| " + " | ".join(headers) + " |\n") + lines.append("| " + " | ".join(["---"] * len(headers)) + " |\n") + for model in sorted(overall_rate_data.keys()): + if overall_rate_data[model]["non_gt"]["mcp"]: + avg_mcp = np.mean(overall_rate_data[model]["non_gt"]["mcp"]) + avg_hard = np.mean(overall_rate_data[model]["non_gt"]["hard"]) + diff = avg_mcp - avg_hard + lines.append( + f"| {model} | {avg_mcp:.2f} | {avg_hard:.2f} | {diff:+.2f} |\n" + ) + lines.append("\n") lines.append("---\n\n") return "".join(lines) @@ -490,12 +516,8 @@ def generate_version_comparison( lines.append("| " + " | ".join(["---"] * len(headers)) + " |\n") for model in sorted(all_models): - val_a = get_metric_value_for_series( - metric, base_project, data_a.get(model, {}), gt_projects=gt_projects - ) - val_b = get_metric_value_for_series( - metric, base_project, data_b.get(model, {}), gt_projects=gt_projects - ) + val_a = data_a.get(model, {}).get(metric, 0) + val_b = data_b.get(model, {}).get(metric, 0) diff = val_a - val_b lines.append(f"| {model} | {val_a:.2f} | {val_b:.2f} | {diff:+.2f} |\n") lines.append("\n") @@ -556,12 +578,8 @@ def generate_version_overall_comparison( } for metric in METRICS: - val_a = get_metric_value_for_series( - metric, project, data_a.get(model, {}), gt_projects=gt_projects - ) - val_b = get_metric_value_for_series( - metric, project, data_b.get(model, {}), gt_projects=gt_projects - ) + val_a = data_a.get(model, {}).get(metric, 0) + val_b = data_b.get(model, {}).get(metric, 0) if metric == "pass_rate": overall_rate_data[model][project_bucket]["a"].append(val_a) overall_rate_data[model][project_bucket]["b"].append(val_b) @@ -844,10 +862,8 @@ def create_legend_pdf_horizontal(output_path): def main(): """Main function to process all subfolders and generate radar charts.""" - base_dir = Path(__file__).resolve().parent - if not list(base_dir.glob("*/evaluation_results.csv")): - base_dir = Path("/Users/wzr/TOSEM-2025/RESULTS/RQ1") - output_dir = Path(__file__).resolve().parent / "RadarCharts" + base_dir = Path("/Users/wzr/TOSEM-2025/RESULTS/RQ1") + output_dir = base_dir / "RadarCharts" output_dir.mkdir(exist_ok=True) # Find all evaluation_results.csv files @@ -870,7 +886,7 @@ def main(): # Process each subfolder for csv_file in sorted(csv_files): scenario_name = csv_file.parent.name - series_name, _ = parse_scenario_name(scenario_name) + series_name = scenario_name.split("-")[0] try: # Read the main evaluation results CSV file @@ -914,28 +930,29 @@ def main(): how="left", ) df = df.drop(columns=["Model"]) - df["pass_rate"] = df["pass_rate"].astype(float) + df["pass_rate"] = df["pass_rate"].fillna(0.0) else: print(f" Warning: success_rate.csv not found for {scenario_name}") - df["pass_rate"] = np.nan + df["pass_rate"] = 0.0 - # Calculate average_score from 3 metrics with weights - # average_score = 0.5 * exact_match + 0.25 * precision + 0.25 * recall - df["average_score"] = ( - 0.5 * df["exact_match"] + 0.25 * df["precision"] + 0.25 * df["recall"] - ) + df["pass_rate_agg"] = df["pass_rate"] - # Determine pass_rate_label based on whether score_summary.csv exists pass_rate_label = "pass_rate" score_file = csv_file.parent / "score_summary.csv" if score_file.exists(): + score_df = pd.read_csv(score_file) + score_df["average_score"] = score_df["Mean_Score"] / 100.0 + df = df.merge( + score_df[["Model", "average_score"]], + left_on="model", + right_on="Model", + how="left", + ) + df["pass_rate"] = df["average_score"].fillna(df["pass_rate"]) + df = df.drop(columns=["Model", "average_score"]) pass_rate_label = "average_score" gt_projects.add(series_name) - df["rate_for_plot"] = df["average_score"].where( - df["average_score"] > 0, df["pass_rate"] - ) - # Check if all required metrics are present missing_metrics = [m for m in METRICS if m not in df.columns] if missing_metrics: @@ -949,9 +966,6 @@ def main(): scenario_data[scenario_name][model_name] = { metric: row[metric] for metric in METRICS } - scenario_data[scenario_name][model_name]["average_score"] = row[ - "average_score" - ] # Accumulate data for overall and series-level charts for idx, row in df.iterrows(): @@ -969,10 +983,8 @@ def main(): output_path = output_dir / f"{scenario_name}_radar.pdf" # Generate radar chart - df_plot = df.copy() - df_plot["pass_rate"] = df_plot["rate_for_plot"].fillna(0.0) create_radar_chart( - df_plot, + df, scenario_name, output_path, pass_rate_label=pass_rate_label, @@ -1147,22 +1159,22 @@ def main(): combined_open_closed_lines.append("\n") # Combine generated markdown files into a single markdown with titles - # if combined_open_closed_lines: - # combined_md_path = output_dir / "All_Open_vs_Closed_summaries.md" - # combined_md_path.write_text( - # "".join(combined_open_closed_lines), - # encoding="utf-8", - # ) - # print(f" Combined markdown created: {combined_md_path}") + if combined_open_closed_lines: + combined_md_path = output_dir / "All_Open_vs_Closed_summaries.md" + combined_md_path.write_text( + "".join(combined_open_closed_lines), + encoding="utf-8", + ) + print(f" Combined markdown created: {combined_md_path}") # Combine all Series_Average_radar.csv into a single markdown with titles - # if combined_series_avg_lines: - # combined_csv_md_path = output_dir / "All_Series_Average_tables.md" - # combined_csv_md_path.write_text( - # "".join(combined_series_avg_lines), - # encoding="utf-8", - # ) - # print(f" Combined Series Average markdown created: {combined_csv_md_path}") + if combined_series_avg_lines: + combined_csv_md_path = output_dir / "All_Series_Average_tables.md" + combined_csv_md_path.write_text( + "".join(combined_series_avg_lines), + encoding="utf-8", + ) + print(f" Combined Series Average markdown created: {combined_csv_md_path}") # Generate overall chart overall_output = output_dir / "Overall_Average_radar.pdf" @@ -1187,9 +1199,9 @@ def main(): gt_projects=gt_projects, ) ) - # comparison1_md_path = output_dir / "Comparison_Powerful_vs_Lightweight.md" - # comparison1_md_path.write_text("".join(comparison1_lines), encoding="utf-8") - # print(f" Powerful vs Lightweight comparison created: {comparison1_md_path}") + comparison1_md_path = output_dir / "Comparison_Powerful_vs_Lightweight.md" + comparison1_md_path.write_text("".join(comparison1_lines), encoding="utf-8") + print(f" Powerful vs Lightweight comparison created: {comparison1_md_path}") # File 2: Reasoning vs Non-Reasoning models comparison2_lines = [] @@ -1212,15 +1224,13 @@ def main(): gt_projects=gt_projects, ) ) - # comparison2_md_path = output_dir / "Comparison_Reasoning_vs_NonReasoning.md" - # comparison2_md_path.write_text("".join(comparison2_lines), encoding="utf-8") - # print( - # f" Reasoning vs Non-Reasoning comparison created: {comparison2_md_path}" - # ) + comparison2_md_path = output_dir / "Comparison_Reasoning_vs_NonReasoning.md" + comparison2_md_path.write_text("".join(comparison2_lines), encoding="utf-8") + print(f" Reasoning vs Non-Reasoning comparison created: {comparison2_md_path}") # File 3: MCP vs Hardcoded comparisons comparison3_lines = [] - projects = discover_projects_for_version_pair(scenario_data, "-MCP", "") + projects = ["MarkdownValidator", "GameBuilder", "EmailResponder"] # Add overall summary first comparison3_lines.append( @@ -1235,15 +1245,19 @@ def main(): generate_mcp_comparison(project, scenario_data, gt_projects=gt_projects) ) - # comparison3_md_path = output_dir / "Comparison_MCP_vs_Hardcoded.md" - # comparison3_md_path.write_text("".join(comparison3_lines), encoding="utf-8") - # print(f" MCP vs Hardcoded comparison created: {comparison3_md_path}") + comparison3_md_path = output_dir / "Comparison_MCP_vs_Hardcoded.md" + comparison3_md_path.write_text("".join(comparison3_lines), encoding="utf-8") + print(f" MCP vs Hardcoded comparison created: {comparison3_md_path}") # File 4: MCP vs A2A comparisons comparison4_lines = [] - version_projects = discover_projects_for_version_pair( - scenario_data, "-MCP", "-A2A" - ) + version_projects = [ + "SQL_assistant", + "intelligent_recruitment_platform", + "landing_page_generator", + "self_evaluation_loop_flow", + "write_a_book_with_flows", + ] # Add overall summary first comparison4_lines.append( @@ -1273,27 +1287,23 @@ def main(): ) ) - # comparison4_md_path = output_dir / "Comparison_MCP_vs_A2A.md" - # comparison4_md_path.write_text("".join(comparison4_lines), encoding="utf-8") - # print(f" MCP vs A2A comparison created: {comparison4_md_path}") + comparison4_md_path = output_dir / "Comparison_MCP_vs_A2A.md" + comparison4_md_path.write_text("".join(comparison4_lines), encoding="utf-8") + print(f" MCP vs A2A comparison created: {comparison4_md_path}") - # File 5: A2A vs H_A2A comparisons + # File 5: A2A vs A2A_mix comparisons comparison5_lines = [] - version_projects = discover_projects_for_version_pair( - scenario_data, "-A2A", "-H_A2A" - ) - # Add overall summary first comparison5_lines.append( generate_version_overall_comparison( version_projects, "-A2A", - "-H_A2A", + "-A2A_mix", scenario_data, "A2A", - "H_A2A", - "A2A vs H_A2A Comparison", + "A2A_mix", + "A2A vs A2A_mix Comparison", gt_projects=gt_projects, ) ) @@ -1304,17 +1314,17 @@ def main(): generate_version_comparison( project, "-A2A", - "-H_A2A", + "-A2A_mix", scenario_data, "A2A", - "H_A2A", + "A2A_mix", gt_projects=gt_projects, ) ) - # comparison5_md_path = output_dir / "Comparison_A2A_vs_H_A2A.md" - # comparison5_md_path.write_text("".join(comparison5_lines), encoding="utf-8") - # print(f" A2A vs H_A2A comparison created: {comparison5_md_path}") + comparison5_md_path = output_dir / "Comparison_A2A_vs_A2A_mix.md" + comparison5_md_path.write_text("".join(comparison5_lines), encoding="utf-8") + print(f" A2A vs A2A_mix comparison created: {comparison5_md_path}") print("=" * 60) print(f" Overall average chart created: {overall_output}") diff --git a/data/processed/RQ1/generate_radar_markdowns.py b/data/processed/RQ1/generate_radar_markdowns.py deleted file mode 100644 index 275db4ffb245eab00829b52616c9982cf2e467f1..0000000000000000000000000000000000000000 --- a/data/processed/RQ1/generate_radar_markdowns.py +++ /dev/null @@ -1,1063 +0,0 @@ -#!/usr/bin/env python3 -"""Generate markdown summaries and comparison tables for RQ1 results. - -This script generates markdown files with proper weighted averaging: -- average_score: Only for GT projects (MarkdownValidator, SQLAssistant, LandingPageGenerator) - Read from score_summary.csv (Mean_Score field) -- pass_rate: For non-GT projects (BookWriter, EmailResponder, GameBuilder, - RecruitmentAssistant, SocialMediaManager) - Read from success_rate.csv (Success_Rate(%) field) -- Other metrics: Read from evaluation_results.csv - -All averages are weighted by sample count (num_samples or Total_Samples). -""" - -import pandas as pd -import numpy as np -from pathlib import Path - -from generate_radar_charts import ( - MODEL_COLORS, - MODEL_CATEGORY, - parse_scenario_name, - discover_projects_for_version_pair, -) - -# GT projects that use score_summary.csv for average_score -GT_PROJECT_PREFIXES = {"MarkdownValidator", "SQLAssistant", "LandingPageGenerator"} - -# Non-GT projects that use success_rate.csv for pass_rate -NON_GT_PROJECT_PREFIXES = { - "BookWriter", - "EmailResponder", - "GameBuilder", - "RecruitmentAssistant", - "SocialMediaManager", -} - -# Metrics that come from evaluation_results.csv (excluding pass_rate) -EVAL_METRICS = [ - "exact_match", - "any_order_match", - "precision", - "recall", - "retry_rate", -] - - -def is_gt_project(series_name: str) -> bool: - """Check if a project series is a GT project (uses average_score).""" - return series_name in GT_PROJECT_PREFIXES - - -def load_scenario_data(csv_file: Path) -> dict: - """Load all data for a single scenario (project folder). - - Returns a dict with: - - 'eval_df': DataFrame from evaluation_results.csv with retry_rate merged - - 'score_df': DataFrame from score_summary.csv (if exists) - - 'success_df': DataFrame from success_rate.csv (if exists) - - 'is_gt': Whether this is a GT project - """ - scenario_name = csv_file.parent.name - series_name, _ = parse_scenario_name(scenario_name) - - result = { - "scenario_name": scenario_name, - "series_name": series_name, - "is_gt": is_gt_project(series_name), - "eval_df": None, - "score_df": None, - "success_df": None, - } - - # Read evaluation_results.csv - df = pd.read_csv(csv_file) - - # Read retry_summary.csv for retry_rate - retry_file = csv_file.parent / "retry_summary.csv" - if retry_file.exists(): - retry_df = pd.read_csv(retry_file) - retry_rate_col = "Retry_Rate(%)" - if ( - retry_rate_col not in retry_df.columns - and "Error_Rate(%)" in retry_df.columns - ): - retry_rate_col = "Error_Rate(%)" - retry_df["retry_rate"] = retry_df[retry_rate_col] / 100.0 - df = df.merge( - retry_df[["Model", "retry_rate"]], - left_on="model", - right_on="Model", - how="left", - ) - df = df.drop(columns=["Model"]) - df["retry_rate"] = df["retry_rate"].fillna(0.0) - else: - df["retry_rate"] = 0.0 - - result["eval_df"] = df - - # Read score_summary.csv for GT projects - score_file = csv_file.parent / "score_summary.csv" - if score_file.exists(): - result["score_df"] = pd.read_csv(score_file) - - # Read success_rate.csv for non-GT projects - success_file = csv_file.parent / "success_rate.csv" - if success_file.exists(): - result["success_df"] = pd.read_csv(success_file) - - return result - - -def weighted_average(values: list, weights: list) -> float: - """Calculate weighted average.""" - if not values or not weights or sum(weights) == 0: - return 0.0 - return sum(v * w for v, w in zip(values, weights)) / sum(weights) - - -def generate_overall_summary_table( - series_averages: dict, - series_weights: dict, -) -> str: - """Generate overall summary table with weighted averages. - - Args: - series_averages: {series_name: {model: {metric: value}}} - series_weights: {series_name: {model: total_samples}} - """ - lines = [] - lines.append("# Overall Summary (All Projects - Weighted Average)\n\n") - lines.append( - "_Note: average_score is only computed for GT projects (MarkdownValidator, SQLAssistant, LandingPageGenerator). pass_rate is only computed for non-GT projects._\n\n" - ) - - # Collect all models - all_models = set() - for series_name, model_data in series_averages.items(): - all_models.update(model_data.keys()) - - # Build data for each model - rows = [] - for model in sorted(all_models): - row = {"model": model} - - # Calculate weighted average for each metric across all series - for metric in EVAL_METRICS: - values = [] - weights = [] - for series_name, model_data in series_averages.items(): - if model in model_data and metric in model_data[model]: - values.append(model_data[model][metric]) - weights.append(series_weights[series_name].get(model, 0)) - row[metric] = weighted_average(values, weights) - - # average_score only from GT projects - avg_score_values = [] - avg_score_weights = [] - for series_name in GT_PROJECT_PREFIXES: - if series_name in series_averages and model in series_averages[series_name]: - if "average_score" in series_averages[series_name][model]: - avg_score_values.append( - series_averages[series_name][model]["average_score"] - ) - avg_score_weights.append(series_weights[series_name].get(model, 0)) - row["average_score"] = weighted_average(avg_score_values, avg_score_weights) - - # pass_rate only from non-GT projects - pass_rate_values = [] - pass_rate_weights = [] - for series_name in NON_GT_PROJECT_PREFIXES: - if series_name in series_averages and model in series_averages[series_name]: - if "pass_rate" in series_averages[series_name][model]: - pass_rate_values.append( - series_averages[series_name][model]["pass_rate"] - ) - pass_rate_weights.append(series_weights[series_name].get(model, 0)) - row["pass_rate"] = weighted_average(pass_rate_values, pass_rate_weights) - - rows.append(row) - - # Create table - headers = ["model"] + EVAL_METRICS + ["average_score", "pass_rate"] - lines.append("| " + " | ".join(headers) + " |\n") - lines.append("| " + " | ".join(["---"] * len(headers)) + " |\n") - - for row in rows: - vals = [row["model"]] - for metric in EVAL_METRICS: - vals.append(f"{row.get(metric, 0):.2f}") - vals.append(f"{row.get('average_score', 0):.2f}") - vals.append(f"{row.get('pass_rate', 0):.2f}") - lines.append("| " + " | ".join(vals) + " |\n") - - return "".join(lines) - - -def generate_series_summary_table( - series_name: str, - model_averages: dict, - is_gt: bool, -) -> str: - """Generate summary table for a single series.""" - lines = [] - lines.append(f"# {series_name} Series Average\n\n") - - if is_gt: - lines.append( - "_This is a GT project series. Using average_score from score_summary.csv._\n\n" - ) - rate_metric = "average_score" - else: - lines.append( - "_This is a non-GT project series. Using pass_rate from success_rate.csv._\n\n" - ) - rate_metric = "pass_rate" - - # Build rows - rows = [] - for model in sorted(model_averages.keys()): - row = {"model": model} - for metric in EVAL_METRICS: - row[metric] = model_averages[model].get(metric, 0) - row[rate_metric] = model_averages[model].get(rate_metric, 0) - rows.append(row) - - # Create table - headers = ["model"] + EVAL_METRICS + [rate_metric] - lines.append("| " + " | ".join(headers) + " |\n") - lines.append("| " + " | ".join(["---"] * len(headers)) + " |\n") - - for row in rows: - vals = [row["model"]] - for metric in EVAL_METRICS: - vals.append(f"{row.get(metric, 0):.2f}") - if rate_metric == "average_score": - vals.append(f"{row.get(rate_metric, 0):.2f}") - else: - vals.append(f"{row.get(rate_metric, 0):.2f}") - lines.append("| " + " | ".join(vals) + " |\n") - - return "".join(lines) - - -def generate_open_vs_closed_table( - title: str, - model_averages: dict, - is_gt: bool = False, - series_name: str = None, -) -> str: - """Generate open vs closed model comparison table.""" - lines = [] - lines.append(f"# {title}\n\n") - - rate_metric = "average_score" if is_gt else "pass_rate" - metrics_to_show = EVAL_METRICS + [rate_metric] - - # Group models by category - group_data = {} - for group_name in ["open", "closed"]: - group_models = [m for m, cat in MODEL_CATEGORY.items() if cat == group_name] - group_values = {metric: [] for metric in metrics_to_show} - model_count = 0 - - for model in group_models: - if model in model_averages: - model_count += 1 - for metric in metrics_to_show: - if metric in model_averages[model]: - group_values[metric].append(model_averages[model][metric]) - - if model_count > 0: - group_data[group_name] = { - "model_count": model_count, - "metrics": { - metric: np.mean(vals) if vals else 0 - for metric, vals in group_values.items() - }, - } - - if not group_data: - return "" - - # Create table - headers = ["Group", "Model Count"] + metrics_to_show - lines.append("| " + " | ".join(headers) + " |\n") - lines.append("| " + " | ".join(["---"] * len(headers)) + " |\n") - - for group_name in ["open", "closed"]: - if group_name not in group_data: - continue - data = group_data[group_name] - vals = [group_name, str(data["model_count"])] - for metric in metrics_to_show: - if metric == "average_score": - vals.append(f"{data['metrics'].get(metric, 0):.2f}") - else: - vals.append(f"{data['metrics'].get(metric, 0):.2f}") - lines.append("| " + " | ".join(vals) + " |\n") - - lines.append("\n") - return "".join(lines) - - -def generate_model_pair_comparison( - model_a: str, - model_b: str, - scenario_data: dict, - comparison_name: str, - gt_projects: set, -) -> str: - """Generate comparison table between two models across all scenarios.""" - lines = [] - lines.append(f"# {comparison_name}\n\n") - - # Group scenarios by base project name - project_series = {} - for scenario_name in scenario_data.keys(): - base_name, _ = parse_scenario_name(scenario_name) - if base_name not in project_series: - project_series[base_name] = [] - project_series[base_name].append(scenario_name) - - # Calculate averaged metrics for each project series (weighted) - series_data = {} - for base_name, scenarios in project_series.items(): - series_data[base_name] = {model_a: {}, model_b: {}} - is_gt = base_name in gt_projects - - rate_metric = "average_score" if is_gt else "pass_rate" - all_metrics = EVAL_METRICS + [rate_metric] - - for metric in all_metrics: - vals_a = [] - vals_b = [] - weights_a = [] - weights_b = [] - - for scenario_name in scenarios: - if scenario_name not in scenario_data: - continue - sdata = scenario_data[scenario_name] - - if model_a in sdata and model_b in sdata: - if metric in sdata[model_a] and metric in sdata[model_b]: - val_a = sdata[model_a].get(metric) - val_b = sdata[model_b].get(metric) - weight_a = sdata[model_a].get("num_samples", 1) - weight_b = sdata[model_b].get("num_samples", 1) - - if val_a is not None and val_b is not None: - vals_a.append(val_a) - vals_b.append(val_b) - weights_a.append(weight_a) - weights_b.append(weight_b) - - if vals_a and vals_b: - series_data[base_name][model_a][metric] = weighted_average( - vals_a, weights_a - ) - series_data[base_name][model_b][metric] = weighted_average( - vals_b, weights_b - ) - - # Calculate overall averages (weighted by series total samples) - overall_avgs = { - metric: {"a": [], "b": [], "wa": [], "wb": []} - for metric in EVAL_METRICS + ["average_score", "pass_rate"] - } - - for base_name in series_data.keys(): - is_gt = base_name in gt_projects - rate_metric = "average_score" if is_gt else "pass_rate" - - for metric in EVAL_METRICS: - if ( - metric in series_data[base_name][model_a] - and metric in series_data[base_name][model_b] - ): - overall_avgs[metric]["a"].append( - series_data[base_name][model_a][metric] - ) - overall_avgs[metric]["b"].append( - series_data[base_name][model_b][metric] - ) - - if ( - rate_metric in series_data[base_name][model_a] - and rate_metric in series_data[base_name][model_b] - ): - overall_avgs[rate_metric]["a"].append( - series_data[base_name][model_a][rate_metric] - ) - overall_avgs[rate_metric]["b"].append( - series_data[base_name][model_b][rate_metric] - ) - - # Add overall summary section - lines.append("## Overall Summary (Averaged Across All Project Series)\n\n") - lines.append( - "_Note: average_score is averaged only across GT projects; pass_rate is averaged only across non-GT projects._\n\n" - ) - lines.append(f"| Metric | {model_a} | {model_b} | Diff (A-B) |\n") - lines.append("| --- | --- | --- | --- |\n") - - for metric in EVAL_METRICS + ["average_score", "pass_rate"]: - if not overall_avgs[metric]["a"]: - continue - avg_a = np.mean(overall_avgs[metric]["a"]) - avg_b = np.mean(overall_avgs[metric]["b"]) - diff = avg_a - avg_b - if metric == "average_score": - lines.append(f"| {metric} | {avg_a:.2f} | {avg_b:.2f} | {diff:+.2f} |\n") - else: - lines.append(f"| {metric} | {avg_a:.2f} | {avg_b:.2f} | {diff:+.2f} |\n") - - lines.append("\n---\n\n") - - # Add per-series comparisons for each metric - for metric in EVAL_METRICS + ["average_score", "pass_rate"]: - lines.append(f"## {metric}\n\n") - headers = ["Project Series", f"{model_a}", f"{model_b}", "Diff (A-B)"] - lines.append("| " + " | ".join(headers) + " |\n") - lines.append("| " + " | ".join(["---"] * len(headers)) + " |\n") - - for base_name in sorted(series_data.keys()): - is_gt = base_name in gt_projects - - # For average_score, only show GT projects - if metric == "average_score" and not is_gt: - continue - # For pass_rate, only show non-GT projects - if metric == "pass_rate" and is_gt: - continue - - if ( - metric in series_data[base_name][model_a] - and metric in series_data[base_name][model_b] - ): - val_a = series_data[base_name][model_a][metric] - val_b = series_data[base_name][model_b][metric] - diff = val_a - val_b - if metric == "average_score": - lines.append( - f"| {base_name} | {val_a:.2f} | {val_b:.2f} | {diff:+.2f} |\n" - ) - else: - lines.append( - f"| {base_name} | {val_a:.2f} | {val_b:.2f} | {diff:+.2f} |\n" - ) - - lines.append("\n") - - return "".join(lines) - - -def generate_version_comparison( - base_project: str, - version_a_suffix: str, - version_b_suffix: str, - scenario_data: dict, - version_a_name: str, - version_b_name: str, - gt_projects: set, -) -> str: - """Generate comparison between two versions of a project.""" - lines = [] - lines.append(f"# {base_project}: {version_a_name} vs {version_b_name}\n\n") - - scenario_a = f"{base_project}{version_a_suffix}" - scenario_b = f"{base_project}{version_b_suffix}" - - if scenario_a not in scenario_data or scenario_b not in scenario_data: - lines.append("_Data not available for comparison_\n\n") - return "".join(lines) - - data_a = scenario_data[scenario_a] - data_b = scenario_data[scenario_b] - - all_models = set(data_a.keys()) | set(data_b.keys()) - is_gt = base_project in gt_projects - rate_metric = "average_score" if is_gt else "pass_rate" - all_metrics = EVAL_METRICS + [rate_metric] - - # Calculate overall averages - overall_avgs = {metric: {"a": [], "b": []} for metric in all_metrics} - for model in all_models: - for metric in all_metrics: - val_a = data_a.get(model, {}).get(metric, 0) - val_b = data_b.get(model, {}).get(metric, 0) - if val_a is not None: - overall_avgs[metric]["a"].append(val_a) - if val_b is not None: - overall_avgs[metric]["b"].append(val_b) - - # Add overall summary section - lines.append("## Overall Summary (Averaged Across All Models)\n\n") - lines.append( - f"| Metric | {version_a_name} | {version_b_name} | Diff ({version_a_name}-{version_b_name}) |\n" - ) - lines.append("| --- | --- | --- | --- |\n") - - for metric in all_metrics: - if overall_avgs[metric]["a"]: - avg_a = np.mean(overall_avgs[metric]["a"]) - avg_b = np.mean(overall_avgs[metric]["b"]) - diff = avg_a - avg_b - if metric == "average_score": - lines.append( - f"| {metric} | {avg_a:.2f} | {avg_b:.2f} | {diff:+.2f} |\n" - ) - else: - lines.append( - f"| {metric} | {avg_a:.2f} | {avg_b:.2f} | {diff:+.2f} |\n" - ) - - lines.append("\n---\n\n") - - # Per-metric tables - for metric in all_metrics: - lines.append(f"## {metric}\n\n") - headers = [ - "Model", - version_a_name, - version_b_name, - f"Diff ({version_a_name}-{version_b_name})", - ] - lines.append("| " + " | ".join(headers) + " |\n") - lines.append("| " + " | ".join(["---"] * len(headers)) + " |\n") - - for model in sorted(all_models): - val_a = data_a.get(model, {}).get(metric, 0) or 0 - val_b = data_b.get(model, {}).get(metric, 0) or 0 - diff = val_a - val_b - if metric == "average_score": - lines.append(f"| {model} | {val_a:.2f} | {val_b:.2f} | {diff:+.2f} |\n") - else: - lines.append(f"| {model} | {val_a:.2f} | {val_b:.2f} | {diff:+.2f} |\n") - - lines.append("\n") - - return "".join(lines) - - -def generate_version_overall_comparison( - projects: list, - version_a_suffix: str, - version_b_suffix: str, - scenario_data: dict, - version_a_name: str, - version_b_name: str, - comparison_title: str, - gt_projects: set, -) -> str: - """Generate overall comparison across all projects for two versions.""" - lines = [] - lines.append(f"# Overall {comparison_title}\n\n") - lines.append(f"Averaged across all projects: {', '.join(projects)}\n\n") - - # Collect data from all projects - overall_data = {} # {model: {metric: {"a": [values], "b": [values]}}} - framework_data = { - metric: {"a": [], "b": []} - for metric in EVAL_METRICS + ["average_score", "pass_rate"] - } - - for project in projects: - scenario_a = f"{project}{version_a_suffix}" - scenario_b = f"{project}{version_b_suffix}" - - if scenario_a not in scenario_data or scenario_b not in scenario_data: - continue - - data_a = scenario_data[scenario_a] - data_b = scenario_data[scenario_b] - - all_models = set(data_a.keys()) | set(data_b.keys()) - is_gt = project in gt_projects - rate_metric = "average_score" if is_gt else "pass_rate" - - for model in all_models: - if model not in overall_data: - overall_data[model] = { - m: {"a": [], "b": []} - for m in EVAL_METRICS + ["average_score", "pass_rate"] - } - - for metric in EVAL_METRICS: - val_a = data_a.get(model, {}).get(metric) - val_b = data_b.get(model, {}).get(metric) - if val_a is not None: - overall_data[model][metric]["a"].append(val_a) - framework_data[metric]["a"].append(val_a) - if val_b is not None: - overall_data[model][metric]["b"].append(val_b) - framework_data[metric]["b"].append(val_b) - - # Rate metric - val_a = data_a.get(model, {}).get(rate_metric) - val_b = data_b.get(model, {}).get(rate_metric) - if val_a is not None: - overall_data[model][rate_metric]["a"].append(val_a) - framework_data[rate_metric]["a"].append(val_a) - if val_b is not None: - overall_data[model][rate_metric]["b"].append(val_b) - framework_data[rate_metric]["b"].append(val_b) - - # Framework-level comparison - lines.append("## Framework-Level Comparison (All Models Averaged)\n\n") - lines.append( - f"| Metric | {version_a_name} | {version_b_name} | Diff ({version_a_name}-{version_b_name}) |\n" - ) - lines.append("| --- | --- | --- | --- |\n") - - for metric in EVAL_METRICS + ["average_score", "pass_rate"]: - if framework_data[metric]["a"]: - avg_a = np.mean(framework_data[metric]["a"]) - avg_b = np.mean(framework_data[metric]["b"]) - diff = avg_a - avg_b - if metric == "average_score": - lines.append( - f"| {metric} | {avg_a:.2f} | {avg_b:.2f} | {diff:+.2f} |\n" - ) - else: - lines.append( - f"| {metric} | {avg_a:.2f} | {avg_b:.2f} | {diff:+.2f} |\n" - ) - - lines.append("\n---\n\n") - - # Per-model summary tables - for metric in EVAL_METRICS + ["average_score", "pass_rate"]: - lines.append(f"## {metric}\n\n") - headers = [ - "Model", - f"{version_a_name} (Avg)", - f"{version_b_name} (Avg)", - f"Diff ({version_a_name}-{version_b_name})", - ] - lines.append("| " + " | ".join(headers) + " |\n") - lines.append("| " + " | ".join(["---"] * len(headers)) + " |\n") - - for model in sorted(overall_data.keys()): - if overall_data[model][metric]["a"]: - avg_a = np.mean(overall_data[model][metric]["a"]) - avg_b = ( - np.mean(overall_data[model][metric]["b"]) - if overall_data[model][metric]["b"] - else 0 - ) - diff = avg_a - avg_b - if metric == "average_score": - lines.append( - f"| {model} | {avg_a:.2f} | {avg_b:.2f} | {diff:+.2f} |\n" - ) - else: - lines.append( - f"| {model} | {avg_a:.2f} | {avg_b:.2f} | {diff:+.2f} |\n" - ) - - lines.append("\n") - - lines.append("---\n\n") - return "".join(lines) - - -def generate_mcp_comparison( - base_project: str, - scenario_data: dict, - gt_projects: set, -) -> str: - """Generate comparison between MCP and hardcoded versions for a project.""" - return generate_version_comparison( - base_project, - "-MCP", - "", - scenario_data, - "MCP", - "Hardcoded", - gt_projects, - ) - - -def generate_mcp_overall_comparison( - projects: list, - scenario_data: dict, - gt_projects: set, -) -> str: - """Generate overall comparison across all MCP vs hardcoded projects.""" - return generate_version_overall_comparison( - projects, - "-MCP", - "", - scenario_data, - "MCP", - "Hardcoded", - "MCP vs Hardcoded Comparison", - gt_projects, - ) - - -def main() -> None: - """Read evaluation CSVs and generate all markdown reports only.""" - - base_dir = Path(__file__).resolve().parent - if not list(base_dir.glob("*/evaluation_results.csv")): - base_dir = Path("/Users/wzr/TOSEM-2025/RESULTS/RQ1") - - output_dir = Path(__file__).resolve().parent / "RadarCharts" - output_dir.mkdir(exist_ok=True) - - # Find all evaluation_results.csv files - csv_files = list(base_dir.glob("*/evaluation_results.csv")) - print(f"Found {len(csv_files)} evaluation_results.csv files") - print("=" * 60) - - # Data structures for aggregation - # scenario_data[scenario_name][model] = {metric: value, num_samples: n} - scenario_data = {} - # series_data[series_name][model] = {metric: [(value, weight), ...]} - series_raw_data = {} - gt_projects = set() - - # Load all scenario data - for csv_file in sorted(csv_files): - scenario_name = csv_file.parent.name - series_name, _ = parse_scenario_name(scenario_name) - - try: - data = load_scenario_data(csv_file) - eval_df = data["eval_df"] - score_df = data["score_df"] - success_df = data["success_df"] - is_gt = data["is_gt"] - - if is_gt: - gt_projects.add(series_name) - - scenario_data[scenario_name] = {} - - for _, row in eval_df.iterrows(): - model_name = row["model"] - num_samples = row.get("num_samples", 1) - - model_data = { - "num_samples": num_samples, - } - - # Read metrics from evaluation_results.csv - for metric in EVAL_METRICS: - if metric in row: - model_data[metric] = row[metric] - - # Read average_score from score_summary.csv for GT projects - if is_gt and score_df is not None: - score_row = score_df[score_df["Model"] == model_name] - if not score_row.empty: - # Mean_Score is in percentage (0-100), convert to decimal (0-1) - model_data["average_score"] = ( - score_row["Mean_Score"].values[0] / 100.0 - ) - model_data["score_samples"] = score_row["Total_Samples"].values[ - 0 - ] - - # Read pass_rate from success_rate.csv for non-GT projects - if not is_gt and success_df is not None: - success_row = success_df[success_df["Model"] == model_name] - if not success_row.empty: - # Success_Rate(%) is in percentage (0-100), convert to decimal (0-1) - model_data["pass_rate"] = ( - success_row["Success_Rate(%)"].values[0] / 100.0 - ) - model_data["pass_rate_samples"] = success_row["Total"].values[0] - - scenario_data[scenario_name][model_name] = model_data - - # Accumulate raw data for series averaging - if series_name not in series_raw_data: - series_raw_data[series_name] = {} - if model_name not in series_raw_data[series_name]: - series_raw_data[series_name][model_name] = { - m: [] for m in EVAL_METRICS + ["average_score", "pass_rate"] - } - series_raw_data[series_name][model_name]["weights"] = [] - series_raw_data[series_name][model_name]["rate_weights"] = [] - - # Store (value, weight) pairs - for metric in EVAL_METRICS: - if metric in model_data: - series_raw_data[series_name][model_name][metric].append( - (model_data[metric], num_samples) - ) - - if "average_score" in model_data: - weight = model_data.get("score_samples", num_samples) - series_raw_data[series_name][model_name]["average_score"].append( - (model_data["average_score"], weight) - ) - - if "pass_rate" in model_data: - weight = model_data.get("pass_rate_samples", num_samples) - series_raw_data[series_name][model_name]["pass_rate"].append( - (model_data["pass_rate"], weight) - ) - - print(f" Loaded {scenario_name} (GT: {is_gt})") - - except Exception as e: - print(f" Error processing {scenario_name}: {e}") - import traceback - - traceback.print_exc() - - print("=" * 60) - print(f"GT projects: {sorted(gt_projects)}") - print("=" * 60) - - # Calculate weighted series averages - # series_averages[series_name][model] = {metric: weighted_avg} - series_averages = {} - series_weights = {} # Total weights per series per model - - for series_name, models_data in series_raw_data.items(): - series_averages[series_name] = {} - series_weights[series_name] = {} - is_gt = series_name in gt_projects - - for model_name, metric_data in models_data.items(): - series_averages[series_name][model_name] = {} - total_weight = 0 - - for metric in EVAL_METRICS: - if metric_data[metric]: - values = [v for v, w in metric_data[metric]] - weights = [w for v, w in metric_data[metric]] - series_averages[series_name][model_name][metric] = weighted_average( - values, weights - ) - total_weight = max(total_weight, sum(weights)) - - if is_gt and metric_data["average_score"]: - values = [v for v, w in metric_data["average_score"]] - weights = [w for v, w in metric_data["average_score"]] - series_averages[series_name][model_name]["average_score"] = ( - weighted_average(values, weights) - ) - - if not is_gt and metric_data["pass_rate"]: - values = [v for v, w in metric_data["pass_rate"]] - weights = [w for v, w in metric_data["pass_rate"]] - series_averages[series_name][model_name]["pass_rate"] = ( - weighted_average(values, weights) - ) - - series_weights[series_name][model_name] = total_weight - - # Generate markdown files - try: - combined_series_avg_lines = [] - combined_open_closed_lines = [] - - # 1) Overall summary - combined_series_avg_lines.append( - generate_overall_summary_table(series_averages, series_weights) - ) - combined_series_avg_lines.append("\n") - - # 2) Per-series summaries - for series_name in sorted(series_averages.keys()): - is_gt = series_name in gt_projects - combined_series_avg_lines.append( - generate_series_summary_table( - series_name, - series_averages[series_name], - is_gt, - ) - ) - combined_series_avg_lines.append("\n") - - # 3) Overall open vs closed - all_models_avg = {} - for series_name, models_data in series_averages.items(): - for model_name, metrics in models_data.items(): - if model_name not in all_models_avg: - all_models_avg[model_name] = { - m: [] for m in EVAL_METRICS + ["average_score", "pass_rate"] - } - for metric, value in metrics.items(): - all_models_avg[model_name][metric].append(value) - - # Average across all series - overall_model_avgs = {} - for model_name, metric_lists in all_models_avg.items(): - overall_model_avgs[model_name] = {} - for metric, values in metric_lists.items(): - if values: - overall_model_avgs[model_name][metric] = np.mean(values) - - combined_open_closed_lines.append( - generate_open_vs_closed_table( - "Overall Open vs Closed Models", - overall_model_avgs, - is_gt=False, # Use pass_rate column for the overall - ) - ) - - # 4) Per-series open vs closed - for series_name in sorted(series_averages.keys()): - is_gt = series_name in gt_projects - combined_open_closed_lines.append( - generate_open_vs_closed_table( - f"{series_name} Open vs Closed Models", - series_averages[series_name], - is_gt=is_gt, - series_name=series_name, - ) - ) - - # Write combined markdowns - combined_csv_md_path = output_dir / "All_Series_Average_tables.md" - combined_csv_md_path.write_text( - "".join(combined_series_avg_lines), encoding="utf-8" - ) - print(f" Combined Series Average markdown created: {combined_csv_md_path}") - - combined_md_path = output_dir / "All_Open_vs_Closed_summaries.md" - combined_md_path.write_text( - "".join(combined_open_closed_lines), encoding="utf-8" - ) - print(f" Combined Open vs Closed markdown created: {combined_md_path}") - - # 5) Pairwise model comparisons - # File 1: GPT-5 vs GPT-4o-mini - comparison1_lines = generate_model_pair_comparison( - "GPT-5", - "GPT-4o-mini", - scenario_data, - "GPT-5 vs GPT-4o-mini (Powerful vs Lightweight)", - gt_projects, - ) - comparison1_md_path = output_dir / "Comparison_Powerful_vs_Lightweight.md" - comparison1_md_path.write_text(comparison1_lines, encoding="utf-8") - print(f" Powerful vs Lightweight comparison created: {comparison1_md_path}") - - # File 2: Reasoning vs Non-Reasoning models - comparison2_lines = [] - comparison2_lines.append( - generate_model_pair_comparison( - "DeepSeek-R1", - "DeepSeek-V3-1", - scenario_data, - "DeepSeek-R1 vs DeepSeek-V3-1 (Reasoning vs Non-Reasoning)", - gt_projects, - ) - ) - comparison2_lines.append("\n---\n\n") - comparison2_lines.append( - generate_model_pair_comparison( - "Gemini-2.5-flash", - "Gemini-2.5-flash-nothinking", - scenario_data, - "Gemini-2.5-flash vs Gemini-2.5-flash-nothinking (Reasoning vs Non-Reasoning)", - gt_projects, - ) - ) - comparison2_md_path = output_dir / "Comparison_Reasoning_vs_NonReasoning.md" - comparison2_md_path.write_text("".join(comparison2_lines), encoding="utf-8") - print(f" Reasoning vs Non-Reasoning comparison created: {comparison2_md_path}") - - # File 3: MCP vs Hardcoded - comparison3_lines = [] - projects = discover_projects_for_version_pair(scenario_data, "-MCP", "") - comparison3_lines.append( - generate_mcp_overall_comparison(projects, scenario_data, gt_projects) - ) - for project in projects: - comparison3_lines.append( - generate_mcp_comparison(project, scenario_data, gt_projects) - ) - comparison3_md_path = output_dir / "Comparison_MCP_vs_Hardcoded.md" - comparison3_md_path.write_text("".join(comparison3_lines), encoding="utf-8") - print(f" MCP vs Hardcoded comparison created: {comparison3_md_path}") - - # File 4: MCP vs A2A - comparison4_lines = [] - version_projects = discover_projects_for_version_pair( - scenario_data, "-MCP", "-A2A" - ) - comparison4_lines.append( - generate_version_overall_comparison( - version_projects, - "-MCP", - "-A2A", - scenario_data, - "MCP", - "A2A", - "MCP vs A2A Comparison", - gt_projects, - ) - ) - for project in version_projects: - comparison4_lines.append( - generate_version_comparison( - project, - "-MCP", - "-A2A", - scenario_data, - "MCP", - "A2A", - gt_projects, - ) - ) - comparison4_md_path = output_dir / "Comparison_MCP_vs_A2A.md" - comparison4_md_path.write_text("".join(comparison4_lines), encoding="utf-8") - print(f" MCP vs A2A comparison created: {comparison4_md_path}") - - # File 5: A2A vs H_A2A - comparison5_lines = [] - version_projects = discover_projects_for_version_pair( - scenario_data, "-A2A", "-H_A2A" - ) - comparison5_lines.append( - generate_version_overall_comparison( - version_projects, - "-A2A", - "-H_A2A", - scenario_data, - "A2A", - "H_A2A", - "A2A vs H_A2A Comparison", - gt_projects, - ) - ) - for project in version_projects: - comparison5_lines.append( - generate_version_comparison( - project, - "-A2A", - "-H_A2A", - scenario_data, - "A2A", - "H_A2A", - gt_projects, - ) - ) - comparison5_md_path = output_dir / "Comparison_A2A_vs_H_A2A.md" - comparison5_md_path.write_text("".join(comparison5_lines), encoding="utf-8") - print(f" A2A vs H_A2A comparison created: {comparison5_md_path}") - - print("=" * 60) - print("All markdown summaries and comparisons generated!") - - except Exception as e: - print(f" Error generating markdown reports: {e}") - import traceback - - traceback.print_exc() - - -if __name__ == "__main__": - main() diff --git a/data/processed/RQ2/Agent-Time-UnifiedY/BookWriter-A2A_agent_time_share_bars.pdf b/data/processed/RQ2/Agent-Time-UnifiedY/BookWriter-A2A_agent_time_share_bars.pdf index a43abd7111544c07afd14c83a5feb8c4bbe64f9a..6106533fddd396555324cd2c594e5967401f49df 100644 --- a/data/processed/RQ2/Agent-Time-UnifiedY/BookWriter-A2A_agent_time_share_bars.pdf +++ b/data/processed/RQ2/Agent-Time-UnifiedY/BookWriter-A2A_agent_time_share_bars.pdf @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:b77b7a12d7fa38f87f9ad6af7c31bbfec95e52053613d69b38d58e5d548747af -size 32338 +oid sha256:df3ca659fa885fd53d09dd40fdcf8d5daaf0d5b04e1d259f924c64dc19c4cf31 +size 26029 diff --git a/data/processed/RQ2/Agent-Time-UnifiedY/BookWriter-H_A2A_agent_time_share_bars.pdf b/data/processed/RQ2/Agent-Time-UnifiedY/BookWriter-H_A2A_agent_time_share_bars.pdf index 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sha256:e0ec2ad24dca3abad770ca36d0cc2d7e12949ff6f84cf2d0798a25a1b7a59569 +size 38768 diff --git a/data/processed/RQ2/ECDFs/A2A_vs_A2A_mix_Detailed_Comparison.md b/data/processed/RQ2/ECDFs/A2A_vs_A2A_mix_Detailed_Comparison.md new file mode 100644 index 0000000000000000000000000000000000000000..de684d940e9229302363f90c392beaa7c6f723c8 --- /dev/null +++ b/data/processed/RQ2/ECDFs/A2A_vs_A2A_mix_Detailed_Comparison.md @@ -0,0 +1,6 @@ +# A2A vs A2A_mix: Detailed Per-Project Comparison + +This document compares A2A and A2A_mix architectures for each project, showing both per-model and overall statistics. + +--- + diff --git a/data/processed/RQ2/ECDFs/A2A_vs_H_A2A_Detailed_Comparison.md b/data/processed/RQ2/ECDFs/A2A_vs_H_A2A_Detailed_Comparison.md deleted file mode 100644 index 540d4aeafe28ce24fdef2773f86ca8b7c0140887..0000000000000000000000000000000000000000 --- a/data/processed/RQ2/ECDFs/A2A_vs_H_A2A_Detailed_Comparison.md +++ /dev/null @@ -1,150 +0,0 @@ -# A2A vs H_A2A: Detailed Per-Project Comparison - -This document compares A2A and H_A2A architectures for each project, showing both per-model and overall statistics. - ---- - -## Overall (All Projects) - -| Project | A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) | -| --- | --- | --- | --- | -| SQLAssistant | 235.62 | 175.78 | -59.84s (-25.4%) | -| RecruitmentAssistant | 501.47 | 344.65 | -156.82s (-31.3%) | -| LandingPageGenerator | 138.60 | 129.66 | -8.94s (-6.5%) | -| SocialMediaManager | 155.30 | 115.68 | -39.62s (-25.5%) | -| BookWriter | 361.87 | 264.19 | -97.68s (-27.0%) | - -## All Projects Combined (Per-Model) - -| Model | A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) | -| --- | --- | --- | --- | -| GPT-5 | 69.35 | 50.18 | -19.17s (-27.6%) | -| GPT-4o-mini | 89.32 | 72.46 | -16.86s (-18.9%) | -| DeepSeek-V3-1 | 258.41 | 191.05 | -67.36s (-26.1%) | -| DeepSeek-R1 | 774.85 | 642.65 | -132.20s (-17.1%) | -| Gemini-2.5-flash | 178.36 | 104.40 | -73.96s (-41.5%) | -| Gemini-2.5-flash-nothinking | 107.27 | 61.33 | -45.94s (-42.8%) | -| Qwen3-235b | 472.44 | 319.85 | -152.58s (-32.3%) | - ---- - -## SQLAssistant - -### Project-Level Summary - -### Overall Summary - -| A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) | -| --- | --- | --- | -| 235.62 | 175.78 | -59.84s (-25.4%) | - -### Per-Model Comparison - -| Model | A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 1130.69 | 898.25 | -232.43s (-20.6%) | -| DeepSeek-V3-1 | 161.80 | 68.15 | -93.65s (-57.9%) | -| GPT-4o-mini | 33.96 | 29.00 | -4.96s (-14.6%) | -| GPT-5 | 21.10 | 28.21 | +7.11s (+33.7%) | -| Gemini-2.5-flash | 151.07 | 83.10 | -67.97s (-45.0%) | -| Gemini-2.5-flash-nothinking | 68.41 | 54.03 | -14.38s (-21.0%) | -| Qwen3-235b | 82.32 | 69.68 | -12.64s (-15.4%) | - ---- - -## RecruitmentAssistant - -### Project-Level Summary - -### Overall Summary - -| A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) | -| --- | --- | --- | -| 501.47 | 344.65 | -156.82s (-31.3%) | - -### Per-Model Comparison - -| Model | A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 922.16 | 774.66 | -147.50s (-16.0%) | -| DeepSeek-V3-1 | 312.32 | 245.49 | -66.83s (-21.4%) | -| GPT-4o-mini | 178.62 | 144.28 | -34.34s (-19.2%) | -| GPT-5 | 67.32 | 52.98 | -14.33s (-21.3%) | -| Gemini-2.5-flash | 357.79 | 127.63 | -230.16s (-64.3%) | -| Gemini-2.5-flash-nothinking | 195.19 | 94.40 | -100.79s (-51.6%) | -| Qwen3-235b | 1476.89 | 973.11 | -503.78s (-34.1%) | - ---- - -## LandingPageGenerator - -### Project-Level Summary - -### Overall Summary - -| A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) | -| --- | --- | --- | -| 138.60 | 129.66 | -8.94s (-6.5%) | - -### Per-Model Comparison - -| Model | A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 302.33 | 283.59 | -18.74s (-6.2%) | -| DeepSeek-V3-1 | 215.52 | 234.43 | +18.91s (+8.8%) | -| GPT-4o-mini | 44.98 | 41.31 | -3.66s (-8.1%) | -| GPT-5 | 23.26 | 30.31 | +7.05s (+30.3%) | -| Gemini-2.5-flash | 76.95 | 73.92 | -3.03s (-3.9%) | -| Gemini-2.5-flash-nothinking | 98.14 | 47.47 | -50.67s (-51.6%) | -| Qwen3-235b | 208.99 | 196.56 | -12.44s (-6.0%) | - ---- - -## SocialMediaManager - -### Project-Level Summary - -### Overall Summary - -| A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) | -| --- | --- | --- | -| 155.30 | 115.68 | -39.62s (-25.5%) | - -### Per-Model Comparison - -| Model | A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 557.94 | 427.00 | -130.94s (-23.5%) | -| DeepSeek-V3-1 | 158.60 | 111.56 | -47.05s (-29.7%) | -| GPT-4o-mini | 87.89 | 46.16 | -41.73s (-47.5%) | -| GPT-5 | 41.51 | 27.98 | -13.53s (-32.6%) | -| Gemini-2.5-flash | 65.45 | 65.04 | -0.42s (-0.6%) | -| Gemini-2.5-flash-nothinking | 40.70 | 29.03 | -11.67s (-28.7%) | -| Qwen3-235b | 135.01 | 102.98 | -32.03s (-23.7%) | - ---- - -## BookWriter - -### Project-Level Summary - -### Overall Summary - -| A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) | -| --- | --- | --- | -| 361.87 | 264.19 | -97.68s (-27.0%) | - -### Per-Model Comparison - -| Model | A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 961.15 | 829.76 | -131.39s (-13.7%) | -| DeepSeek-V3-1 | 443.80 | 295.61 | -148.19s (-33.4%) | -| GPT-4o-mini | 101.15 | 101.57 | +0.42s (+0.4%) | -| GPT-5 | 193.58 | 111.41 | -82.17s (-42.4%) | -| Gemini-2.5-flash | 240.55 | 172.33 | -68.22s (-28.4%) | -| Gemini-2.5-flash-nothinking | 133.91 | 81.73 | -52.18s (-39.0%) | -| Qwen3-235b | 458.96 | 256.93 | -202.03s (-44.0%) | - ---- - diff --git a/data/processed/RQ2/ECDFs/ECDF_Model_Legend_horizontal.pdf b/data/processed/RQ2/ECDFs/ECDF_Model_Legend_horizontal.pdf index aac267744c7cc48a9e11c2c00f4948aea41ec567..9e9d728d56e5b0bd5d27a520fad68637f4332d4a 100644 --- a/data/processed/RQ2/ECDFs/ECDF_Model_Legend_horizontal.pdf +++ b/data/processed/RQ2/ECDFs/ECDF_Model_Legend_horizontal.pdf @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:3b70c6e36875b8117bad54bbb657a55ee3ff5dd39c65f1bc30dfb66230823983 +oid sha256:581845294c8d1c503cd99fbf0a72ac0b4781b027311e2e91c63b4875a6e32286 size 13138 diff --git a/data/processed/RQ2/ECDFs/Per_Project_Model_Comparison.md b/data/processed/RQ2/ECDFs/Per_Project_Model_Comparison.md index cd276344019df1b37d5aa20b89c76e29364ebfdf..085b3795058cc1cd732c747c35ed3409bfae9d02 100644 --- a/data/processed/RQ2/ECDFs/Per_Project_Model_Comparison.md +++ b/data/processed/RQ2/ECDFs/Per_Project_Model_Comparison.md @@ -3,7 +3,7 @@ This document compares model performance within each of the 21 projects. Each project shows: -- Model statistics (mean) +- Model statistics (mean, median, count, total time) - Relative performance compared to the fastest model --- @@ -12,15 +12,15 @@ Each project shows: ### Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| GPT-5 | 45.52 | -| GPT-4o-mini | 69.33 | -| DeepSeek-V3-1 | 191.58 | -| DeepSeek-R1 | 589.60 | -| Gemini-2.5-flash | 175.03 | -| Gemini-2.5-flash-nothinking | 110.57 | -| Qwen3-235b | 307.27 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| GPT-5 | 45.52 | 27.00 | 1270 | 57816.36 | +| GPT-4o-mini | 69.33 | 49.05 | 1270 | 88053.45 | +| DeepSeek-V3-1 | 191.58 | 151.97 | 1270 | 243310.25 | +| DeepSeek-R1 | 589.60 | 472.63 | 1268 | 747611.27 | +| Gemini-2.5-flash | 175.03 | 93.36 | 1270 | 222285.01 | +| Gemini-2.5-flash-nothinking | 110.57 | 56.68 | 1270 | 140427.64 | +| Qwen3-235b | 307.27 | 98.62 | 1270 | 390226.65 | ### Relative Performance (vs. Fastest Model) @@ -36,7 +36,7 @@ Baseline (fastest): **GPT-5** (45.52s mean) | Gemini-2.5-flash-nothinking | 110.57 | +142.9% | | Qwen3-235b | 307.27 | +574.9% | -**Slowest model:** DeepSeek-R1 (589.60s mean, +1195.1% slower than baseline GPT-5) +**Slowest model:** DeepSeek-R1 (589.60s mean, +1195.1% slower than baseline) --- @@ -44,15 +44,15 @@ Baseline (fastest): **GPT-5** (45.52s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 961.15 | -| DeepSeek-V3-1 | 443.80 | -| GPT-4o-mini | 101.15 | -| GPT-5 | 193.58 | -| Gemini-2.5-flash | 240.55 | -| Gemini-2.5-flash-nothinking | 133.91 | -| Qwen3-235b | 458.96 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 961.15 | 894.17 | 60 | 57669.13 | +| DeepSeek-V3-1 | 443.80 | 425.58 | 60 | 26627.97 | +| GPT-4o-mini | 101.15 | 97.92 | 60 | 6069.04 | +| GPT-5 | 193.58 | 201.17 | 60 | 11614.51 | +| Gemini-2.5-flash | 240.55 | 238.57 | 60 | 14433.15 | +| Gemini-2.5-flash-nothinking | 133.91 | 129.30 | 60 | 8034.51 | +| Qwen3-235b | 458.96 | 403.43 | 60 | 27537.69 | ## Relative Performance (vs. Fastest Model) @@ -68,7 +68,7 @@ Baseline (fastest): **GPT-4o-mini** (101.15s mean) | Gemini-2.5-flash-nothinking | 133.91 | +32.4% | | Qwen3-235b | 458.96 | +353.7% | -**Slowest model:** DeepSeek-R1 (961.15s mean, +850.2% slower than baseline GPT-4o-mini) +**Slowest model:** DeepSeek-R1 (961.15s mean, +850.2% slower than baseline) --- @@ -76,15 +76,15 @@ Baseline (fastest): **GPT-4o-mini** (101.15s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 829.76 | -| DeepSeek-V3-1 | 295.61 | -| GPT-4o-mini | 101.57 | -| GPT-5 | 111.41 | -| Gemini-2.5-flash | 172.33 | -| Gemini-2.5-flash-nothinking | 81.73 | -| Qwen3-235b | 256.93 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 829.76 | 792.88 | 60 | 49785.81 | +| DeepSeek-V3-1 | 295.61 | 283.52 | 60 | 17736.69 | +| GPT-4o-mini | 101.57 | 97.49 | 60 | 6094.34 | +| GPT-5 | 111.41 | 99.91 | 60 | 6684.57 | +| Gemini-2.5-flash | 172.33 | 160.45 | 60 | 10339.79 | +| Gemini-2.5-flash-nothinking | 81.73 | 80.19 | 60 | 4903.86 | +| Qwen3-235b | 256.93 | 217.75 | 60 | 15416.09 | ## Relative Performance (vs. Fastest Model) @@ -100,7 +100,7 @@ Baseline (fastest): **Gemini-2.5-flash-nothinking** (81.73s mean) | Gemini-2.5-flash-nothinking | 81.73 | +0.0% | | Qwen3-235b | 256.93 | +214.4% | -**Slowest model:** DeepSeek-R1 (829.76s mean, +915.2% slower than baseline Gemini-2.5-flash-nothinking) +**Slowest model:** DeepSeek-R1 (829.76s mean, +915.2% slower than baseline) --- @@ -108,15 +108,15 @@ Baseline (fastest): **Gemini-2.5-flash-nothinking** (81.73s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 960.29 | -| DeepSeek-V3-1 | 395.67 | -| GPT-4o-mini | 112.23 | -| GPT-5 | 140.17 | -| Gemini-2.5-flash | 183.47 | -| Gemini-2.5-flash-nothinking | 115.47 | -| Qwen3-235b | 442.38 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 960.29 | 818.51 | 60 | 57617.52 | +| DeepSeek-V3-1 | 395.67 | 361.70 | 60 | 23740.13 | +| GPT-4o-mini | 112.23 | 103.68 | 60 | 6733.76 | +| GPT-5 | 140.17 | 143.72 | 60 | 8409.92 | +| Gemini-2.5-flash | 183.47 | 184.77 | 60 | 11008.22 | +| Gemini-2.5-flash-nothinking | 115.47 | 108.62 | 60 | 6928.23 | +| Qwen3-235b | 442.38 | 404.23 | 60 | 26543.00 | ## Relative Performance (vs. Fastest Model) @@ -132,7 +132,7 @@ Baseline (fastest): **GPT-4o-mini** (112.23s mean) | Gemini-2.5-flash-nothinking | 115.47 | +2.9% | | Qwen3-235b | 442.38 | +294.2% | -**Slowest model:** DeepSeek-R1 (960.29s mean, +755.7% slower than baseline GPT-4o-mini) +**Slowest model:** DeepSeek-R1 (960.29s mean, +755.7% slower than baseline) --- @@ -140,15 +140,15 @@ Baseline (fastest): **GPT-4o-mini** (112.23s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 233.49 | -| DeepSeek-V3-1 | 47.23 | -| GPT-4o-mini | 17.90 | -| GPT-5 | 9.41 | -| Gemini-2.5-flash | 180.03 | -| Gemini-2.5-flash-nothinking | 217.85 | -| Qwen3-235b | 37.41 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 233.49 | 185.78 | 98 | 22882.07 | +| DeepSeek-V3-1 | 47.23 | 43.01 | 100 | 4723.35 | +| GPT-4o-mini | 17.90 | 13.83 | 100 | 1789.63 | +| GPT-5 | 9.41 | 8.42 | 100 | 941.33 | +| Gemini-2.5-flash | 180.03 | 42.84 | 100 | 18003.41 | +| Gemini-2.5-flash-nothinking | 217.85 | 32.45 | 100 | 21785.38 | +| Qwen3-235b | 37.41 | 33.02 | 100 | 3740.70 | ## Relative Performance (vs. Fastest Model) @@ -164,7 +164,7 @@ Baseline (fastest): **GPT-5** (9.41s mean) | Gemini-2.5-flash-nothinking | 217.85 | +2214.3% | | Qwen3-235b | 37.41 | +297.4% | -**Slowest model:** DeepSeek-R1 (233.49s mean, +2380.4% slower than baseline GPT-5) +**Slowest model:** DeepSeek-R1 (233.49s mean, +2380.4% slower than baseline) --- @@ -172,15 +172,15 @@ Baseline (fastest): **GPT-5** (9.41s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 141.80 | -| DeepSeek-V3-1 | 44.42 | -| GPT-4o-mini | 18.15 | -| GPT-5 | 8.25 | -| Gemini-2.5-flash | 115.85 | -| Gemini-2.5-flash-nothinking | 151.00 | -| Qwen3-235b | 38.30 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 141.80 | 125.64 | 100 | 14179.71 | +| DeepSeek-V3-1 | 44.42 | 39.60 | 100 | 4442.01 | +| GPT-4o-mini | 18.15 | 15.84 | 100 | 1814.60 | +| GPT-5 | 8.25 | 7.88 | 100 | 825.09 | +| Gemini-2.5-flash | 115.85 | 29.18 | 100 | 11585.49 | +| Gemini-2.5-flash-nothinking | 151.00 | 21.38 | 100 | 15099.93 | +| Qwen3-235b | 38.30 | 33.32 | 100 | 3830.01 | ## Relative Performance (vs. Fastest Model) @@ -196,7 +196,7 @@ Baseline (fastest): **GPT-5** (8.25s mean) | Gemini-2.5-flash-nothinking | 151.00 | +1730.1% | | Qwen3-235b | 38.30 | +364.2% | -**Slowest model:** Gemini-2.5-flash-nothinking (151.00s mean, +1730.1% slower than baseline GPT-5) +**Slowest model:** Gemini-2.5-flash-nothinking (151.00s mean, +1730.1% slower than baseline) --- @@ -204,15 +204,15 @@ Baseline (fastest): **GPT-5** (8.25s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 914.94 | -| DeepSeek-V3-1 | 518.26 | -| GPT-4o-mini | 94.49 | -| GPT-5 | 45.35 | -| Gemini-2.5-flash | 163.51 | -| Gemini-2.5-flash-nothinking | 169.19 | -| Qwen3-235b | 317.52 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 914.94 | 930.18 | 25 | 22873.47 | +| DeepSeek-V3-1 | 518.26 | 492.54 | 25 | 12956.58 | +| GPT-4o-mini | 94.49 | 87.41 | 25 | 2362.27 | +| GPT-5 | 45.35 | 45.41 | 25 | 1133.78 | +| Gemini-2.5-flash | 163.51 | 133.48 | 25 | 4087.85 | +| Gemini-2.5-flash-nothinking | 169.19 | 133.32 | 25 | 4229.82 | +| Qwen3-235b | 317.52 | 253.19 | 25 | 7938.00 | ## Relative Performance (vs. Fastest Model) @@ -228,7 +228,7 @@ Baseline (fastest): **GPT-5** (45.35s mean) | Gemini-2.5-flash-nothinking | 169.19 | +273.1% | | Qwen3-235b | 317.52 | +600.1% | -**Slowest model:** DeepSeek-R1 (914.94s mean, +1917.5% slower than baseline GPT-5) +**Slowest model:** DeepSeek-R1 (914.94s mean, +1917.5% slower than baseline) --- @@ -236,15 +236,15 @@ Baseline (fastest): **GPT-5** (45.35s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 827.60 | -| DeepSeek-V3-1 | 516.71 | -| GPT-4o-mini | 136.40 | -| GPT-5 | 43.16 | -| Gemini-2.5-flash | 185.74 | -| Gemini-2.5-flash-nothinking | 120.75 | -| Qwen3-235b | 316.86 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 827.60 | 802.12 | 25 | 20690.04 | +| DeepSeek-V3-1 | 516.71 | 462.37 | 25 | 12917.73 | +| GPT-4o-mini | 136.40 | 92.29 | 25 | 3409.88 | +| GPT-5 | 43.16 | 43.45 | 25 | 1078.90 | +| Gemini-2.5-flash | 185.74 | 185.03 | 25 | 4643.56 | +| Gemini-2.5-flash-nothinking | 120.75 | 111.85 | 25 | 3018.77 | +| Qwen3-235b | 316.86 | 313.26 | 25 | 7921.47 | ## Relative Performance (vs. Fastest Model) @@ -260,7 +260,7 @@ Baseline (fastest): **GPT-5** (43.16s mean) | Gemini-2.5-flash-nothinking | 120.75 | +179.8% | | Qwen3-235b | 316.86 | +634.2% | -**Slowest model:** DeepSeek-R1 (827.60s mean, +1817.7% slower than baseline GPT-5) +**Slowest model:** DeepSeek-R1 (827.60s mean, +1817.7% slower than baseline) --- @@ -268,15 +268,15 @@ Baseline (fastest): **GPT-5** (43.16s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 302.33 | -| DeepSeek-V3-1 | 215.52 | -| GPT-4o-mini | 44.98 | -| GPT-5 | 23.26 | -| Gemini-2.5-flash | 76.95 | -| Gemini-2.5-flash-nothinking | 98.14 | -| Qwen3-235b | 208.99 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 302.33 | 254.54 | 60 | 18139.89 | +| DeepSeek-V3-1 | 215.52 | 175.65 | 60 | 12931.05 | +| GPT-4o-mini | 44.98 | 43.88 | 60 | 2698.52 | +| GPT-5 | 23.26 | 18.74 | 60 | 1395.76 | +| Gemini-2.5-flash | 76.95 | 70.58 | 60 | 4617.19 | +| Gemini-2.5-flash-nothinking | 98.14 | 69.14 | 60 | 5888.27 | +| Qwen3-235b | 208.99 | 128.85 | 60 | 12539.60 | ## Relative Performance (vs. Fastest Model) @@ -292,7 +292,7 @@ Baseline (fastest): **GPT-5** (23.26s mean) | Gemini-2.5-flash-nothinking | 98.14 | +321.9% | | Qwen3-235b | 208.99 | +798.4% | -**Slowest model:** DeepSeek-R1 (302.33s mean, +1199.6% slower than baseline GPT-5) +**Slowest model:** DeepSeek-R1 (302.33s mean, +1199.6% slower than baseline) --- @@ -300,15 +300,15 @@ Baseline (fastest): **GPT-5** (23.26s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 283.59 | -| DeepSeek-V3-1 | 234.43 | -| GPT-4o-mini | 41.31 | -| GPT-5 | 30.31 | -| Gemini-2.5-flash | 73.92 | -| Gemini-2.5-flash-nothinking | 47.47 | -| Qwen3-235b | 196.56 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 283.59 | 240.55 | 60 | 17015.44 | +| DeepSeek-V3-1 | 234.43 | 193.30 | 60 | 14065.93 | +| GPT-4o-mini | 41.31 | 39.94 | 60 | 2478.65 | +| GPT-5 | 30.31 | 32.22 | 60 | 1818.49 | +| Gemini-2.5-flash | 73.92 | 52.93 | 60 | 4435.47 | +| Gemini-2.5-flash-nothinking | 47.47 | 42.83 | 60 | 2848.08 | +| Qwen3-235b | 196.56 | 119.09 | 60 | 11793.38 | ## Relative Performance (vs. Fastest Model) @@ -324,7 +324,7 @@ Baseline (fastest): **GPT-5** (30.31s mean) | Gemini-2.5-flash-nothinking | 47.47 | +56.6% | | Qwen3-235b | 196.56 | +548.5% | -**Slowest model:** DeepSeek-R1 (283.59s mean, +835.7% slower than baseline GPT-5) +**Slowest model:** DeepSeek-R1 (283.59s mean, +835.7% slower than baseline) --- @@ -332,15 +332,15 @@ Baseline (fastest): **GPT-5** (30.31s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 274.53 | -| DeepSeek-V3-1 | 135.84 | -| GPT-4o-mini | 55.93 | -| GPT-5 | 23.98 | -| Gemini-2.5-flash | 91.42 | -| Gemini-2.5-flash-nothinking | 97.29 | -| Qwen3-235b | 92.07 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 274.53 | 246.57 | 60 | 16471.81 | +| DeepSeek-V3-1 | 135.84 | 119.45 | 60 | 8150.36 | +| GPT-4o-mini | 55.93 | 54.65 | 60 | 3355.75 | +| GPT-5 | 23.98 | 23.42 | 60 | 1438.98 | +| Gemini-2.5-flash | 91.42 | 87.19 | 60 | 5484.96 | +| Gemini-2.5-flash-nothinking | 97.29 | 69.47 | 60 | 5837.31 | +| Qwen3-235b | 92.07 | 86.16 | 60 | 5524.20 | ## Relative Performance (vs. Fastest Model) @@ -356,7 +356,7 @@ Baseline (fastest): **GPT-5** (23.98s mean) | Gemini-2.5-flash-nothinking | 97.29 | +305.7% | | Qwen3-235b | 92.07 | +283.9% | -**Slowest model:** DeepSeek-R1 (274.53s mean, +1044.7% slower than baseline GPT-5) +**Slowest model:** DeepSeek-R1 (274.53s mean, +1044.7% slower than baseline) --- @@ -364,15 +364,15 @@ Baseline (fastest): **GPT-5** (23.98s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 147.98 | -| DeepSeek-V3-1 | 10.29 | -| GPT-4o-mini | 8.47 | -| GPT-5 | 7.32 | -| Gemini-2.5-flash | 554.35 | -| Gemini-2.5-flash-nothinking | 132.33 | -| Qwen3-235b | 9.54 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 147.98 | 128.24 | 60 | 8878.66 | +| DeepSeek-V3-1 | 10.29 | 8.95 | 60 | 617.43 | +| GPT-4o-mini | 8.47 | 3.01 | 60 | 508.36 | +| GPT-5 | 7.32 | 2.60 | 60 | 439.11 | +| Gemini-2.5-flash | 554.35 | 221.42 | 60 | 33260.97 | +| Gemini-2.5-flash-nothinking | 132.33 | 7.61 | 60 | 7939.80 | +| Qwen3-235b | 9.54 | 7.38 | 60 | 572.29 | ## Relative Performance (vs. Fastest Model) @@ -388,7 +388,7 @@ Baseline (fastest): **GPT-5** (7.32s mean) | Gemini-2.5-flash-nothinking | 132.33 | +1708.2% | | Qwen3-235b | 9.54 | +30.3% | -**Slowest model:** Gemini-2.5-flash (554.35s mean, +7474.7% slower than baseline GPT-5) +**Slowest model:** Gemini-2.5-flash (554.35s mean, +7474.7% slower than baseline) --- @@ -396,15 +396,15 @@ Baseline (fastest): **GPT-5** (7.32s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 129.20 | -| DeepSeek-V3-1 | 10.69 | -| GPT-4o-mini | 3.58 | -| GPT-5 | 2.98 | -| Gemini-2.5-flash | 457.36 | -| Gemini-2.5-flash-nothinking | 168.14 | -| Qwen3-235b | 10.49 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 129.20 | 113.74 | 60 | 7752.24 | +| DeepSeek-V3-1 | 10.69 | 9.03 | 60 | 641.59 | +| GPT-4o-mini | 3.58 | 3.13 | 60 | 214.72 | +| GPT-5 | 2.98 | 2.80 | 60 | 179.05 | +| Gemini-2.5-flash | 457.36 | 251.42 | 60 | 27441.47 | +| Gemini-2.5-flash-nothinking | 168.14 | 11.26 | 60 | 10088.45 | +| Qwen3-235b | 10.49 | 7.17 | 60 | 629.39 | ## Relative Performance (vs. Fastest Model) @@ -420,7 +420,7 @@ Baseline (fastest): **GPT-5** (2.98s mean) | Gemini-2.5-flash-nothinking | 168.14 | +5534.3% | | Qwen3-235b | 10.49 | +251.5% | -**Slowest model:** Gemini-2.5-flash (457.36s mean, +15225.7% slower than baseline GPT-5) +**Slowest model:** Gemini-2.5-flash (457.36s mean, +15225.7% slower than baseline) --- @@ -428,15 +428,15 @@ Baseline (fastest): **GPT-5** (2.98s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 922.16 | -| DeepSeek-V3-1 | 312.32 | -| GPT-4o-mini | 178.62 | -| GPT-5 | 67.32 | -| Gemini-2.5-flash | 357.79 | -| Gemini-2.5-flash-nothinking | 195.19 | -| Qwen3-235b | 1476.89 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 922.16 | 669.36 | 60 | 55329.75 | +| DeepSeek-V3-1 | 312.32 | 300.68 | 60 | 18739.34 | +| GPT-4o-mini | 178.62 | 174.95 | 60 | 10717.18 | +| GPT-5 | 67.32 | 66.63 | 60 | 4038.91 | +| Gemini-2.5-flash | 357.79 | 300.61 | 60 | 21467.37 | +| Gemini-2.5-flash-nothinking | 195.19 | 194.60 | 60 | 11711.41 | +| Qwen3-235b | 1476.89 | 1057.75 | 60 | 88613.16 | ## Relative Performance (vs. Fastest Model) @@ -452,7 +452,7 @@ Baseline (fastest): **GPT-5** (67.32s mean) | Gemini-2.5-flash-nothinking | 195.19 | +190.0% | | Qwen3-235b | 1476.89 | +2094.0% | -**Slowest model:** Qwen3-235b (1476.89s mean, +2094.0% slower than baseline GPT-5) +**Slowest model:** Qwen3-235b (1476.89s mean, +2094.0% slower than baseline) --- @@ -460,15 +460,15 @@ Baseline (fastest): **GPT-5** (67.32s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 774.66 | -| DeepSeek-V3-1 | 245.49 | -| GPT-4o-mini | 144.28 | -| GPT-5 | 52.98 | -| Gemini-2.5-flash | 127.63 | -| Gemini-2.5-flash-nothinking | 94.40 | -| Qwen3-235b | 973.11 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 774.66 | 644.06 | 60 | 46479.85 | +| DeepSeek-V3-1 | 245.49 | 249.80 | 60 | 14729.69 | +| GPT-4o-mini | 144.28 | 131.84 | 60 | 8656.53 | +| GPT-5 | 52.98 | 50.55 | 60 | 3179.00 | +| Gemini-2.5-flash | 127.63 | 120.13 | 60 | 7657.88 | +| Gemini-2.5-flash-nothinking | 94.40 | 94.66 | 60 | 5664.18 | +| Qwen3-235b | 973.11 | 912.25 | 60 | 58386.42 | ## Relative Performance (vs. Fastest Model) @@ -484,7 +484,7 @@ Baseline (fastest): **GPT-5** (52.98s mean) | Gemini-2.5-flash-nothinking | 94.40 | +78.2% | | Qwen3-235b | 973.11 | +1736.6% | -**Slowest model:** Qwen3-235b (973.11s mean, +1736.6% slower than baseline GPT-5) +**Slowest model:** Qwen3-235b (973.11s mean, +1736.6% slower than baseline) --- @@ -492,15 +492,15 @@ Baseline (fastest): **GPT-5** (52.98s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 839.96 | -| DeepSeek-V3-1 | 339.42 | -| GPT-4o-mini | 129.31 | -| GPT-5 | 59.50 | -| Gemini-2.5-flash | 201.33 | -| Gemini-2.5-flash-nothinking | 163.09 | -| Qwen3-235b | 1381.73 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 839.96 | 711.17 | 60 | 50397.41 | +| DeepSeek-V3-1 | 339.42 | 328.97 | 60 | 20365.50 | +| GPT-4o-mini | 129.31 | 107.80 | 60 | 7758.66 | +| GPT-5 | 59.50 | 57.94 | 60 | 3570.10 | +| Gemini-2.5-flash | 201.33 | 185.48 | 60 | 12079.56 | +| Gemini-2.5-flash-nothinking | 163.09 | 156.11 | 60 | 9785.44 | +| Qwen3-235b | 1381.73 | 1226.34 | 60 | 82903.53 | ## Relative Performance (vs. Fastest Model) @@ -516,7 +516,7 @@ Baseline (fastest): **GPT-5** (59.50s mean) | Gemini-2.5-flash-nothinking | 163.09 | +174.1% | | Qwen3-235b | 1381.73 | +2222.2% | -**Slowest model:** Qwen3-235b (1381.73s mean, +2222.2% slower than baseline GPT-5) +**Slowest model:** Qwen3-235b (1381.73s mean, +2222.2% slower than baseline) --- @@ -524,15 +524,15 @@ Baseline (fastest): **GPT-5** (59.50s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 1130.69 | -| DeepSeek-V3-1 | 161.80 | -| GPT-4o-mini | 33.96 | -| GPT-5 | 21.10 | -| Gemini-2.5-flash | 151.07 | -| Gemini-2.5-flash-nothinking | 68.41 | -| Qwen3-235b | 82.32 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 1130.69 | 1048.39 | 60 | 67841.22 | +| DeepSeek-V3-1 | 161.80 | 144.55 | 60 | 9707.97 | +| GPT-4o-mini | 33.96 | 30.11 | 60 | 2037.42 | +| GPT-5 | 21.10 | 21.42 | 60 | 1266.15 | +| Gemini-2.5-flash | 151.07 | 118.63 | 60 | 9063.97 | +| Gemini-2.5-flash-nothinking | 68.41 | 53.95 | 60 | 4104.37 | +| Qwen3-235b | 82.32 | 75.72 | 60 | 4939.32 | ## Relative Performance (vs. Fastest Model) @@ -548,7 +548,7 @@ Baseline (fastest): **GPT-5** (21.10s mean) | Gemini-2.5-flash-nothinking | 68.41 | +224.2% | | Qwen3-235b | 82.32 | +290.1% | -**Slowest model:** DeepSeek-R1 (1130.69s mean, +5258.1% slower than baseline GPT-5) +**Slowest model:** DeepSeek-R1 (1130.69s mean, +5258.1% slower than baseline) --- @@ -556,15 +556,15 @@ Baseline (fastest): **GPT-5** (21.10s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 898.25 | -| DeepSeek-V3-1 | 68.15 | -| GPT-4o-mini | 29.00 | -| GPT-5 | 28.21 | -| Gemini-2.5-flash | 83.10 | -| Gemini-2.5-flash-nothinking | 54.03 | -| Qwen3-235b | 69.68 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 898.25 | 659.13 | 60 | 53895.23 | +| DeepSeek-V3-1 | 68.15 | 57.57 | 60 | 4089.18 | +| GPT-4o-mini | 29.00 | 22.22 | 60 | 1739.97 | +| GPT-5 | 28.21 | 23.74 | 60 | 1692.83 | +| Gemini-2.5-flash | 83.10 | 64.11 | 60 | 4985.79 | +| Gemini-2.5-flash-nothinking | 54.03 | 29.85 | 60 | 3241.76 | +| Qwen3-235b | 69.68 | 57.27 | 60 | 4181.03 | ## Relative Performance (vs. Fastest Model) @@ -580,7 +580,7 @@ Baseline (fastest): **GPT-5** (28.21s mean) | Gemini-2.5-flash-nothinking | 54.03 | +91.5% | | Qwen3-235b | 69.68 | +147.0% | -**Slowest model:** DeepSeek-R1 (898.25s mean, +3083.7% slower than baseline GPT-5) +**Slowest model:** DeepSeek-R1 (898.25s mean, +3083.7% slower than baseline) --- @@ -588,15 +588,15 @@ Baseline (fastest): **GPT-5** (28.21s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 941.69 | -| DeepSeek-V3-1 | 112.55 | -| GPT-4o-mini | 48.84 | -| GPT-5 | 26.09 | -| Gemini-2.5-flash | 104.38 | -| Gemini-2.5-flash-nothinking | 46.95 | -| Qwen3-235b | 89.03 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 941.69 | 908.10 | 60 | 56501.59 | +| DeepSeek-V3-1 | 112.55 | 103.47 | 60 | 6753.21 | +| GPT-4o-mini | 48.84 | 46.84 | 60 | 2930.67 | +| GPT-5 | 26.09 | 26.63 | 60 | 1565.16 | +| Gemini-2.5-flash | 104.38 | 61.00 | 60 | 6262.88 | +| Gemini-2.5-flash-nothinking | 46.95 | 29.87 | 60 | 2816.70 | +| Qwen3-235b | 89.03 | 80.44 | 60 | 5341.82 | ## Relative Performance (vs. Fastest Model) @@ -612,7 +612,7 @@ Baseline (fastest): **GPT-5** (26.09s mean) | Gemini-2.5-flash-nothinking | 46.95 | +80.0% | | Qwen3-235b | 89.03 | +241.3% | -**Slowest model:** DeepSeek-R1 (941.69s mean, +3509.9% slower than baseline GPT-5) +**Slowest model:** DeepSeek-R1 (941.69s mean, +3509.9% slower than baseline) --- @@ -620,15 +620,15 @@ Baseline (fastest): **GPT-5** (26.09s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 557.94 | -| DeepSeek-V3-1 | 158.60 | -| GPT-4o-mini | 87.89 | -| GPT-5 | 41.51 | -| Gemini-2.5-flash | 65.45 | -| Gemini-2.5-flash-nothinking | 40.70 | -| Qwen3-235b | 135.01 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 557.94 | 497.03 | 60 | 33476.23 | +| DeepSeek-V3-1 | 158.60 | 151.02 | 60 | 9516.19 | +| GPT-4o-mini | 87.89 | 75.81 | 60 | 5273.64 | +| GPT-5 | 41.51 | 33.49 | 60 | 2490.59 | +| Gemini-2.5-flash | 65.45 | 58.31 | 60 | 3927.28 | +| Gemini-2.5-flash-nothinking | 40.70 | 37.84 | 60 | 2441.80 | +| Qwen3-235b | 135.01 | 113.26 | 60 | 8100.81 | ## Relative Performance (vs. Fastest Model) @@ -644,7 +644,7 @@ Baseline (fastest): **Gemini-2.5-flash-nothinking** (40.70s mean) | Gemini-2.5-flash-nothinking | 40.70 | +0.0% | | Qwen3-235b | 135.01 | +231.8% | -**Slowest model:** DeepSeek-R1 (557.94s mean, +1271.0% slower than baseline Gemini-2.5-flash-nothinking) +**Slowest model:** DeepSeek-R1 (557.94s mean, +1271.0% slower than baseline) --- @@ -652,15 +652,15 @@ Baseline (fastest): **Gemini-2.5-flash-nothinking** (40.70s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 427.00 | -| DeepSeek-V3-1 | 111.56 | -| GPT-4o-mini | 46.16 | -| GPT-5 | 27.98 | -| Gemini-2.5-flash | 65.04 | -| Gemini-2.5-flash-nothinking | 29.03 | -| Qwen3-235b | 102.98 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 427.00 | 316.96 | 60 | 25620.06 | +| DeepSeek-V3-1 | 111.56 | 109.53 | 60 | 6693.43 | +| GPT-4o-mini | 46.16 | 38.91 | 60 | 2769.74 | +| GPT-5 | 27.98 | 25.56 | 60 | 1678.90 | +| Gemini-2.5-flash | 65.04 | 54.59 | 60 | 3902.31 | +| Gemini-2.5-flash-nothinking | 29.03 | 23.80 | 60 | 1741.76 | +| Qwen3-235b | 102.98 | 70.84 | 60 | 6178.94 | ## Relative Performance (vs. Fastest Model) @@ -676,7 +676,7 @@ Baseline (fastest): **GPT-5** (27.98s mean) | Gemini-2.5-flash-nothinking | 29.03 | +3.7% | | Qwen3-235b | 102.98 | +268.0% | -**Slowest model:** DeepSeek-R1 (427.00s mean, +1426.0% slower than baseline GPT-5) +**Slowest model:** DeepSeek-R1 (427.00s mean, +1426.0% slower than baseline) --- @@ -684,15 +684,15 @@ Baseline (fastest): **GPT-5** (27.98s mean) ## Model Statistics Summary -| Model | Mean (s) | -| --- | --- | -| DeepSeek-R1 | 735.24 | -| DeepSeek-V3-1 | 219.42 | -| GPT-4o-mini | 144.00 | -| GPT-5 | 39.59 | -| Gemini-2.5-flash | 59.94 | -| Gemini-2.5-flash-nothinking | 38.63 | -| Qwen3-235b | 126.60 | +| Model | Mean (s) | Median (s) | Count | Total (s) | +| --- | --- | --- | --- | --- | +| DeepSeek-R1 | 735.24 | 628.71 | 60 | 44114.13 | +| DeepSeek-V3-1 | 219.42 | 197.26 | 60 | 13164.95 | +| GPT-4o-mini | 144.00 | 111.81 | 60 | 8640.11 | +| GPT-5 | 39.59 | 33.41 | 60 | 2375.23 | +| Gemini-2.5-flash | 59.94 | 55.27 | 60 | 3596.43 | +| Gemini-2.5-flash-nothinking | 38.63 | 37.92 | 60 | 2317.81 | +| Qwen3-235b | 126.60 | 105.77 | 60 | 7595.78 | ## Relative Performance (vs. Fastest Model) @@ -708,7 +708,7 @@ Baseline (fastest): **Gemini-2.5-flash-nothinking** (38.63s mean) | Gemini-2.5-flash-nothinking | 38.63 | +0.0% | | Qwen3-235b | 126.60 | +227.7% | -**Slowest model:** DeepSeek-R1 (735.24s mean, +1803.3% slower than baseline Gemini-2.5-flash-nothinking) +**Slowest model:** DeepSeek-R1 (735.24s mean, +1803.3% slower than baseline) --- diff --git a/data/processed/RQ2/ECDFs/Time_Comparison_A2A_vs_A2A_mix.md b/data/processed/RQ2/ECDFs/Time_Comparison_A2A_vs_A2A_mix.md new file mode 100644 index 0000000000000000000000000000000000000000..84cc10ac29154aeccf21d383bd44d26dad168db4 --- /dev/null +++ b/data/processed/RQ2/ECDFs/Time_Comparison_A2A_vs_A2A_mix.md @@ -0,0 +1,38 @@ +# Overall A2A vs A2A_mix Time Comparison + +Averaged across all projects: SQL_assistant, intelligent_recruitment_platform, landing_page_generator, self_evaluation_loop_flow, write_a_book_with_flows + +## Framework-Level Comparison (All Models Averaged) + +| A2A Mean (s) | A2A_mix Mean (s) | Diff (A2A-A2A_mix) | +| --- | --- | --- | + +--- + +## Per-Model Summary + +| Model | A2A Mean (s) | A2A_mix Mean (s) | Diff (A2A-A2A_mix) | +| --- | --- | --- | --- | + +--- + +# SQL_assistant: A2A vs A2A_mix + +_Data not available for comparison_ + +# intelligent_recruitment_platform: A2A vs A2A_mix + +_Data not available for comparison_ + +# landing_page_generator: A2A vs A2A_mix + +_Data not available for comparison_ + +# self_evaluation_loop_flow: A2A vs A2A_mix + +_Data not available for comparison_ + +# write_a_book_with_flows: A2A vs A2A_mix + +_Data not available for comparison_ + diff --git a/data/processed/RQ2/ECDFs/Time_Comparison_A2A_vs_H_A2A.md b/data/processed/RQ2/ECDFs/Time_Comparison_A2A_vs_H_A2A.md deleted file mode 100644 index c939d0f14233021bedaa3f0ffb80816d1304794f..0000000000000000000000000000000000000000 --- a/data/processed/RQ2/ECDFs/Time_Comparison_A2A_vs_H_A2A.md +++ /dev/null @@ -1,136 +0,0 @@ -# Overall A2A vs H_A2A Time Comparison - -Averaged across all projects: SQLAssistant, RecruitmentAssistant, LandingPageGenerator, SocialMediaManager, BookWriter - -## Framework-Level Comparison (All Models Averaged) - -| A2A Mean (s) | H_A2A Mean (s) | Diff (A2A-H_A2A) | -| --- | --- | --- | -| 278.57 | 205.99 | +72.58s (+35.2%) | - ---- - -## Per-Model Summary - -| Model | A2A Mean (s) | H_A2A Mean (s) | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 774.85 | 642.65 | +132.20s (+20.6%) | -| DeepSeek-V3-1 | 258.41 | 191.05 | +67.36s (+35.3%) | -| GPT-4o-mini | 89.32 | 72.46 | +16.86s (+23.3%) | -| GPT-5 | 69.35 | 50.18 | +19.17s (+38.2%) | -| Gemini-2.5-flash | 178.36 | 104.40 | +73.96s (+70.8%) | -| Gemini-2.5-flash-nothinking | 107.27 | 61.33 | +45.94s (+74.9%) | -| Qwen3-235b | 472.44 | 319.85 | +152.58s (+47.7%) | - ---- - -# SQLAssistant: A2A vs H_A2A - -## Overall Summary (Averaged Across All Models) - -| A2A Mean (s) | H_A2A Mean (s) | Diff (A2A-H_A2A) | -| --- | --- | --- | -| 235.62 | 175.78 | +59.84s (+34.0%) | - ---- - -## Per-Model Comparison - -| Model | A2A Mean (s) | H_A2A Mean (s) | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 1130.69 | 898.25 | +232.43s (+25.9%) | -| DeepSeek-V3-1 | 161.80 | 68.15 | +93.65s (+137.4%) | -| GPT-4o-mini | 33.96 | 29.00 | +4.96s (+17.1%) | -| GPT-5 | 21.10 | 28.21 | -7.11s (-25.2%) | -| Gemini-2.5-flash | 151.07 | 83.10 | +67.97s (+81.8%) | -| Gemini-2.5-flash-nothinking | 68.41 | 54.03 | +14.38s (+26.6%) | -| Qwen3-235b | 82.32 | 69.68 | +12.64s (+18.1%) | - -# RecruitmentAssistant: A2A vs H_A2A - -## Overall Summary (Averaged Across All Models) - -| A2A Mean (s) | H_A2A Mean (s) | Diff (A2A-H_A2A) | -| --- | --- | --- | -| 501.47 | 344.65 | +156.82s (+45.5%) | - ---- - -## Per-Model Comparison - -| Model | A2A Mean (s) | H_A2A Mean (s) | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 922.16 | 774.66 | +147.50s (+19.0%) | -| DeepSeek-V3-1 | 312.32 | 245.49 | +66.83s (+27.2%) | -| GPT-4o-mini | 178.62 | 144.28 | +34.34s (+23.8%) | -| GPT-5 | 67.32 | 52.98 | +14.33s (+27.0%) | -| Gemini-2.5-flash | 357.79 | 127.63 | +230.16s (+180.3%) | -| Gemini-2.5-flash-nothinking | 195.19 | 94.40 | +100.79s (+106.8%) | -| Qwen3-235b | 1476.89 | 973.11 | +503.78s (+51.8%) | - -# LandingPageGenerator: A2A vs H_A2A - -## Overall Summary (Averaged Across All Models) - -| A2A Mean (s) | H_A2A Mean (s) | Diff (A2A-H_A2A) | -| --- | --- | --- | -| 138.60 | 129.66 | +8.94s (+6.9%) | - ---- - -## Per-Model Comparison - -| Model | A2A Mean (s) | H_A2A Mean (s) | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 302.33 | 283.59 | +18.74s (+6.6%) | -| DeepSeek-V3-1 | 215.52 | 234.43 | -18.91s (-8.1%) | -| GPT-4o-mini | 44.98 | 41.31 | +3.66s (+8.9%) | -| GPT-5 | 23.26 | 30.31 | -7.05s (-23.2%) | -| Gemini-2.5-flash | 76.95 | 73.92 | +3.03s (+4.1%) | -| Gemini-2.5-flash-nothinking | 98.14 | 47.47 | +50.67s (+106.7%) | -| Qwen3-235b | 208.99 | 196.56 | +12.44s (+6.3%) | - -# SocialMediaManager: A2A vs H_A2A - -## Overall Summary (Averaged Across All Models) - -| A2A Mean (s) | H_A2A Mean (s) | Diff (A2A-H_A2A) | -| --- | --- | --- | -| 155.30 | 115.68 | +39.62s (+34.3%) | - ---- - -## Per-Model Comparison - -| Model | A2A Mean (s) | H_A2A Mean (s) | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 557.94 | 427.00 | +130.94s (+30.7%) | -| DeepSeek-V3-1 | 158.60 | 111.56 | +47.05s (+42.2%) | -| GPT-4o-mini | 87.89 | 46.16 | +41.73s (+90.4%) | -| GPT-5 | 41.51 | 27.98 | +13.53s (+48.3%) | -| Gemini-2.5-flash | 65.45 | 65.04 | +0.42s (+0.6%) | -| Gemini-2.5-flash-nothinking | 40.70 | 29.03 | +11.67s (+40.2%) | -| Qwen3-235b | 135.01 | 102.98 | +32.03s (+31.1%) | - -# BookWriter: A2A vs H_A2A - -## Overall Summary (Averaged Across All Models) - -| A2A Mean (s) | H_A2A Mean (s) | Diff (A2A-H_A2A) | -| --- | --- | --- | -| 361.87 | 264.19 | +97.68s (+37.0%) | - ---- - -## Per-Model Comparison - -| Model | A2A Mean (s) | H_A2A Mean (s) | Diff (A2A-H_A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 961.15 | 829.76 | +131.39s (+15.8%) | -| DeepSeek-V3-1 | 443.80 | 295.61 | +148.19s (+50.1%) | -| GPT-4o-mini | 101.15 | 101.57 | -0.42s (-0.4%) | -| GPT-5 | 193.58 | 111.41 | +82.17s (+73.8%) | -| Gemini-2.5-flash | 240.55 | 172.33 | +68.22s (+39.6%) | -| Gemini-2.5-flash-nothinking | 133.91 | 81.73 | +52.18s (+63.8%) | -| Qwen3-235b | 458.96 | 256.93 | +202.03s (+78.6%) | - diff --git a/data/processed/RQ2/ECDFs/Time_Comparison_MCP_vs_A2A.md b/data/processed/RQ2/ECDFs/Time_Comparison_MCP_vs_A2A.md index 913f81bce04c82cee31086ad218a3a35c4c82e3c..97cc224a70d6d70b357cd2f64b34c8700bccd7bc 100644 --- a/data/processed/RQ2/ECDFs/Time_Comparison_MCP_vs_A2A.md +++ b/data/processed/RQ2/ECDFs/Time_Comparison_MCP_vs_A2A.md @@ -1,12 +1,11 @@ # Overall MCP vs A2A Time Comparison -Averaged across all projects: SQLAssistant, RecruitmentAssistant, LandingPageGenerator, SocialMediaManager, BookWriter +Averaged across all projects: SQL_assistant, intelligent_recruitment_platform, landing_page_generator, self_evaluation_loop_flow, write_a_book_with_flows ## Framework-Level Comparison (All Models Averaged) | MCP Mean (s) | A2A Mean (s) | Diff (MCP-A2A) | | --- | --- | --- | -| 256.23 | 278.57 | -22.34s (-8.0%) | --- @@ -14,123 +13,26 @@ Averaged across all projects: SQLAssistant, RecruitmentAssistant, LandingPageGen | Model | MCP Mean (s) | A2A Mean (s) | Diff (MCP-A2A) | | --- | --- | --- | --- | -| DeepSeek-R1 | 750.34 | 774.85 | -24.51s (-3.2%) | -| DeepSeek-V3-1 | 240.58 | 258.41 | -17.83s (-6.9%) | -| GPT-4o-mini | 98.06 | 89.32 | +8.74s (+9.8%) | -| GPT-5 | 57.86 | 69.35 | -11.49s (-16.6%) | -| Gemini-2.5-flash | 128.11 | 178.36 | -50.26s (-28.2%) | -| Gemini-2.5-flash-nothinking | 92.28 | 107.27 | -14.98s (-14.0%) | -| Qwen3-235b | 426.36 | 472.44 | -46.07s (-9.8%) | --- -# SQLAssistant: MCP vs A2A +# SQL_assistant: MCP vs A2A -## Overall Summary (Averaged Across All Models) +_Data not available for comparison_ -| MCP Mean (s) | A2A Mean (s) | Diff (MCP-A2A) | -| --- | --- | --- | -| 195.65 | 235.62 | -39.97s (-17.0%) | - ---- - -## Per-Model Comparison +# intelligent_recruitment_platform: MCP vs A2A -| Model | MCP Mean (s) | A2A Mean (s) | Diff (MCP-A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 941.69 | 1130.69 | -188.99s (-16.7%) | -| DeepSeek-V3-1 | 112.55 | 161.80 | -49.25s (-30.4%) | -| GPT-4o-mini | 48.84 | 33.96 | +14.89s (+43.8%) | -| GPT-5 | 26.09 | 21.10 | +4.98s (+23.6%) | -| Gemini-2.5-flash | 104.38 | 151.07 | -46.68s (-30.9%) | -| Gemini-2.5-flash-nothinking | 46.95 | 68.41 | -21.46s (-31.4%) | -| Qwen3-235b | 89.03 | 82.32 | +6.71s (+8.1%) | +_Data not available for comparison_ -# RecruitmentAssistant: MCP vs A2A +# landing_page_generator: MCP vs A2A -## Overall Summary (Averaged Across All Models) +_Data not available for comparison_ -| MCP Mean (s) | A2A Mean (s) | Diff (MCP-A2A) | -| --- | --- | --- | -| 444.91 | 501.47 | -56.56s (-11.3%) | +# self_evaluation_loop_flow: MCP vs A2A ---- +_Data not available for comparison_ -## Per-Model Comparison +# write_a_book_with_flows: MCP vs A2A -| Model | MCP Mean (s) | A2A Mean (s) | Diff (MCP-A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 839.96 | 922.16 | -82.21s (-8.9%) | -| DeepSeek-V3-1 | 339.42 | 312.32 | +27.10s (+8.7%) | -| GPT-4o-mini | 129.31 | 178.62 | -49.31s (-27.6%) | -| GPT-5 | 59.50 | 67.32 | -7.81s (-11.6%) | -| Gemini-2.5-flash | 201.33 | 357.79 | -156.46s (-43.7%) | -| Gemini-2.5-flash-nothinking | 163.09 | 195.19 | -32.10s (-16.4%) | -| Qwen3-235b | 1381.73 | 1476.89 | -95.16s (-6.4%) | - -# LandingPageGenerator: MCP vs A2A - -## Overall Summary (Averaged Across All Models) - -| MCP Mean (s) | A2A Mean (s) | Diff (MCP-A2A) | -| --- | --- | --- | -| 110.15 | 138.60 | -28.44s (-20.5%) | - ---- - -## Per-Model Comparison - -| Model | MCP Mean (s) | A2A Mean (s) | Diff (MCP-A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 274.53 | 302.33 | -27.80s (-9.2%) | -| DeepSeek-V3-1 | 135.84 | 215.52 | -79.68s (-37.0%) | -| GPT-4o-mini | 55.93 | 44.98 | +10.95s (+24.4%) | -| GPT-5 | 23.98 | 23.26 | +0.72s (+3.1%) | -| Gemini-2.5-flash | 91.42 | 76.95 | +14.46s (+18.8%) | -| Gemini-2.5-flash-nothinking | 97.29 | 98.14 | -0.85s (-0.9%) | -| Qwen3-235b | 92.07 | 208.99 | -116.92s (-55.9%) | - -# SocialMediaManager: MCP vs A2A - -## Overall Summary (Averaged Across All Models) - -| MCP Mean (s) | A2A Mean (s) | Diff (MCP-A2A) | -| --- | --- | --- | -| 194.77 | 155.30 | +39.47s (+25.4%) | - ---- - -## Per-Model Comparison - -| Model | MCP Mean (s) | A2A Mean (s) | Diff (MCP-A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 735.24 | 557.94 | +177.30s (+31.8%) | -| DeepSeek-V3-1 | 219.42 | 158.60 | +60.81s (+38.3%) | -| GPT-4o-mini | 144.00 | 87.89 | +56.11s (+63.8%) | -| GPT-5 | 39.59 | 41.51 | -1.92s (-4.6%) | -| Gemini-2.5-flash | 59.94 | 65.45 | -5.51s (-8.4%) | -| Gemini-2.5-flash-nothinking | 38.63 | 40.70 | -2.07s (-5.1%) | -| Qwen3-235b | 126.60 | 135.01 | -8.42s (-6.2%) | - -# BookWriter: MCP vs A2A - -## Overall Summary (Averaged Across All Models) - -| MCP Mean (s) | A2A Mean (s) | Diff (MCP-A2A) | -| --- | --- | --- | -| 335.67 | 361.87 | -26.20s (-7.2%) | - ---- - -## Per-Model Comparison - -| Model | MCP Mean (s) | A2A Mean (s) | Diff (MCP-A2A) | -| --- | --- | --- | --- | -| DeepSeek-R1 | 960.29 | 961.15 | -0.86s (-0.1%) | -| DeepSeek-V3-1 | 395.67 | 443.80 | -48.13s (-10.8%) | -| GPT-4o-mini | 112.23 | 101.15 | +11.08s (+11.0%) | -| GPT-5 | 140.17 | 193.58 | -53.41s (-27.6%) | -| Gemini-2.5-flash | 183.47 | 240.55 | -57.08s (-23.7%) | -| Gemini-2.5-flash-nothinking | 115.47 | 133.91 | -18.44s (-13.8%) | -| Qwen3-235b | 442.38 | 458.96 | -16.58s (-3.6%) | +_Data not available for comparison_ diff --git a/data/processed/RQ2/ECDFs/ecdf_BookWriter-A2A.pdf b/data/processed/RQ2/ECDFs/ecdf_BookWriter-A2A.pdf index 5fceb34cdee177f08fa11187a4a0a5ce2e41a257..53a2c0934571b0f6d67d72b3c71c7769936a6a77 100644 --- a/data/processed/RQ2/ECDFs/ecdf_BookWriter-A2A.pdf +++ b/data/processed/RQ2/ECDFs/ecdf_BookWriter-A2A.pdf @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:46c9971b6541ca196a33b2bc82d5faf18f83d8c8b59f43b96a0e9ffb3ff22839 -size 15954 +oid sha256:65a03e930a4629e2746b559e9add8f35a9d8abe8f78e67dd8e0c58376c2aa951 +size 16991 diff --git a/data/processed/RQ2/ECDFs/ecdf_BookWriter-H_A2A.pdf b/data/processed/RQ2/ECDFs/ecdf_BookWriter-H_A2A.pdf index 1adcab6c14a941ed92e1edfcc9a0946866290c1c..c01b63493ce8e2623957657a89e240abd3a14030 100644 --- a/data/processed/RQ2/ECDFs/ecdf_BookWriter-H_A2A.pdf +++ b/data/processed/RQ2/ECDFs/ecdf_BookWriter-H_A2A.pdf @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:483a08b0763a40ba68ff1bf88588d0e48e8a3c6e68b998486d2527f4c19ab411 -size 15894 +oid sha256:7cf98dd637bfa6ad324360b5582844d733c71573f00685b6d455dbb087012e9f +size 16906 diff --git a/data/processed/RQ2/ECDFs/ecdf_BookWriter-MCP.pdf b/data/processed/RQ2/ECDFs/ecdf_BookWriter-MCP.pdf index 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e5143ae29bef48ce64720c93ee9c8aff232ada7c..30ec4de65c9ff8ab93350e46091d412dea268137 100644 --- a/data/processed/RQ2/plot_agent_time_share_bars_unified_y_by_series.py +++ b/data/processed/RQ2/plot_agent_time_share_bars_unified_y_by_series.py @@ -54,6 +54,24 @@ AGENT_COLOR_CYCLE: List[str] = [ KNOWN_SUFFIXES = {"A2A", "A2A_mix", "MCP"} +SERIES_LEGEND_NCOLS: Dict[str, int] = { + "BookWriter": 3, + "SQLAssistant": 2, + "RecruitmentAssistant": 2, + "LandingPageGenerator": 3, + "SocialMediaManager": 3, +} + +SERIES_LEGEND_ROW1: Dict[str, int] = { + "BookWriter": 2, +} + +SERIES_YMAX_OVERRIDE: Dict[str, float] = { + "EmailResponder": 25.0, + "GameBuilder": 25.0, + "LandingPageGenerator": 20.0, +} + SERIES_LEGEND_ROWS: Dict[str, List[List[Optional[str]]]] = { "intelligent_recruitment_platform": [ ["Job Requirement Analyst", "Candidate Evaluator"], @@ -246,7 +264,7 @@ def plot_agent_time_share_bars_unified_y( for agent, val in zip(agent_order, ks_vals): shares_by_agent[agent].append(val) - fig, ax = plt.subplots(figsize=(7.5, 5)) + fig, ax = plt.subplots(figsize=(7.5, 9.0)) bottoms = [0.0 for _ in x] bar_handles: Dict[str, any] = {} @@ -272,7 +290,7 @@ def plot_agent_time_share_bars_unified_y( min_height_for_label = label_ref_ks * 0.03 min_gap = label_ref_ks * 0.045 - label_fontsize = 9 + label_fontsize = 21 for model_idx in range(len(MODEL_ORDER)): label_entries = [] @@ -299,8 +317,14 @@ def plot_agent_time_share_bars_unified_y( bar = bar_handles[agent][model_idx] x_center = bar.get_x() + bar.get_width() / 2.0 - percent_text = f"${percent:.1f}\\%$" - label_entries.append([y_center, x_center, percent_text]) + percent_text = f"{percent:.1f}%" + label_entries.append([y_center, x_center, percent_text, share_val]) + + # Cap: at most n_agents-1 labels per bar; keep largest segments + _max_labels = max(1, len(agent_order) - 1) + if len(label_entries) > _max_labels: + label_entries.sort(key=lambda e: e[3], reverse=True) + label_entries = label_entries[:_max_labels] if len(label_entries) > 1: max_iterations = 50 @@ -320,7 +344,8 @@ def plot_agent_time_share_bars_unified_y( if not changed: break - for y_center, x_center, percent_text in label_entries: + for entry in label_entries: + y_center, x_center, percent_text = entry[0], entry[1], entry[2] ax.text( x_center, y_center, @@ -332,15 +357,41 @@ def plot_agent_time_share_bars_unified_y( fontweight="bold", ) - tick_labels = [MODEL_LABELS.get(m, m) for m in MODEL_ORDER] + _wrap_map = { + "GPT-4o-mini": "GPT-4o\nmini", + "DeepSeek-V3.1": "DeepSeek\nV3.1", + "DeepSeek-R1": "DeepSeek\nR1", + "Gemini-2.5": "Gemini\n2.5", + "Gemini-2.5-NT": "Gemini\n2.5-NT", + "Qwen3-235b": "Qwen3\n235b", + } + tick_labels = [ + _wrap_map.get(MODEL_LABELS.get(m, m), MODEL_LABELS.get(m, m)) + for m in MODEL_ORDER + ] ax.set_xticks(x) - ax.set_xticklabels(tick_labels, rotation=0, ha="center") + ax.set_xticklabels(tick_labels, rotation=0, ha="center", linespacing=1.1) - ax.tick_params(axis="x", labelsize=10) - ax.tick_params(axis="y", labelsize=14) + ax.tick_params(axis="x", labelsize=16, pad=6) + ax.tick_params(axis="y", labelsize=21) + for label in ax.get_xticklabels(): + label.set_fontweight("bold") + for label in ax.get_yticklabels(): + label.set_fontweight("bold") ax.set_ylim(0.0, y_max_ks) - ax.set_ylabel("Latency Breakdown(Ɨ10³s)", fontsize=18) + ax.set_ylabel("") + ax.text( + 0.025, + -0.035, + "(Ɨ10³ s)", + transform=ax.transAxes, + ha="right", + va="top", + fontsize=18, + fontweight="bold", + clip_on=False, + ) project_max_ks = 0.0 for idx in range(len(MODEL_ORDER)): @@ -359,86 +410,132 @@ def plot_agent_time_share_bars_unified_y( f"{total_time:.2f}", ha="center", va="bottom", - fontsize=14, + fontsize=25, fontweight="bold", ) heading_handle = plt.Rectangle((0, 0), 1, 1, facecolor="none", edgecolor="none") - legend_rows = SERIES_LEGEND_ROWS.get(series) - legend_anchor_y = 1.06 if legend_rows else 1.04 - heading_legend_x = -0.08 + heading_legend_x = -0.038 + + # Build legend list in bar top-to-bottom order + _ordered_agents = list(reversed(agent_order)) # top bar first + n_agents = len(_ordered_agents) + + # Determine ncols: use per-series override if set, else prefer 1 row, fall back to 2 rows + if series in SERIES_LEGEND_NCOLS: + ncols_agents = SERIES_LEGEND_NCOLS[series] + else: + _char_width_in = 17.0 / 72.0 * 0.50 + _handle_in = (0.6 + 0.4) * _char_width_in * 2 + _col_spacing_in = 0.6 * _char_width_in * 2 + _heading_in = len("Agent") * _char_width_in + _handle_in + _axes_width_in = 7.5 * 0.90 - _heading_in + _item_widths = [ + len(lbl) * _char_width_in + _handle_in for lbl in _ordered_agents + ] + _total_1row = sum(_item_widths) + _col_spacing_in * (n_agents - 1) + if n_agents == 0: + ncols_agents = 1 + elif _total_1row <= _axes_width_in: + ncols_agents = n_agents + else: + ncols_agents = math.ceil(n_agents / 2) + ncols_agents = max(1, min(ncols_agents, n_agents)) + + # Compute legend_anchor_y so the top edge of the legend block sits at a fixed + # distance above axes top (y=1.0), regardless of number of rows. + # With loc='center left', anchor = vertical center of the legend block. + # row_height_frac = (fontsize_pt * line_spacing) / 72 / fig_height_in / axes_height_frac + _fig_h = fig.get_figheight() + _axes_h_frac = 0.90 - 0.167 # top - bottom from subplots_adjust + _row_h_frac = (17.0 * 1.5) / 72.0 / _fig_h / _axes_h_frac + _legend_top_gap = -0.005 # fixed gap above axes top in axes fraction + + # Matplotlib fills legend column-major; reorder input to column-major so + # visual reading order (left→right, top→bottom) matches bar top→bottom order. + nrows_leg = math.ceil(n_agents / ncols_agents) + + legend_anchor_y = 1.0 + _legend_top_gap + nrows_leg * _row_h_frac * 0.5 + heading_legend = ax.legend( [heading_handle], ["Agent"], - fontsize=12, + fontsize=15, loc="center left", bbox_to_anchor=(heading_legend_x, legend_anchor_y), ncol=1, frameon=False, columnspacing=0.8, - handletextpad=0.5, + handletextpad=0.0, + handlelength=0.0, + prop={"weight": "bold", "size": 17}, ) + # If row1 count is specified, insert padding after row1 items so row1 has fewer cols + _row1_count = SERIES_LEGEND_ROW1.get(series) + if _row1_count is not None and nrows_leg == 2: + _pad_count = ncols_agents - _row1_count + _padded = ( + _ordered_agents[:_row1_count] + + [None] * _pad_count + + _ordered_agents[_row1_count:] + ) + _padded = _padded + [None] * max(0, nrows_leg * ncols_agents - len(_padded)) + else: + _padded = _ordered_agents + [None] * (nrows_leg * ncols_agents - n_agents) + # _padded[r*ncols + c] = desired visual item at (row r, col c) + # matplotlib reads column-major: item i → col=i//nrows, row=i%nrows + # so feed in column-major: for col c, row r → _padded[r*ncols+c] + _colmajor: List[Optional[str]] = [] + for c in range(ncols_agents): + for r in range(nrows_leg): + _colmajor.append(_padded[r * ncols_agents + c]) + handles = [] labels = [] - - if legend_rows: - nrows = len(legend_rows) - ncols = max(len(r) for r in legend_rows) - row_major: List[Optional[str]] = [] - for r in legend_rows: - padded = list(r) + [None] * (ncols - len(r)) - row_major.extend(padded) - - for col in range(ncols): - for row in range(nrows): - idx = row * ncols + col - name = row_major[idx] if idx < len(row_major) else None - if not name: - handles.append( - plt.Rectangle((0, 0), 1, 1, facecolor="none", edgecolor="none") - ) - labels.append("") - continue - if name not in agent_colors: - continue - handles.append( - plt.Rectangle( - (0, 0), 1, 1, facecolor=agent_colors[name], edgecolor="none" - ) - ) - labels.append(name) - - ncols_agents = ncols - else: - for agent in reversed(agent_order): + for agent in _colmajor: + if agent is None: + handles.append( + plt.Rectangle((0, 0), 1, 1, facecolor="none", edgecolor="none") + ) + labels.append("") + else: handles.append( plt.Rectangle( (0, 0), 1, 1, facecolor=agent_colors[agent], edgecolor="none" ) ) labels.append(agent) - ncols_agents = len(labels) if labels else 1 + + # Place agent legend immediately after "Agent" text, no overlap + _fig_width_in = fig.get_figwidth() + _axes_frac_per_in = 1.0 / (_fig_width_in * 0.91) + _agent_text_in = len("Agent") * (17.0 / 72.0) * 0.60 + _small_gap_in = 17.0 / 72.0 * 0.3 + _agents_x = heading_legend_x + (_agent_text_in + _small_gap_in) * _axes_frac_per_in agent_legend = ax.legend( handles, labels, - fontsize=12, + fontsize=17, loc="center left", - bbox_to_anchor=(0.1, legend_anchor_y), + bbox_to_anchor=(_agents_x, legend_anchor_y), ncol=ncols_agents, frameon=False, - columnspacing=0.8, - handletextpad=0.5, + columnspacing=0.2, + handletextpad=0.3, + handlelength=0.5, + prop={"weight": "bold", "size": 17}, ) ax.add_artist(heading_legend) legend_texts = heading_legend.get_texts() if legend_texts: - legend_texts[0].set_fontsize(18) + legend_texts[0].set_fontsize(17) + legend_texts[0].set_fontweight("bold") - fig.subplots_adjust(left=0.09, right=0.999, bottom=0.167, top=0.88) + fig.subplots_adjust(left=0.09, right=0.999, bottom=0.167, top=0.90) out_dir.mkdir(parents=True, exist_ok=True) out_file = out_dir / f"{project_name}_agent_time_share_bars.pdf" @@ -649,8 +746,8 @@ def parse_args() -> argparse.Namespace: parser.add_argument( "--headroom", type=float, - default=1.15, - help="Y-axis headroom multiplier.", + default=1.30, + help="Y-axis headroom multiplier (unused when smart y_max is active).", ) parser.add_argument( "--label-scale-floor", @@ -736,7 +833,14 @@ def main() -> None: if series_max <= 0.0: continue - y_max_ks = series_max * float(args.headroom) + _sm = series_max + _step = 5.0 + _rounded = math.ceil(_sm / _step) * _step + if _rounded <= _sm: + _rounded += _step + y_max_ks = _rounded + _step + if series in SERIES_YMAX_OVERRIDE: + y_max_ks = SERIES_YMAX_OVERRIDE[series] project_max = float(info["project_max_ks"]) or 0.0 label_ref = max(project_max, series_max * float(args.label_scale_floor)) diff --git a/data/processed/RQ2/plot_framework_overhead_ratio_mix_vs_a2a.py b/data/processed/RQ2/plot_framework_overhead_ratio_mix_vs_a2a.py index e0e208affd8b434489bb7d022a968b4cebdf5d4c..046db6ce96fe4fec237b5dca5a012a448c455a8c 100644 --- a/data/processed/RQ2/plot_framework_overhead_ratio_mix_vs_a2a.py +++ b/data/processed/RQ2/plot_framework_overhead_ratio_mix_vs_a2a.py @@ -98,8 +98,8 @@ def find_a2a_mix_pairs(root: Path) -> List[Tuple[Path, Path]]: if not sub.is_dir(): continue name = sub.name - if name.endswith("-A2A_mix"): - base = name[: -len("_mix")] + if name.endswith("-H_A2A"): + base = name[: -len("-H_A2A")] + "-A2A" mix_dirs[base] = sub elif name.endswith("-A2A"): a2a_dirs[name] = sub @@ -196,7 +196,7 @@ def plot_framework_ratios( for key in FRAMEWORK_KEYS: shares_by_comp[key].append(float(shares.get(key, 0.0) or 0.0)) - fig, ax = plt.subplots(figsize=(max(7.5, 1.0 * len(project_names)), 5)) + fig, ax = plt.subplots(figsize=(max(7.5, 1.0 * len(project_names)), 7)) # Build stacked bars bottoms = [0.0 for _ in x] @@ -249,7 +249,8 @@ def plot_framework_ratios( f"{percent:.2f}", ha="center", va="center", - fontsize=16, + fontsize=22, + fontweight="bold", ) # X axis labels: one per project (use short labels where available) @@ -258,13 +259,18 @@ def plot_framework_ratios( ax.set_xticklabels(tick_labels, rotation=30, ha="right") # Enlarge tick label fonts on both axes - ax.tick_params(axis="both", labelsize=14) + ax.tick_params(axis="x", labelsize=18, pad=6) + ax.tick_params(axis="y", labelsize=24) + for label in ax.get_xticklabels(): + label.set_fontweight("bold") + for label in ax.get_yticklabels(): + label.set_fontweight("bold") ax.set_ylim(0.0, 1.0) - ax.set_ylabel("Framework Overhead Breakdown (%)", fontsize=18) + ax.set_ylabel("(%)", fontsize=21, fontweight="bold", labelpad=-10) - # Show y-axis ticks as percentages (0-100) - ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: f"{y * 100:.2f}")) + # Show y-axis ticks as plain integers + ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: f"{y * 100:.0f}")) # Legend at top, horizontal, with inline label handles = [ @@ -287,18 +293,21 @@ def plot_framework_ratios( legend = ax.legend( handles, labels, - fontsize=14, + fontsize=19, loc="upper left", - bbox_to_anchor=(-0.08, 1.12), + bbox_to_anchor=(-0.06, 1.09), ncol=len(labels), frameon=False, columnspacing=0.8, handletextpad=0.5, + handlelength=0.3, + prop={"weight": "bold", "size": 19}, ) legend_texts = legend.get_texts() if legend_texts: - legend_texts[0].set_fontsize(18) + legend_texts[0].set_fontsize(20) + legend_texts[0].set_fontweight("bold") # Tighten layout and crop extra whitespace fig.tight_layout(pad=0.0) diff --git a/data/processed/RQ2/plot_llm_share_heatmap.py b/data/processed/RQ2/plot_llm_share_heatmap.py index 9577cae7f35d03902fa6b4cfebbd49894fa7fb69..922e4ea5280b52625e13ce8699c42e349b224ace 100644 --- a/data/processed/RQ2/plot_llm_share_heatmap.py +++ b/data/processed/RQ2/plot_llm_share_heatmap.py @@ -58,15 +58,29 @@ def plot_llm_share_heatmap(): ax.set_xticks(np.arange(len(df.columns))) ax.set_yticks(np.arange(len(df.index))) - ax.set_xticklabels(suffixes, rotation=0, ha="center", fontsize=9) + ax.set_xticklabels( + suffixes, rotation=0, ha="center", fontsize=12, fontweight="bold" + ) y_labels = [] for label in df.index: if "Gemini-2.5-flash-nothinking" in label: - y_labels.append("Gemini-2.5-flash\n-nothinking") + y_labels.append("Gemini-2.5\n-flash\n-nothinking") + elif "Gemini-2.5-flash" in label: + y_labels.append("Gemini-2.5\n-flash") + elif "DeepSeek-V3" in label: + y_labels.append("DeepSeek\n-V3") + elif "DeepSeek-R1" in label: + y_labels.append("DeepSeek\n-R1") + elif "GPT-4o-mini" in label: + y_labels.append("GPT-4o\n-mini") + elif "Qwen3-235b" in label: + y_labels.append("Qwen3\n-235b") + elif "Overall" in label: + y_labels.append("Overall\nAverage") else: y_labels.append(label) - ax.set_yticklabels(y_labels, fontsize=10) + ax.set_yticklabels(y_labels, fontsize=12, fontweight="bold") for i in range(len(df.index)): for j in range(len(df.columns)): @@ -88,7 +102,9 @@ def plot_llm_share_heatmap(): ) cbar = fig.colorbar(im, ax=ax, fraction=0.03, pad=0.01) - cbar.ax.tick_params(labelsize=10) + cbar.ax.tick_params(labelsize=13) + for label in cbar.ax.get_yticklabels(): + label.set_fontweight("bold") cbar.ax.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: f"{x * 100:.2f}%")) cbar.set_label("") diff --git a/data/processed/RQ2/plot_model_overhead_bars_a2a.py b/data/processed/RQ2/plot_model_overhead_bars_a2a.py index d34bc7c25fea164c7c885735fbc892d64a4d4c72..5cf3c61cbd0c9286e40f0b5df27fa455a058de5e 100644 --- a/data/processed/RQ2/plot_model_overhead_bars_a2a.py +++ b/data/processed/RQ2/plot_model_overhead_bars_a2a.py @@ -231,7 +231,7 @@ def plot_model_overhead_bars_a2a( for key in COMPONENT_KEYS: shares_by_comp[key].append(float(shares.get(key, 0.0) or 0.0)) - fig, ax = plt.subplots(figsize=(max(7.5, 1.0 * len(project_names)), 5)) + fig, ax = plt.subplots(figsize=(max(7.5, 1.0 * len(project_names)), 7)) # Build stacked bars bottoms = [0.0 for _ in x] @@ -258,6 +258,7 @@ def plot_model_overhead_bars_a2a( ax.set_xlim(-0.5 + margin, len(project_names) - 0.5 - margin) # Annotate percentage values for each segment (skip zeros) + min_gap = 0.04 # minimum vertical gap between labels (in data coords 0-1) for idx, name in enumerate(project_names): shares = [ float(project_shares.get(name, {}).get(k, 0.0) or 0.0) @@ -268,6 +269,9 @@ def plot_model_overhead_bars_a2a( continue active_shares = [shares[i] for i in active_indices] active_percents = compute_percent_labels(active_shares) + + # Build label entries: [y_center, x_center, text] + label_entries = [] for local_pos, comp_idx in enumerate(active_indices): key = COMPONENT_KEYS[comp_idx] share = shares[comp_idx] @@ -276,16 +280,35 @@ def plot_model_overhead_bars_a2a( continue bars = bar_handles[key] bar = bars[idx] - # Explicitly compute the horizontal center of the bar segment x_center = bar.get_x() + bar.get_width() / 2.0 y_center = bar.get_y() + bar.get_height() / 2.0 + label_entries.append([y_center, x_center, f"{percent:.2f}"]) + + # Nudge overlapping labels apart + if len(label_entries) > 1: + for _ in range(50): + label_entries.sort(key=lambda e: e[0]) + changed = False + for i in range(1, len(label_entries)): + y_prev = label_entries[i - 1][0] + y_curr = label_entries[i][0] + if y_curr - y_prev < min_gap: + needed = min_gap - (y_curr - y_prev) + label_entries[i - 1][0] = y_prev - needed / 2.0 + label_entries[i][0] = y_curr + needed / 2.0 + changed = True + if not changed: + break + + for y_center, x_center, text in label_entries: ax.text( x_center, y_center, - f"{percent:.2f}", + text, ha="center", va="center", - fontsize=16, + fontsize=22, + fontweight="bold", ) # X axis labels: one per A2A project (use short labels where available) @@ -294,13 +317,18 @@ def plot_model_overhead_bars_a2a( ax.set_xticklabels(tick_labels, rotation=30, ha="right") # Enlarge tick label fonts on both axes - ax.tick_params(axis="both", labelsize=14) + ax.tick_params(axis="x", labelsize=18, pad=6) + ax.tick_params(axis="y", labelsize=24) + for label in ax.get_xticklabels(): + label.set_fontweight("bold") + for label in ax.get_yticklabels(): + label.set_fontweight("bold") ax.set_ylim(0.0, 1.0) - ax.set_ylabel("Latency Breakdown (%)", fontsize=18) + ax.set_ylabel("(%)", fontsize=21, fontweight="bold", labelpad=-10) - # Show y-axis ticks as percentages (0-100), using a formatter to avoid warnings - ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: f"{y * 100:.2f}")) + # Show y-axis ticks as plain integers + ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: f"{y * 100:.0f}")) # Legend at top, horizontal, with inline "Component" label. # Use border-free patches in the legend as well. @@ -326,19 +354,22 @@ def plot_model_overhead_bars_a2a( legend = ax.legend( handles, labels, - fontsize=14, + fontsize=19, loc="upper left", - bbox_to_anchor=(-0.08, 1.12), + bbox_to_anchor=(-0.06, 1.09), ncol=len(labels), # all legend items in a single horizontal row frameon=False, columnspacing=0.8, handletextpad=0.5, + handlelength=0.3, + prop={"weight": "bold", "size": 19}, ) - # Make only the "Component" label in the legend match the y-axis label size + # Make the heading label slightly larger legend_texts = legend.get_texts() if legend_texts: - legend_texts[0].set_fontsize(18) + legend_texts[0].set_fontsize(20) + legend_texts[0].set_fontweight("bold") # Tighten layout and crop extra whitespace (especially left/right) fig.tight_layout(pad=0.0) diff --git a/data/processed/RQ2/plot_model_overhead_bars_mix.py b/data/processed/RQ2/plot_model_overhead_bars_mix.py index 51daae43b472fb17dbc93b977045f9db56b4534d..df300aad991460985a69fc4de86cf7fe1c2c06b2 100644 --- a/data/processed/RQ2/plot_model_overhead_bars_mix.py +++ b/data/processed/RQ2/plot_model_overhead_bars_mix.py @@ -105,7 +105,7 @@ def find_model_summary_csvs(root: Path) -> List[Path]: for sub in root.iterdir(): if not sub.is_dir(): continue - if not sub.name.endswith("mix"): + if not sub.name.endswith("H_A2A"): continue candidate = sub / "performance_breakdown_summary_by_model.csv" if candidate.exists(): @@ -208,7 +208,7 @@ def plot_model_overhead_bars( for key in COMPONENT_KEYS: shares_by_comp[key].append(float(shares.get(key, 0.0) or 0.0)) - fig, ax = plt.subplots(figsize=(max(7.5, 1.0 * len(project_names)), 5)) + fig, ax = plt.subplots(figsize=(max(7.5, 1.0 * len(project_names)), 7)) # Build stacked bars bottoms = [0.0 for _ in x] @@ -261,7 +261,8 @@ def plot_model_overhead_bars( f"{percent:.2f}", ha="center", va="center", - fontsize=16, + fontsize=22, + fontweight="bold", ) # X axis labels: one per mix project (use short labels where available) @@ -270,13 +271,18 @@ def plot_model_overhead_bars( ax.set_xticklabels(tick_labels, rotation=30, ha="right") # Enlarge tick label fonts on both axes - ax.tick_params(axis="both", labelsize=14) + ax.tick_params(axis="x", labelsize=18, pad=6) + ax.tick_params(axis="y", labelsize=24) + for label in ax.get_xticklabels(): + label.set_fontweight("bold") + for label in ax.get_yticklabels(): + label.set_fontweight("bold") ax.set_ylim(0.0, 1.0) - ax.set_ylabel("Latency Breakdown (%)", fontsize=18) + ax.set_ylabel("(%)", fontsize=21, fontweight="bold", labelpad=-10) - # Show y-axis ticks as percentages (0-100), using a formatter to avoid warnings - ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: f"{y * 100:.2f}")) + # Show y-axis ticks as plain integers + ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: f"{y * 100:.0f}")) # Legend at top, horizontal, with inline "Component" label. # Use border-free patches in the legend as well. @@ -302,22 +308,25 @@ def plot_model_overhead_bars( legend = ax.legend( handles, labels, - fontsize=14, + fontsize=19, loc="upper left", # Keep Component near the y-axis label, but avoid extending the # legend too far left, which would create extra whitespace on the # right after tight cropping. - bbox_to_anchor=(-0.08, 1.12), + bbox_to_anchor=(-0.06, 1.09), ncol=len(labels), # all legend items in a single horizontal row frameon=False, columnspacing=0.8, handletextpad=0.5, + handlelength=0.3, + prop={"weight": "bold", "size": 19}, ) - # Make only the "Component" label in the legend match the y-axis label size + # Make the heading label slightly larger legend_texts = legend.get_texts() if legend_texts: - legend_texts[0].set_fontsize(18) + legend_texts[0].set_fontsize(20) + legend_texts[0].set_fontweight("bold") # Tighten layout and crop extra whitespace (especially left/right) fig.tight_layout(pad=0.0) @@ -337,12 +346,14 @@ def main() -> None: # Collect all *mix subdirectories under Part2 (whether they have data or not) mix_dirs = [ - sub for sub in part2_dir.iterdir() if sub.is_dir() and sub.name.endswith("mix") + sub + for sub in part2_dir.iterdir() + if sub.is_dir() and sub.name.endswith("H_A2A") ] mix_dirs.sort(key=lambda p: p.name) if not mix_dirs: - print("no *mix subdirs found under Part2") + print("no *H_A2A subdirs found under Part2") return mix_shares: Dict[str, Dict[str, float]] = {} diff --git a/data/processed/RQ2/plot_total_classified_ecdf.py b/data/processed/RQ2/plot_total_classified_ecdf.py index 04aa41a00b2035d7c46f111e208f8087fcf9872f..2204abe392d13c270ca7c688f4bd7cbd05860399 100644 --- a/data/processed/RQ2/plot_total_classified_ecdf.py +++ b/data/processed/RQ2/plot_total_classified_ecdf.py @@ -186,14 +186,16 @@ def plot_overall_ecdf( print("no ECDF lines drawn for overall, skip figure") return - ax.set_xlabel("Trace duration [10^3 s]", fontsize=18) - ax.set_ylabel("", fontsize=18) + ax.set_xlabel(r"$\mathbf{Trace\ duration\ [10^3\ s]}$", fontsize=22) + ax.set_ylabel("", fontsize=22) ax.set_ylim(0.0, 1.0) ax.grid(True, which="both", axis="both", linestyle="-", linewidth=0.5, alpha=0.4) - ax.tick_params(axis="both", labelsize=14) + ax.tick_params(axis="both", labelsize=22) + plt.setp(ax.get_xticklabels(), fontweight="bold") + plt.setp(ax.get_yticklabels(), fontweight="bold") ax.margins(x=0.01) fig.tight_layout(pad=0.0) @@ -546,6 +548,8 @@ def generate_version_overall_comparison( def get_base_project_name(scenario: str) -> str: + if scenario.endswith("-A2A_mix"): + return scenario[: -len("-A2A_mix")] if scenario.endswith("-H_A2A"): return scenario[: -len("-H_A2A")] if scenario.endswith("-A2A"): @@ -589,12 +593,15 @@ def generate_overall_model_comparison( # Summary table lines.append("### Model Statistics Summary\n\n") - lines.append("| Model | Mean (s) |\n") - lines.append("| --- | --- |\n") + lines.append("| Model | Mean (s) | Median (s) | Count | Total (s) |\n") + lines.append("| --- | --- | --- | --- | --- |\n") for model in all_models: stats = model_stats[model] - lines.append(f"| {model} | {stats['mean']:.2f} |\n") + lines.append( + f"| {model} | {stats['mean']:.2f} | {stats['median']:.2f} | " + f"{stats['count']} | {stats['total']:.2f} |\n" + ) # Relative performance vs fastest (only if at least two models with data) positive_models = [m for m in all_models if model_stats[m]["mean"] > 0] @@ -627,7 +634,7 @@ def generate_overall_model_comparison( slowest_slowdown = (slowest_mean - fastest_mean) / fastest_mean * 100 lines.append( f"\n**Slowest model:** {slowest_model} ({slowest_mean:.2f}s mean, " - f"{slowest_slowdown:+.1f}% slower than baseline {fastest_model})\n" + f"{slowest_slowdown:+.1f}% slower than baseline)\n" ) lines.append("\n") @@ -663,12 +670,15 @@ def generate_project_model_comparison( # Summary table lines.append("## Model Statistics Summary\n\n") - lines.append("| Model | Mean (s) |\n") - lines.append("| --- | --- |\n") + lines.append("| Model | Mean (s) | Median (s) | Count | Total (s) |\n") + lines.append("| --- | --- | --- | --- | --- |\n") for model in all_models: stats = model_stats[model] - lines.append(f"| {model} | {stats['mean']:.2f} |\n") + lines.append( + f"| {model} | {stats['mean']:.2f} | {stats['median']:.2f} | " + f"{stats['count']} | {stats['total']:.2f} |\n" + ) # Pairwise comparison: compare each model against the fastest one if len(all_models) > 1: @@ -709,7 +719,7 @@ def generate_project_model_comparison( slowest_slowdown = (slowest_mean - fastest_mean) / fastest_mean * 100 lines.append( f"\n**Slowest model:** {slowest_model} ({slowest_mean:.2f}s mean, " - f"{slowest_slowdown:+.1f}% slower than baseline {fastest_model})\n" + f"{slowest_slowdown:+.1f}% slower than baseline)\n" ) lines.append("\n") @@ -771,17 +781,19 @@ def plot_ecdf_for_project( if x_max_plot > 0.0: ax.set_xlim(0.0, x_max_plot) - # Show axis units directly in terms of 10^3 s using plain text (no LaTeX). - ax.set_xlabel("Trace duration [10^3 s]", fontsize=18) + # Show axis units directly in terms of 10^3 s using LaTeX bold. + ax.set_xlabel(r"$\mathbf{Trace\ duration\ [10^3\ s]}$", fontsize=22) # No explicit y-axis label (ECDF) to keep the figure clean. - ax.set_ylabel("", fontsize=18) + ax.set_ylabel("", fontsize=22) ax.set_ylim(0.0, 1.0) # Add light grid similar to typical ECDF examples ax.grid(True, which="both", axis="both", linestyle="-", linewidth=0.5, alpha=0.4) - ax.tick_params(axis="both", labelsize=14) + ax.tick_params(axis="both", labelsize=22) + plt.setp(ax.get_xticklabels(), fontweight="bold") + plt.setp(ax.get_yticklabels(), fontweight="bold") # Slightly reduce margins so curves fill the axes area; no in-figure legend or title ax.margins(x=0.01) @@ -881,37 +893,42 @@ def main() -> None: print("Generating time comparison summaries...") print("=" * 60) + # 1. MCP vs Hardcoded comparisons mcp_hardcoded_projects = [ "MarkdownValidator", "GameBuilder", "EmailResponder", ] - version_projects = [ - "SQLAssistant", - "RecruitmentAssistant", - "LandingPageGenerator", - "SocialMediaManager", - "BookWriter", - ] - - # 1. MCP vs Hardcoded comparisons comparison_lines = [] + # Add overall summary first comparison_lines.append( generate_mcp_vs_hardcoded_overall_comparison( mcp_hardcoded_projects, scenario_time_data ) ) + + # Then add per-project comparisons for project in mcp_hardcoded_projects: comparison_lines.append( generate_mcp_vs_hardcoded_comparison(project, scenario_time_data) ) + comparison_md_path = out_dir / "Time_Comparison_MCP_vs_Hardcoded.md" comparison_md_path.write_text("".join(comparison_lines), encoding="utf-8") print(f"Created: {comparison_md_path}") # 2. MCP vs A2A comparisons + version_projects = [ + "SQL_assistant", + "intelligent_recruitment_platform", + "landing_page_generator", + "self_evaluation_loop_flow", + "write_a_book_with_flows", + ] + comparison_lines = [] + # Add overall summary first comparison_lines.append( generate_version_overall_comparison( version_projects, @@ -923,47 +940,54 @@ def main() -> None: "MCP vs A2A Time Comparison", ) ) + + # Then add per-project comparisons for project in version_projects: comparison_lines.append( generate_version_comparison( project, "-MCP", "-A2A", scenario_time_data, "MCP", "A2A" ) ) + comparison_md_path = out_dir / "Time_Comparison_MCP_vs_A2A.md" comparison_md_path.write_text("".join(comparison_lines), encoding="utf-8") print(f"Created: {comparison_md_path}") - # 3. A2A vs H_A2A comparisons + # 3. A2A vs A2A_mix comparisons comparison_lines = [] + # Add overall summary first comparison_lines.append( generate_version_overall_comparison( version_projects, "-A2A", - "-H_A2A", + "-A2A_mix", scenario_time_data, "A2A", - "H_A2A", - "A2A vs H_A2A Time Comparison", + "A2A_mix", + "A2A vs A2A_mix Time Comparison", ) ) + + # Then add per-project comparisons for project in version_projects: comparison_lines.append( generate_version_comparison( - project, "-A2A", "-H_A2A", scenario_time_data, "A2A", "H_A2A" + project, "-A2A", "-A2A_mix", scenario_time_data, "A2A", "A2A_mix" ) ) - comparison_md_path = out_dir / "Time_Comparison_A2A_vs_H_A2A.md" + + comparison_md_path = out_dir / "Time_Comparison_A2A_vs_A2A_mix.md" comparison_md_path.write_text("".join(comparison_lines), encoding="utf-8") print(f"Created: {comparison_md_path}") - # 4. Generate detailed A2A vs H_A2A comparison for each project - print("\nGenerating detailed A2A vs H_A2A per-project comparisons...") + # 4. Generate detailed A2A vs A2A_mix comparison for each project + print("\nGenerating detailed A2A vs A2A_mix per-project comparisons...") a2a_mix_comparison_lines = [] a2a_mix_comparison_lines.append( - "# A2A vs H_A2A: Detailed Per-Project Comparison\n\n" + "# A2A vs A2A_mix: Detailed Per-Project Comparison\n\n" ) a2a_mix_comparison_lines.append( - "This document compares A2A and H_A2A architectures for each project, " + "This document compares A2A and A2A_mix architectures for each project, " ) a2a_mix_comparison_lines.append( "showing both per-model and overall statistics.\n\n" @@ -981,7 +1005,7 @@ def main() -> None: for project in version_projects: scenario_a2a = f"{project}-A2A" - scenario_a2a_mix = f"{project}-H_A2A" + scenario_a2a_mix = f"{project}-A2A_mix" if ( scenario_a2a not in scenario_time_data @@ -1022,7 +1046,7 @@ def main() -> None: pct = (diff / avg_a2a * 100) if avg_a2a > 0 else 0 a2a_mix_comparison_lines.append("### Overall Summary\n\n") a2a_mix_comparison_lines.append( - "| A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) |\n" + "| A2A Mean (s) | A2A_mix Mean (s) | Diff (A2A_mix - A2A) |\n" ) a2a_mix_comparison_lines.append("| --- | --- | --- |\n") a2a_mix_comparison_lines.append( @@ -1041,7 +1065,7 @@ def main() -> None: # Per-model comparison a2a_mix_comparison_lines.append("### Per-Model Comparison\n\n") a2a_mix_comparison_lines.append( - "| Model | A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) |\n" + "| Model | A2A Mean (s) | A2A_mix Mean (s) | Diff (A2A_mix - A2A) |\n" ) a2a_mix_comparison_lines.append("| --- | --- | --- | --- |\n") @@ -1064,7 +1088,7 @@ def main() -> None: a2a_mix_comparison_lines.insert( 4, "## Overall (All Projects)\n\n" - "| Project | A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) |\n" + "| Project | A2A Mean (s) | A2A_mix Mean (s) | Diff (A2A_mix - A2A) |\n" "| --- | --- | --- | --- |\n" + "".join( f"| {d['project']} | {d['a2a']:.2f} | {d['mix']:.2f} | {d['diff']:+.2f}s ({d['pct']:+.1f}%) |\n" @@ -1077,7 +1101,7 @@ def main() -> None: per_model_lines = [] per_model_lines.append("## All Projects Combined (Per-Model)\n\n") per_model_lines.append( - "| Model | A2A Mean (s) | H_A2A Mean (s) | Diff (H_A2A - A2A) |\n" + "| Model | A2A Mean (s) | A2A_mix Mean (s) | Diff (A2A_mix - A2A) |\n" ) per_model_lines.append("| --- | --- | --- | --- |\n") for model in MODEL_ORDER: @@ -1099,7 +1123,7 @@ def main() -> None: # insert after top intro (after first 4 elements added earlier) a2a_mix_comparison_lines[5:5] = per_model_lines - a2a_mix_md_path = out_dir / "A2A_vs_H_A2A_Detailed_Comparison.md" + a2a_mix_md_path = out_dir / "A2A_vs_A2A_mix_Detailed_Comparison.md" a2a_mix_md_path.write_text("".join(a2a_mix_comparison_lines), encoding="utf-8") print(f"Created: {a2a_mix_md_path}") @@ -1113,7 +1137,9 @@ def main() -> None: f"This document compares model performance within each of the {len(all_project_names)} projects.\n\n" ) model_comparison_lines.append("Each project shows:\n") - model_comparison_lines.append("- Model statistics (mean)\n") + model_comparison_lines.append( + "- Model statistics (mean, median, count, total time)\n" + ) model_comparison_lines.append( "- Relative performance compared to the fastest model\n\n" ) diff --git a/data/processed/RQ3/Violin/BookWriter-A2A_total_tokens_violin.pdf b/data/processed/RQ3/Violin/BookWriter-A2A_total_tokens_violin.pdf index a50755b0511b14b2a51dad4c0b77e2f5b324cb07..1d9738595c9ad31d27fc364b391da2718fc92505 100644 --- a/data/processed/RQ3/Violin/BookWriter-A2A_total_tokens_violin.pdf +++ b/data/processed/RQ3/Violin/BookWriter-A2A_total_tokens_violin.pdf @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f2261cbfcbc6b44b06e5ab875757b738772d763bb3c19c9ad891cfa7e9acbc50 -size 30168 +oid sha256:a808d2b8d8909f9f5518fd3f13a3fd8cb1e7bf46c2cbad468a0efb236abaa14d +size 29232 diff --git a/data/processed/RQ3/Violin/BookWriter-H-A2A_total_tokens_violin.pdf 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sha256:4815259199a2e5179fe614b6d8f9cc1fb180ad0961a9b540cabeb4a74911d6ae -size 44043 +oid sha256:58b702d4d4094bb757169ba593121bdf06bdc50087b8a65182d4d8daa750e4c1 +size 43226 diff --git a/data/processed/RQ3/Violin/project_model_distribution.md b/data/processed/RQ3/Violin/project_model_distribution.md index 9f6287d5f177e50a37c9e26323af276b66ae1895..be153062043203e3788673194b69cd8313a515a9 100644 --- a/data/processed/RQ3/Violin/project_model_distribution.md +++ b/data/processed/RQ3/Violin/project_model_distribution.md @@ -2,9 +2,9 @@ Per-project, per-model total token usage with breakdown by execution outcome. Only the mean total tokens are reported. Baseline vs maximum is computed from the overall mean aggregated across all three statuses. -## TOTAL AGGREGATION (All Projects) +## Global Summary (All Projects Combined) -### Overall per-model total tokens (all statuses) +### Overall per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 1270 | 38,411 | @@ -15,65 +15,16 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 1270 | 38,578 | | Qwen3-235b | 1270 | 39,751 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 1051 | 40,620 | -| GPT-4o-mini | 1116 | 47,293 | -| DeepSeek-V3.1 | 1098 | 59,360 | -| DeepSeek-R1 | 826 | 60,570 | -| Gemini-2.5 | 862 | 54,661 | -| Gemini-2.5-NT | 896 | 36,901 | -| Qwen3-235b | 1000 | 35,776 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 137 | 32,233 | -| GPT-4o-mini | 144 | 81,392 | -| DeepSeek-V3.1 | 165 | 64,056 | -| DeepSeek-R1 | 308 | 114,919 | -| Gemini-2.5 | 138 | 92,688 | -| Gemini-2.5-NT | 91 | 111,927 | -| Qwen3-235b | 196 | 66,003 | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 82 | 20,410 | -| GPT-4o-mini | 10 | 160,542 | -| DeepSeek-V3.1 | 7 | 26,352 | -| DeepSeek-R1 | 136 | 51,229 | -| Gemini-2.5 | 270 | 29,101 | -| Gemini-2.5-NT | 283 | 20,300 | -| Qwen3-235b | 74 | 23,934 | - -### Baseline vs maximum (overall mean tokens) +### Baseline vs maximum (Overall per-model total tokens) - Baseline (lowest mean): GPT-5 = 38,411 tokens - Maximum (highest mean): DeepSeek-R1 = 72,750 tokens - Delta: 34,340 (89.4%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): Qwen3-235b = 35,776 tokens -- Maximum (highest mean): DeepSeek-R1 = 60,570 tokens -- Delta: 24,794 (69.3%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): GPT-5 = 32,233 tokens -- Maximum (highest mean): DeepSeek-R1 = 114,919 tokens -- Delta: 82,686 (256.5%) +## Series Aggregates (Aggregated by Base Task) -### Baseline vs maximum — Failure -- Baseline (lowest mean): Gemini-2.5-NT = 20,300 tokens -- Maximum (highest mean): GPT-4o-mini = 160,542 tokens -- Delta: 140,242 (690.8%) +### Series: BookWriter ---- -# Series Aggregates - -## Series: BookWriter - -### Overall per-model total tokens (all statuses) +#### Aggregated tokens for BookWriter | Model | n | Mean | | --- | --- | --- | | GPT-5 | 180 | 163,074 | @@ -84,49 +35,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 180 | 78,207 | | Qwen3-235b | 180 | 101,878 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 180 | 163,074 | -| GPT-4o-mini | 180 | 125,190 | -| DeepSeek-V3.1 | 179 | 198,154 | -| DeepSeek-R1 | 163 | 156,596 | -| Gemini-2.5 | 180 | 102,955 | -| Gemini-2.5-NT | 179 | 78,117 | -| Qwen3-235b | 179 | 101,880 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 1 | 398,139 | -| DeepSeek-R1 | 17 | 288,873 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 1 | 94,226 | -| Qwen3-235b | 1 | 101,570 | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Aggregated tokens for BookWriter) - Baseline (lowest mean): Gemini-2.5-NT = 78,207 tokens - Maximum (highest mean): DeepSeek-V3.1 = 199,265 tokens - Delta: 121,058 (154.8%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): Gemini-2.5-NT = 78,117 tokens -- Maximum (highest mean): DeepSeek-V3.1 = 198,154 tokens -- Delta: 120,036 (153.7%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): Gemini-2.5-NT = 94,226 tokens -- Maximum (highest mean): DeepSeek-V3.1 = 398,139 tokens -- Delta: 303,913 (322.5%) - -### Baseline vs maximum — Failure -- No data to compare. +### Series: EmailResponder -## Series: EmailResponder - -### Overall per-model total tokens (all statuses) +#### Aggregated tokens for EmailResponder | Model | n | Mean | | --- | --- | --- | | GPT-5 | 200 | 6,232 | @@ -137,62 +53,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 200 | 38,777 | | Qwen3-235b | 200 | 6,667 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 200 | 6,232 | -| GPT-4o-mini | 200 | 8,741 | -| DeepSeek-V3.1 | 196 | 8,441 | -| DeepSeek-R1 | 168 | 14,238 | -| Gemini-2.5 | 181 | 22,482 | -| Gemini-2.5-NT | 176 | 12,595 | -| Qwen3-235b | 200 | 6,667 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 4 | 11,697 | -| DeepSeek-R1 | 30 | 27,119 | -| Gemini-2.5 | 18 | 158,396 | -| Gemini-2.5-NT | 24 | 230,778 | -| Qwen3-235b | 0 | - | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 2 | 16,502 | -| Gemini-2.5 | 1 | 405,135 | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 0 | - | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Aggregated tokens for EmailResponder) - Baseline (lowest mean): GPT-5 = 6,232 tokens - Maximum (highest mean): Gemini-2.5-NT = 38,777 tokens - Delta: 32,545 (522.2%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): GPT-5 = 6,232 tokens -- Maximum (highest mean): Gemini-2.5 = 22,482 tokens -- Delta: 16,250 (260.7%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): DeepSeek-V3.1 = 11,697 tokens -- Maximum (highest mean): Gemini-2.5-NT = 230,778 tokens -- Delta: 219,081 (1872.9%) - -### Baseline vs maximum — Failure -- Baseline (lowest mean): DeepSeek-R1 = 16,502 tokens -- Maximum (highest mean): Gemini-2.5 = 405,135 tokens -- Delta: 388,633 (2355.1%) - -## Series: GameBuilder +### Series: GameBuilder -### Overall per-model total tokens (all statuses) +#### Aggregated tokens for GameBuilder | Model | n | Mean | | --- | --- | --- | | GPT-5 | 50 | 22,665 | @@ -203,62 +71,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 50 | 59,373 | | Qwen3-235b | 50 | 24,125 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 50 | 22,665 | -| GPT-4o-mini | 50 | 32,909 | -| DeepSeek-V3.1 | 49 | 47,722 | -| DeepSeek-R1 | 50 | 44,694 | -| Gemini-2.5 | 50 | 74,145 | -| Gemini-2.5-NT | 48 | 52,527 | -| Qwen3-235b | 50 | 24,125 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 0 | - | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 2 | 223,682 | -| Qwen3-235b | 0 | - | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 1 | 77,354 | -| DeepSeek-R1 | 0 | - | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 0 | - | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Aggregated tokens for GameBuilder) - Baseline (lowest mean): GPT-5 = 22,665 tokens - Maximum (highest mean): Gemini-2.5 = 74,145 tokens - Delta: 51,480 (227.1%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): GPT-5 = 22,665 tokens -- Maximum (highest mean): Gemini-2.5 = 74,145 tokens -- Delta: 51,480 (227.1%) +### Series: LandingPageGenerator -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): Gemini-2.5-NT = 223,682 tokens -- Maximum (highest mean): Gemini-2.5-NT = 223,682 tokens -- Delta: 0 (0.0%) - -### Baseline vs maximum — Failure -- Baseline (lowest mean): DeepSeek-V3.1 = 77,354 tokens -- Maximum (highest mean): DeepSeek-V3.1 = 77,354 tokens -- Delta: 0 (0.0%) - -## Series: LandingPageGenerator - -### Overall per-model total tokens (all statuses) +#### Aggregated tokens for LandingPageGenerator | Model | n | Mean | | --- | --- | --- | | GPT-5 | 180 | 17,638 | @@ -269,62 +89,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 180 | 23,734 | | Qwen3-235b | 180 | 16,749 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 101 | 17,805 | -| GPT-4o-mini | 177 | 27,799 | -| DeepSeek-V3.1 | 172 | 26,666 | -| DeepSeek-R1 | 49 | 35,431 | -| Gemini-2.5 | 1 | 55,158 | -| Gemini-2.5-NT | 2 | 188,992 | -| Qwen3-235b | 109 | 17,466 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 2 | 28,430 | -| DeepSeek-R1 | 8 | 61,223 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 5 | 33,835 | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 79 | 17,425 | -| GPT-4o-mini | 3 | 23,420 | -| DeepSeek-V3.1 | 6 | 17,852 | -| DeepSeek-R1 | 123 | 37,626 | -| Gemini-2.5 | 179 | 30,876 | -| Gemini-2.5-NT | 178 | 21,877 | -| Qwen3-235b | 66 | 14,271 | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Aggregated tokens for LandingPageGenerator) - Baseline (lowest mean): Qwen3-235b = 16,749 tokens - Maximum (highest mean): DeepSeek-R1 = 38,077 tokens - Delta: 21,328 (127.3%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): Qwen3-235b = 17,466 tokens -- Maximum (highest mean): Gemini-2.5-NT = 188,992 tokens -- Delta: 171,526 (982.0%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): DeepSeek-V3.1 = 28,430 tokens -- Maximum (highest mean): DeepSeek-R1 = 61,223 tokens -- Delta: 32,793 (115.3%) - -### Baseline vs maximum — Failure -- Baseline (lowest mean): Qwen3-235b = 14,271 tokens -- Maximum (highest mean): DeepSeek-R1 = 37,626 tokens -- Delta: 23,355 (163.7%) - -## Series: MarkdownValidator +### Series: MarkdownValidator -### Overall per-model total tokens (all statuses) +#### Aggregated tokens for MarkdownValidator | Model | n | Mean | | --- | --- | --- | | GPT-5 | 120 | 1,910 | @@ -335,49 +107,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 120 | 46,614 | | Qwen3-235b | 120 | 1,924 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 120 | 1,910 | -| GPT-4o-mini | 120 | 1,897 | -| DeepSeek-V3.1 | 120 | 2,006 | -| DeepSeek-R1 | 120 | 5,898 | -| Gemini-2.5 | 95 | 74,526 | -| Gemini-2.5-NT | 113 | 27,744 | -| Qwen3-235b | 120 | 1,924 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 0 | - | -| Gemini-2.5 | 25 | 270,547 | -| Gemini-2.5-NT | 7 | 351,232 | -| Qwen3-235b | 0 | - | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Aggregated tokens for MarkdownValidator) - Baseline (lowest mean): GPT-4o-mini = 1,897 tokens - Maximum (highest mean): Gemini-2.5 = 115,364 tokens - Delta: 113,466 (5980.0%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): GPT-4o-mini = 1,897 tokens -- Maximum (highest mean): Gemini-2.5 = 74,526 tokens -- Delta: 72,629 (3827.7%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): Gemini-2.5 = 270,547 tokens -- Maximum (highest mean): Gemini-2.5-NT = 351,232 tokens -- Delta: 80,686 (29.8%) +### Series: RecruitmentAssistant -### Baseline vs maximum — Failure -- No data to compare. - -## Series: RecruitmentAssistant - -### Overall per-model total tokens (all statuses) +#### Aggregated tokens for RecruitmentAssistant | Model | n | Mean | | --- | --- | --- | | GPT-5 | 180 | 32,358 | @@ -388,62 +125,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 180 | 48,983 | | Qwen3-235b | 180 | 86,025 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 180 | 32,358 | -| GPT-4o-mini | 180 | 92,274 | -| DeepSeek-V3.1 | 180 | 65,486 | -| DeepSeek-R1 | 171 | 77,784 | -| Gemini-2.5 | 180 | 63,570 | -| Gemini-2.5-NT | 180 | 48,983 | -| Qwen3-235b | 133 | 69,503 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 8 | 108,468 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 44 | 132,115 | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 1 | 32,043 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 3 | 142,502 | - -### Baseline vs maximum (overall mean tokens) -- Baseline (lowest mean): GPT-5 = 32,358 tokens -- Maximum (highest mean): GPT-4o-mini = 92,274 tokens -- Delta: 59,916 (185.2%) - -### Baseline vs maximum — Direct Success +#### Baseline vs maximum (Aggregated tokens for RecruitmentAssistant) - Baseline (lowest mean): GPT-5 = 32,358 tokens - Maximum (highest mean): GPT-4o-mini = 92,274 tokens - Delta: 59,916 (185.2%) -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): DeepSeek-R1 = 108,468 tokens -- Maximum (highest mean): Qwen3-235b = 132,115 tokens -- Delta: 23,647 (21.8%) +### Series: SQLAssistant -### Baseline vs maximum — Failure -- Baseline (lowest mean): DeepSeek-R1 = 32,043 tokens -- Maximum (highest mean): Qwen3-235b = 142,502 tokens -- Delta: 110,459 (344.7%) - -## Series: SQLAssistant - -### Overall per-model total tokens (all statuses) +#### Aggregated tokens for SQLAssistant | Model | n | Mean | | --- | --- | --- | | GPT-5 | 180 | 11,342 | @@ -454,62 +143,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 180 | 19,757 | | Qwen3-235b | 180 | 16,464 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 101 | 7,480 | -| GPT-4o-mini | 114 | 13,910 | -| DeepSeek-V3.1 | 70 | 13,760 | -| DeepSeek-R1 | 36 | 33,567 | -| Gemini-2.5 | 33 | 15,045 | -| Gemini-2.5-NT | 45 | 11,328 | -| Qwen3-235b | 112 | 12,176 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 79 | 16,279 | -| GPT-4o-mini | 66 | 27,437 | -| DeepSeek-V3.1 | 110 | 34,291 | -| DeepSeek-R1 | 144 | 94,256 | -| Gemini-2.5 | 57 | 37,607 | -| Gemini-2.5-NT | 30 | 39,861 | -| Qwen3-235b | 66 | 23,950 | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 0 | - | -| Gemini-2.5 | 90 | 21,392 | -| Gemini-2.5-NT | 105 | 17,626 | -| Qwen3-235b | 2 | 9,587 | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Aggregated tokens for SQLAssistant) - Baseline (lowest mean): GPT-5 = 11,342 tokens - Maximum (highest mean): DeepSeek-R1 = 82,118 tokens - Delta: 70,776 (624.0%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): GPT-5 = 7,480 tokens -- Maximum (highest mean): DeepSeek-R1 = 33,567 tokens -- Delta: 26,087 (348.8%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): GPT-5 = 16,279 tokens -- Maximum (highest mean): DeepSeek-R1 = 94,256 tokens -- Delta: 77,977 (479.0%) +### Series: SocialMediaManager -### Baseline vs maximum — Failure -- Baseline (lowest mean): Qwen3-235b = 9,587 tokens -- Maximum (highest mean): Gemini-2.5 = 21,392 tokens -- Delta: 11,805 (123.1%) - -## Series: SocialMediaManager - -### Overall per-model total tokens (all statuses) +#### Aggregated tokens for SocialMediaManager | Model | n | Mean | | --- | --- | --- | | GPT-5 | 180 | 32,102 | @@ -520,65 +161,16 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 180 | 10,850 | | Qwen3-235b | 180 | 43,954 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 119 | 19,760 | -| GPT-4o-mini | 95 | 36,934 | -| DeepSeek-V3.1 | 132 | 61,643 | -| DeepSeek-R1 | 69 | 42,398 | -| Gemini-2.5 | 142 | 12,219 | -| Gemini-2.5-NT | 153 | 9,820 | -| Qwen3-235b | 97 | 23,270 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 58 | 53,963 | -| GPT-4o-mini | 78 | 127,046 | -| DeepSeek-V3.1 | 48 | 131,154 | -| DeepSeek-R1 | 101 | 145,943 | -| Gemini-2.5 | 38 | 27,172 | -| Gemini-2.5-NT | 27 | 16,691 | -| Qwen3-235b | 80 | 65,900 | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 3 | 99,016 | -| GPT-4o-mini | 7 | 219,309 | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 10 | 227,416 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 3 | 127,530 | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Aggregated tokens for SocialMediaManager) - Baseline (lowest mean): Gemini-2.5-NT = 10,850 tokens - Maximum (highest mean): DeepSeek-R1 = 110,777 tokens - Delta: 99,927 (920.9%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): Gemini-2.5-NT = 9,820 tokens -- Maximum (highest mean): DeepSeek-V3.1 = 61,643 tokens -- Delta: 51,823 (527.7%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): Gemini-2.5-NT = 16,691 tokens -- Maximum (highest mean): DeepSeek-R1 = 145,943 tokens -- Delta: 129,251 (774.4%) +## Individual Project Details -### Baseline vs maximum — Failure -- Baseline (lowest mean): GPT-5 = 99,016 tokens -- Maximum (highest mean): DeepSeek-R1 = 227,416 tokens -- Delta: 128,400 (129.7%) +### BookWriter-MCP ---- -# Individual Project Details - -## BookWriter-MCP - -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 190,842 | @@ -589,49 +181,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 82,546 | | Qwen3-235b | 60 | 110,716 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 60 | 190,842 | -| GPT-4o-mini | 60 | 133,339 | -| DeepSeek-V3.1 | 60 | 184,315 | -| DeepSeek-R1 | 52 | 171,033 | -| Gemini-2.5 | 60 | 107,154 | -| Gemini-2.5-NT | 59 | 82,348 | -| Qwen3-235b | 59 | 110,871 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 8 | 318,572 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 1 | 94,226 | -| Qwen3-235b | 1 | 101,570 | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): Gemini-2.5-NT = 82,546 tokens - Maximum (highest mean): GPT-5 = 190,842 tokens - Delta: 108,296 (131.2%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): Gemini-2.5-NT = 82,348 tokens -- Maximum (highest mean): GPT-5 = 190,842 tokens -- Delta: 108,494 (131.8%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): Gemini-2.5-NT = 94,226 tokens -- Maximum (highest mean): DeepSeek-R1 = 318,572 tokens -- Delta: 224,346 (238.1%) - -### Baseline vs maximum — Failure -- No data to compare. - -## BookWriter-A2A +### BookWriter-A2A -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 181,022 | @@ -642,49 +199,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 86,249 | | Qwen3-235b | 60 | 118,406 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 60 | 181,022 | -| GPT-4o-mini | 60 | 139,665 | -| DeepSeek-V3.1 | 59 | 270,142 | -| DeepSeek-R1 | 52 | 173,528 | -| Gemini-2.5 | 60 | 110,126 | -| Gemini-2.5-NT | 60 | 86,249 | -| Qwen3-235b | 60 | 118,406 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 1 | 398,139 | -| DeepSeek-R1 | 8 | 272,152 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 0 | - | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): Gemini-2.5-NT = 86,249 tokens - Maximum (highest mean): DeepSeek-V3.1 = 272,275 tokens - Delta: 186,026 (215.7%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): Gemini-2.5-NT = 86,249 tokens -- Maximum (highest mean): DeepSeek-V3.1 = 270,142 tokens -- Delta: 183,893 (213.2%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): DeepSeek-R1 = 272,152 tokens -- Maximum (highest mean): DeepSeek-V3.1 = 398,139 tokens -- Delta: 125,988 (46.3%) +### BookWriter-H-A2A -### Baseline vs maximum — Failure -- No data to compare. - -## BookWriter-H-A2A - -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 117,358 | @@ -695,49 +217,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 65,826 | | Qwen3-235b | 60 | 76,512 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 60 | 117,358 | -| GPT-4o-mini | 60 | 102,564 | -| DeepSeek-V3.1 | 60 | 141,204 | -| DeepSeek-R1 | 59 | 128,948 | -| Gemini-2.5 | 60 | 91,585 | -| Gemini-2.5-NT | 60 | 65,826 | -| Qwen3-235b | 60 | 76,512 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 1 | 185,051 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 0 | - | - -### Baseline vs maximum (overall mean tokens) -- Baseline (lowest mean): Gemini-2.5-NT = 65,826 tokens -- Maximum (highest mean): DeepSeek-V3.1 = 141,204 tokens -- Delta: 75,378 (114.5%) - -### Baseline vs maximum — Direct Success +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): Gemini-2.5-NT = 65,826 tokens - Maximum (highest mean): DeepSeek-V3.1 = 141,204 tokens - Delta: 75,378 (114.5%) -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): DeepSeek-R1 = 185,051 tokens -- Maximum (highest mean): DeepSeek-R1 = 185,051 tokens -- Delta: 0 (0.0%) - -### Baseline vs maximum — Failure -- No data to compare. +### EmailResponder -## EmailResponder - -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 100 | 5,562 | @@ -748,62 +235,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 100 | 42,421 | | Qwen3-235b | 100 | 5,900 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 100 | 5,562 | -| GPT-4o-mini | 100 | 8,044 | -| DeepSeek-V3.1 | 98 | 7,350 | -| DeepSeek-R1 | 72 | 13,490 | -| Gemini-2.5 | 90 | 11,211 | -| Gemini-2.5-NT | 88 | 12,494 | -| Qwen3-235b | 100 | 5,900 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 2 | 11,414 | -| DeepSeek-R1 | 26 | 26,688 | -| Gemini-2.5 | 9 | 233,862 | -| Gemini-2.5-NT | 12 | 261,883 | -| Qwen3-235b | 0 | - | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 2 | 16,502 | -| Gemini-2.5 | 1 | 405,135 | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 0 | - | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): GPT-5 = 5,562 tokens - Maximum (highest mean): Gemini-2.5-NT = 42,421 tokens - Delta: 36,859 (662.8%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): GPT-5 = 5,562 tokens -- Maximum (highest mean): DeepSeek-R1 = 13,490 tokens -- Delta: 7,929 (142.6%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): DeepSeek-V3.1 = 11,414 tokens -- Maximum (highest mean): Gemini-2.5-NT = 261,883 tokens -- Delta: 250,469 (2194.5%) - -### Baseline vs maximum — Failure -- Baseline (lowest mean): DeepSeek-R1 = 16,502 tokens -- Maximum (highest mean): Gemini-2.5 = 405,135 tokens -- Delta: 388,633 (2355.1%) - -## EmailResponder-MCP +### EmailResponder-MCP -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 100 | 6,903 | @@ -814,49 +253,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 100 | 35,133 | | Qwen3-235b | 100 | 7,434 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 100 | 6,903 | -| GPT-4o-mini | 100 | 9,437 | -| DeepSeek-V3.1 | 98 | 9,531 | -| DeepSeek-R1 | 96 | 14,798 | -| Gemini-2.5 | 91 | 33,629 | -| Gemini-2.5-NT | 88 | 12,696 | -| Qwen3-235b | 100 | 7,434 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 2 | 11,981 | -| DeepSeek-R1 | 4 | 29,922 | -| Gemini-2.5 | 9 | 82,929 | -| Gemini-2.5-NT | 12 | 199,673 | -| Qwen3-235b | 0 | - | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): GPT-5 = 6,903 tokens - Maximum (highest mean): Gemini-2.5 = 38,066 tokens - Delta: 31,163 (451.4%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): GPT-5 = 6,903 tokens -- Maximum (highest mean): Gemini-2.5 = 33,629 tokens -- Delta: 26,726 (387.2%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): DeepSeek-V3.1 = 11,981 tokens -- Maximum (highest mean): Gemini-2.5-NT = 199,673 tokens -- Delta: 187,692 (1566.6%) - -### Baseline vs maximum — Failure -- No data to compare. - -## GameBuilder +### GameBuilder -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 25 | 22,904 | @@ -867,73 +271,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 25 | 67,964 | | Qwen3-235b | 25 | 24,212 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 25 | 22,904 | -| GPT-4o-mini | 25 | 17,823 | -| DeepSeek-V3.1 | 24 | 39,841 | -| DeepSeek-R1 | 25 | 44,536 | -| Gemini-2.5 | 25 | 72,772 | -| Gemini-2.5-NT | 23 | 54,423 | -| Qwen3-235b | 25 | 24,212 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 0 | - | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 2 | 223,682 | -| Qwen3-235b | 0 | - | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 1 | 77,354 | -| DeepSeek-R1 | 0 | - | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 0 | - | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): GPT-4o-mini = 17,823 tokens - Maximum (highest mean): Gemini-2.5 = 72,772 tokens - Delta: 54,949 (308.3%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): GPT-4o-mini = 17,823 tokens -- Maximum (highest mean): Gemini-2.5 = 72,772 tokens -- Delta: 54,949 (308.3%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): Gemini-2.5-NT = 223,682 tokens -- Maximum (highest mean): Gemini-2.5-NT = 223,682 tokens -- Delta: 0 (0.0%) - -### Baseline vs maximum — Failure -- Baseline (lowest mean): DeepSeek-V3.1 = 77,354 tokens -- Maximum (highest mean): DeepSeek-V3.1 = 77,354 tokens -- Delta: 0 (0.0%) - -## GameBuilder-MCP - -### Overall per-model total tokens (all statuses) -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 25 | 22,425 | -| GPT-4o-mini | 25 | 47,996 | -| DeepSeek-V3.1 | 25 | 55,289 | -| DeepSeek-R1 | 25 | 44,851 | -| Gemini-2.5 | 25 | 75,519 | -| Gemini-2.5-NT | 25 | 50,782 | -| Qwen3-235b | 25 | 24,039 | +### GameBuilder-MCP -### Direct Success distribution +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 25 | 22,425 | @@ -944,25 +289,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 25 | 50,782 | | Qwen3-235b | 25 | 24,039 | -### Baseline vs maximum (overall mean tokens) -- Baseline (lowest mean): GPT-5 = 22,425 tokens -- Maximum (highest mean): Gemini-2.5 = 75,519 tokens -- Delta: 53,093 (236.8%) - -### Baseline vs maximum — Direct Success +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): GPT-5 = 22,425 tokens - Maximum (highest mean): Gemini-2.5 = 75,519 tokens - Delta: 53,093 (236.8%) -### Baseline vs maximum — Retry Success -- No data to compare. - -### Baseline vs maximum — Failure -- No data to compare. +### LandingPageGenerator-MCP -## LandingPageGenerator-MCP - -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 12,653 | @@ -973,62 +307,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 33,428 | | Qwen3-235b | 60 | 14,478 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 60 | 12,653 | -| GPT-4o-mini | 59 | 19,333 | -| DeepSeek-V3.1 | 55 | 15,912 | -| DeepSeek-R1 | 33 | 24,081 | -| Gemini-2.5 | 1 | 55,158 | -| Gemini-2.5-NT | 2 | 188,992 | -| Qwen3-235b | 53 | 15,068 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 8 | 61,223 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 0 | - | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 1 | 14,308 | -| DeepSeek-V3.1 | 5 | 15,362 | -| DeepSeek-R1 | 19 | 21,990 | -| Gemini-2.5 | 59 | 26,041 | -| Gemini-2.5-NT | 58 | 28,064 | -| Qwen3-235b | 7 | 10,009 | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): GPT-5 = 12,653 tokens - Maximum (highest mean): Gemini-2.5-NT = 33,428 tokens - Delta: 20,775 (164.2%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): GPT-5 = 12,653 tokens -- Maximum (highest mean): Gemini-2.5-NT = 188,992 tokens -- Delta: 176,339 (1393.6%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): DeepSeek-R1 = 61,223 tokens -- Maximum (highest mean): DeepSeek-R1 = 61,223 tokens -- Delta: 0 (0.0%) - -### Baseline vs maximum — Failure -- Baseline (lowest mean): Qwen3-235b = 10,009 tokens -- Maximum (highest mean): Gemini-2.5-NT = 28,064 tokens -- Delta: 18,055 (180.4%) - -## LandingPageGenerator-A2A +### LandingPageGenerator-A2A -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 14,965 | @@ -1039,62 +325,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 22,826 | | Qwen3-235b | 60 | 18,142 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 16 | 18,863 | -| GPT-4o-mini | 58 | 20,076 | -| DeepSeek-V3.1 | 60 | 28,814 | -| DeepSeek-R1 | 7 | 46,577 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 27 | 20,066 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 0 | - | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 3 | 34,203 | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 44 | 13,548 | -| GPT-4o-mini | 2 | 27,976 | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 53 | 26,291 | -| Gemini-2.5 | 60 | 21,256 | -| Gemini-2.5-NT | 60 | 22,826 | -| Qwen3-235b | 30 | 14,804 | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): GPT-5 = 14,965 tokens - Maximum (highest mean): DeepSeek-V3.1 = 28,814 tokens - Delta: 13,849 (92.5%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): GPT-5 = 18,863 tokens -- Maximum (highest mean): DeepSeek-R1 = 46,577 tokens -- Delta: 27,714 (146.9%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): Qwen3-235b = 34,203 tokens -- Maximum (highest mean): Qwen3-235b = 34,203 tokens -- Delta: 0 (0.0%) +### LandingPageGenerator-H-A2A -### Baseline vs maximum — Failure -- Baseline (lowest mean): GPT-5 = 13,548 tokens -- Maximum (highest mean): GPT-4o-mini = 27,976 tokens -- Delta: 14,429 (106.5%) - -## LandingPageGenerator-H-A2A - -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 25,296 | @@ -1105,62 +343,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 14,949 | | Qwen3-235b | 60 | 17,628 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 25 | 29,492 | -| GPT-4o-mini | 60 | 43,589 | -| DeepSeek-V3.1 | 57 | 34,783 | -| DeepSeek-R1 | 9 | 68,378 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 29 | 19,428 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 2 | 28,430 | -| DeepSeek-R1 | 0 | - | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 2 | 33,284 | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 35 | 22,299 | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 1 | 30,301 | -| DeepSeek-R1 | 51 | 55,231 | -| Gemini-2.5 | 60 | 45,250 | -| Gemini-2.5-NT | 60 | 14,949 | -| Qwen3-235b | 29 | 14,748 | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): Gemini-2.5-NT = 14,949 tokens - Maximum (highest mean): DeepSeek-R1 = 57,203 tokens - Delta: 42,254 (282.6%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): Qwen3-235b = 19,428 tokens -- Maximum (highest mean): DeepSeek-R1 = 68,378 tokens -- Delta: 48,950 (252.0%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): DeepSeek-V3.1 = 28,430 tokens -- Maximum (highest mean): Qwen3-235b = 33,284 tokens -- Delta: 4,854 (17.1%) - -### Baseline vs maximum — Failure -- Baseline (lowest mean): Qwen3-235b = 14,748 tokens -- Maximum (highest mean): DeepSeek-R1 = 55,231 tokens -- Delta: 40,483 (274.5%) +### MarkdownValidator -## MarkdownValidator - -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 1,853 | @@ -1171,49 +361,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 31,330 | | Qwen3-235b | 60 | 1,854 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 60 | 1,853 | -| GPT-4o-mini | 60 | 1,853 | -| DeepSeek-V3.1 | 60 | 1,948 | -| DeepSeek-R1 | 60 | 5,912 | -| Gemini-2.5 | 47 | 81,437 | -| Gemini-2.5-NT | 57 | 16,246 | -| Qwen3-235b | 60 | 1,854 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 0 | - | -| Gemini-2.5 | 13 | 329,685 | -| Gemini-2.5-NT | 3 | 317,934 | -| Qwen3-235b | 0 | - | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): GPT-5 = 1,853 tokens - Maximum (highest mean): Gemini-2.5 = 135,224 tokens - Delta: 133,372 (7199.5%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): GPT-5 = 1,853 tokens -- Maximum (highest mean): Gemini-2.5 = 81,437 tokens -- Delta: 79,585 (4296.0%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): Gemini-2.5-NT = 317,934 tokens -- Maximum (highest mean): Gemini-2.5 = 329,685 tokens -- Delta: 11,751 (3.7%) - -### Baseline vs maximum — Failure -- No data to compare. - -## MarkdownValidator-MCP +### MarkdownValidator-MCP -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 1,967 | @@ -1224,49 +379,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 61,899 | | Qwen3-235b | 60 | 1,994 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 60 | 1,967 | -| GPT-4o-mini | 60 | 1,942 | -| DeepSeek-V3.1 | 60 | 2,063 | -| DeepSeek-R1 | 60 | 5,885 | -| Gemini-2.5 | 48 | 67,759 | -| Gemini-2.5-NT | 56 | 39,448 | -| Qwen3-235b | 60 | 1,994 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 0 | - | -| Gemini-2.5 | 12 | 206,480 | -| Gemini-2.5-NT | 4 | 376,206 | -| Qwen3-235b | 0 | - | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): GPT-4o-mini = 1,942 tokens - Maximum (highest mean): Gemini-2.5 = 95,503 tokens - Delta: 93,561 (4819.0%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): GPT-4o-mini = 1,942 tokens -- Maximum (highest mean): Gemini-2.5 = 67,759 tokens -- Delta: 65,817 (3390.0%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): Gemini-2.5 = 206,480 tokens -- Maximum (highest mean): Gemini-2.5-NT = 376,206 tokens -- Delta: 169,726 (82.2%) +### RecruitmentAssistant-MCP -### Baseline vs maximum — Failure -- No data to compare. - -## RecruitmentAssistant-MCP - -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 32,411 | @@ -1277,62 +397,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 54,402 | | Qwen3-235b | 60 | 87,098 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 60 | 32,411 | -| GPT-4o-mini | 60 | 99,933 | -| DeepSeek-V3.1 | 60 | 65,748 | -| DeepSeek-R1 | 58 | 82,498 | -| Gemini-2.5 | 60 | 64,864 | -| Gemini-2.5-NT | 60 | 54,402 | -| Qwen3-235b | 40 | 68,264 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 2 | 148,137 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 18 | 124,320 | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 0 | - | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 2 | 128,765 | - -### Baseline vs maximum (overall mean tokens) -- Baseline (lowest mean): GPT-5 = 32,411 tokens -- Maximum (highest mean): GPT-4o-mini = 99,933 tokens -- Delta: 67,523 (208.3%) - -### Baseline vs maximum — Direct Success +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): GPT-5 = 32,411 tokens - Maximum (highest mean): GPT-4o-mini = 99,933 tokens - Delta: 67,523 (208.3%) -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): Qwen3-235b = 124,320 tokens -- Maximum (highest mean): DeepSeek-R1 = 148,137 tokens -- Delta: 23,817 (19.2%) +### RecruitmentAssistant-A2A -### Baseline vs maximum — Failure -- Baseline (lowest mean): Qwen3-235b = 128,765 tokens -- Maximum (highest mean): Qwen3-235b = 128,765 tokens -- Delta: 0 (0.0%) - -## RecruitmentAssistant-A2A - -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 31,991 | @@ -1343,62 +415,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 55,660 | | Qwen3-235b | 60 | 97,717 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 60 | 31,991 | -| GPT-4o-mini | 60 | 74,951 | -| DeepSeek-V3.1 | 60 | 65,662 | -| DeepSeek-R1 | 56 | 73,964 | -| Gemini-2.5 | 60 | 68,719 | -| Gemini-2.5-NT | 60 | 55,660 | -| Qwen3-235b | 44 | 73,316 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 3 | 68,009 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 15 | 164,476 | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 1 | 32,043 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 1 | 169,975 | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): GPT-5 = 31,991 tokens - Maximum (highest mean): Qwen3-235b = 97,717 tokens - Delta: 65,726 (205.5%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): GPT-5 = 31,991 tokens -- Maximum (highest mean): GPT-4o-mini = 74,951 tokens -- Delta: 42,960 (134.3%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): DeepSeek-R1 = 68,009 tokens -- Maximum (highest mean): Qwen3-235b = 164,476 tokens -- Delta: 96,467 (141.8%) - -### Baseline vs maximum — Failure -- Baseline (lowest mean): DeepSeek-R1 = 32,043 tokens -- Maximum (highest mean): Qwen3-235b = 169,975 tokens -- Delta: 137,932 (430.5%) - -## RecruitmentAssistant-H-A2A +### RecruitmentAssistant-H-A2A -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 32,672 | @@ -1409,49 +433,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 36,887 | | Qwen3-235b | 60 | 73,259 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 60 | 32,672 | -| GPT-4o-mini | 60 | 101,936 | -| DeepSeek-V3.1 | 60 | 65,048 | -| DeepSeek-R1 | 57 | 76,741 | -| Gemini-2.5 | 60 | 57,128 | -| Gemini-2.5-NT | 60 | 36,887 | -| Qwen3-235b | 49 | 67,089 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 3 | 122,480 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 11 | 100,743 | - -### Baseline vs maximum (overall mean tokens) -- Baseline (lowest mean): GPT-5 = 32,672 tokens -- Maximum (highest mean): GPT-4o-mini = 101,936 tokens -- Delta: 69,264 (212.0%) - -### Baseline vs maximum — Direct Success +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): GPT-5 = 32,672 tokens - Maximum (highest mean): GPT-4o-mini = 101,936 tokens - Delta: 69,264 (212.0%) -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): Qwen3-235b = 100,743 tokens -- Maximum (highest mean): DeepSeek-R1 = 122,480 tokens -- Delta: 21,738 (21.6%) - -### Baseline vs maximum — Failure -- No data to compare. +### SQLAssistant-MCP -## SQLAssistant-MCP - -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 13,881 | @@ -1462,62 +451,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 15,014 | | Qwen3-235b | 60 | 17,232 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 19 | 6,378 | -| GPT-4o-mini | 21 | 14,334 | -| DeepSeek-V3.1 | 26 | 15,771 | -| DeepSeek-R1 | 13 | 35,319 | -| Gemini-2.5 | 6 | 15,321 | -| Gemini-2.5-NT | 10 | 12,100 | -| Qwen3-235b | 37 | 12,304 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 41 | 17,358 | -| GPT-4o-mini | 39 | 29,576 | -| DeepSeek-V3.1 | 34 | 33,181 | -| DeepSeek-R1 | 47 | 93,025 | -| Gemini-2.5 | 11 | 42,818 | -| Gemini-2.5-NT | 6 | 33,419 | -| Qwen3-235b | 23 | 25,159 | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 0 | - | -| Gemini-2.5 | 43 | 13,573 | -| Gemini-2.5-NT | 44 | 13,166 | -| Qwen3-235b | 0 | - | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): GPT-5 = 13,881 tokens - Maximum (highest mean): DeepSeek-R1 = 80,522 tokens - Delta: 66,641 (480.1%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): GPT-5 = 6,378 tokens -- Maximum (highest mean): DeepSeek-R1 = 35,319 tokens -- Delta: 28,941 (453.8%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): GPT-5 = 17,358 tokens -- Maximum (highest mean): DeepSeek-R1 = 93,025 tokens -- Delta: 75,667 (435.9%) - -### Baseline vs maximum — Failure -- Baseline (lowest mean): Gemini-2.5-NT = 13,166 tokens -- Maximum (highest mean): Gemini-2.5 = 13,573 tokens -- Delta: 407 (3.1%) +### SQLAssistant-A2A -## SQLAssistant-A2A - -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 11,189 | @@ -1528,62 +469,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 25,782 | | Qwen3-235b | 60 | 17,381 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 36 | 8,736 | -| GPT-4o-mini | 50 | 17,092 | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 3 | 55,457 | -| Gemini-2.5 | 2 | 23,118 | -| Gemini-2.5-NT | 2 | 36,636 | -| Qwen3-235b | 35 | 13,848 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 24 | 14,869 | -| GPT-4o-mini | 10 | 26,350 | -| DeepSeek-V3.1 | 60 | 36,805 | -| DeepSeek-R1 | 57 | 98,627 | -| Gemini-2.5 | 20 | 44,005 | -| Gemini-2.5-NT | 5 | 71,593 | -| Qwen3-235b | 23 | 23,436 | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 0 | - | -| Gemini-2.5 | 38 | 26,988 | -| Gemini-2.5-NT | 53 | 21,051 | -| Qwen3-235b | 2 | 9,587 | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): GPT-5 = 11,189 tokens - Maximum (highest mean): DeepSeek-R1 = 96,469 tokens - Delta: 85,280 (762.2%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): GPT-5 = 8,736 tokens -- Maximum (highest mean): DeepSeek-R1 = 55,457 tokens -- Delta: 46,721 (534.8%) +### SQLAssistant-H-A2A -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): GPT-5 = 14,869 tokens -- Maximum (highest mean): DeepSeek-R1 = 98,627 tokens -- Delta: 83,759 (563.3%) - -### Baseline vs maximum — Failure -- Baseline (lowest mean): Qwen3-235b = 9,587 tokens -- Maximum (highest mean): Gemini-2.5 = 26,988 tokens -- Delta: 17,401 (181.5%) - -## SQLAssistant-H-A2A - -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 8,956 | @@ -1594,62 +487,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 18,476 | | Qwen3-235b | 60 | 14,779 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 46 | 6,953 | -| GPT-4o-mini | 43 | 10,004 | -| DeepSeek-V3.1 | 44 | 12,571 | -| DeepSeek-R1 | 20 | 29,145 | -| Gemini-2.5 | 25 | 14,332 | -| Gemini-2.5-NT | 33 | 9,561 | -| Qwen3-235b | 40 | 10,594 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 14 | 15,537 | -| GPT-4o-mini | 17 | 23,168 | -| DeepSeek-V3.1 | 16 | 27,226 | -| DeepSeek-R1 | 40 | 89,473 | -| Gemini-2.5 | 26 | 30,481 | -| Gemini-2.5-NT | 19 | 33,544 | -| Qwen3-235b | 20 | 23,149 | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 0 | - | -| Gemini-2.5 | 9 | 35,120 | -| Gemini-2.5-NT | 8 | 19,462 | -| Qwen3-235b | 0 | - | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): GPT-5 = 8,956 tokens - Maximum (highest mean): DeepSeek-R1 = 69,364 tokens - Delta: 60,408 (674.5%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): GPT-5 = 6,953 tokens -- Maximum (highest mean): DeepSeek-R1 = 29,145 tokens -- Delta: 22,193 (319.2%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): GPT-5 = 15,537 tokens -- Maximum (highest mean): DeepSeek-R1 = 89,473 tokens -- Delta: 73,936 (475.9%) - -### Baseline vs maximum — Failure -- Baseline (lowest mean): Gemini-2.5-NT = 19,462 tokens -- Maximum (highest mean): Gemini-2.5 = 35,120 tokens -- Delta: 15,658 (80.5%) - -## SocialMediaManager-MCP +### SocialMediaManager-MCP -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 38,940 | @@ -1660,62 +505,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 11,480 | | Qwen3-235b | 60 | 46,557 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 33 | 22,287 | -| GPT-4o-mini | 20 | 40,555 | -| DeepSeek-V3.1 | 37 | 64,742 | -| DeepSeek-R1 | 21 | 41,849 | -| Gemini-2.5 | 51 | 11,390 | -| Gemini-2.5-NT | 55 | 10,904 | -| Qwen3-235b | 35 | 27,216 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 26 | 57,749 | -| GPT-4o-mini | 37 | 134,943 | -| DeepSeek-V3.1 | 23 | 145,557 | -| DeepSeek-R1 | 36 | 138,306 | -| Gemini-2.5 | 9 | 20,447 | -| Gemini-2.5-NT | 5 | 17,821 | -| Qwen3-235b | 24 | 72,110 | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 1 | 99,433 | -| GPT-4o-mini | 3 | 227,117 | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 3 | 230,298 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 1 | 110,239 | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): Gemini-2.5-NT = 11,480 tokens - Maximum (highest mean): DeepSeek-R1 = 109,146 tokens - Delta: 97,666 (850.7%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): Gemini-2.5-NT = 10,904 tokens -- Maximum (highest mean): DeepSeek-V3.1 = 64,742 tokens -- Delta: 53,839 (493.8%) +### SocialMediaManager-A2A -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): Gemini-2.5-NT = 17,821 tokens -- Maximum (highest mean): DeepSeek-V3.1 = 145,557 tokens -- Delta: 127,736 (716.8%) - -### Baseline vs maximum — Failure -- Baseline (lowest mean): GPT-5 = 99,433 tokens -- Maximum (highest mean): DeepSeek-R1 = 230,298 tokens -- Delta: 130,865 (131.6%) - -## SocialMediaManager-A2A - -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 40,570 | @@ -1726,62 +523,14 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 11,429 | | Qwen3-235b | 60 | 55,866 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 31 | 23,996 | -| GPT-4o-mini | 24 | 42,258 | -| DeepSeek-V3.1 | 46 | 70,407 | -| DeepSeek-R1 | 21 | 45,776 | -| Gemini-2.5 | 56 | 12,176 | -| Gemini-2.5-NT | 53 | 10,675 | -| Qwen3-235b | 27 | 30,147 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 27 | 55,285 | -| GPT-4o-mini | 32 | 125,026 | -| DeepSeek-V3.1 | 14 | 143,090 | -| DeepSeek-R1 | 34 | 165,493 | -| Gemini-2.5 | 4 | 23,860 | -| Gemini-2.5-NT | 7 | 17,136 | -| Qwen3-235b | 32 | 72,094 | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 2 | 98,808 | -| GPT-4o-mini | 4 | 213,454 | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 5 | 181,907 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 1 | 230,991 | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): Gemini-2.5-NT = 11,429 tokens - Maximum (highest mean): DeepSeek-R1 = 124,960 tokens - Delta: 113,531 (993.3%) -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): Gemini-2.5-NT = 10,675 tokens -- Maximum (highest mean): DeepSeek-V3.1 = 70,407 tokens -- Delta: 59,732 (559.5%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): Gemini-2.5-NT = 17,136 tokens -- Maximum (highest mean): DeepSeek-R1 = 165,493 tokens -- Delta: 148,357 (865.8%) - -### Baseline vs maximum — Failure -- Baseline (lowest mean): GPT-5 = 98,808 tokens -- Maximum (highest mean): Qwen3-235b = 230,991 tokens -- Delta: 132,183 (133.8%) - -## SocialMediaManager-H-A2A +### SocialMediaManager-H-A2A -### Overall per-model total tokens (all statuses) +#### Per-model total tokens | Model | n | Mean | | --- | --- | --- | | GPT-5 | 60 | 16,796 | @@ -1792,55 +541,7 @@ Per-project, per-model total token usage with breakdown by execution outcome. On | Gemini-2.5-NT | 60 | 9,642 | | Qwen3-235b | 60 | 29,440 | -### Direct Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 55 | 15,857 | -| GPT-4o-mini | 51 | 33,008 | -| DeepSeek-V3.1 | 49 | 51,074 | -| DeepSeek-R1 | 27 | 40,199 | -| Gemini-2.5 | 35 | 13,495 | -| Gemini-2.5-NT | 45 | 7,487 | -| Qwen3-235b | 35 | 14,019 | - -### Retry Success distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 5 | 27,133 | -| GPT-4o-mini | 9 | 101,766 | -| DeepSeek-V3.1 | 11 | 85,846 | -| DeepSeek-R1 | 31 | 133,368 | -| Gemini-2.5 | 25 | 30,123 | -| Gemini-2.5-NT | 15 | 16,107 | -| Qwen3-235b | 24 | 51,432 | - -### Failure distribution -| Model | n | Mean | -| --- | --- | --- | -| GPT-5 | 0 | - | -| GPT-4o-mini | 0 | - | -| DeepSeek-V3.1 | 0 | - | -| DeepSeek-R1 | 2 | 336,866 | -| Gemini-2.5 | 0 | - | -| Gemini-2.5-NT | 0 | - | -| Qwen3-235b | 1 | 41,361 | - -### Baseline vs maximum (overall mean tokens) +#### Baseline vs maximum (Per-model total tokens) - Baseline (lowest mean): Gemini-2.5-NT = 9,642 tokens - Maximum (highest mean): DeepSeek-R1 = 98,225 tokens -- Delta: 88,583 (918.7%) - -### Baseline vs maximum — Direct Success -- Baseline (lowest mean): Gemini-2.5-NT = 7,487 tokens -- Maximum (highest mean): DeepSeek-V3.1 = 51,074 tokens -- Delta: 43,587 (582.2%) - -### Baseline vs maximum — Retry Success -- Baseline (lowest mean): Gemini-2.5-NT = 16,107 tokens -- Maximum (highest mean): DeepSeek-R1 = 133,368 tokens -- Delta: 117,261 (728.0%) - -### Baseline vs maximum — Failure -- Baseline (lowest mean): Qwen3-235b = 41,361 tokens -- Maximum (highest mean): DeepSeek-R1 = 336,866 tokens -- Delta: 295,505 (714.5%) \ No newline at end of file +- Delta: 88,583 (918.7%) \ No newline at end of file diff --git a/data/processed/RQ3/Violin/project_token_stats_pass_vs_failure.md b/data/processed/RQ3/Violin/project_token_stats_pass_vs_failure.md index 6f94aa7c6c5001d525cd761aa37f9207003d1a7f..e3bb566ad03f8578b46c778dedfe898cfb592ee1 100644 --- a/data/processed/RQ3/Violin/project_token_stats_pass_vs_failure.md +++ b/data/processed/RQ3/Violin/project_token_stats_pass_vs_failure.md @@ -4,12 +4,12 @@ | Status | n | Mean | | --- | --- | --- | -| Direct Success | 6849 | 47,691 | +| Pass (no retries) | 6849 | 47,691 | | Failure | 862 | 29,935 | ## BookWriter-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -21,9 +21,9 @@ | Gemini-2.5-NT | 59 | 82,348 | | Qwen3-235b | 59 | 110,871 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 190,842 | - | - | | GPT-4o-mini | 133,339 | - | - | @@ -35,7 +35,7 @@ ## BookWriter-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -47,9 +47,9 @@ | Gemini-2.5-NT | 60 | 86,249 | | Qwen3-235b | 60 | 118,406 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 181,022 | - | - | | GPT-4o-mini | 139,665 | - | - | @@ -61,7 +61,7 @@ ## BookWriter-H-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -73,9 +73,9 @@ | Gemini-2.5-NT | 60 | 65,826 | | Qwen3-235b | 60 | 76,512 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 117,358 | - | - | | GPT-4o-mini | 102,564 | - | - | @@ -87,7 +87,7 @@ ## EmailResponder -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -111,9 +111,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 0 | - | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 5,562 | - | - | | GPT-4o-mini | 8,044 | - | - | @@ -125,7 +125,7 @@ ## EmailResponder-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -137,9 +137,9 @@ | Gemini-2.5-NT | 88 | 12,696 | | Qwen3-235b | 100 | 7,434 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 6,903 | - | - | | GPT-4o-mini | 9,437 | - | - | @@ -151,7 +151,7 @@ ## GameBuilder -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -175,9 +175,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 0 | - | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 22,904 | - | - | | GPT-4o-mini | 17,823 | - | - | @@ -189,7 +189,7 @@ ## GameBuilder-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -201,9 +201,9 @@ | Gemini-2.5-NT | 25 | 50,782 | | Qwen3-235b | 25 | 24,039 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 22,425 | - | - | | GPT-4o-mini | 47,996 | - | - | @@ -215,7 +215,7 @@ ## LandingPageGenerator-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -239,9 +239,9 @@ | Gemini-2.5-NT | 58 | 28,064 | | Qwen3-235b | 7 | 10,009 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 12,653 | - | - | | GPT-4o-mini | 19,333 | 14,308 | -5,025 (-26.0%) | @@ -253,7 +253,7 @@ ## LandingPageGenerator-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -277,9 +277,9 @@ | Gemini-2.5-NT | 60 | 22,826 | | Qwen3-235b | 30 | 14,804 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 18,863 | 13,548 | -5,315 (-28.2%) | | GPT-4o-mini | 20,076 | 27,976 | +7,900 (39.4%) | @@ -291,7 +291,7 @@ ## LandingPageGenerator-H-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -315,9 +315,9 @@ | Gemini-2.5-NT | 60 | 14,949 | | Qwen3-235b | 29 | 14,748 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 29,492 | 22,299 | -7,194 (-24.4%) | | GPT-4o-mini | 43,589 | - | - | @@ -329,7 +329,7 @@ ## MarkdownValidator -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -341,9 +341,9 @@ | Gemini-2.5-NT | 57 | 16,246 | | Qwen3-235b | 60 | 1,854 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 1,853 | - | - | | GPT-4o-mini | 1,853 | - | - | @@ -355,7 +355,7 @@ ## MarkdownValidator-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -367,9 +367,9 @@ | Gemini-2.5-NT | 56 | 39,448 | | Qwen3-235b | 60 | 1,994 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 1,967 | - | - | | GPT-4o-mini | 1,942 | - | - | @@ -381,7 +381,7 @@ ## RecruitmentAssistant-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -405,9 +405,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 2 | 128,765 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 32,411 | - | - | | GPT-4o-mini | 99,933 | - | - | @@ -419,7 +419,7 @@ ## RecruitmentAssistant-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -443,9 +443,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 1 | 169,975 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 31,991 | - | - | | GPT-4o-mini | 74,951 | - | - | @@ -457,7 +457,7 @@ ## RecruitmentAssistant-H-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -469,9 +469,9 @@ | Gemini-2.5-NT | 60 | 36,887 | | Qwen3-235b | 49 | 67,089 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 32,672 | - | - | | GPT-4o-mini | 101,936 | - | - | @@ -483,7 +483,7 @@ ## SQLAssistant-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -507,9 +507,9 @@ | Gemini-2.5-NT | 44 | 13,166 | | Qwen3-235b | 0 | - | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 6,378 | - | - | | GPT-4o-mini | 14,334 | - | - | @@ -521,7 +521,7 @@ ## SQLAssistant-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -545,9 +545,9 @@ | Gemini-2.5-NT | 53 | 21,051 | | Qwen3-235b | 2 | 9,587 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 8,736 | - | - | | GPT-4o-mini | 17,092 | - | - | @@ -559,7 +559,7 @@ ## SQLAssistant-H-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -583,9 +583,9 @@ | Gemini-2.5-NT | 8 | 19,462 | | Qwen3-235b | 0 | - | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 6,953 | - | - | | GPT-4o-mini | 10,004 | - | - | @@ -597,7 +597,7 @@ ## SocialMediaManager-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -621,9 +621,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 1 | 110,239 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 22,287 | 99,433 | +77,146 (346.1%) | | GPT-4o-mini | 40,555 | 227,117 | +186,562 (460.0%) | @@ -635,7 +635,7 @@ ## SocialMediaManager-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -659,9 +659,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 1 | 230,991 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 23,996 | 98,808 | +74,812 (311.8%) | | GPT-4o-mini | 42,258 | 213,454 | +171,196 (405.1%) | @@ -673,7 +673,7 @@ ## SocialMediaManager-H-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -697,9 +697,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 1 | 41,361 | -### Direct Success vs Failure (mean, abs & %) +### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 15,857 | - | - | | GPT-4o-mini | 33,008 | - | - | @@ -713,7 +713,7 @@ ### BookWriter (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -725,9 +725,9 @@ | Gemini-2.5-NT | 179 | 78,117 | | Qwen3-235b | 179 | 101,880 | -#### Direct Success vs Failure (mean, abs & %) +#### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 163,074 | - | - | | GPT-4o-mini | 125,190 | - | - | @@ -739,7 +739,7 @@ ### EmailResponder (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -763,9 +763,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 0 | - | -#### Direct Success vs Failure (mean, abs & %) +#### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 6,232 | - | - | | GPT-4o-mini | 8,741 | - | - | @@ -777,7 +777,7 @@ ### GameBuilder (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -801,9 +801,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 0 | - | -#### Direct Success vs Failure (mean, abs & %) +#### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 22,665 | - | - | | GPT-4o-mini | 32,909 | - | - | @@ -815,7 +815,7 @@ ### LandingPageGenerator (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -839,9 +839,9 @@ | Gemini-2.5-NT | 178 | 21,877 | | Qwen3-235b | 66 | 14,271 | -#### Direct Success vs Failure (mean, abs & %) +#### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 17,805 | 17,425 | -381 (-2.1%) | | GPT-4o-mini | 27,799 | 23,420 | -4,379 (-15.8%) | @@ -853,7 +853,7 @@ ### MarkdownValidator (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -865,9 +865,9 @@ | Gemini-2.5-NT | 113 | 27,744 | | Qwen3-235b | 120 | 1,924 | -#### Direct Success vs Failure (mean, abs & %) +#### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 1,910 | - | - | | GPT-4o-mini | 1,897 | - | - | @@ -879,7 +879,7 @@ ### RecruitmentAssistant (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -903,9 +903,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 3 | 142,502 | -#### Direct Success vs Failure (mean, abs & %) +#### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 32,358 | - | - | | GPT-4o-mini | 92,274 | - | - | @@ -917,7 +917,7 @@ ### SQLAssistant (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -941,9 +941,9 @@ | Gemini-2.5-NT | 105 | 17,626 | | Qwen3-235b | 2 | 9,587 | -#### Direct Success vs Failure (mean, abs & %) +#### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 7,480 | - | - | | GPT-4o-mini | 13,910 | - | - | @@ -955,7 +955,7 @@ ### SocialMediaManager (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -979,9 +979,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 3 | 127,530 | -#### Direct Success vs Failure (mean, abs & %) +#### Pass (no retries) vs Failure (mean, abs & %) -| Model | Direct Success mean | Failure mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Failure mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 19,760 | 99,016 | +79,256 (401.1%) | | GPT-4o-mini | 36,934 | 219,309 | +182,375 (493.8%) | diff --git a/data/processed/RQ3/Violin/project_token_stats_pass_vs_retry.md b/data/processed/RQ3/Violin/project_token_stats_pass_vs_retry.md index bcd4bde9567c7d5196d0e60b93cc1fd7e2bf613f..41c42d7cdcf35d1e2287356f047868d90fcaf6a5 100644 --- a/data/processed/RQ3/Violin/project_token_stats_pass_vs_retry.md +++ b/data/processed/RQ3/Violin/project_token_stats_pass_vs_retry.md @@ -4,12 +4,12 @@ | Status | n | Mean | | --- | --- | --- | -| Direct Success | 6849 | 47,691 | -| Retry Success | 1179 | 83,133 | +| Pass (no retries) | 6849 | 47,691 | +| Pass (with retries) | 1179 | 83,133 | ## BookWriter-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -21,7 +21,7 @@ | Gemini-2.5-NT | 59 | 82,348 | | Qwen3-235b | 59 | 110,871 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -33,9 +33,9 @@ | Gemini-2.5-NT | 1 | 94,226 | | Qwen3-235b | 1 | 101,570 | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 190,842 | - | - | | GPT-4o-mini | 133,339 | - | - | @@ -47,7 +47,7 @@ ## BookWriter-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -59,7 +59,7 @@ | Gemini-2.5-NT | 60 | 86,249 | | Qwen3-235b | 60 | 118,406 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -71,9 +71,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 0 | - | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 181,022 | - | - | | GPT-4o-mini | 139,665 | - | - | @@ -85,7 +85,7 @@ ## BookWriter-H-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -97,7 +97,7 @@ | Gemini-2.5-NT | 60 | 65,826 | | Qwen3-235b | 60 | 76,512 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -109,9 +109,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 0 | - | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 117,358 | - | - | | GPT-4o-mini | 102,564 | - | - | @@ -123,7 +123,7 @@ ## EmailResponder -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -135,7 +135,7 @@ | Gemini-2.5-NT | 88 | 12,494 | | Qwen3-235b | 100 | 5,900 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -147,9 +147,9 @@ | Gemini-2.5-NT | 12 | 261,883 | | Qwen3-235b | 0 | - | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 5,562 | - | - | | GPT-4o-mini | 8,044 | - | - | @@ -161,7 +161,7 @@ ## EmailResponder-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -173,7 +173,7 @@ | Gemini-2.5-NT | 88 | 12,696 | | Qwen3-235b | 100 | 7,434 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -185,9 +185,9 @@ | Gemini-2.5-NT | 12 | 199,673 | | Qwen3-235b | 0 | - | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 6,903 | - | - | | GPT-4o-mini | 9,437 | - | - | @@ -199,7 +199,7 @@ ## GameBuilder -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -211,7 +211,7 @@ | Gemini-2.5-NT | 23 | 54,423 | | Qwen3-235b | 25 | 24,212 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -223,9 +223,9 @@ | Gemini-2.5-NT | 2 | 223,682 | | Qwen3-235b | 0 | - | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 22,904 | - | - | | GPT-4o-mini | 17,823 | - | - | @@ -237,7 +237,7 @@ ## GameBuilder-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -249,9 +249,9 @@ | Gemini-2.5-NT | 25 | 50,782 | | Qwen3-235b | 25 | 24,039 | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 22,425 | - | - | | GPT-4o-mini | 47,996 | - | - | @@ -263,7 +263,7 @@ ## LandingPageGenerator-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -275,7 +275,7 @@ | Gemini-2.5-NT | 2 | 188,992 | | Qwen3-235b | 53 | 15,068 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -287,9 +287,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 0 | - | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 12,653 | - | - | | GPT-4o-mini | 19,333 | - | - | @@ -301,7 +301,7 @@ ## LandingPageGenerator-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -313,7 +313,7 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 27 | 20,066 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -325,9 +325,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 3 | 34,203 | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 18,863 | - | - | | GPT-4o-mini | 20,076 | - | - | @@ -339,7 +339,7 @@ ## LandingPageGenerator-H-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -351,7 +351,7 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 29 | 19,428 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -363,9 +363,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 2 | 33,284 | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 29,492 | - | - | | GPT-4o-mini | 43,589 | - | - | @@ -377,7 +377,7 @@ ## MarkdownValidator -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -389,7 +389,7 @@ | Gemini-2.5-NT | 57 | 16,246 | | Qwen3-235b | 60 | 1,854 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -401,9 +401,9 @@ | Gemini-2.5-NT | 3 | 317,934 | | Qwen3-235b | 0 | - | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 1,853 | - | - | | GPT-4o-mini | 1,853 | - | - | @@ -415,7 +415,7 @@ ## MarkdownValidator-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -427,7 +427,7 @@ | Gemini-2.5-NT | 56 | 39,448 | | Qwen3-235b | 60 | 1,994 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -439,9 +439,9 @@ | Gemini-2.5-NT | 4 | 376,206 | | Qwen3-235b | 0 | - | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 1,967 | - | - | | GPT-4o-mini | 1,942 | - | - | @@ -453,7 +453,7 @@ ## RecruitmentAssistant-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -465,7 +465,7 @@ | Gemini-2.5-NT | 60 | 54,402 | | Qwen3-235b | 40 | 68,264 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -477,9 +477,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 18 | 124,320 | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 32,411 | - | - | | GPT-4o-mini | 99,933 | - | - | @@ -491,7 +491,7 @@ ## RecruitmentAssistant-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -503,7 +503,7 @@ | Gemini-2.5-NT | 60 | 55,660 | | Qwen3-235b | 44 | 73,316 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -515,9 +515,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 15 | 164,476 | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 31,991 | - | - | | GPT-4o-mini | 74,951 | - | - | @@ -529,7 +529,7 @@ ## RecruitmentAssistant-H-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -541,7 +541,7 @@ | Gemini-2.5-NT | 60 | 36,887 | | Qwen3-235b | 49 | 67,089 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -553,9 +553,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 11 | 100,743 | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 32,672 | - | - | | GPT-4o-mini | 101,936 | - | - | @@ -567,7 +567,7 @@ ## SQLAssistant-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -579,7 +579,7 @@ | Gemini-2.5-NT | 10 | 12,100 | | Qwen3-235b | 37 | 12,304 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -591,9 +591,9 @@ | Gemini-2.5-NT | 6 | 33,419 | | Qwen3-235b | 23 | 25,159 | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 6,378 | 17,358 | +10,981 (172.2%) | | GPT-4o-mini | 14,334 | 29,576 | +15,242 (106.3%) | @@ -605,7 +605,7 @@ ## SQLAssistant-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -617,7 +617,7 @@ | Gemini-2.5-NT | 2 | 36,636 | | Qwen3-235b | 35 | 13,848 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -629,9 +629,9 @@ | Gemini-2.5-NT | 5 | 71,593 | | Qwen3-235b | 23 | 23,436 | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 8,736 | 14,869 | +6,133 (70.2%) | | GPT-4o-mini | 17,092 | 26,350 | +9,258 (54.2%) | @@ -643,7 +643,7 @@ ## SQLAssistant-H-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -655,7 +655,7 @@ | Gemini-2.5-NT | 33 | 9,561 | | Qwen3-235b | 40 | 10,594 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -667,9 +667,9 @@ | Gemini-2.5-NT | 19 | 33,544 | | Qwen3-235b | 20 | 23,149 | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 6,953 | 15,537 | +8,584 (123.5%) | | GPT-4o-mini | 10,004 | 23,168 | +13,165 (131.6%) | @@ -681,7 +681,7 @@ ## SocialMediaManager-MCP -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -693,7 +693,7 @@ | Gemini-2.5-NT | 55 | 10,904 | | Qwen3-235b | 35 | 27,216 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -705,9 +705,9 @@ | Gemini-2.5-NT | 5 | 17,821 | | Qwen3-235b | 24 | 72,110 | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 22,287 | 57,749 | +35,462 (159.1%) | | GPT-4o-mini | 40,555 | 134,943 | +94,388 (232.7%) | @@ -719,7 +719,7 @@ ## SocialMediaManager-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -731,7 +731,7 @@ | Gemini-2.5-NT | 53 | 10,675 | | Qwen3-235b | 27 | 30,147 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -743,9 +743,9 @@ | Gemini-2.5-NT | 7 | 17,136 | | Qwen3-235b | 32 | 72,094 | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 23,996 | 55,285 | +31,289 (130.4%) | | GPT-4o-mini | 42,258 | 125,026 | +82,768 (195.9%) | @@ -757,7 +757,7 @@ ## SocialMediaManager-H-A2A -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -769,7 +769,7 @@ | Gemini-2.5-NT | 45 | 7,487 | | Qwen3-235b | 35 | 14,019 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -781,9 +781,9 @@ | Gemini-2.5-NT | 15 | 16,107 | | Qwen3-235b | 24 | 51,432 | -### Direct Success vs Retry Success (mean, abs & %) +### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 15,857 | 27,133 | +11,276 (71.1%) | | GPT-4o-mini | 33,008 | 101,766 | +68,758 (208.3%) | @@ -797,7 +797,7 @@ ### BookWriter (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -809,7 +809,7 @@ | Gemini-2.5-NT | 179 | 78,117 | | Qwen3-235b | 179 | 101,880 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -821,9 +821,9 @@ | Gemini-2.5-NT | 1 | 94,226 | | Qwen3-235b | 1 | 101,570 | -#### Direct Success vs Retry Success (mean, abs & %) +#### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 163,074 | - | - | | GPT-4o-mini | 125,190 | - | - | @@ -835,7 +835,7 @@ ### EmailResponder (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -847,7 +847,7 @@ | Gemini-2.5-NT | 176 | 12,595 | | Qwen3-235b | 200 | 6,667 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -859,9 +859,9 @@ | Gemini-2.5-NT | 24 | 230,778 | | Qwen3-235b | 0 | - | -#### Direct Success vs Retry Success (mean, abs & %) +#### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 6,232 | - | - | | GPT-4o-mini | 8,741 | - | - | @@ -873,7 +873,7 @@ ### GameBuilder (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -885,7 +885,7 @@ | Gemini-2.5-NT | 48 | 52,527 | | Qwen3-235b | 50 | 24,125 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -897,9 +897,9 @@ | Gemini-2.5-NT | 2 | 223,682 | | Qwen3-235b | 0 | - | -#### Direct Success vs Retry Success (mean, abs & %) +#### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 22,665 | - | - | | GPT-4o-mini | 32,909 | - | - | @@ -911,7 +911,7 @@ ### LandingPageGenerator (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -923,7 +923,7 @@ | Gemini-2.5-NT | 2 | 188,992 | | Qwen3-235b | 109 | 17,466 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -935,9 +935,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 5 | 33,835 | -#### Direct Success vs Retry Success (mean, abs & %) +#### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 17,805 | - | - | | GPT-4o-mini | 27,799 | - | - | @@ -949,7 +949,7 @@ ### MarkdownValidator (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -961,7 +961,7 @@ | Gemini-2.5-NT | 113 | 27,744 | | Qwen3-235b | 120 | 1,924 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -973,9 +973,9 @@ | Gemini-2.5-NT | 7 | 351,232 | | Qwen3-235b | 0 | - | -#### Direct Success vs Retry Success (mean, abs & %) +#### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 1,910 | - | - | | GPT-4o-mini | 1,897 | - | - | @@ -987,7 +987,7 @@ ### RecruitmentAssistant (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -999,7 +999,7 @@ | Gemini-2.5-NT | 180 | 48,983 | | Qwen3-235b | 133 | 69,503 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -1011,9 +1011,9 @@ | Gemini-2.5-NT | 0 | - | | Qwen3-235b | 44 | 132,115 | -#### Direct Success vs Retry Success (mean, abs & %) +#### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 32,358 | - | - | | GPT-4o-mini | 92,274 | - | - | @@ -1025,7 +1025,7 @@ ### SQLAssistant (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -1037,7 +1037,7 @@ | Gemini-2.5-NT | 45 | 11,328 | | Qwen3-235b | 112 | 12,176 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -1049,9 +1049,9 @@ | Gemini-2.5-NT | 30 | 39,861 | | Qwen3-235b | 66 | 23,950 | -#### Direct Success vs Retry Success (mean, abs & %) +#### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 7,480 | 16,279 | +8,799 (117.6%) | | GPT-4o-mini | 13,910 | 27,437 | +13,527 (97.2%) | @@ -1063,7 +1063,7 @@ ### SocialMediaManager (aggregated across variants) -### Direct Success +### Pass (no retries) | Model | n | Mean | | --- | --- | --- | @@ -1075,7 +1075,7 @@ | Gemini-2.5-NT | 153 | 9,820 | | Qwen3-235b | 97 | 23,270 | -### Retry Success +### Pass (with retries) | Model | n | Mean | | --- | --- | --- | @@ -1087,9 +1087,9 @@ | Gemini-2.5-NT | 27 | 16,691 | | Qwen3-235b | 80 | 65,900 | -#### Direct Success vs Retry Success (mean, abs & %) +#### Pass (no retries) vs Pass (with retries) (mean, abs & %) -| Model | Direct Success mean | Retry Success mean | Ī” vs Direct Success | +| Model | Pass (no retries) mean | Pass (with retries) mean | Ī” vs Pass (no retries) | | --- | --- | --- | --- | | GPT-5 | 19,760 | 53,963 | +34,202 (173.1%) | | GPT-4o-mini | 36,934 | 127,046 | +90,112 (244.0%) | diff --git a/data/processed/RQ3/analyze_performance.py b/data/processed/RQ3/analyze_performance.py index 12fc032d66fd6974463d492fbc9770153cec41d8..fd359e89be1157b6187cae4571c8778578ca288f 100644 --- a/data/processed/RQ3/analyze_performance.py +++ b/data/processed/RQ3/analyze_performance.py @@ -25,24 +25,6 @@ logging.basicConfig( logger = logging.getLogger(__name__) -def normalize_task_name(task: Optional[str]) -> str: - raw = (task or "").strip() - if not raw: - return "" - - # Map the actual project/task directory names to the canonical task names - # used by success checking logic. - mapping = { - "SocialMediaManager": "self_evaluation_loop_flow", - "SQLAssistant": "SQL_assistant", - "LandingPageGenerator": "landing_page_generator", - "RecruitmentAssistant": "intelligent_recruitment_platform", - "BookWriter": "write_a_book_with_flows", - } - - return mapping.get(raw, raw) - - class ExecutionPathParser: """Parser for execution_path.md files""" @@ -88,21 +70,16 @@ class ExecutionPathParser: # Extract architecture from task name if "-MCP" in task_full: self.architecture = "MCP" - self.task = normalize_task_name(task_full.replace("-MCP", "")) + self.task = task_full.replace("-MCP", "") elif "-A2A_mix" in task_full: self.architecture = "A2A_mix" - self.task = normalize_task_name(task_full.replace("-A2A_mix", "")) - elif "-H_A2A" in task_full or "-H-A2A" in task_full: - self.architecture = "A2A_mix" - self.task = normalize_task_name( - task_full.replace("-H_A2A", "").replace("-H-A2A", "") - ) + self.task = task_full.replace("-A2A_mix", "") elif "-A2A" in task_full: self.architecture = "A2A" - self.task = normalize_task_name(task_full.replace("-A2A", "")) + self.task = task_full.replace("-A2A", "") else: # Check in file content for architecture info - self.task = normalize_task_name(task_full) + self.task = task_full self.architecture = "Unknown" logger.debug( @@ -622,10 +599,24 @@ class PerformanceAnalyzer: # Get the directory containing execution_path.md session_dir = Path(parser.file_path).parent task = parser.task - task_norm = normalize_task_name(task) + + if task.endswith("-H_A2A"): + task = task[: -len("-H_A2A")] + + task_aliases = { + "BookWriter": "write_a_book_with_flows", + "SQLAssistant": "SQL_assistant", + "SocialMediaManager": "self_evaluation_loop_flow", + "LandingPageGenerator": "landing_page_generator", + "RecruitmentAssistant": "intelligent_recruitment_platform", + "EmailResponder": "EmailResponder", + "GameBuilder": "GameBuilder", + "MarkdownValidator": "MarkdownValidator", + } + task = task_aliases.get(task, task) # 1. write_a_book_with_flows - if task_norm == "write_a_book_with_flows": + if task == "write_a_book_with_flows": chapters_dir = session_dir / "chapters" if chapters_dir.exists() and chapters_dir.is_dir(): md_files = list(chapters_dir.glob("*.md")) @@ -634,7 +625,7 @@ class PerformanceAnalyzer: return "fail" # 2. SQL_assistant - elif task_norm == "SQL_assistant": + elif task == "SQL_assistant": with open(parser.file_path, "r", encoding="utf-8") as f: content = f.read() if re.search(r"\[Tool\]\s+get_database_schema", content): @@ -642,7 +633,7 @@ class PerformanceAnalyzer: return "fail" # 3. self_evaluation_loop_flow - elif task_norm == "self_evaluation_loop_flow": + elif task == "self_evaluation_loop_flow": metadata_file = session_dir / "metadata.json" if metadata_file.exists(): with open(metadata_file, "r", encoding="utf-8") as f: @@ -652,7 +643,7 @@ class PerformanceAnalyzer: return "fail" # 4. MarkdownValidator - elif task_norm == "MarkdownValidator": + elif task == "MarkdownValidator": execution_info_file = session_dir / "execution_info.json" if execution_info_file.exists(): with open(execution_info_file, "r", encoding="utf-8") as f: @@ -662,7 +653,7 @@ class PerformanceAnalyzer: return "fail" # 5. landing_page_generator - elif task_norm == "landing_page_generator": + elif task == "landing_page_generator": html_validation_file = session_dir / "html_validation.json" if html_validation_file.exists(): with open(html_validation_file, "r", encoding="utf-8") as f: @@ -672,7 +663,7 @@ class PerformanceAnalyzer: return "fail" # 6. intelligent_recruitment_platform - elif task_norm == "intelligent_recruitment_platform": + elif task == "intelligent_recruitment_platform": reports_dir = session_dir / "reports" if reports_dir.exists() and reports_dir.is_dir(): md_files = list(reports_dir.glob("*.md")) @@ -681,7 +672,7 @@ class PerformanceAnalyzer: return "fail" # 7. GameBuilder - elif task_norm == "GameBuilder": + elif task == "GameBuilder": validation_result_file = session_dir / "validation_result.json" if validation_result_file.exists(): with open(validation_result_file, "r", encoding="utf-8") as f: @@ -691,7 +682,7 @@ class PerformanceAnalyzer: return "fail" # 8. EmailResponder - elif task_norm == "EmailResponder": + elif task == "EmailResponder": execution_log_file = session_dir / "execution_log.json" if execution_log_file.exists(): with open(execution_log_file, "r", encoding="utf-8") as f: @@ -702,10 +693,8 @@ class PerformanceAnalyzer: # Unknown task else: - logger.warning( - f"Unknown task type for success check: task={task_norm}, session_dir={session_dir}" - ) - return "unknown" + logger.warning(f"Unknown task type for success check: {task}") + return "fail" except Exception as e: logger.error(f"Error checking task success for {parser.file_path}: {e}") @@ -718,10 +707,11 @@ class PerformanceAnalyzer: results = [] for parser in self.data: - if parser.total_tokens["total"] <= 0: - continue - try: + if parser.total_tokens["total"] > 0: + # Check task success status status = self.check_task_success(parser) + + # Check if execution contains retry markers has_retry = self.check_has_retry(parser) result = { @@ -730,7 +720,9 @@ class PerformanceAnalyzer: "architecture": parser.architecture, "model": parser.model, "status": status, - "with_retry": str(has_retry).lower(), + "with_retry": str( + has_retry + ).lower(), # Convert to 'true' or 'false' "input_tokens": parser.total_tokens["input"], "output_tokens": parser.total_tokens["output"], "reasoning_tokens": parser.total_tokens["reasoning"], @@ -738,21 +730,12 @@ class PerformanceAnalyzer: "total_tokens": parser.total_tokens["total"], } results.append(result) - except Exception as e: - logger.error( - f"Error generating token details for {getattr(parser, 'file_path', '')}: {e}", - exc_info=True, - ) # Sort by task, architecture, model, and file path results.sort( key=lambda x: (x["task"], x["architecture"], x["model"], x["file_path"]) ) - out_dir = os.path.dirname(output_file) - if out_dir: - os.makedirs(out_dir, exist_ok=True) - # Write to CSV with open(output_file, "w", newline="", encoding="utf-8") as f: writer = csv.DictWriter( diff --git a/data/processed/RQ3/performance_reports/agent_time_statistics.csv b/data/processed/RQ3/performance_reports/agent_time_statistics.csv index 69ceeed4cb8621259997f0570bb604021a32dba7..fe051c0e0ee653647d6af5262a164251a0f5316c 100644 --- a/data/processed/RQ3/performance_reports/agent_time_statistics.csv +++ b/data/processed/RQ3/performance_reports/agent_time_statistics.csv @@ -1,109 +1,4 @@ task,architecture,agent,model,count,mean_time,p90_time,p99_time,cv_time -BookWriter,A2A,Expert Research Agent for Book Outline Planning,DeepSeek-R1,64,144.78250000000003,243.03,321.64969999999994,45.44380569633287 -BookWriter,A2A,Expert Research Agent for Book Outline Planning,DeepSeek-V3-1,61,104.9444262295082,120.56,172.68399999999997,22.396354136388027 -BookWriter,A2A,Expert Research Agent for Book Outline Planning,GPT-4o-mini,60,27.16866666666667,36.252,43.662899999999986,24.223705464497797 -BookWriter,A2A,Expert Research Agent for Book Outline Planning,GPT-5,60,27.134166666666665,36.286,43.24419999999999,29.754999493227714 -BookWriter,A2A,Expert Research Agent for Book Outline Planning,Gemini-2.5-flash,60,67.25133333333333,87.479,137.8359999999999,32.41328070122893 -BookWriter,A2A,Expert Research Agent for Book Outline Planning,Gemini-2.5-flash-nothinking,60,38.554,65.897,88.76909999999992,48.05612710382457 -BookWriter,A2A,Expert Research Agent for Book Outline Planning,Qwen3-235b,60,62.43650000000001,136.27800000000002,207.1812999999999,75.0261795426685 -BookWriter,A2A,Professional Chapter Content Writer,DeepSeek-R1,220,255.13886363636365,543.655,1320.3811000000003,97.02236646554641 -BookWriter,A2A,Professional Chapter Content Writer,DeepSeek-V3-1,232,154.70650862068968,174.685,224.84899999999996,15.488915941677442 -BookWriter,A2A,Professional Chapter Content Writer,GPT-4o-mini,212,11.96245283018868,19.175000000000008,34.356899999999975,54.75244975636861 -BookWriter,A2A,Professional Chapter Content Writer,GPT-5,290,37.54393103448276,62.768,73.5011,47.16052718560357 -BookWriter,A2A,Professional Chapter Content Writer,Gemini-2.5-flash,193,54.65259067357513,72.83200000000001,92.18239999999986,27.83777713766345 -BookWriter,A2A,Professional Chapter Content Writer,Gemini-2.5-flash-nothinking,184,26.05353260869565,41.807,54.22929999999992,46.24276987335762 -BookWriter,A2A,Professional Chapter Content Writer,Qwen3-235b,228,76.18583333333335,140.84500000000008,564.5445999999996,139.76711847201108 -BookWriter,A2A,Senior Book Editor and Quality Assurance Specialist,DeepSeek-R1,61,92.86114754098361,139.21,229.45599999999996,49.11996402318214 -BookWriter,A2A,Senior Book Editor and Quality Assurance Specialist,DeepSeek-V3-1,61,60.99475409836065,64.97,71.604,8.815895328923675 -BookWriter,A2A,Senior Book Editor and Quality Assurance Specialist,GPT-4o-mini,60,19.260666666666665,23.243000000000002,36.56259999999999,23.88538150223277 -BookWriter,A2A,Senior Book Editor and Quality Assurance Specialist,GPT-5,60,28.282833333333333,34.074,39.209599999999995,20.042950718209262 -BookWriter,A2A,Senior Book Editor and Quality Assurance Specialist,Gemini-2.5-flash,60,32.096333333333334,45.687,64.40609999999997,32.29967309854577 -BookWriter,A2A,Senior Book Editor and Quality Assurance Specialist,Gemini-2.5-flash-nothinking,60,18.303833333333333,22.359,38.91689999999991,32.52917320426369 -BookWriter,A2A,Senior Book Editor and Quality Assurance Specialist,Qwen3-235b,60,154.5448333333333,191.982,207.7757,23.130431993102878 -BookWriter,A2A,Specialized Chapter Research Agent,DeepSeek-R1,224,140.86892857142857,242.54100000000003,379.29790000000014,52.851675434069556 -BookWriter,A2A,Specialized Chapter Research Agent,DeepSeek-V3-1,232,61.22711206896552,84.119,98.43129999999998,26.986257540204345 -BookWriter,A2A,Specialized Chapter Research Agent,GPT-4o-mini,212,19.69353773584906,29.992,41.20079999999995,37.42285254849124 -BookWriter,A2A,Specialized Chapter Research Agent,GPT-5,290,21.545344827586206,29.496,34.2066,28.0114844333462 -BookWriter,A2A,Specialized Chapter Research Agent,Gemini-2.5-flash,193,46.15792746113989,65.86600000000001,110.19119999999991,59.42639152581316 -BookWriter,A2A,Specialized Chapter Research Agent,Gemini-2.5-flash-nothinking,184,26.28418478260869,37.312000000000005,67.41029999999984,129.67393004046454 -BookWriter,A2A,Specialized Chapter Research Agent,Qwen3-235b,228,49.93864035087719,83.513,144.8107,51.61311296112605 -BookWriter,A2A,Strategic Book Structure Architect,DeepSeek-R1,63,64.50523809523811,122.55000000000001,191.70580000000007,65.41438801856357 -BookWriter,A2A,Strategic Book Structure Architect,DeepSeek-V3-1,61,13.14983606557377,15.73,20.995999999999988,18.96975430151972 -BookWriter,A2A,Strategic Book Structure Architect,GPT-4o-mini,60,9.853333333333333,13.374,25.005199999999988,39.96853655325949 -BookWriter,A2A,Strategic Book Structure Architect,GPT-5,60,9.256333333333334,12.536,14.935799999999999,31.442297651029026 -BookWriter,A2A,Strategic Book Structure Architect,Gemini-2.5-flash,60,16.065499999999997,20.132,25.825399999999973,22.13011561587052 -BookWriter,A2A,Strategic Book Structure Architect,Gemini-2.5-flash-nothinking,60,3.742833333333333,4.028,10.962099999999994,39.03135709963338 -BookWriter,A2A,Strategic Book Structure Architect,Qwen3-235b,60,13.183,18.809,41.421599999999984,51.366223941849334 -BookWriter,MCP,Expert Research Agent for Book Outline Planning,DeepSeek-R1,64,140.5703125,219.39600000000002,319.07919999999996,42.874423907305605 -BookWriter,MCP,Expert Research Agent for Book Outline Planning,DeepSeek-V3-1,60,54.135666666666665,63.831,184.9633999999999,55.36233287159625 -BookWriter,MCP,Expert Research Agent for Book Outline Planning,GPT-4o-mini,60,28.726000000000003,40.04,54.77439999999998,29.721794614304937 -BookWriter,MCP,Expert Research Agent for Book Outline Planning,GPT-5,60,18.533,23.753,29.882799999999992,21.92278706254531 -BookWriter,MCP,Expert Research Agent for Book Outline Planning,Gemini-2.5-flash,60,53.25249999999999,77.863,89.82419999999999,33.66986456921156 -BookWriter,MCP,Expert Research Agent for Book Outline Planning,Gemini-2.5-flash-nothinking,61,31.654426229508196,49.88,74.73399999999998,52.009187360036194 -BookWriter,MCP,Expert Research Agent for Book Outline Planning,Qwen3-235b,60,57.120666666666665,103.759,129.19359999999998,50.9036934482483 -BookWriter,MCP,Professional Chapter Content Writer,DeepSeek-R1,229,253.36410480349343,540.5699999999999,1314.9699999999993,97.92895080133856 -BookWriter,MCP,Professional Chapter Content Writer,DeepSeek-V3-1,230,175.96439130434788,198.694,417.0484,30.306687116724863 -BookWriter,MCP,Professional Chapter Content Writer,GPT-4o-mini,221,13.561402714932125,22.26,35.57,47.723305426838 -BookWriter,MCP,Professional Chapter Content Writer,GPT-5,293,28.279761092150174,44.484,55.42639999999999,44.00016861229022 -BookWriter,MCP,Professional Chapter Content Writer,Gemini-2.5-flash,188,45.70015957446808,58.09400000000001,72.3943,24.321185946750496 -BookWriter,MCP,Professional Chapter Content Writer,Gemini-2.5-flash-nothinking,187,26.51566844919786,41.150000000000006,49.64579999999999,40.27472010997589 -BookWriter,MCP,Professional Chapter Content Writer,Qwen3-235b,225,70.80248888888889,138.73599999999996,491.1003999999996,146.9250777835174 -BookWriter,MCP,Senior Book Editor and Quality Assurance Specialist,DeepSeek-R1,64,91.2065625,157.978,254.6833,55.748249565034904 -BookWriter,MCP,Senior Book Editor and Quality Assurance Specialist,DeepSeek-V3-1,60,72.82683333333334,79.455,170.05709999999993,28.1832790853699 -BookWriter,MCP,Senior Book Editor and Quality Assurance Specialist,GPT-4o-mini,60,21.762833333333333,32.457,36.5606,29.269366438400766 -BookWriter,MCP,Senior Book Editor and Quality Assurance Specialist,GPT-5,60,20.128833333333333,24.346,26.222599999999996,13.064574635157214 -BookWriter,MCP,Senior Book Editor and Quality Assurance Specialist,Gemini-2.5-flash,60,25.394333333333336,35.225,43.25549999999999,24.6952682022162 -BookWriter,MCP,Senior Book Editor and Quality Assurance Specialist,Gemini-2.5-flash-nothinking,61,17.13590163934426,20.8,29.971999999999998,21.512176801350826 -BookWriter,MCP,Senior Book Editor and Quality Assurance Specialist,Qwen3-235b,60,149.73000000000002,193.65900000000002,275.77429999999947,34.150530872593386 -BookWriter,MCP,Specialized Chapter Research Agent,DeepSeek-R1,233,120.5107296137339,194.20000000000005,326.93240000000003,46.379951353555214 -BookWriter,MCP,Specialized Chapter Research Agent,DeepSeek-V3-1,230,45.038304347826084,54.98,161.43470000000056,42.931548671864405 -BookWriter,MCP,Specialized Chapter Research Agent,GPT-4o-mini,221,23.614570135746604,37.12,46.63,47.68634360462982 -BookWriter,MCP,Specialized Chapter Research Agent,GPT-5,293,15.804812286689417,20.378,23.807199999999995,20.31499338804929 -BookWriter,MCP,Specialized Chapter Research Agent,Gemini-2.5-flash,188,34.847925531914896,48.00900000000001,71.54829999999995,34.17547010233117 -BookWriter,MCP,Specialized Chapter Research Agent,Gemini-2.5-flash-nothinking,187,21.674812834224596,31.656000000000002,45.33719999999997,106.90579029371439 -BookWriter,MCP,Specialized Chapter Research Agent,Qwen3-235b,225,51.412088888888896,80.456,133.10279999999995,44.3742111186785 -BookWriter,MCP,Strategic Book Structure Architect,DeepSeek-R1,64,47.70546875,74.403,115.4653,47.26333528905559 -BookWriter,MCP,Strategic Book Structure Architect,DeepSeek-V3-1,60,14.213166666666668,16.573000000000004,21.513799999999982,17.41307522220299 -BookWriter,MCP,Strategic Book Structure Architect,GPT-4o-mini,60,10.53516666666667,14.676,18.583799999999993,29.531019116974335 -BookWriter,MCP,Strategic Book Structure Architect,GPT-5,60,6.713166666666667,8.7,10.919599999999997,23.96894785754281 -BookWriter,MCP,Strategic Book Structure Architect,Gemini-2.5-flash,60,13.380833333333333,16.931,21.135999999999996,21.63768101892417 -BookWriter,MCP,Strategic Book Structure Architect,Gemini-2.5-flash-nothinking,61,3.5603278688524593,4.02,7.711999999999993,30.738466556046586 -BookWriter,MCP,Strategic Book Structure Architect,Qwen3-235b,60,16.964666666666666,32.873,47.9787,62.87649102703432 -BookWriter-H_A2A,A2A_mix,Professional Chapter Content Writer,DeepSeek-R1,210,243.3452380952381,519.036,1086.2659999999994,88.53411564806972 -BookWriter-H_A2A,A2A_mix,Professional Chapter Content Writer,DeepSeek-V3-1,217,164.1221658986175,185.094,272.6960000000001,17.386081914401252 -BookWriter-H_A2A,A2A_mix,Professional Chapter Content Writer,GPT-4o-mini,234,17.14696581196581,27.989000000000004,43.92049999999998,47.737132184924356 -BookWriter-H_A2A,A2A_mix,Professional Chapter Content Writer,GPT-5,255,36.9992156862745,59.641999999999996,66.6674,45.719484110707334 -BookWriter-H_A2A,A2A_mix,Professional Chapter Content Writer,Gemini-2.5-flash,180,59.018,76.64599999999999,110.63090000000001,29.366190500327615 -BookWriter-H_A2A,A2A_mix,Professional Chapter Content Writer,Gemini-2.5-flash-nothinking,183,27.981092896174857,40.7,52.991800000000005,38.89840755274651 -BookWriter-H_A2A,A2A_mix,Professional Chapter Content Writer,Qwen3-235b,211,64.63469194312795,129.6,398.95000000000044,123.40976643999863 -BookWriter-H_A2A,A2A_mix,Specialized Chapter Research Agent,DeepSeek-R1,210,112.02438095238095,180.59199999999998,221.4854,39.07334775112287 -BookWriter-H_A2A,A2A_mix,Specialized Chapter Research Agent,DeepSeek-V3-1,217,39.57493087557603,46.566,57.193200000000004,17.113918739252604 -BookWriter-H_A2A,A2A_mix,Specialized Chapter Research Agent,GPT-4o-mini,234,27.131410256410255,41.247,56.5504,44.4256988196998 -BookWriter-H_A2A,A2A_mix,Specialized Chapter Research Agent,GPT-5,255,20.603529411764704,28.232,33.97,28.378438462465855 -BookWriter-H_A2A,A2A_mix,Specialized Chapter Research Agent,Gemini-2.5-flash,180,50.535833333333336,65.85099999999998,161.5877000000018,70.35650750018996 -BookWriter-H_A2A,A2A_mix,Specialized Chapter Research Agent,Gemini-2.5-flash-nothinking,183,23.40054644808743,35.260000000000005,48.5544,38.66970598556082 -BookWriter-H_A2A,A2A_mix,Specialized Chapter Research Agent,Qwen3-235b,211,53.23549763033175,87.67,144.149,54.908443717019026 -BookWriter-H_A2A,A2A_mix,agent,DeepSeek-R1,172,30.455755813953484,66.886,147.19969999999975,99.7841433462617 -BookWriter-H_A2A,A2A_mix,agent,DeepSeek-V3-1,135,11.482814814814814,13.988,15.9498,23.11621050568151 -BookWriter-H_A2A,A2A_mix,agent,GPT-4o-mini,120,4.3646666666666665,5.612000000000001,9.388600000000004,29.87218044930834 -BookWriter-H_A2A,A2A_mix,agent,GPT-5,75,2.224533333333334,2.8080000000000003,3.0742000000000007,22.502644874726567 -BookWriter-H_A2A,A2A_mix,agent,Gemini-2.5-flash,121,6.413388429752066,8.22,18.947999999999993,49.60080770930897 -BookWriter-H_A2A,A2A_mix,agent,Gemini-2.5-flash-nothinking,120,3.4103333333333334,4.374000000000001,7.500600000000005,32.439078869552304 -BookWriter-H_A2A,A2A_mix,agent,Qwen3-235b,300,6.839266666666666,11.331,47.51639999999996,110.93971769421391 -BookWriter-H_A2A,A2A_mix,outliner,DeepSeek-R1,60,49.0345,68.948,80.1553,27.552219772477404 -BookWriter-H_A2A,A2A_mix,outliner,DeepSeek-V3-1,60,9.793499999999998,11.738000000000001,13.04,15.511122889029938 -BookWriter-H_A2A,A2A_mix,outliner,GPT-4o-mini,60,4.514666666666667,6.158,11.266199999999998,39.77707870503432 -BookWriter-H_A2A,A2A_mix,outliner,GPT-5,60,5.106166666666667,7.0280000000000005,8.292199999999996,27.82362885840812 -BookWriter-H_A2A,A2A_mix,outliner,Gemini-2.5-flash,60,13.603833333333334,15.388000000000002,26.697799999999955,26.09480171607064 -BookWriter-H_A2A,A2A_mix,outliner,Gemini-2.5-flash-nothinking,60,3.330666666666666,3.962,5.111299999999994,17.09800366006143 -BookWriter-H_A2A,A2A_mix,outliner,Qwen3-235b,60,14.776999999999997,33.092,39.87799999999999,70.39158865511024 -BookWriter-H_A2A,A2A_mix,researcher,DeepSeek-R1,60,101.26233333333334,158.907,187.66709999999998,35.702375137629296 -BookWriter-H_A2A,A2A_mix,researcher,DeepSeek-V3-1,60,28.312333333333335,34.719,36.7823,17.29351720773788 -BookWriter-H_A2A,A2A_mix,researcher,GPT-4o-mini,60,15.066499999999998,20.631999999999998,24.801299999999976,25.636585760966852 -BookWriter-H_A2A,A2A_mix,researcher,GPT-5,60,1.9015,1.983,10.91559999999994,137.6346108654473 -BookWriter-H_A2A,A2A_mix,researcher,Gemini-2.5-flash,60,8.424333333333333,9.879000000000001,13.122799999999996,16.168417284560803 -BookWriter-H_A2A,A2A_mix,researcher,Gemini-2.5-flash-nothinking,60,5.611833333333334,6.4830000000000005,7.678899999999999,12.67644255571099 -BookWriter-H_A2A,A2A_mix,researcher,Qwen3-235b,60,22.663333333333334,37.093,69.01679999999982,62.242097802535625 EmailResponder,MCP,Expert Professional Email Response Composer and Communication Strategist,DeepSeek-R1,100,30.096,84.34200000000001,176.5222000000001,113.6595001651245 EmailResponder,MCP,Expert Professional Email Response Composer and Communication Strategist,DeepSeek-V3-1,100,14.240700000000002,33.54600000000001,38.814500000000024,73.30899953137248 EmailResponder,MCP,Expert Professional Email Response Composer and Communication Strategist,GPT-4o-mini,100,5.566300000000001,10.111000000000006,18.115600000000015,71.67474212261352 @@ -174,69 +69,6 @@ GameBuilder,Unknown,Software Quality Control Engineer,GPT-5,25,17.15160000000000 GameBuilder,Unknown,Software Quality Control Engineer,Gemini-2.5-flash,25,67.678,126.49400000000003,160.32239999999996,57.09595305371432 GameBuilder,Unknown,Software Quality Control Engineer,Gemini-2.5-flash-nothinking,27,76.8351851851852,125.83800000000004,432.4909999999994,127.80954184391817 GameBuilder,Unknown,Software Quality Control Engineer,Qwen3-235b,25,126.54799999999999,248.53200000000015,472.0555999999998,85.41150664919827 -LandingPageGenerator,A2A,Landing Page Content Generator,DeepSeek-R1,62,177.37274193548387,371.47999999999996,642.6985000000002,81.64608828922493 -LandingPageGenerator,A2A,Landing Page Content Generator,DeepSeek-V3-1,60,188.02116666666663,190.758,823.0604999999999,82.45458086813 -LandingPageGenerator,A2A,Landing Page Content Generator,GPT-4o-mini,60,27.733833333333333,40.005,47.47079999999997,29.302393412397443 -LandingPageGenerator,A2A,Landing Page Content Generator,GPT-5,60,14.573500000000001,27.134000000000004,40.18609999999997,65.35962264430147 -LandingPageGenerator,A2A,Landing Page Content Generator,Gemini-2.5-flash,60,49.102,76.885,120.33199999999997,42.70809428560347 -LandingPageGenerator,A2A,Landing Page Content Generator,Gemini-2.5-flash-nothinking,61,67.31,70.7,724.2699999999984,261.63316734315714 -LandingPageGenerator,A2A,Landing Page Content Generator,Qwen3-235b,63,171.33523809523805,446.04400000000004,1000.6026,132.61603886235724 -LandingPageGenerator,A2A,Product Idea Analyst,DeepSeek-R1,62,70.74854838709679,98.863,252.1266000000001,65.34019693648871 -LandingPageGenerator,A2A,Product Idea Analyst,DeepSeek-V3-1,60,17.035833333333333,24.791,34.11199999999997,31.203661936496996 -LandingPageGenerator,A2A,Product Idea Analyst,GPT-4o-mini,60,13.109833333333333,23.715,30.83099999999997,57.932328862845964 -LandingPageGenerator,A2A,Product Idea Analyst,GPT-5,60,4.5136666666666665,6.3629999999999995,7.441899999999995,22.901083732666137 -LandingPageGenerator,A2A,Product Idea Analyst,Gemini-2.5-flash,60,21.058166666666672,28.130000000000003,32.779699999999984,26.52766641016793 -LandingPageGenerator,A2A,Product Idea Analyst,Gemini-2.5-flash-nothinking,64,20.181093750000002,26.391000000000002,40.321499999999965,36.27971934100415 -LandingPageGenerator,A2A,Product Idea Analyst,Qwen3-235b,63,19.787619047619046,33.370000000000005,54.53780000000003,55.78858333085751 -LandingPageGenerator,A2A,Template Selection Specialist,DeepSeek-R1,62,42.770322580645164,86.734,147.14340000000007,80.08749083847539 -LandingPageGenerator,A2A,Template Selection Specialist,DeepSeek-V3-1,60,8.904333333333334,10.516,17.991399999999985,25.17655278269393 -LandingPageGenerator,A2A,Template Selection Specialist,GPT-4o-mini,60,2.353666666666667,2.875,5.4405,28.74798134305061 -LandingPageGenerator,A2A,Template Selection Specialist,GPT-5,60,2.385833333333333,2.652,3.3617999999999966,11.978846061110126 -LandingPageGenerator,A2A,Template Selection Specialist,Gemini-2.5-flash,60,4.715833333333334,5.526000000000001,12.38999999999999,40.43736992880102 -LandingPageGenerator,A2A,Template Selection Specialist,Gemini-2.5-flash-nothinking,61,5.827049180327869,10.8,20.92999999999999,66.08038401820447 -LandingPageGenerator,A2A,Template Selection Specialist,Qwen3-235b,63,6.3331746031746015,10.870000000000001,14.351800000000008,45.358025794297134 -LandingPageGenerator,MCP,Landing Page Content Generator,DeepSeek-R1,68,131.39191176470587,234.60000000000005,308.49069999999995,50.78453241734455 -LandingPageGenerator,MCP,Landing Page Content Generator,DeepSeek-V3-1,60,106.70566666666666,159.979,365.95919999999853,70.21737296526494 -LandingPageGenerator,MCP,Landing Page Content Generator,GPT-4o-mini,60,37.97166666666667,50.991,67.74979999999994,31.226295673607336 -LandingPageGenerator,MCP,Landing Page Content Generator,GPT-5,60,17.087,19.473000000000003,20.900499999999997,9.892037885404035 -LandingPageGenerator,MCP,Landing Page Content Generator,Gemini-2.5-flash,60,66.37833333333333,98.432,182.61829999999975,54.46665597311644 -LandingPageGenerator,MCP,Landing Page Content Generator,Gemini-2.5-flash-nothinking,61,72.30344262295083,119.54,498.1559999999997,137.25208128560269 -LandingPageGenerator,MCP,Landing Page Content Generator,Qwen3-235b,60,70.16100000000002,103.57600000000001,233.34259999999904,69.70806237202166 -LandingPageGenerator,MCP,Product Idea Analyst,DeepSeek-R1,70,64.99471428571428,96.85600000000002,195.86040000000017,56.11687417345953 -LandingPageGenerator,MCP,Product Idea Analyst,DeepSeek-V3-1,60,19.672666666666665,28.249000000000002,43.45309999999999,35.72804141474059 -LandingPageGenerator,MCP,Product Idea Analyst,GPT-4o-mini,60,15.749500000000001,30.292,39.485499999999995,59.850805725404484 -LandingPageGenerator,MCP,Product Idea Analyst,GPT-5,60,4.572000000000001,6.119000000000001,7.235699999999998,21.071611346657573 -LandingPageGenerator,MCP,Product Idea Analyst,Gemini-2.5-flash,60,21.272,27.750999999999998,31.817099999999996,20.166543191746683 -LandingPageGenerator,MCP,Product Idea Analyst,Gemini-2.5-flash-nothinking,62,18.9708064516129,25.839,32.79460000000001,33.732608615726015 -LandingPageGenerator,MCP,Product Idea Analyst,Qwen3-235b,60,16.71333333333333,28.246000000000002,43.40119999999999,53.498397090428625 -LandingPageGenerator,MCP,Template Selection Specialist,DeepSeek-R1,70,42.72157142857142,77.20500000000001,160.07160000000016,80.95921344867699 -LandingPageGenerator,MCP,Template Selection Specialist,DeepSeek-V3-1,60,9.467333333333332,11.433,14.618699999999997,16.72926462554838 -LandingPageGenerator,MCP,Template Selection Specialist,GPT-4o-mini,60,2.252333333333333,2.843,4.1043999999999965,23.144457778061525 -LandingPageGenerator,MCP,Template Selection Specialist,GPT-5,60,2.320666666666667,2.491,3.2786999999999997,10.791262684543113 -LandingPageGenerator,MCP,Template Selection Specialist,Gemini-2.5-flash,60,4.043500000000001,4.847,8.446099999999982,30.394246395589747 -LandingPageGenerator,MCP,Template Selection Specialist,Gemini-2.5-flash-nothinking,61,4.205901639344263,6.02,11.143999999999998,41.92762116616369 -LandingPageGenerator,MCP,Template Selection Specialist,Qwen3-235b,60,5.2005,6.630000000000001,9.901599999999993,30.249357606000054 -LandingPageGenerator-H_A2A,A2A_mix,Landing Page Content Generator,DeepSeek-R1,60,170.94116666666665,370.769,647.9275999999994,90.33359861533803 -LandingPageGenerator-H_A2A,A2A_mix,Landing Page Content Generator,DeepSeek-V3-1,60,206.86499999999995,233.62700000000012,755.2991999999994,74.07681612790536 -LandingPageGenerator-H_A2A,A2A_mix,Landing Page Content Generator,GPT-4o-mini,60,28.240666666666662,37.712,44.987199999999994,23.510473478736568 -LandingPageGenerator-H_A2A,A2A_mix,Landing Page Content Generator,GPT-5,60,20.350833333333334,36.772,45.38799999999995,57.64300175130867 -LandingPageGenerator-H_A2A,A2A_mix,Landing Page Content Generator,Gemini-2.5-flash,60,58.37166666666667,71.24400000000001,467.82869999999843,166.86938459593568 -LandingPageGenerator-H_A2A,A2A_mix,Landing Page Content Generator,Gemini-2.5-flash-nothinking,60,37.109333333333325,60.09,80.62979999999993,40.750913411439925 -LandingPageGenerator-H_A2A,A2A_mix,Landing Page Content Generator,Qwen3-235b,63,175.21444444444444,389.36600000000067,1145.0794000000005,145.46863621000844 -LandingPageGenerator-H_A2A,A2A_mix,agent,DeepSeek-R1,120,38.44716666666667,81.07500000000002,104.12540000000001,70.23252399041972 -LandingPageGenerator-H_A2A,A2A_mix,agent,DeepSeek-V3-1,123,9.405121951219511,13.696,16.3898,29.15872651725727 -LandingPageGenerator-H_A2A,A2A_mix,agent,GPT-4o-mini,120,3.4458333333333333,6.559,10.207800000000004,65.1535679250944 -LandingPageGenerator-H_A2A,A2A_mix,agent,GPT-5,119,3.7556302521008407,8.188,10.378999999999994,74.20754625264755 -LandingPageGenerator-H_A2A,A2A_mix,agent,Gemini-2.5-flash,120,3.45525,4.851000000000002,10.233200000000002,50.55236802615638 -LandingPageGenerator-H_A2A,A2A_mix,agent,Gemini-2.5-flash-nothinking,120,2.655333333333333,3.4580000000000015,13.5888,90.23845078103918 -LandingPageGenerator-H_A2A,A2A_mix,agent,Qwen3-235b,126,4.306190476190477,6.7,14.727500000000001,70.45963587905699 -LandingPageGenerator-H_A2A,A2A_mix,senior_react_engineer,DeepSeek-R1,60,30.346999999999998,82.42200000000001,216.04609999999923,166.25703731788153 -LandingPageGenerator-H_A2A,A2A_mix,senior_react_engineer,DeepSeek-V3-1,60,14.053333333333336,22.580000000000002,32.82399999999997,50.06228132750382 -LandingPageGenerator-H_A2A,A2A_mix,senior_react_engineer,GPT-4o-mini,60,3.7871666666666663,5.02,5.536899999999999,26.753805201242987 -LandingPageGenerator-H_A2A,A2A_mix,senior_react_engineer,GPT-5,56,1.523035714285714,1.95,2.679000000000003,25.985481135665317 -LandingPageGenerator-H_A2A,A2A_mix,senior_react_engineer,Gemini-2.5-flash,60,6.126000000000001,7.924000000000002,22.198699999999988,64.60300750875898 -LandingPageGenerator-H_A2A,A2A_mix,senior_react_engineer,Gemini-2.5-flash-nothinking,60,7.092666666666665,11.370999999999999,22.44389999999998,58.305988653216545 -LandingPageGenerator-H_A2A,A2A_mix,senior_react_engineer,Qwen3-235b,62,1.6912903225806448,2.32,2.8034,23.654575787664978 MarkdownValidator,MCP,Requirements Manager,DeepSeek-R1,60,129.19133333333332,208.619,245.03149999999982,38.23297996334468 MarkdownValidator,MCP,Requirements Manager,DeepSeek-V3-1,60,10.701999999999996,16.729,28.447999999999993,42.36213601809079 MarkdownValidator,MCP,Requirements Manager,GPT-4o-mini,60,3.5645,5.125,10.687099999999976,52.391495444512124 @@ -251,236 +83,380 @@ MarkdownValidator,Unknown,Requirements Manager,GPT-5,60,7.299666666666666,4.118, MarkdownValidator,Unknown,Requirements Manager,Gemini-2.5-flash,77,438.05233766233766,1326.7659999999998,1421.775999999999,116.38441771561996 MarkdownValidator,Unknown,Requirements Manager,Gemini-2.5-flash-nothinking,64,125.235625,377.44000000000017,1345.4674,262.5261403466183 MarkdownValidator,Unknown,Requirements Manager,Qwen3-235b,60,9.525833333333333,17.553,26.4775,60.68876050451443 -RecruitmentAssistant,A2A,Senior Candidate Evaluator,DeepSeek-R1,66,482.2822727272728,894.74,1589.9264999999996,72.37375570440209 -RecruitmentAssistant,A2A,Senior Candidate Evaluator,DeepSeek-V3-1,60,165.93366666666665,220.633,257.38959999999986,22.714070473384883 -RecruitmentAssistant,A2A,Senior Candidate Evaluator,GPT-4o-mini,60,113.64116666666665,196.942,315.11449999999934,60.5074188227274 -RecruitmentAssistant,A2A,Senior Candidate Evaluator,GPT-5,60,27.725,35.755,38.9192,23.717881374765692 -RecruitmentAssistant,A2A,Senior Candidate Evaluator,Gemini-2.5-flash,60,166.0688333333333,279.96100000000007,559.7442999999998,74.46640561446888 -RecruitmentAssistant,A2A,Senior Candidate Evaluator,Gemini-2.5-flash-nothinking,60,85.31750000000001,121.487,273.58459999999945,58.295850755747125 -RecruitmentAssistant,A2A,Senior Candidate Evaluator,Qwen3-235b,84,928.6871428571427,1564.0890000000002,2122.626200000001,53.18297604787774 -RecruitmentAssistant,A2A,Senior Interview Materials Coordinator,DeepSeek-R1,59,206.74694915254236,323.5640000000001,724.7176000000006,65.52443608606401 -RecruitmentAssistant,A2A,Senior Interview Materials Coordinator,DeepSeek-V3-1,60,84.92750000000002,94.15100000000001,101.81889999999999,9.590720114479625 -RecruitmentAssistant,A2A,Senior Interview Materials Coordinator,GPT-4o-mini,60,29.495000000000005,39.144,55.74299999999993,28.779410132458207 -RecruitmentAssistant,A2A,Senior Interview Materials Coordinator,GPT-5,60,16.673,19.773,24.038099999999986,14.889921290193477 -RecruitmentAssistant,A2A,Senior Interview Materials Coordinator,Gemini-2.5-flash,60,102.49050000000001,162.637,279.9419999999998,51.43570470741998 -RecruitmentAssistant,A2A,Senior Interview Materials Coordinator,Gemini-2.5-flash-nothinking,60,59.13583333333333,85.762,154.1239999999995,50.313584442665025 -RecruitmentAssistant,A2A,Senior Interview Materials Coordinator,Qwen3-235b,59,80.3657627118644,118.15000000000003,188.8300000000001,40.7166650084163 -RecruitmentAssistant,A2A,Senior Job Requirements Analyst,DeepSeek-R1,66,168.61424242424243,245.7,1101.2504999999976,117.89154800997554 -RecruitmentAssistant,A2A,Senior Job Requirements Analyst,DeepSeek-V3-1,60,59.03966666666667,70.324,133.71999999999957,37.0852088913006 -RecruitmentAssistant,A2A,Senior Job Requirements Analyst,GPT-4o-mini,60,32.686166666666665,52.70300000000002,114.64809999999993,69.61685473825099 -RecruitmentAssistant,A2A,Senior Job Requirements Analyst,GPT-5,60,20.7715,24.212,29.753899999999998,14.589490602632466 -RecruitmentAssistant,A2A,Senior Job Requirements Analyst,Gemini-2.5-flash,60,86.3855,135.882,292.50779999999963,63.096017988364295 -RecruitmentAssistant,A2A,Senior Job Requirements Analyst,Gemini-2.5-flash-nothinking,60,48.35666666666666,68.52600000000001,194.96289999999914,90.17903477689882 -RecruitmentAssistant,A2A,Senior Job Requirements Analyst,Qwen3-235b,84,68.37702380952383,128.38000000000002,182.58240000000004,60.59765765765134 -RecruitmentAssistant,MCP,Senior Candidate Evaluator,DeepSeek-R1,61,507.7860655737705,1003.26,1340.6599999999999,65.54616198975312 -RecruitmentAssistant,MCP,Senior Candidate Evaluator,DeepSeek-V3-1,60,181.6111666666667,241.187,426.6646999999993,36.43568976679267 -RecruitmentAssistant,MCP,Senior Candidate Evaluator,GPT-4o-mini,60,91.36733333333332,170.648,363.8679999999991,86.05607435163387 -RecruitmentAssistant,MCP,Senior Candidate Evaluator,GPT-5,60,23.312333333333335,31.564,37.3966,28.551021956942645 -RecruitmentAssistant,MCP,Senior Candidate Evaluator,Gemini-2.5-flash,60,91.94200000000001,152.084,212.99929999999975,51.15058707526449 -RecruitmentAssistant,MCP,Senior Candidate Evaluator,Gemini-2.5-flash-nothinking,60,68.89833333333333,107.016,137.40139999999997,42.565075806564074 -RecruitmentAssistant,MCP,Senior Candidate Evaluator,Qwen3-235b,84,862.7088095238095,1419.4959999999999,1649.2959000000008,50.069411751658635 -RecruitmentAssistant,MCP,Senior Interview Materials Coordinator,DeepSeek-R1,61,178.0060655737705,278.01,401.2199999999999,43.32874246428282 -RecruitmentAssistant,MCP,Senior Interview Materials Coordinator,DeepSeek-V3-1,60,95.02600000000002,111.278,197.0470999999995,30.895864644176086 -RecruitmentAssistant,MCP,Senior Interview Materials Coordinator,GPT-4o-mini,60,16.37483333333333,20.852999999999998,24.680399999999995,22.574374628194807 -RecruitmentAssistant,MCP,Senior Interview Materials Coordinator,GPT-5,60,16.001166666666666,18.822,24.198199999999975,16.21761121300057 -RecruitmentAssistant,MCP,Senior Interview Materials Coordinator,Gemini-2.5-flash,60,63.66716666666666,103.61800000000001,181.01939999999973,51.19663644262158 -RecruitmentAssistant,MCP,Senior Interview Materials Coordinator,Gemini-2.5-flash-nothinking,60,56.25466666666666,81.843,192.45489999999927,69.77989047360981 -RecruitmentAssistant,MCP,Senior Interview Materials Coordinator,Qwen3-235b,58,82.67603448275861,124.04700000000001,220.94309999999996,43.73322979014188 -RecruitmentAssistant,MCP,Senior Job Requirements Analyst,DeepSeek-R1,62,138.4190322580645,230.742,281.51779999999997,43.393496872752905 -RecruitmentAssistant,MCP,Senior Job Requirements Analyst,DeepSeek-V3-1,60,62.85633333333333,81.169,93.4834,28.553678427507567 -RecruitmentAssistant,MCP,Senior Job Requirements Analyst,GPT-4o-mini,60,21.720666666666666,31.005000000000003,57.27609999999995,46.28720660653235 -RecruitmentAssistant,MCP,Senior Job Requirements Analyst,GPT-5,60,20.220499999999998,24.590999999999998,26.667799999999996,14.896803583382734 -RecruitmentAssistant,MCP,Senior Job Requirements Analyst,Gemini-2.5-flash,60,48.28083333333334,58.21,186.9028999999992,80.75562845740082 -RecruitmentAssistant,MCP,Senior Job Requirements Analyst,Gemini-2.5-flash-nothinking,60,38.291999999999994,62.74900000000001,101.7066999999999,59.94364219715042 -RecruitmentAssistant,MCP,Senior Job Requirements Analyst,Qwen3-235b,84,67.24738095238096,117.236,178.38000000000005,54.78247055147199 -RecruitmentAssistant-H_A2A,A2A,Senior Candidate Evaluator,DeepSeek-R1,64,470.15515625,761.1330000000002,1555.8469999999988,63.348074757582765 -RecruitmentAssistant-H_A2A,A2A,Senior Candidate Evaluator,DeepSeek-V3-1,60,160.84433333333337,207.644,224.99749999999997,21.50462241846168 -RecruitmentAssistant-H_A2A,A2A,Senior Candidate Evaluator,GPT-4o-mini,60,99.26650000000001,171.419,312.96289999999976,64.81157877918369 -RecruitmentAssistant-H_A2A,A2A,Senior Candidate Evaluator,GPT-5,60,26.981833333333334,39.864,46.77079999999998,31.407689487717256 -RecruitmentAssistant-H_A2A,A2A,Senior Candidate Evaluator,Gemini-2.5-flash,60,70.23633333333333,117.13300000000001,157.13699999999994,45.88249310100194 -RecruitmentAssistant-H_A2A,A2A,Senior Candidate Evaluator,Gemini-2.5-flash-nothinking,60,50.517833333333336,70.51299999999999,91.52019999999996,31.935452430027354 -RecruitmentAssistant-H_A2A,A2A,Senior Candidate Evaluator,Qwen3-235b,72,664.6062500000002,1072.156,1621.509700000001,56.75729644490956 -RecruitmentAssistant-H_A2A,A2A,agent,DeepSeek-R1,128,50.4896875,82.72099999999999,114.92070000000001,47.52808132546662 -RecruitmentAssistant-H_A2A,A2A,agent,DeepSeek-V3-1,120,9.985999999999999,11.411000000000003,39.632900000000014,55.01406601658895 -RecruitmentAssistant-H_A2A,A2A,agent,GPT-4o-mini,120,4.082,7.008000000000001,9.653200000000002,50.45287086416841 -RecruitmentAssistant-H_A2A,A2A,agent,GPT-5,120,3.0318333333333336,6.181,10.4046,67.70014907724561 -RecruitmentAssistant-H_A2A,A2A,agent,Gemini-2.5-flash,123,5.984715447154471,9.158,13.451200000000002,51.78364614160436 -RecruitmentAssistant-H_A2A,A2A,agent,Gemini-2.5-flash-nothinking,120,4.0925,8.994000000000003,16.907400000000003,92.29489925611661 -RecruitmentAssistant-H_A2A,A2A,agent,Qwen3-235b,188,4.718617021276596,9.438000000000002,13.950499999999982,79.63587507472865 -RecruitmentAssistant-H_A2A,A2A,interview_coordinator,DeepSeek-R1,60,144.31050000000002,204.10399999999998,242.03829999999996,29.084905938728717 -RecruitmentAssistant-H_A2A,A2A,interview_coordinator,DeepSeek-V3-1,60,61.934666666666665,79.12899999999999,88.8034,21.614527807242247 -RecruitmentAssistant-H_A2A,A2A,interview_coordinator,GPT-4o-mini,60,31.336666666666666,41.57,54.0492,25.484921054604236 -RecruitmentAssistant-H_A2A,A2A,interview_coordinator,GPT-5,60,18.41433333333333,23.607,27.472599999999993,23.763992579662307 -RecruitmentAssistant-H_A2A,A2A,interview_coordinator,Gemini-2.5-flash,60,36.23616666666666,44.214000000000006,61.15339999999995,21.94037257766498 -RecruitmentAssistant-H_A2A,A2A,interview_coordinator,Gemini-2.5-flash-nothinking,60,32.06166666666666,47.532000000000004,60.46049999999998,36.153268091416734 -RecruitmentAssistant-H_A2A,A2A,interview_coordinator,Qwen3-235b,60,158.7245,248.218,296.45999999999987,39.60749300865382 -SQLAssistant,A2A,Business Intelligence Analyst,DeepSeek-R1,8,152.99375,336.65999999999997,388.32,89.34330597009786 -SQLAssistant,A2A,Business Intelligence Analyst,GPT-4o-mini,60,16.526333333333334,24.145,31.291,33.72392231828864 -SQLAssistant,A2A,Business Intelligence Analyst,GPT-5,54,6.70074074074074,10.292,16.750899999999998,46.81680634843712 -SQLAssistant,A2A,Business Intelligence Analyst,Gemini-2.5-flash,22,27.928181818181823,44.34400000000002,52.25209999999999,36.06389350311745 -SQLAssistant,A2A,Business Intelligence Analyst,Gemini-2.5-flash-nothinking,40,19.540499999999998,39.099,61.37909999999999,69.70866774846289 -SQLAssistant,A2A,Business Intelligence Analyst,Qwen3-235b,54,29.829629629629633,49.59400000000001,79.39739999999996,53.712578664155785 -SQLAssistant,A2A,Data Security and Compliance Auditor,DeepSeek-R1,177,43.75768361581921,78.52600000000002,223.69560000000013,94.88455226839085 -SQLAssistant,A2A,Data Security and Compliance Auditor,DeepSeek-V3-1,195,6.146358974358975,9.602,18.7722,47.72378387355977 -SQLAssistant,A2A,Data Security and Compliance Auditor,GPT-4o-mini,71,4.104788732394367,4.75,18.068999999999992,69.47840217444188 -SQLAssistant,A2A,Data Security and Compliance Auditor,GPT-5,93,2.551935483870968,3.13,4.055199999999991,32.28225391114768 -SQLAssistant,A2A,Data Security and Compliance Auditor,Gemini-2.5-flash,161,19.553167701863355,23.79,139.002,134.1958241395214 -SQLAssistant,A2A,Data Security and Compliance Auditor,Gemini-2.5-flash-nothinking,127,4.267874015748032,7.844000000000001,18.882399999999997,84.93357270363518 -SQLAssistant,A2A,Data Security and Compliance Auditor,Qwen3-235b,90,7.941111111111112,15.523000000000001,25.7322,69.0329136718855 -SQLAssistant,A2A,Expert SQL Query Generator,DeepSeek-R1,181,138.38082872928177,255.86,562.9539999999965,123.99785820145858 -SQLAssistant,A2A,Expert SQL Query Generator,DeepSeek-V3-1,209,23.113397129186602,36.374000000000024,65.57799999999999,56.49724577840352 -SQLAssistant,A2A,Expert SQL Query Generator,GPT-4o-mini,71,4.1874647887323935,6.13,11.806999999999988,50.25149138618599 -SQLAssistant,A2A,Expert SQL Query Generator,GPT-5,93,3.20989247311828,4.2,5.70799999999999,33.27520068300834 -SQLAssistant,A2A,Expert SQL Query Generator,Gemini-2.5-flash,161,18.361055900621118,28.34,186.09600000000015,179.05070287192157 -SQLAssistant,A2A,Expert SQL Query Generator,Gemini-2.5-flash-nothinking,127,13.611653543307087,17.37600000000001,109.15899999999999,208.90974435869109 -SQLAssistant,A2A,Expert SQL Query Generator,Qwen3-235b,90,13.79733333333333,22.614,45.1722,57.84503768311274 -SQLAssistant,A2A,Senior SQL Code Reviewer,DeepSeek-R1,180,186.51716666666664,372.5329999999999,742.1253,90.50106301986447 -SQLAssistant,A2A,Senior SQL Code Reviewer,DeepSeek-V3-1,198,17.378434343434343,25.921,41.73810000000001,48.191310363114184 -SQLAssistant,A2A,Senior SQL Code Reviewer,GPT-4o-mini,71,4.902676056338028,9.16,20.627999999999965,80.94114404491415 -SQLAssistant,A2A,Senior SQL Code Reviewer,GPT-5,93,2.340215053763441,3.632,4.222799999999999,40.40667420442891 -SQLAssistant,A2A,Senior SQL Code Reviewer,Gemini-2.5-flash,161,13.470745341614906,20.07,46.520000000000024,201.8952006323121 -SQLAssistant,A2A,Senior SQL Code Reviewer,Gemini-2.5-flash-nothinking,127,6.916771653543308,10.316000000000003,55.012599999999836,152.37685054996223 -SQLAssistant,A2A,Senior SQL Code Reviewer,Qwen3-235b,90,13.920555555555554,26.415,41.1359,63.571333034232325 -SQLAssistant,MCP,Business Intelligence Analyst,DeepSeek-R1,29,76.85862068965517,160.192,170.7064,64.4249461450751 -SQLAssistant,MCP,Business Intelligence Analyst,DeepSeek-V3-1,51,34.86941176470588,53.86,56.715,33.446822382524935 -SQLAssistant,MCP,Business Intelligence Analyst,GPT-4o-mini,34,15.604411764705882,25.866,35.99900000000002,49.96253789905594 -SQLAssistant,MCP,Business Intelligence Analyst,GPT-5,35,5.514285714285714,6.8260000000000005,7.688799999999998,18.832711686400575 -SQLAssistant,MCP,Business Intelligence Analyst,Gemini-2.5-flash,55,10.48745454545454,16.424,24.6992,42.600887529463165 -SQLAssistant,MCP,Business Intelligence Analyst,Gemini-2.5-flash-nothinking,59,6.734406779661017,9.152000000000001,40.97880000000011,145.85974373889064 -SQLAssistant,MCP,Business Intelligence Analyst,Qwen3-235b,56,20.928392857142857,41.075,55.040500000000016,63.87784626455202 -SQLAssistant,MCP,Data Security and Compliance Auditor,DeepSeek-R1,142,52.294225352112676,108.50000000000001,178.95750000000004,74.45623194194523 -SQLAssistant,MCP,Data Security and Compliance Auditor,DeepSeek-V3-1,110,16.03709090909091,25.44000000000001,29.3224,41.121923592095456 -SQLAssistant,MCP,Data Security and Compliance Auditor,GPT-4o-mini,130,8.321538461538463,13.664000000000003,23.730999999999995,51.58528340634948 -SQLAssistant,MCP,Data Security and Compliance Auditor,GPT-5,129,4.794651162790697,7.726,13.055999999999997,55.038997732453424 -SQLAssistant,MCP,Data Security and Compliance Auditor,Gemini-2.5-flash,86,16.85116279069768,28.575,56.05050000000008,58.82417931439906 -SQLAssistant,MCP,Data Security and Compliance Auditor,Gemini-2.5-flash-nothinking,84,13.892380952380952,14.717000000000002,143.57400000000027,228.68829271520985 -SQLAssistant,MCP,Data Security and Compliance Auditor,Qwen3-235b,93,17.71279569892473,32.867999999999995,47.99399999999999,58.6024585657597 -SQLAssistant,MCP,Expert SQL Query Generator,DeepSeek-R1,155,102.98774193548387,188.518,514.8036000000005,98.4930454120539 -SQLAssistant,MCP,Expert SQL Query Generator,DeepSeek-V3-1,115,16.048695652173915,24.90600000000001,35.825399999999995,39.03555612535344 -SQLAssistant,MCP,Expert SQL Query Generator,GPT-4o-mini,130,4.2050769230769225,6.192000000000007,11.819899999999995,42.416424388977624 -SQLAssistant,MCP,Expert SQL Query Generator,GPT-5,129,3.35906976744186,4.654,7.2383999999999995,32.43504202615352 -SQLAssistant,MCP,Expert SQL Query Generator,Gemini-2.5-flash,87,25.549310344827582,42.12600000000002,185.7066000000001,157.79209034869254 -SQLAssistant,MCP,Expert SQL Query Generator,Gemini-2.5-flash-nothinking,84,8.698214285714286,14.727000000000002,31.890800000000038,76.23671680002671 -SQLAssistant,MCP,Expert SQL Query Generator,Qwen3-235b,93,13.93010752688172,20.016,48.65999999999981,109.40215206134505 -SQLAssistant,MCP,Senior SQL Code Reviewer,DeepSeek-R1,155,199.28335483870967,392.1739999999999,672.0562000000003,94.01790497971322 -SQLAssistant,MCP,Senior SQL Code Reviewer,DeepSeek-V3-1,111,12.311171171171171,18.12,21.09500000000001,32.35602786749792 -SQLAssistant,MCP,Senior SQL Code Reviewer,GPT-4o-mini,130,6.124153846153847,10.410000000000002,25.408899999999967,78.4190645371273 -SQLAssistant,MCP,Senior SQL Code Reviewer,GPT-5,127,2.49763779527559,3.8220000000000005,6.669399999999999,49.405323237755574 -SQLAssistant,MCP,Senior SQL Code Reviewer,Gemini-2.5-flash,87,23.517126436781613,17.35400000000002,176.66080000000053,457.88875544005793 -SQLAssistant,MCP,Senior SQL Code Reviewer,Gemini-2.5-flash-nothinking,84,6.199404761904764,9.440999999999999,27.580400000000015,80.35254317347493 -SQLAssistant,MCP,Senior SQL Code Reviewer,Qwen3-235b,93,13.205591397849462,23.345999999999997,35.285599999999995,55.639212566793525 -SQLAssistant-H_A2A,A2A,Expert SQL Query Generator,DeepSeek-R1,124,161.91064516129032,282.331,911.4582999999992,138.53700841435628 -SQLAssistant-H_A2A,A2A,Expert SQL Query Generator,DeepSeek-V3-1,81,16.38666666666667,27.11,49.524000000000015,54.59687049314284 -SQLAssistant-H_A2A,A2A,Expert SQL Query Generator,GPT-4o-mini,83,3.6742168674698794,4.117999999999999,7.79239999999994,36.687708110615254 -SQLAssistant-H_A2A,A2A,Expert SQL Query Generator,GPT-5,75,3.748,4.976000000000003,6.442400000000004,22.64947470362157 -SQLAssistant-H_A2A,A2A,Expert SQL Query Generator,Gemini-2.5-flash,93,21.91440860215054,31.427999999999997,211.61559999999977,180.4848457494644 -SQLAssistant-H_A2A,A2A,Expert SQL Query Generator,Gemini-2.5-flash-nothinking,86,12.023139534883722,15.605,79.18100000000106,212.9470135445072 -SQLAssistant-H_A2A,A2A,Expert SQL Query Generator,Qwen3-235b,88,13.965227272727274,24.325,41.55949999999994,53.9543392251959 -SQLAssistant-H_A2A,A2A,Senior SQL Code Reviewer,DeepSeek-R1,124,206.54798387096776,471.083,840.5824999999995,94.13797345806513 -SQLAssistant-H_A2A,A2A,Senior SQL Code Reviewer,DeepSeek-V3-1,78,11.829871794871796,15.245,24.111000000000026,30.230623021018065 -SQLAssistant-H_A2A,A2A,Senior SQL Code Reviewer,GPT-4o-mini,83,4.090481927710843,6.436,9.004199999999987,43.48626544121849 -SQLAssistant-H_A2A,A2A,Senior SQL Code Reviewer,GPT-5,75,3.0263999999999998,4.288,8.751400000000013,53.56023781278151 -SQLAssistant-H_A2A,A2A,Senior SQL Code Reviewer,Gemini-2.5-flash,93,12.430645161290322,21.272,54.32479999999989,104.39009214381478 -SQLAssistant-H_A2A,A2A,Senior SQL Code Reviewer,Gemini-2.5-flash-nothinking,86,8.72674418604651,9.665,64.79750000000115,292.1430068871592 -SQLAssistant-H_A2A,A2A,Senior SQL Code Reviewer,Qwen3-235b,88,15.048977272727273,24.491999999999994,37.582299999999975,48.394477694811854 -SQLAssistant-H_A2A,A2A,agent,DeepSeek-R1,226,2.031548672566372,3.83,9.1725,78.95611007950538 -SQLAssistant-H_A2A,A2A,agent,DeepSeek-V3-1,167,4.259880239520958,11.332,21.24080000000001,102.28209822844218 -SQLAssistant-H_A2A,A2A,agent,GPT-4o-mini,162,2.59,5.870000000000002,11.687499999999982,85.81134459066172 -SQLAssistant-H_A2A,A2A,agent,GPT-5,134,3.667835820895522,7.829,17.92479999999997,97.48766810923782 -SQLAssistant-H_A2A,A2A,agent,Gemini-2.5-flash,179,2.9906145251396645,6.736000000000001,19.8584,114.71087670507252 -SQLAssistant-H_A2A,A2A,agent,Gemini-2.5-flash-nothinking,229,2.4059388646288213,4.84,8.953199999999997,78.21448263059027 -SQLAssistant-H_A2A,A2A,agent,Qwen3-235b,206,2.8290291262135923,7.54,13.1495,104.40664894036873 -SQLAssistant-H_A2A,A2A,result_interpreter,DeepSeek-R1,39,26.876410256410256,34.330000000000034,86.41339999999992,56.685578616077194 -SQLAssistant-H_A2A,A2A,result_interpreter,DeepSeek-V3-1,56,10.034464285714284,12.61,15.069500000000003,24.244406492291436 -SQLAssistant-H_A2A,A2A,result_interpreter,GPT-4o-mini,54,2.9146296296296295,3.697,4.0382,20.1586215422541 -SQLAssistant-H_A2A,A2A,result_interpreter,GPT-5,59,4.338135593220339,5.914000000000001,7.4136,26.95344353131761 -SQLAssistant-H_A2A,A2A,result_interpreter,Gemini-2.5-flash,50,7.9918000000000005,10.849,15.071399999999999,30.79448981288138 -SQLAssistant-H_A2A,A2A,result_interpreter,Gemini-2.5-flash-nothinking,50,2.7754000000000003,3.6390000000000002,4.4738,26.68717828449652 -SQLAssistant-H_A2A,A2A,result_interpreter,Qwen3-235b,56,10.866964285714285,26.619999999999997,32.5435,71.0589957681135 -SQLAssistant-H_A2A,A2A_mix,Expert SQL Query Generator,DeepSeek-R1,14,110.83928571428571,213.49800000000002,230.27459999999996,71.06469935426793 -SQLAssistant-H_A2A,A2A_mix,Expert SQL Query Generator,DeepSeek-V3-1,2,10.91,12.39,12.722999999999999,23.98070660302681 -SQLAssistant-H_A2A,A2A_mix,Expert SQL Query Generator,GPT-4o-mini,3,5.1499999999999995,6.896,7.4036,40.814129838299095 -SQLAssistant-H_A2A,A2A_mix,Expert SQL Query Generator,GPT-5,2,3.125,3.2489999999999997,3.2769,7.014499269370543 -SQLAssistant-H_A2A,A2A_mix,Expert SQL Query Generator,Gemini-2.5-flash,8,23.833750000000002,57.41799999999999,89.14479999999999,126.65033977821226 -SQLAssistant-H_A2A,A2A_mix,Expert SQL Query Generator,Gemini-2.5-flash-nothinking,9,19.72111111111111,44.62600000000001,89.10759999999999,147.78608530983644 -SQLAssistant-H_A2A,A2A_mix,Senior SQL Code Reviewer,DeepSeek-R1,14,312.4057142857142,591.9860000000001,1170.5149999999996,101.51646700757884 -SQLAssistant-H_A2A,A2A_mix,Senior SQL Code Reviewer,DeepSeek-V3-1,2,12.31,12.446,12.476600000000001,1.9530162924729981 -SQLAssistant-H_A2A,A2A_mix,Senior SQL Code Reviewer,GPT-4o-mini,3,3.4,4.962,5.5272,55.94501131162134 -SQLAssistant-H_A2A,A2A_mix,Senior SQL Code Reviewer,GPT-5,2,1.7349999999999999,1.915,1.9555,18.339945333368668 -SQLAssistant-H_A2A,A2A_mix,Senior SQL Code Reviewer,Gemini-2.5-flash,8,15.361250000000002,27.145,28.7515,55.895693081779484 -SQLAssistant-H_A2A,A2A_mix,Senior SQL Code Reviewer,Gemini-2.5-flash-nothinking,9,9.786666666666667,17.424000000000007,40.47839999999999,129.45849588326172 -SQLAssistant-H_A2A,A2A_mix,agent,DeepSeek-R1,41,1.5414634146341464,2.17,4.578000000000001,50.01432557956748 -SQLAssistant-H_A2A,A2A_mix,agent,DeepSeek-V3-1,9,1.4377777777777778,1.9040000000000001,2.0624000000000002,28.349874784379704 -SQLAssistant-H_A2A,A2A_mix,agent,GPT-4o-mini,7,2.5785714285714283,5.82,6.899999999999999,94.20349021029175 -SQLAssistant-H_A2A,A2A_mix,agent,GPT-5,6,2.4650000000000003,5.2250000000000005,8.811500000000002,134.11319260907072 -SQLAssistant-H_A2A,A2A_mix,agent,Gemini-2.5-flash,22,1.6872727272727273,2.6840000000000006,4.630099999999999,57.16087109639808 -SQLAssistant-H_A2A,A2A_mix,agent,Gemini-2.5-flash-nothinking,39,1.648717948717949,2.646000000000003,5.255199999999996,61.934782055450185 -SQLAssistant-H_A2A,A2A_mix,result_interpreter,DeepSeek-R1,3,21.816666666666666,23.71,24.367,10.49509432258021 -SQLAssistant-H_A2A,A2A_mix,result_interpreter,DeepSeek-V3-1,1,11.5,11.5,11.5,0.0 -SQLAssistant-H_A2A,A2A_mix,result_interpreter,GPT-4o-mini,1,2.65,2.65,2.65,0.0 -SQLAssistant-H_A2A,A2A_mix,result_interpreter,GPT-5,1,8.55,8.55,8.55,0.0 -SQLAssistant-H_A2A,A2A_mix,result_interpreter,Gemini-2.5-flash,2,5.890000000000001,6.474,6.6054,17.527604423299817 -SQLAssistant-H_A2A,A2A_mix,result_interpreter,Gemini-2.5-flash-nothinking,1,1.84,1.84,1.84,0.0 -SocialMediaManager,A2A,Shakespearean Bard,DeepSeek-R1,158,69.00601265822785,145.97700000000006,267.71580000000074,84.99901452322209 -SocialMediaManager,A2A,Shakespearean Bard,DeepSeek-V3-1,77,66.50649350649351,123.042,138.59759999999997,62.99570487660172 -SocialMediaManager,A2A,Shakespearean Bard,GPT-4o-mini,136,19.23220588235294,31.580000000000002,47.514500000000005,63.13860454420024 -SocialMediaManager,A2A,Shakespearean Bard,GPT-5,112,4.393035714285714,5.537000000000001,14.7361,51.4658281883755 -SocialMediaManager,A2A,Shakespearean Bard,Gemini-2.5-flash,64,17.41265625,22.860000000000003,31.279799999999998,26.79830853879214 -SocialMediaManager,A2A,Shakespearean Bard,Gemini-2.5-flash-nothinking,67,9.615820895522386,12.24,18.524600000000014,28.996956707906808 -SocialMediaManager,A2A,Shakespearean Bard,Qwen3-235b,109,25.940275229357802,57.87800000000002,160.8496,140.83983458608108 -SocialMediaManager,A2A,Topic Analysis Expert,DeepSeek-R1,75,45.812666666666665,73.84,128.2814000000002,50.561751829138444 -SocialMediaManager,A2A,Topic Analysis Expert,DeepSeek-V3-1,60,31.006000000000004,37.497,43.36829999999999,13.079275659436835 -SocialMediaManager,A2A,Topic Analysis Expert,GPT-4o-mini,60,11.346166666666669,17.437,29.29289999999999,45.959204434986155 -SocialMediaManager,A2A,Topic Analysis Expert,GPT-5,60,11.912333333333333,14.768,18.9153,20.520720824060636 -SocialMediaManager,A2A,Topic Analysis Expert,Gemini-2.5-flash,60,27.386333333333333,44.285,78.68659999999994,50.57653093451727 -SocialMediaManager,A2A,Topic Analysis Expert,Gemini-2.5-flash-nothinking,60,19.02816666666667,24.562,30.556499999999982,23.822408316961628 -SocialMediaManager,A2A,Topic Analysis Expert,Qwen3-235b,60,27.251333333333335,49.053,73.38069999999999,53.19862655846502 -SocialMediaManager,A2A,X Post Verifier,DeepSeek-R1,151,125.62039735099339,242.76,334.305,68.84533294732434 -SocialMediaManager,A2A,X Post Verifier,DeepSeek-V3-1,77,31.607662337662344,36.226000000000006,39.694399999999995,10.578787159533881 -SocialMediaManager,A2A,X Post Verifier,GPT-4o-mini,136,12.869044117647059,17.525,25.786500000000004,33.274671791057216 -SocialMediaManager,A2A,X Post Verifier,GPT-5,112,9.838214285714287,11.66,13.5382,13.845318965837178 -SocialMediaManager,A2A,X Post Verifier,Gemini-2.5-flash,64,16.338749999999997,22.121000000000002,41.651699999999934,42.94415391027609 -SocialMediaManager,A2A,X Post Verifier,Gemini-2.5-flash-nothinking,67,6.839850746268657,8.27,12.410600000000006,26.02896814908106 -SocialMediaManager,A2A,X Post Verifier,Qwen3-235b,109,31.740550458715596,45.106,63.6368,41.302985060161426 -SocialMediaManager,MCP,Shakespearean Bard,DeepSeek-R1,153,102.23777777777778,222.32200000000003,431.9996,93.27672022373112 -SocialMediaManager,MCP,Shakespearean Bard,DeepSeek-V3-1,90,76.66222222222221,150.43,179.62959999999998,70.07441243351609 -SocialMediaManager,MCP,Shakespearean Bard,GPT-4o-mini,148,31.31885135135135,55.036000000000115,172.14330000000004,100.47998291342448 -SocialMediaManager,MCP,Shakespearean Bard,GPT-5,112,4.374910714285714,5.604000000000001,10.6886,44.16157993057751 -SocialMediaManager,MCP,Shakespearean Bard,Gemini-2.5-flash,70,15.783000000000003,20.092,28.170200000000012,23.09879563134621 -SocialMediaManager,MCP,Shakespearean Bard,Gemini-2.5-flash-nothinking,65,9.911076923076923,12.366,15.8292,23.268219950103223 -SocialMediaManager,MCP,Shakespearean Bard,Qwen3-235b,101,22.318811881188125,51.9,143.47,117.58259854236563 -SocialMediaManager,MCP,Topic Analysis Expert,DeepSeek-R1,76,47.9325,68.0,176.50750000000002,66.36167479320035 -SocialMediaManager,MCP,Topic Analysis Expert,DeepSeek-V3-1,60,38.785666666666664,46.992000000000004,71.18179999999997,23.48729408483621 -SocialMediaManager,MCP,Topic Analysis Expert,GPT-4o-mini,61,15.760983606557378,20.68,40.39999999999997,43.10697887463873 -SocialMediaManager,MCP,Topic Analysis Expert,GPT-5,60,13.111500000000001,15.431,46.220499999999994,50.482747270821946 -SocialMediaManager,MCP,Topic Analysis Expert,Gemini-2.5-flash,60,24.097833333333334,32.007,46.2848,29.541962972487664 -SocialMediaManager,MCP,Topic Analysis Expert,Gemini-2.5-flash-nothinking,60,19.818833333333334,25.22,26.987599999999997,20.471323062688775 -SocialMediaManager,MCP,Topic Analysis Expert,Qwen3-235b,60,28.404166666666672,56.39300000000001,73.67519999999999,55.462431280669534 -SocialMediaManager,MCP,X Post Verifier,DeepSeek-R1,145,171.3425517241379,323.21599999999995,586.8852000000005,69.14367713765127 -SocialMediaManager,MCP,X Post Verifier,DeepSeek-V3-1,90,43.86066666666667,54.31700000000001,83.44489999999999,22.492648250383052 -SocialMediaManager,MCP,X Post Verifier,GPT-4o-mini,147,20.807414965986396,29.108,119.13219999999959,89.77652004913766 -SocialMediaManager,MCP,X Post Verifier,GPT-5,112,9.862142857142857,12.269,27.475000000000005,34.307029160351114 -SocialMediaManager,MCP,X Post Verifier,Gemini-2.5-flash,70,14.948857142857143,19.341,29.922000000000004,29.277165641231118 -SocialMediaManager,MCP,X Post Verifier,Gemini-2.5-flash-nothinking,65,7.4404615384615385,9.686,13.5552,25.19793755666113 -SocialMediaManager,MCP,X Post Verifier,Qwen3-235b,101,36.06128712871287,55.87,78.31,40.03906692349581 -SocialMediaManager-H_A2A,A2A_mix,Shakespearean Bard,DeepSeek-R1,124,66.43758064516129,141.966,199.8815,73.92515828799226 -SocialMediaManager-H_A2A,A2A_mix,Shakespearean Bard,DeepSeek-V3-1,71,59.04746478873239,110.27,151.07399999999998,66.88496431650725 -SocialMediaManager-H_A2A,A2A_mix,Shakespearean Bard,GPT-4o-mini,72,20.248333333333335,33.36899999999999,65.06150000000008,64.04113059691956 -SocialMediaManager-H_A2A,A2A_mix,Shakespearean Bard,GPT-5,66,5.587121212121212,13.15,18.821499999999972,71.53262108073706 -SocialMediaManager-H_A2A,A2A_mix,Shakespearean Bard,Gemini-2.5-flash,94,18.83287234042553,27.814,54.53379999999999,41.64185996463634 -SocialMediaManager-H_A2A,A2A_mix,Shakespearean Bard,Gemini-2.5-flash-nothinking,80,9.839250000000002,13.292000000000002,17.97139999999997,28.148094562475695 -SocialMediaManager-H_A2A,A2A_mix,Shakespearean Bard,Qwen3-235b,106,19.783396226415093,46.425,135.09650000000008,127.20838715884497 -SocialMediaManager-H_A2A,A2A_mix,agent,DeepSeek-R1,133,32.10556390977444,67.886,101.474,86.02873813869319 -SocialMediaManager-H_A2A,A2A_mix,agent,DeepSeek-V3-1,129,6.2731782945736425,9.412,12.0848,39.241317948766444 -SocialMediaManager-H_A2A,A2A_mix,agent,GPT-4o-mini,119,3.4233613445378146,6.196,10.143399999999994,66.3822782825157 -SocialMediaManager-H_A2A,A2A_mix,agent,GPT-5,144,2.860694444444445,6.0000000000000036,9.257499999999995,70.81659869136651 -SocialMediaManager-H_A2A,A2A_mix,agent,Gemini-2.5-flash,120,4.077083333333333,5.32,15.985600000000025,116.5039556987084 -SocialMediaManager-H_A2A,A2A_mix,agent,Gemini-2.5-flash-nothinking,120,2.711833333333333,4.337000000000001,15.411500000000016,85.07290037226844 -SocialMediaManager-H_A2A,A2A_mix,agent,Qwen3-235b,139,5.752877697841727,10.152000000000001,28.97780000000003,95.20041306095841 -SocialMediaManager-H_A2A,A2A_mix,x_post_verifier,DeepSeek-R1,116,103.1662068965517,172.04,233.31849999999997,52.694110259857084 -SocialMediaManager-H_A2A,A2A_mix,x_post_verifier,DeepSeek-V3-1,71,22.479295774647888,24.89,30.191999999999982,14.604875578200627 -SocialMediaManager-H_A2A,A2A_mix,x_post_verifier,GPT-4o-mini,72,10.582777777777778,14.584,23.239500000000028,33.029355122007274 -SocialMediaManager-H_A2A,A2A_mix,x_post_verifier,GPT-5,66,12.182424242424243,15.030000000000001,18.38949999999999,16.784026105046248 -SocialMediaManager-H_A2A,A2A_mix,x_post_verifier,Gemini-2.5-flash,94,14.911382978723402,20.275000000000002,30.464199999999714,48.515771502171226 -SocialMediaManager-H_A2A,A2A_mix,x_post_verifier,Gemini-2.5-flash-nothinking,80,6.468625,9.342,14.8515,34.906113762033236 -SocialMediaManager-H_A2A,A2A_mix,x_post_verifier,Qwen3-235b,106,29.625849056603776,51.269999999999996,80.88350000000001,47.11875140983777 +SQL_assistant,A2A,Business Intelligence Analyst,DeepSeek-R1,8,152.99375,336.65999999999997,388.32,89.34330597009786 +SQL_assistant,A2A,Business Intelligence Analyst,GPT-4o-mini,60,16.526333333333334,24.145,31.291,33.72392231828864 +SQL_assistant,A2A,Business Intelligence Analyst,GPT-5,54,6.70074074074074,10.292,16.750899999999998,46.81680634843712 +SQL_assistant,A2A,Business Intelligence Analyst,Gemini-2.5-flash,22,27.928181818181823,44.34400000000002,52.25209999999999,36.06389350311745 +SQL_assistant,A2A,Business Intelligence Analyst,Gemini-2.5-flash-nothinking,40,19.540499999999998,39.099,61.37909999999999,69.70866774846289 +SQL_assistant,A2A,Business Intelligence Analyst,Qwen3-235b,54,29.829629629629633,49.59400000000001,79.39739999999996,53.712578664155785 +SQL_assistant,A2A,Data Security and Compliance Auditor,DeepSeek-R1,177,43.75768361581921,78.52600000000002,223.69560000000013,94.88455226839085 +SQL_assistant,A2A,Data Security and Compliance Auditor,DeepSeek-V3-1,195,6.146358974358975,9.602,18.7722,47.72378387355977 +SQL_assistant,A2A,Data Security and Compliance Auditor,GPT-4o-mini,71,4.104788732394367,4.75,18.068999999999992,69.47840217444188 +SQL_assistant,A2A,Data Security and Compliance Auditor,GPT-5,93,2.551935483870968,3.13,4.055199999999991,32.28225391114768 +SQL_assistant,A2A,Data Security and Compliance Auditor,Gemini-2.5-flash,161,19.553167701863355,23.79,139.002,134.1958241395214 +SQL_assistant,A2A,Data Security and Compliance Auditor,Gemini-2.5-flash-nothinking,127,4.267874015748032,7.844000000000001,18.882399999999997,84.93357270363518 +SQL_assistant,A2A,Data Security and Compliance Auditor,Qwen3-235b,90,7.941111111111112,15.523000000000001,25.7322,69.0329136718855 +SQL_assistant,A2A,Expert SQL Query Generator,DeepSeek-R1,181,138.38082872928177,255.86,562.9539999999965,123.99785820145858 +SQL_assistant,A2A,Expert SQL Query Generator,DeepSeek-V3-1,209,23.113397129186602,36.374000000000024,65.57799999999999,56.49724577840352 +SQL_assistant,A2A,Expert SQL Query Generator,GPT-4o-mini,71,4.1874647887323935,6.13,11.806999999999988,50.25149138618599 +SQL_assistant,A2A,Expert SQL Query Generator,GPT-5,93,3.20989247311828,4.2,5.70799999999999,33.27520068300834 +SQL_assistant,A2A,Expert SQL Query Generator,Gemini-2.5-flash,161,18.361055900621118,28.34,186.09600000000015,179.05070287192157 +SQL_assistant,A2A,Expert SQL Query Generator,Gemini-2.5-flash-nothinking,127,13.611653543307087,17.37600000000001,109.15899999999999,208.90974435869109 +SQL_assistant,A2A,Expert SQL Query Generator,Qwen3-235b,90,13.79733333333333,22.614,45.1722,57.84503768311274 +SQL_assistant,A2A,Senior SQL Code Reviewer,DeepSeek-R1,180,186.51716666666664,372.5329999999999,742.1253,90.50106301986447 +SQL_assistant,A2A,Senior SQL Code Reviewer,DeepSeek-V3-1,198,17.378434343434343,25.921,41.73810000000001,48.191310363114184 +SQL_assistant,A2A,Senior SQL Code Reviewer,GPT-4o-mini,71,4.902676056338028,9.16,20.627999999999965,80.94114404491415 +SQL_assistant,A2A,Senior SQL Code Reviewer,GPT-5,93,2.340215053763441,3.632,4.222799999999999,40.40667420442891 +SQL_assistant,A2A,Senior SQL Code Reviewer,Gemini-2.5-flash,161,13.470745341614906,20.07,46.520000000000024,201.8952006323121 +SQL_assistant,A2A,Senior SQL Code Reviewer,Gemini-2.5-flash-nothinking,127,6.916771653543308,10.316000000000003,55.012599999999836,152.37685054996223 +SQL_assistant,A2A,Senior SQL Code Reviewer,Qwen3-235b,90,13.920555555555554,26.415,41.1359,63.571333034232325 +SQL_assistant,A2A_mix,Expert SQL Query Generator,DeepSeek-R1,138,156.72949275362316,272.573,883.6276999999991,136.84474550799604 +SQL_assistant,A2A_mix,Expert SQL Query Generator,DeepSeek-V3-1,83,16.254698795180722,26.971999999999998,49.40759999999996,54.641630296898455 +SQL_assistant,A2A_mix,Expert SQL Query Generator,GPT-4o-mini,86,3.7256976744186043,4.255000000000001,8.51600000000004,37.29856609611088 +SQL_assistant,A2A_mix,Expert SQL Query Generator,GPT-5,77,3.731818181818182,4.924000000000001,6.427599999999996,22.61498667290226 +SQL_assistant,A2A_mix,Expert SQL Query Generator,Gemini-2.5-flash,101,22.066435643564358,35.45,201.47,175.70637615529694 +SQL_assistant,A2A_mix,Expert SQL Query Generator,Gemini-2.5-flash-nothinking,95,12.752421052631579,17.28000000000002,102.68160000000033,203.00273461078967 +SQL_assistant,A2A_mix,Expert SQL Query Generator,Qwen3-235b,88,13.965227272727274,24.325,41.55949999999994,53.9543392251959 +SQL_assistant,A2A_mix,Senior SQL Code Reviewer,DeepSeek-R1,138,217.28717391304346,489.372,1046.6777999999988,97.10159311413351 +SQL_assistant,A2A_mix,Senior SQL Code Reviewer,DeepSeek-V3-1,80,11.841874999999998,15.215,23.976999999999958,29.822930441359507 +SQL_assistant,A2A_mix,Senior SQL Code Reviewer,GPT-4o-mini,86,4.066395348837209,6.415,8.953500000000009,43.67257068226419 +SQL_assistant,A2A_mix,Senior SQL Code Reviewer,GPT-5,77,2.992857142857143,4.282,8.703599999999987,53.90157378605546 +SQL_assistant,A2A_mix,Senior SQL Code Reviewer,Gemini-2.5-flash,101,12.662772277227726,22.46,49.12,100.1130150240139 +SQL_assistant,A2A_mix,Senior SQL Code Reviewer,Gemini-2.5-flash-nothinking,95,8.82715789473684,10.354000000000008,54.75200000000044,277.84148273755255 +SQL_assistant,A2A_mix,Senior SQL Code Reviewer,Qwen3-235b,88,15.048977272727273,24.491999999999994,37.582299999999975,48.394477694811854 +SQL_assistant,A2A_mix,agent,DeepSeek-R1,267,1.9562921348314608,3.8,9.028999999999991,77.47328796645122 +SQL_assistant,A2A_mix,agent,DeepSeek-V3-1,176,4.1155681818181815,11.12,21.005000000000003,104.2385748366506 +SQL_assistant,A2A_mix,agent,GPT-4o-mini,169,2.589526627218935,5.934000000000003,11.59999999999999,85.86979649620842 +SQL_assistant,A2A_mix,agent,GPT-5,140,3.616285714285714,7.864000000000001,17.778399999999962,98.49383195927057 +SQL_assistant,A2A_mix,agent,Gemini-2.5-flash,201,2.847960199004975,6.57,19.81,115.06227252347718 +SQL_assistant,A2A_mix,agent,Gemini-2.5-flash-nothinking,268,2.2957462686567163,4.552000000000003,8.149799999999967,78.45260111898702 +SQL_assistant,A2A_mix,agent,Qwen3-235b,206,2.8290291262135923,7.54,13.1495,104.40664894036873 +SQL_assistant,A2A_mix,result_interpreter,DeepSeek-R1,42,26.515000000000004,32.041000000000004,85.49629999999988,55.57209289549361 +SQL_assistant,A2A_mix,result_interpreter,DeepSeek-V3-1,57,10.06017543859649,12.552,15.060399999999998,24.043107867952493 +SQL_assistant,A2A_mix,result_interpreter,GPT-4o-mini,55,2.909818181818182,3.696,4.0376,20.041669410152103 +SQL_assistant,A2A_mix,result_interpreter,GPT-5,60,4.408333333333334,6.096,7.906899999999997,29.047423907724212 +SQL_assistant,A2A_mix,result_interpreter,Gemini-2.5-flash,52,7.910961538461539,10.78,15.048600000000002,30.980375623731106 +SQL_assistant,A2A_mix,result_interpreter,Gemini-2.5-flash-nothinking,51,2.757058823529412,3.63,4.470000000000001,27.015710396301053 +SQL_assistant,A2A_mix,result_interpreter,Qwen3-235b,56,10.866964285714285,26.619999999999997,32.5435,71.0589957681135 +SQL_assistant,MCP,Business Intelligence Analyst,DeepSeek-R1,29,76.85862068965517,160.192,170.7064,64.4249461450751 +SQL_assistant,MCP,Business Intelligence Analyst,DeepSeek-V3-1,51,34.86941176470588,53.86,56.715,33.446822382524935 +SQL_assistant,MCP,Business Intelligence Analyst,GPT-4o-mini,34,15.604411764705882,25.866,35.99900000000002,49.96253789905594 +SQL_assistant,MCP,Business Intelligence Analyst,GPT-5,35,5.514285714285714,6.8260000000000005,7.688799999999998,18.832711686400575 +SQL_assistant,MCP,Business Intelligence Analyst,Gemini-2.5-flash,55,10.48745454545454,16.424,24.6992,42.600887529463165 +SQL_assistant,MCP,Business Intelligence Analyst,Gemini-2.5-flash-nothinking,59,6.734406779661017,9.152000000000001,40.97880000000011,145.85974373889064 +SQL_assistant,MCP,Business Intelligence Analyst,Qwen3-235b,56,20.928392857142857,41.075,55.040500000000016,63.87784626455202 +SQL_assistant,MCP,Data Security and Compliance Auditor,DeepSeek-R1,142,52.294225352112676,108.50000000000001,178.95750000000004,74.45623194194523 +SQL_assistant,MCP,Data Security and Compliance Auditor,DeepSeek-V3-1,110,16.03709090909091,25.44000000000001,29.3224,41.121923592095456 +SQL_assistant,MCP,Data Security and Compliance Auditor,GPT-4o-mini,130,8.321538461538463,13.664000000000003,23.730999999999995,51.58528340634948 +SQL_assistant,MCP,Data Security and Compliance Auditor,GPT-5,129,4.794651162790697,7.726,13.055999999999997,55.038997732453424 +SQL_assistant,MCP,Data Security and Compliance Auditor,Gemini-2.5-flash,86,16.85116279069768,28.575,56.05050000000008,58.82417931439906 +SQL_assistant,MCP,Data Security and Compliance Auditor,Gemini-2.5-flash-nothinking,84,13.892380952380952,14.717000000000002,143.57400000000027,228.68829271520985 +SQL_assistant,MCP,Data Security and Compliance Auditor,Qwen3-235b,93,17.71279569892473,32.867999999999995,47.99399999999999,58.6024585657597 +SQL_assistant,MCP,Expert SQL Query Generator,DeepSeek-R1,155,102.98774193548387,188.518,514.8036000000005,98.4930454120539 +SQL_assistant,MCP,Expert SQL Query Generator,DeepSeek-V3-1,115,16.048695652173915,24.90600000000001,35.825399999999995,39.03555612535344 +SQL_assistant,MCP,Expert SQL Query Generator,GPT-4o-mini,130,4.2050769230769225,6.192000000000007,11.819899999999995,42.416424388977624 +SQL_assistant,MCP,Expert SQL Query Generator,GPT-5,129,3.35906976744186,4.654,7.2383999999999995,32.43504202615352 +SQL_assistant,MCP,Expert SQL Query Generator,Gemini-2.5-flash,87,25.549310344827582,42.12600000000002,185.7066000000001,157.79209034869254 +SQL_assistant,MCP,Expert SQL Query Generator,Gemini-2.5-flash-nothinking,84,8.698214285714286,14.727000000000002,31.890800000000038,76.23671680002671 +SQL_assistant,MCP,Expert SQL Query Generator,Qwen3-235b,93,13.93010752688172,20.016,48.65999999999981,109.40215206134505 +SQL_assistant,MCP,Senior SQL Code Reviewer,DeepSeek-R1,155,199.28335483870967,392.1739999999999,672.0562000000003,94.01790497971322 +SQL_assistant,MCP,Senior SQL Code Reviewer,DeepSeek-V3-1,111,12.311171171171171,18.12,21.09500000000001,32.35602786749792 +SQL_assistant,MCP,Senior SQL Code Reviewer,GPT-4o-mini,130,6.124153846153847,10.410000000000002,25.408899999999967,78.4190645371273 +SQL_assistant,MCP,Senior SQL Code Reviewer,GPT-5,127,2.49763779527559,3.8220000000000005,6.669399999999999,49.405323237755574 +SQL_assistant,MCP,Senior SQL Code Reviewer,Gemini-2.5-flash,87,23.517126436781613,17.35400000000002,176.66080000000053,457.88875544005793 +SQL_assistant,MCP,Senior SQL Code Reviewer,Gemini-2.5-flash-nothinking,84,6.199404761904764,9.440999999999999,27.580400000000015,80.35254317347493 +SQL_assistant,MCP,Senior SQL Code Reviewer,Qwen3-235b,93,13.205591397849462,23.345999999999997,35.285599999999995,55.639212566793525 +intelligent_recruitment_platform,A2A,Senior Candidate Evaluator,DeepSeek-R1,66,482.2822727272728,894.74,1589.9264999999996,72.37375570440209 +intelligent_recruitment_platform,A2A,Senior Candidate Evaluator,DeepSeek-V3-1,60,165.93366666666665,220.633,257.38959999999986,22.714070473384883 +intelligent_recruitment_platform,A2A,Senior Candidate Evaluator,GPT-4o-mini,60,113.64116666666665,196.942,315.11449999999934,60.5074188227274 +intelligent_recruitment_platform,A2A,Senior Candidate Evaluator,GPT-5,60,27.725,35.755,38.9192,23.717881374765692 +intelligent_recruitment_platform,A2A,Senior Candidate Evaluator,Gemini-2.5-flash,60,166.0688333333333,279.96100000000007,559.7442999999998,74.46640561446888 +intelligent_recruitment_platform,A2A,Senior Candidate Evaluator,Gemini-2.5-flash-nothinking,60,85.31750000000001,121.487,273.58459999999945,58.295850755747125 +intelligent_recruitment_platform,A2A,Senior Candidate Evaluator,Qwen3-235b,84,928.6871428571427,1564.0890000000002,2122.626200000001,53.18297604787774 +intelligent_recruitment_platform,A2A,Senior Interview Materials Coordinator,DeepSeek-R1,59,206.74694915254236,323.5640000000001,724.7176000000006,65.52443608606401 +intelligent_recruitment_platform,A2A,Senior Interview Materials Coordinator,DeepSeek-V3-1,60,84.92750000000002,94.15100000000001,101.81889999999999,9.590720114479625 +intelligent_recruitment_platform,A2A,Senior Interview Materials Coordinator,GPT-4o-mini,60,29.495000000000005,39.144,55.74299999999993,28.779410132458207 +intelligent_recruitment_platform,A2A,Senior Interview Materials Coordinator,GPT-5,60,16.673,19.773,24.038099999999986,14.889921290193477 +intelligent_recruitment_platform,A2A,Senior Interview Materials Coordinator,Gemini-2.5-flash,60,102.49050000000001,162.637,279.9419999999998,51.43570470741998 +intelligent_recruitment_platform,A2A,Senior Interview Materials Coordinator,Gemini-2.5-flash-nothinking,60,59.13583333333333,85.762,154.1239999999995,50.313584442665025 +intelligent_recruitment_platform,A2A,Senior Interview Materials Coordinator,Qwen3-235b,59,80.3657627118644,118.15000000000003,188.8300000000001,40.7166650084163 +intelligent_recruitment_platform,A2A,Senior Job Requirements Analyst,DeepSeek-R1,66,168.61424242424243,245.7,1101.2504999999976,117.89154800997554 +intelligent_recruitment_platform,A2A,Senior Job Requirements Analyst,DeepSeek-V3-1,60,59.03966666666667,70.324,133.71999999999957,37.0852088913006 +intelligent_recruitment_platform,A2A,Senior Job Requirements Analyst,GPT-4o-mini,60,32.686166666666665,52.70300000000002,114.64809999999993,69.61685473825099 +intelligent_recruitment_platform,A2A,Senior Job Requirements Analyst,GPT-5,60,20.7715,24.212,29.753899999999998,14.589490602632466 +intelligent_recruitment_platform,A2A,Senior Job Requirements Analyst,Gemini-2.5-flash,60,86.3855,135.882,292.50779999999963,63.096017988364295 +intelligent_recruitment_platform,A2A,Senior Job Requirements Analyst,Gemini-2.5-flash-nothinking,60,48.35666666666666,68.52600000000001,194.96289999999914,90.17903477689882 +intelligent_recruitment_platform,A2A,Senior Job Requirements Analyst,Qwen3-235b,84,68.37702380952383,128.38000000000002,182.58240000000004,60.59765765765134 +intelligent_recruitment_platform,A2A_mix,Senior Candidate Evaluator,DeepSeek-R1,64,470.15515625,761.1330000000002,1555.8469999999988,63.348074757582765 +intelligent_recruitment_platform,A2A_mix,Senior Candidate Evaluator,DeepSeek-V3-1,60,160.84433333333337,207.644,224.99749999999997,21.50462241846168 +intelligent_recruitment_platform,A2A_mix,Senior Candidate Evaluator,GPT-4o-mini,60,99.26650000000001,171.419,312.96289999999976,64.81157877918369 +intelligent_recruitment_platform,A2A_mix,Senior Candidate Evaluator,GPT-5,60,26.981833333333334,39.864,46.77079999999998,31.407689487717256 +intelligent_recruitment_platform,A2A_mix,Senior Candidate Evaluator,Gemini-2.5-flash,60,70.23633333333333,117.13300000000001,157.13699999999994,45.88249310100194 +intelligent_recruitment_platform,A2A_mix,Senior Candidate Evaluator,Gemini-2.5-flash-nothinking,60,50.517833333333336,70.51299999999999,91.52019999999996,31.935452430027354 +intelligent_recruitment_platform,A2A_mix,Senior Candidate Evaluator,Qwen3-235b,72,664.6062500000002,1072.156,1621.509700000001,56.75729644490956 +intelligent_recruitment_platform,A2A_mix,agent,DeepSeek-R1,128,50.4896875,82.72099999999999,114.92070000000001,47.52808132546662 +intelligent_recruitment_platform,A2A_mix,agent,DeepSeek-V3-1,120,9.985999999999999,11.411000000000003,39.632900000000014,55.01406601658895 +intelligent_recruitment_platform,A2A_mix,agent,GPT-4o-mini,120,4.082,7.008000000000001,9.653200000000002,50.45287086416841 +intelligent_recruitment_platform,A2A_mix,agent,GPT-5,120,3.0318333333333336,6.181,10.4046,67.70014907724561 +intelligent_recruitment_platform,A2A_mix,agent,Gemini-2.5-flash,123,5.984715447154471,9.158,13.451200000000002,51.78364614160436 +intelligent_recruitment_platform,A2A_mix,agent,Gemini-2.5-flash-nothinking,120,4.0925,8.994000000000003,16.907400000000003,92.29489925611661 +intelligent_recruitment_platform,A2A_mix,agent,Qwen3-235b,188,4.718617021276596,9.438000000000002,13.950499999999982,79.63587507472865 +intelligent_recruitment_platform,A2A_mix,interview_coordinator,DeepSeek-R1,60,144.31050000000002,204.10399999999998,242.03829999999996,29.084905938728717 +intelligent_recruitment_platform,A2A_mix,interview_coordinator,DeepSeek-V3-1,60,61.934666666666665,79.12899999999999,88.8034,21.614527807242247 +intelligent_recruitment_platform,A2A_mix,interview_coordinator,GPT-4o-mini,60,31.336666666666666,41.57,54.0492,25.484921054604236 +intelligent_recruitment_platform,A2A_mix,interview_coordinator,GPT-5,60,18.41433333333333,23.607,27.472599999999993,23.763992579662307 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Evaluator,GPT-5,60,23.312333333333335,31.564,37.3966,28.551021956942645 +intelligent_recruitment_platform,MCP,Senior Candidate Evaluator,Gemini-2.5-flash,60,91.94200000000001,152.084,212.99929999999975,51.15058707526449 +intelligent_recruitment_platform,MCP,Senior Candidate Evaluator,Gemini-2.5-flash-nothinking,60,68.89833333333333,107.016,137.40139999999997,42.565075806564074 +intelligent_recruitment_platform,MCP,Senior Candidate Evaluator,Qwen3-235b,84,862.7088095238095,1419.4959999999999,1649.2959000000008,50.069411751658635 +intelligent_recruitment_platform,MCP,Senior Interview Materials Coordinator,DeepSeek-R1,61,178.0060655737705,278.01,401.2199999999999,43.32874246428282 +intelligent_recruitment_platform,MCP,Senior Interview Materials Coordinator,DeepSeek-V3-1,60,95.02600000000002,111.278,197.0470999999995,30.895864644176086 +intelligent_recruitment_platform,MCP,Senior Interview Materials Coordinator,GPT-4o-mini,60,16.37483333333333,20.852999999999998,24.680399999999995,22.574374628194807 +intelligent_recruitment_platform,MCP,Senior Interview Materials Coordinator,GPT-5,60,16.001166666666666,18.822,24.198199999999975,16.21761121300057 +intelligent_recruitment_platform,MCP,Senior Interview Materials Coordinator,Gemini-2.5-flash,60,63.66716666666666,103.61800000000001,181.01939999999973,51.19663644262158 +intelligent_recruitment_platform,MCP,Senior Interview Materials Coordinator,Gemini-2.5-flash-nothinking,60,56.25466666666666,81.843,192.45489999999927,69.77989047360981 +intelligent_recruitment_platform,MCP,Senior Interview Materials Coordinator,Qwen3-235b,58,82.67603448275861,124.04700000000001,220.94309999999996,43.73322979014188 +intelligent_recruitment_platform,MCP,Senior Job Requirements Analyst,DeepSeek-R1,62,138.4190322580645,230.742,281.51779999999997,43.393496872752905 +intelligent_recruitment_platform,MCP,Senior Job Requirements Analyst,DeepSeek-V3-1,60,62.85633333333333,81.169,93.4834,28.553678427507567 +intelligent_recruitment_platform,MCP,Senior Job Requirements Analyst,GPT-4o-mini,60,21.720666666666666,31.005000000000003,57.27609999999995,46.28720660653235 +intelligent_recruitment_platform,MCP,Senior Job Requirements Analyst,GPT-5,60,20.220499999999998,24.590999999999998,26.667799999999996,14.896803583382734 +intelligent_recruitment_platform,MCP,Senior Job Requirements Analyst,Gemini-2.5-flash,60,48.28083333333334,58.21,186.9028999999992,80.75562845740082 +intelligent_recruitment_platform,MCP,Senior Job Requirements Analyst,Gemini-2.5-flash-nothinking,60,38.291999999999994,62.74900000000001,101.7066999999999,59.94364219715042 +intelligent_recruitment_platform,MCP,Senior Job Requirements Analyst,Qwen3-235b,84,67.24738095238096,117.236,178.38000000000005,54.78247055147199 +landing_page_generator,A2A,Landing Page Content Generator,DeepSeek-R1,62,177.37274193548387,371.47999999999996,642.6985000000002,81.64608828922493 +landing_page_generator,A2A,Landing Page Content Generator,DeepSeek-V3-1,60,188.02116666666663,190.758,823.0604999999999,82.45458086813 +landing_page_generator,A2A,Landing Page Content Generator,GPT-4o-mini,60,27.733833333333333,40.005,47.47079999999997,29.302393412397443 +landing_page_generator,A2A,Landing Page Content Generator,GPT-5,60,14.573500000000001,27.134000000000004,40.18609999999997,65.35962264430147 +landing_page_generator,A2A,Landing Page Content Generator,Gemini-2.5-flash,60,49.102,76.885,120.33199999999997,42.70809428560347 +landing_page_generator,A2A,Landing Page Content Generator,Gemini-2.5-flash-nothinking,61,67.31,70.7,724.2699999999984,261.63316734315714 +landing_page_generator,A2A,Landing Page Content Generator,Qwen3-235b,63,171.33523809523805,446.04400000000004,1000.6026,132.61603886235724 +landing_page_generator,A2A,Product Idea Analyst,DeepSeek-R1,62,70.74854838709679,98.863,252.1266000000001,65.34019693648871 +landing_page_generator,A2A,Product Idea Analyst,DeepSeek-V3-1,60,17.035833333333333,24.791,34.11199999999997,31.203661936496996 +landing_page_generator,A2A,Product Idea Analyst,GPT-4o-mini,60,13.109833333333333,23.715,30.83099999999997,57.932328862845964 +landing_page_generator,A2A,Product Idea Analyst,GPT-5,60,4.5136666666666665,6.3629999999999995,7.441899999999995,22.901083732666137 +landing_page_generator,A2A,Product Idea Analyst,Gemini-2.5-flash,60,21.058166666666672,28.130000000000003,32.779699999999984,26.52766641016793 +landing_page_generator,A2A,Product Idea Analyst,Gemini-2.5-flash-nothinking,64,20.181093750000002,26.391000000000002,40.321499999999965,36.27971934100415 +landing_page_generator,A2A,Product Idea Analyst,Qwen3-235b,63,19.787619047619046,33.370000000000005,54.53780000000003,55.78858333085751 +landing_page_generator,A2A,Template Selection Specialist,DeepSeek-R1,62,42.770322580645164,86.734,147.14340000000007,80.08749083847539 +landing_page_generator,A2A,Template Selection Specialist,DeepSeek-V3-1,60,8.904333333333334,10.516,17.991399999999985,25.17655278269393 +landing_page_generator,A2A,Template Selection Specialist,GPT-4o-mini,60,2.353666666666667,2.875,5.4405,28.74798134305061 +landing_page_generator,A2A,Template Selection Specialist,GPT-5,60,2.385833333333333,2.652,3.3617999999999966,11.978846061110126 +landing_page_generator,A2A,Template Selection Specialist,Gemini-2.5-flash,60,4.715833333333334,5.526000000000001,12.38999999999999,40.43736992880102 +landing_page_generator,A2A,Template Selection Specialist,Gemini-2.5-flash-nothinking,61,5.827049180327869,10.8,20.92999999999999,66.08038401820447 +landing_page_generator,A2A,Template Selection Specialist,Qwen3-235b,63,6.3331746031746015,10.870000000000001,14.351800000000008,45.358025794297134 +landing_page_generator,A2A_mix,Landing Page Content Generator,DeepSeek-R1,60,170.94116666666665,370.769,647.9275999999994,90.33359861533803 +landing_page_generator,A2A_mix,Landing Page Content Generator,DeepSeek-V3-1,60,206.86499999999995,233.62700000000012,755.2991999999994,74.07681612790536 +landing_page_generator,A2A_mix,Landing Page Content Generator,GPT-4o-mini,60,28.240666666666662,37.712,44.987199999999994,23.510473478736568 +landing_page_generator,A2A_mix,Landing Page Content Generator,GPT-5,60,20.350833333333334,36.772,45.38799999999995,57.64300175130867 +landing_page_generator,A2A_mix,Landing Page Content Generator,Gemini-2.5-flash,60,58.37166666666667,71.24400000000001,467.82869999999843,166.86938459593568 +landing_page_generator,A2A_mix,Landing Page Content Generator,Gemini-2.5-flash-nothinking,60,37.109333333333325,60.09,80.62979999999993,40.750913411439925 +landing_page_generator,A2A_mix,Landing Page Content Generator,Qwen3-235b,63,175.21444444444444,389.36600000000067,1145.0794000000005,145.46863621000844 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Selection Specialist,DeepSeek-V3-1,60,9.467333333333332,11.433,14.618699999999997,16.72926462554838 +landing_page_generator,MCP,Template Selection Specialist,GPT-4o-mini,60,2.252333333333333,2.843,4.1043999999999965,23.144457778061525 +landing_page_generator,MCP,Template Selection Specialist,GPT-5,60,2.320666666666667,2.491,3.2786999999999997,10.791262684543113 +landing_page_generator,MCP,Template Selection Specialist,Gemini-2.5-flash,60,4.043500000000001,4.847,8.446099999999982,30.394246395589747 +landing_page_generator,MCP,Template Selection Specialist,Gemini-2.5-flash-nothinking,61,4.205901639344263,6.02,11.143999999999998,41.92762116616369 +landing_page_generator,MCP,Template Selection Specialist,Qwen3-235b,60,5.2005,6.630000000000001,9.901599999999993,30.249357606000054 +self_evaluation_loop_flow,A2A,Shakespearean Bard,DeepSeek-R1,158,69.00601265822785,145.97700000000006,267.71580000000074,84.99901452322209 +self_evaluation_loop_flow,A2A,Shakespearean Bard,DeepSeek-V3-1,77,66.50649350649351,123.042,138.59759999999997,62.99570487660172 +self_evaluation_loop_flow,A2A,Shakespearean Bard,GPT-4o-mini,136,19.23220588235294,31.580000000000002,47.514500000000005,63.13860454420024 +self_evaluation_loop_flow,A2A,Shakespearean Bard,GPT-5,112,4.393035714285714,5.537000000000001,14.7361,51.4658281883755 +self_evaluation_loop_flow,A2A,Shakespearean Bard,Gemini-2.5-flash,64,17.41265625,22.860000000000003,31.279799999999998,26.79830853879214 +self_evaluation_loop_flow,A2A,Shakespearean Bard,Gemini-2.5-flash-nothinking,67,9.615820895522386,12.24,18.524600000000014,28.996956707906808 +self_evaluation_loop_flow,A2A,Shakespearean Bard,Qwen3-235b,109,25.940275229357802,57.87800000000002,160.8496,140.83983458608108 +self_evaluation_loop_flow,A2A,Topic Analysis Expert,DeepSeek-R1,75,45.812666666666665,73.84,128.2814000000002,50.561751829138444 +self_evaluation_loop_flow,A2A,Topic Analysis 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Research Agent for Book Outline Planning,GPT-4o-mini,60,28.726000000000003,40.04,54.77439999999998,29.721794614304937 +write_a_book_with_flows,MCP,Expert Research Agent for Book Outline Planning,GPT-5,60,18.533,23.753,29.882799999999992,21.92278706254531 +write_a_book_with_flows,MCP,Expert Research Agent for Book Outline Planning,Gemini-2.5-flash,60,53.25249999999999,77.863,89.82419999999999,33.66986456921156 +write_a_book_with_flows,MCP,Expert Research Agent for Book Outline Planning,Gemini-2.5-flash-nothinking,61,31.654426229508196,49.88,74.73399999999998,52.009187360036194 +write_a_book_with_flows,MCP,Expert Research Agent for Book Outline Planning,Qwen3-235b,60,57.120666666666665,103.759,129.19359999999998,50.9036934482483 +write_a_book_with_flows,MCP,Professional Chapter Content Writer,DeepSeek-R1,229,253.36410480349343,540.5699999999999,1314.9699999999993,97.92895080133856 +write_a_book_with_flows,MCP,Professional Chapter Content Writer,DeepSeek-V3-1,230,175.96439130434788,198.694,417.0484,30.306687116724863 +write_a_book_with_flows,MCP,Professional Chapter Content Writer,GPT-4o-mini,221,13.561402714932125,22.26,35.57,47.723305426838 +write_a_book_with_flows,MCP,Professional Chapter Content Writer,GPT-5,293,28.279761092150174,44.484,55.42639999999999,44.00016861229022 +write_a_book_with_flows,MCP,Professional Chapter Content Writer,Gemini-2.5-flash,188,45.70015957446808,58.09400000000001,72.3943,24.321185946750496 +write_a_book_with_flows,MCP,Professional Chapter Content Writer,Gemini-2.5-flash-nothinking,187,26.51566844919786,41.150000000000006,49.64579999999999,40.27472010997589 +write_a_book_with_flows,MCP,Professional Chapter Content Writer,Qwen3-235b,225,70.80248888888889,138.73599999999996,491.1003999999996,146.9250777835174 +write_a_book_with_flows,MCP,Senior Book Editor and Quality Assurance Specialist,DeepSeek-R1,64,91.2065625,157.978,254.6833,55.748249565034904 +write_a_book_with_flows,MCP,Senior Book Editor and Quality Assurance Specialist,DeepSeek-V3-1,60,72.82683333333334,79.455,170.05709999999993,28.1832790853699 +write_a_book_with_flows,MCP,Senior Book Editor and Quality Assurance Specialist,GPT-4o-mini,60,21.762833333333333,32.457,36.5606,29.269366438400766 +write_a_book_with_flows,MCP,Senior Book Editor and Quality Assurance Specialist,GPT-5,60,20.128833333333333,24.346,26.222599999999996,13.064574635157214 +write_a_book_with_flows,MCP,Senior Book Editor and Quality Assurance Specialist,Gemini-2.5-flash,60,25.394333333333336,35.225,43.25549999999999,24.6952682022162 +write_a_book_with_flows,MCP,Senior Book Editor and Quality Assurance Specialist,Gemini-2.5-flash-nothinking,61,17.13590163934426,20.8,29.971999999999998,21.512176801350826 +write_a_book_with_flows,MCP,Senior Book Editor and Quality Assurance Specialist,Qwen3-235b,60,149.73000000000002,193.65900000000002,275.77429999999947,34.150530872593386 +write_a_book_with_flows,MCP,Specialized Chapter Research Agent,DeepSeek-R1,233,120.5107296137339,194.20000000000005,326.93240000000003,46.379951353555214 +write_a_book_with_flows,MCP,Specialized Chapter Research Agent,DeepSeek-V3-1,230,45.038304347826084,54.98,161.43470000000056,42.931548671864405 +write_a_book_with_flows,MCP,Specialized Chapter Research Agent,GPT-4o-mini,221,23.614570135746604,37.12,46.63,47.68634360462982 +write_a_book_with_flows,MCP,Specialized Chapter Research Agent,GPT-5,293,15.804812286689417,20.378,23.807199999999995,20.31499338804929 +write_a_book_with_flows,MCP,Specialized Chapter Research Agent,Gemini-2.5-flash,188,34.847925531914896,48.00900000000001,71.54829999999995,34.17547010233117 +write_a_book_with_flows,MCP,Specialized Chapter Research Agent,Gemini-2.5-flash-nothinking,187,21.674812834224596,31.656000000000002,45.33719999999997,106.90579029371439 +write_a_book_with_flows,MCP,Specialized Chapter Research Agent,Qwen3-235b,225,51.412088888888896,80.456,133.10279999999995,44.3742111186785 +write_a_book_with_flows,MCP,Strategic Book Structure Architect,DeepSeek-R1,64,47.70546875,74.403,115.4653,47.26333528905559 +write_a_book_with_flows,MCP,Strategic Book Structure Architect,DeepSeek-V3-1,60,14.213166666666668,16.573000000000004,21.513799999999982,17.41307522220299 +write_a_book_with_flows,MCP,Strategic Book Structure Architect,GPT-4o-mini,60,10.53516666666667,14.676,18.583799999999993,29.531019116974335 +write_a_book_with_flows,MCP,Strategic Book Structure Architect,GPT-5,60,6.713166666666667,8.7,10.919599999999997,23.96894785754281 +write_a_book_with_flows,MCP,Strategic Book Structure Architect,Gemini-2.5-flash,60,13.380833333333333,16.931,21.135999999999996,21.63768101892417 +write_a_book_with_flows,MCP,Strategic Book Structure Architect,Gemini-2.5-flash-nothinking,61,3.5603278688524593,4.02,7.711999999999993,30.738466556046586 +write_a_book_with_flows,MCP,Strategic Book Structure Architect,Qwen3-235b,60,16.964666666666666,32.873,47.9787,62.87649102703432 diff --git a/data/processed/RQ3/performance_reports/agent_token_statistics.csv b/data/processed/RQ3/performance_reports/agent_token_statistics.csv index 5b7a31255d22b2daa8d1d5c9b35be3687f7155b3..b48429a51964676932557ed0dc1f838d8eaa7148 100644 --- a/data/processed/RQ3/performance_reports/agent_token_statistics.csv +++ b/data/processed/RQ3/performance_reports/agent_token_statistics.csv @@ -1,108 +1,4 @@ task,architecture,agent,model,count,mean_input,cv_input,mean_output,cv_output,mean_reasoning,cv_reasoning,mean_result,cv_result,mean_total,cv_total -BookWriter,A2A,Expert Research Agent for Book Outline Planning,DeepSeek-R1,64,10361.828125,67.41484742410444,4074.546875,40.631701633413535,1382.5,47.02664582784801,2692.046875,51.46832836598371,14436.375,56.87006953441834 -BookWriter,A2A,Expert Research Agent for Book Outline Planning,DeepSeek-V3-1,61,25631.06557377049,33.27355144223436,2774.590163934426,23.572391877858305,0.0,0,2774.590163934426,23.572391877858305,28405.655737704918,31.89944392601143 -BookWriter,A2A,Expert Research Agent for Book Outline Planning,GPT-4o-mini,60,19805.533333333333,33.20189292289845,1952.2833333333333,20.597993332066984,0.0,0,1952.2833333333333,20.597993332066984,21757.816666666666,31.454504360058767 -BookWriter,A2A,Expert Research Agent for Book Outline Planning,GPT-5,60,4533.366666666667,27.912885200417875,1686.7333333333333,10.60507657111148,0.0,0,1686.7333333333333,10.60507657111148,6220.1,22.1231284354429 -BookWriter,A2A,Expert Research Agent for Book Outline Planning,Gemini-2.5-flash,60,5730.483333333334,86.02309097219712,8714.3,29.50697992698816,229.95,45.89958951040056,8484.35,29.90969349304268,14444.783333333333,48.97116979491752 -BookWriter,A2A,Expert Research Agent for Book Outline Planning,Gemini-2.5-flash-nothinking,60,2974.05,79.3090412190428,7487.816666666667,58.40888380657488,0.0,0,7487.816666666667,58.40888380657488,10461.866666666667,51.23980632020449 -BookWriter,A2A,Expert Research Agent for Book Outline Planning,Qwen3-235b,60,5315.383333333333,64.819917752919,1434.6333333333334,55.22283284745203,0.0,0,1434.6333333333334,55.22283284745203,6750.016666666666,58.740386653593546 -BookWriter,A2A,Professional Chapter Content Writer,DeepSeek-R1,220,13251.077272727272,170.87945133276364,6097.9,69.49089835907527,2310.190909090909,116.90409227404545,3787.7090909090907,76.54972329983892,19348.977272727272,131.31889733925595 -BookWriter,A2A,Professional Chapter Content Writer,DeepSeek-V3-1,232,19916.219827586207,17.05662106685926,4704.788793103448,16.125016345238805,0.0,0,4704.788793103448,16.125016345238805,24621.008620689656,16.468822539622117 -BookWriter,A2A,Professional Chapter Content Writer,GPT-4o-mini,212,3213.132075471698,11.604311922448723,1046.5094339622642,33.28575130189195,0.0,0,1046.5094339622642,33.28575130189195,4259.641509433963,16.111933666855474 -BookWriter,A2A,Professional Chapter Content Writer,GPT-5,290,12555.675862068965,87.06887285435508,3369.0172413793102,67.41281123586998,0.0,0,3369.0172413793102,67.41281123586998,15924.693103448275,82.70461505845509 -BookWriter,A2A,Professional Chapter Content Writer,Gemini-2.5-flash,193,5504.357512953368,50.089913895180096,7455.4715025906735,26.87358738857965,50.19170984455958,504.60698276759234,7405.279792746114,27.28072162940356,12959.82901554404,27.647809826832788 -BookWriter,A2A,Professional Chapter Content Writer,Gemini-2.5-flash-nothinking,184,5174.358695652174,64.88519649529152,4875.554347826087,57.46345896533697,0.0,0,4875.554347826087,57.46345896533697,10049.91304347826,50.10193153680501 -BookWriter,A2A,Professional Chapter Content Writer,Qwen3-235b,228,6421.350877192983,134.86817766326695,1619.5219298245613,90.0902942125588,0.0,0,1619.5219298245613,90.0902942125588,8040.872807017544,124.76157621832951 -BookWriter,A2A,Senior Book Editor and Quality Assurance Specialist,DeepSeek-R1,61,32441.344262295082,74.10836386373715,2517.245901639344,48.85449588605152,357.27868852459017,93.23302885720148,2159.967213114754,54.02550403397143,34958.59016393442,70.72060870430141 -BookWriter,A2A,Senior Book Editor and Quality Assurance Specialist,DeepSeek-V3-1,61,41474.72131147541,14.09491083122219,1362.672131147541,13.286892370515178,0.0,0,1362.672131147541,13.286892370515178,42837.39344262295,14.001781747912059 -BookWriter,A2A,Senior Book Editor and Quality Assurance Specialist,GPT-4o-mini,60,35133.166666666664,9.626733866429644,1159.0833333333333,5.351348731388179,0.0,0,1159.0833333333333,5.351348731388179,36292.25,9.404969927388478 -BookWriter,A2A,Senior Book Editor and Quality Assurance Specialist,GPT-5,60,53322.083333333336,7.493212686915587,1574.75,6.508877797168585,0.0,0,1574.75,6.508877797168585,54896.833333333336,7.319130240856827 -BookWriter,A2A,Senior Book Editor and Quality Assurance Specialist,Gemini-2.5-flash,60,10498.366666666667,57.06202359756707,3808.2166666666667,33.923714627743465,84.71666666666667,121.87810348538024,3723.5,34.75435505241889,14306.583333333334,49.34886718140945 -BookWriter,A2A,Senior Book Editor and Quality Assurance Specialist,Gemini-2.5-flash-nothinking,60,9365.0,37.159155723119305,2715.883333333333,23.610703563664593,0.0,0,2715.883333333333,23.610703563664593,12080.883333333333,29.84736764628768 -BookWriter,A2A,Senior Book Editor and Quality Assurance Specialist,Qwen3-235b,60,37660.76666666667,14.190516763367937,1530.1666666666667,8.2305296376385,0.0,0,1530.1666666666667,8.2305296376385,39190.933333333334,13.670221209107481 -BookWriter,A2A,Specialized Chapter Research Agent,DeepSeek-R1,224,12597.763392857143,73.35817538473954,3589.2276785714284,48.37796267291225,1641.4107142857142,61.239304704799444,1947.8169642857142,56.16262938093516,16186.99107142857,65.38175321830074 -BookWriter,A2A,Specialized Chapter Research Agent,DeepSeek-V3-1,232,24926.55172413793,37.70732211664461,1381.3706896551723,28.39652209378268,0.0,0,1381.3706896551723,28.39652209378268,26307.922413793105,36.96250503107319 -BookWriter,A2A,Specialized Chapter Research Agent,GPT-4o-mini,212,16700.622641509435,37.290749744699944,1190.188679245283,35.778480316918994,0.0,0,1190.188679245283,35.778480316918994,17890.811320754718,36.38847848140847 -BookWriter,A2A,Specialized Chapter Research Agent,GPT-5,290,6950.841379310345,23.204457337303637,1251.1172413793104,8.746699529886095,0.0,0,1251.1172413793104,8.746699529886095,8201.958620689655,20.136869563835546 -BookWriter,A2A,Specialized Chapter Research Agent,Gemini-2.5-flash,193,5151.782383419689,68.5943159599579,5461.119170984456,36.25927373330967,206.00518134715026,106.37406033951197,5255.113989637306,36.64600286783082,10612.901554404145,47.636940142953584 -BookWriter,A2A,Specialized Chapter Research Agent,Gemini-2.5-flash-nothinking,184,4795.494565217391,310.90615041399536,4733.271739130435,211.6385840316679,0.0,0,4733.271739130435,211.6385840316679,9528.766304347826,260.6393982095932 -BookWriter,A2A,Specialized Chapter Research Agent,Qwen3-235b,228,9180.986842105263,37.25133703027434,1079.0263157894738,24.464136133509008,0.0,0,1079.0263157894738,24.464136133509008,10260.013157894737,34.99561424522148 -BookWriter,A2A,Strategic Book Structure Architect,DeepSeek-R1,63,2448.8888888888887,24.200949595804886,1703.6507936507937,71.03128216379778,1344.888888888889,90.2434601302901,358.76190476190476,35.86258975687904,4152.539682539683,30.69951388751204 -BookWriter,A2A,Strategic Book Structure Architect,DeepSeek-V3-1,61,2531.2131147540986,8.08356126118702,340.39344262295083,20.485616431777252,0.0,0,340.39344262295083,20.485616431777252,2871.6065573770493,7.732603188966533 -BookWriter,A2A,Strategic Book Structure Architect,GPT-4o-mini,60,2668.766666666667,8.133633421035109,681.5666666666667,16.90451632390839,0.0,0,681.5666666666667,16.90451632390839,3350.3333333333335,8.97625121841687 -BookWriter,A2A,Strategic Book Structure Architect,GPT-5,60,2683.516666666667,22.03673317449624,609.3666666666667,29.66779681474016,0.0,0,609.3666666666667,29.66779681474016,3292.883333333333,18.918660209559455 -BookWriter,A2A,Strategic Book Structure Architect,Gemini-2.5-flash,60,3658.35,9.457898466400911,1891.1,24.39511396565831,1590.55,28.486385000020924,300.55,16.018984529560683,5549.45,10.684571806915105 -BookWriter,A2A,Strategic Book Structure Architect,Gemini-2.5-flash-nothinking,60,3342.9166666666665,16.61776481046472,321.7,33.083055197012705,0.0,0,321.7,33.083055197012705,3664.616666666667,15.131922403413123 -BookWriter,A2A,Strategic Book Structure Architect,Qwen3-235b,60,2567.9333333333334,22.717436257764277,353.3333333333333,22.569119967305273,0.0,0,353.3333333333333,22.569119967305273,2921.266666666667,21.12221837643799 -BookWriter,MCP,Expert Research Agent for Book Outline Planning,DeepSeek-R1,64,9690.5,70.6309859765369,4043.484375,39.49519809386777,1542.6875,48.26674337729842,2500.796875,56.5232594633389,13733.984375,59.46444934598388 -BookWriter,MCP,Expert Research Agent for Book Outline Planning,DeepSeek-V3-1,60,6036.9,40.95889863873784,1314.1333333333334,49.21024336961946,0.0,0,1314.1333333333334,49.21024336961946,7351.033333333334,41.39816107146624 -BookWriter,MCP,Expert Research Agent for Book Outline Planning,GPT-4o-mini,60,14101.266666666666,22.95184305671251,1880.4833333333333,22.897811268707535,0.0,0,1880.4833333333333,22.897811268707535,15981.75,22.661491614000475 -BookWriter,MCP,Expert Research Agent for Book Outline Planning,GPT-5,60,4443.75,25.383394664466213,1681.4,10.351549713740196,0.0,0,1681.4,10.351549713740196,6125.15,20.047187880496857 -BookWriter,MCP,Expert Research Agent for Book Outline Planning,Gemini-2.5-flash,60,5944.066666666667,74.7741125659151,8193.75,35.17640830104612,214.71666666666667,65.05115165511039,7979.033333333334,35.50664700581903,14137.816666666668,45.49712439383524 -BookWriter,MCP,Expert Research Agent for Book Outline Planning,Gemini-2.5-flash-nothinking,61,2843.688524590164,75.28619373545106,5881.213114754099,54.66352985374048,0.0,0,5881.213114754099,54.66352985374048,8724.901639344262,44.14936961898731 -BookWriter,MCP,Expert Research Agent for Book Outline Planning,Qwen3-235b,60,5422.5,65.11952979269911,1551.5333333333333,44.42240323058732,0.0,0,1551.5333333333333,44.42240323058732,6974.033333333334,56.694464390944646 -BookWriter,MCP,Professional Chapter Content Writer,DeepSeek-R1,229,12548.480349344978,184.83096081083187,6378.397379912663,77.00706380422628,2619.2489082969432,111.7847257486081,3759.1484716157206,87.32363767424455,18926.87772925764,142.04048656403967 -BookWriter,MCP,Professional Chapter Content Writer,DeepSeek-V3-1,230,19659.934782608696,22.530306178883063,4722.186956521739,16.230416989817112,0.0,0,4722.186956521739,16.230416989817112,24382.121739130434,20.737820172104524 -BookWriter,MCP,Professional Chapter Content Writer,GPT-4o-mini,221,3201.004524886878,13.361628402715361,1079.158371040724,34.090132234701045,0.0,0,1079.158371040724,34.090132234701045,4280.162895927602,17.76915570559501 -BookWriter,MCP,Professional Chapter Content Writer,GPT-5,293,13712.296928327645,76.03412960576338,3657.4709897610924,60.59801464864714,0.0,0,3657.4709897610924,60.59801464864714,17369.76791808874,72.65533929238057 -BookWriter,MCP,Professional Chapter Content Writer,Gemini-2.5-flash,188,5905.537234042553,59.71052626977882,7667.904255319149,25.97328230885238,21.154255319148938,737.1750086528276,7646.75,26.276605081484245,13573.441489361701,32.56556756109358 -BookWriter,MCP,Professional Chapter Content Writer,Gemini-2.5-flash-nothinking,187,5456.0695187165775,63.27989557371774,5312.117647058823,47.83084038812565,0.0,0,5312.117647058823,47.83084038812565,10768.187165775402,46.69514281144027 -BookWriter,MCP,Professional Chapter Content Writer,Qwen3-235b,225,6007.155555555555,140.3099156762884,1506.951111111111,103.13962039638731,0.0,0,1506.951111111111,103.13962039638731,7514.106666666667,131.97875673145052 -BookWriter,MCP,Senior Book Editor and Quality Assurance Specialist,DeepSeek-R1,64,37983.703125,89.38729485464695,2570.0625,54.51676922521056,315.21875,68.69598848507043,2254.84375,59.80620578450752,40553.765625,85.82510583840293 -BookWriter,MCP,Senior Book Editor and Quality Assurance Specialist,DeepSeek-V3-1,60,42369.46666666667,8.568909501861329,1376.0333333333333,5.525114591622007,0.0,0,1376.0333333333333,5.525114591622007,43745.5,8.375875183240945 -BookWriter,MCP,Senior Book Editor and Quality Assurance Specialist,GPT-4o-mini,60,36034.53333333333,10.335092642706027,1172.6833333333334,4.905800395733966,0.0,0,1172.6833333333334,4.905800395733966,37207.21666666667,10.06454563778402 -BookWriter,MCP,Senior Book Editor and Quality Assurance Specialist,GPT-5,60,53949.3,6.034502662418836,1552.1666666666667,7.004392744486282,0.0,0,1552.1666666666667,7.004392744486282,55501.46666666667,5.8897666034458345 -BookWriter,MCP,Senior Book Editor and Quality Assurance Specialist,Gemini-2.5-flash,60,9983.65,47.90951715147879,3618.733333333333,25.22332833931965,114.78333333333333,101.95207135521986,3503.95,26.50394318395112,13602.383333333333,40.54868271458845 -BookWriter,MCP,Senior Book Editor and Quality Assurance Specialist,Gemini-2.5-flash-nothinking,61,10084.0,37.537841139544774,2804.6721311475408,22.503192147055636,0.0,0,2804.6721311475408,22.503192147055636,12888.672131147541,30.582871810102674 -BookWriter,MCP,Senior Book Editor and Quality Assurance Specialist,Qwen3-235b,60,35864.15,29.027600833894223,1638.0666666666666,50.229707675563006,5.75,774.5966692414834,1632.3166666666666,50.11614653162201,37502.21666666667,29.096080121470518 -BookWriter,MCP,Specialized Chapter Research Agent,DeepSeek-R1,233,11169.32618025751,71.81641939349647,3330.2317596566522,40.821277187235296,1488.961373390558,59.780311942998,1841.2703862660944,53.04422883770449,14499.557939914162,62.301815975855234 -BookWriter,MCP,Specialized Chapter Research Agent,DeepSeek-V3-1,230,8707.804347826086,28.74686906808646,984.1130434782609,18.330839293032145,0.0,0,984.1130434782609,18.330839293032145,9691.917391304349,27.186667918876356 -BookWriter,MCP,Specialized Chapter Research Agent,GPT-4o-mini,221,15300.171945701357,33.91417736988102,1285.9411764705883,39.91245427617763,0.0,0,1285.9411764705883,39.91245427617763,16586.113122171944,34.04705317144145 -BookWriter,MCP,Specialized Chapter Research Agent,GPT-5,293,7152.791808873721,22.358991739542038,1258.1399317406144,9.107124734788682,0.0,0,1258.1399317406144,9.107124734788682,8410.931740614335,19.568423530656094 -BookWriter,MCP,Specialized Chapter Research Agent,Gemini-2.5-flash,188,4907.882978723404,66.63652284383063,5129.765957446809,36.268789495727866,205.13829787234042,107.96315347732997,4924.627659574468,36.90481925508839,10037.648936170213,46.2611747258881 -BookWriter,MCP,Specialized Chapter Research Agent,Gemini-2.5-flash-nothinking,187,3766.9732620320856,140.91650625834046,3790.9786096256685,135.18922556290212,0.0,0,3790.9786096256685,135.18922556290212,7557.951871657754,134.46872978651447 -BookWriter,MCP,Specialized Chapter Research Agent,Qwen3-235b,225,8218.684444444445,43.9977953540838,1132.3733333333332,21.600803725632854,0.0,0,1132.3733333333332,21.600803725632854,9351.057777777778,39.593743828780255 -BookWriter,MCP,Strategic Book Structure Architect,DeepSeek-R1,64,2616.96875,24.63477238866856,1371.109375,53.60677107835645,1035.890625,70.90269540440075,335.21875,47.518682515158545,3988.078125,22.858356134329927 -BookWriter,MCP,Strategic Book Structure Architect,DeepSeek-V3-1,60,2259.4166666666665,9.055156270452724,341.51666666666665,14.04869336949735,0.0,0,341.51666666666665,14.04869336949735,2600.9333333333334,8.044446973935177 -BookWriter,MCP,Strategic Book Structure Architect,GPT-4o-mini,60,2599.65,8.67574923358922,693.3,17.40172853337561,0.0,0,693.3,17.40172853337561,3292.95,8.839321052530904 -BookWriter,MCP,Strategic Book Structure Architect,GPT-5,60,2664.7,19.779759778241647,655.25,31.09195210638837,0.0,0,655.25,31.09195210638837,3319.95,15.513018262513661 -BookWriter,MCP,Strategic Book Structure Architect,Gemini-2.5-flash,60,3545.6666666666665,16.600005061442097,1886.3166666666666,22.91616679099501,1564.5333333333333,30.799356442377118,321.78333333333336,61.344709943458575,5431.983333333334,14.509904048748012 -BookWriter,MCP,Strategic Book Structure Architect,Gemini-2.5-flash-nothinking,61,3077.7377049180327,26.963651532723226,321.1803278688525,30.655800176560653,0.0,0,321.1803278688525,30.655800176560653,3398.9180327868853,24.05565493001902 -BookWriter,MCP,Strategic Book Structure Architect,Qwen3-235b,60,2649.266666666667,20.596948188858573,346.1166666666667,27.9353069036048,0.0,0,346.1166666666667,27.9353069036048,2995.383333333333,19.434697949333124 -BookWriter-H_A2A,A2A_mix,Professional Chapter Content Writer,DeepSeek-R1,210,9071.180952380952,159.4476392435725,5769.104761904762,69.7468532283938,2383.842857142857,105.89301771470554,3385.2619047619046,76.05722674501209,14840.285714285714,116.16493530130842 -BookWriter-H_A2A,A2A_mix,Professional Chapter Content Writer,DeepSeek-V3-1,217,20275.764976958526,26.570354508540127,4817.944700460829,16.388349228630023,0.0,0,4817.944700460829,16.388349228630023,25093.709677419356,24.0535729589754 -BookWriter-H_A2A,A2A_mix,Professional Chapter Content Writer,GPT-4o-mini,234,3136.905982905983,12.207799354958706,1063.0726495726497,30.46021146887555,0.0,0,1063.0726495726497,30.46021146887555,4199.978632478633,15.93171824023823 -BookWriter-H_A2A,A2A_mix,Professional Chapter Content Writer,GPT-5,255,13306.76862745098,79.25983385740174,3657.5803921568627,63.092431655477654,0.0,0,3657.5803921568627,63.092431655477654,16964.349019607842,75.5766558046713 -BookWriter-H_A2A,A2A_mix,Professional Chapter Content Writer,Gemini-2.5-flash,180,5921.833333333333,58.957875258646276,7608.366666666667,29.33137125755558,119.16111111111111,883.0222187127703,7489.205555555555,24.09642775870188,13530.2,33.244320637553244 -BookWriter-H_A2A,A2A_mix,Professional Chapter Content Writer,Gemini-2.5-flash-nothinking,183,5429.7213114754095,61.5173420690852,5222.934426229508,50.235496479400574,0.0,0,5222.934426229508,50.235496479400574,10652.655737704918,46.493942855483155 -BookWriter-H_A2A,A2A_mix,Professional Chapter Content Writer,Qwen3-235b,211,5217.028436018957,136.38496546266526,1396.3886255924172,86.66999858073363,0.0,0,1396.3886255924172,86.66999858073363,6613.417061611374,124.89148439302883 -BookWriter-H_A2A,A2A_mix,Specialized Chapter Research Agent,DeepSeek-R1,210,10772.07142857143,69.56432258814573,3136.057142857143,39.219233154808435,1427.2666666666667,52.53659665474794,1708.7904761904763,50.55953851271391,13908.128571428571,59.8900135130332 -BookWriter-H_A2A,A2A_mix,Specialized Chapter Research Agent,DeepSeek-V3-1,217,8022.198156682028,25.7610305503158,961.7880184331797,14.088123711597142,0.0,0,961.7880184331797,14.088123711597142,8983.986175115208,23.874000982327235 -BookWriter-H_A2A,A2A_mix,Specialized Chapter Research Agent,GPT-4o-mini,234,15370.423076923076,32.06052304643358,1309.320512820513,39.77874345446647,0.0,0,1309.320512820513,39.77874345446647,16679.74358974359,32.3348707636347 -BookWriter-H_A2A,A2A_mix,Specialized Chapter Research Agent,GPT-5,255,6721.733333333334,23.134099978057705,1265.9058823529413,9.880959928840587,0.0,0,1265.9058823529413,9.880959928840587,7987.639215686275,20.088497541958926 -BookWriter-H_A2A,A2A_mix,Specialized Chapter Research Agent,Gemini-2.5-flash,180,5060.083333333333,66.7710537885546,5479.233333333334,33.31501405607243,242.63888888888889,115.63795504297664,5236.594444444445,32.95815653780201,10539.316666666668,44.4261774284995 -BookWriter-H_A2A,A2A_mix,Specialized Chapter Research Agent,Gemini-2.5-flash-nothinking,183,3571.2677595628415,61.462661519653416,3730.9836065573772,45.25080488866611,0.0,0,3730.9836065573772,45.25080488866611,7302.251366120218,41.590996073984286 -BookWriter-H_A2A,A2A_mix,Specialized Chapter Research Agent,Qwen3-235b,211,8973.189573459716,38.82516798028017,1055.1279620853081,28.431656831529136,0.0,0,1055.1279620853081,28.431656831529136,10028.317535545024,36.392040299385656 -BookWriter-H_A2A,A2A_mix,agent,DeepSeek-R1,172,2572.9127906976746,81.19791164085946,872.6802325581396,101.56053368567764,510.50581395348837,148.869515269278,362.1744186046512,74.33653890919224,3445.593023255814,64.09432956117521 -BookWriter-H_A2A,A2A_mix,agent,DeepSeek-V3-1,135,4360.688888888889,69.77909467105562,288.2,24.724672218170745,0.0,0,288.2,24.724672218170745,4648.888888888889,65.94832924387029 -BookWriter-H_A2A,A2A_mix,agent,GPT-4o-mini,120,3600.375,78.84676678931586,284.35833333333335,14.037530428841317,0.0,0,284.35833333333335,14.037530428841317,3884.733333333333,72.14224473692114 -BookWriter-H_A2A,A2A_mix,agent,GPT-5,75,7052.4,49.01817334719725,90.89333333333333,24.652535877815076,0.0,0,90.89333333333333,24.652535877815076,7143.293333333333,48.63205705951344 -BookWriter-H_A2A,A2A_mix,agent,Gemini-2.5-flash-nothinking,120,0.0,0,0.0,0,0.0,0,0.0,0,3835.4,71.20887828304095 -BookWriter-H_A2A,A2A_mix,agent,Qwen3-235b,300,2069.7566666666667,107.75567936388693,123.49666666666667,82.86503172701403,0.0,0,123.49666666666667,82.86503172701403,2193.2533333333336,100.86132313520369 -BookWriter-H_A2A,A2A_mix,outliner,DeepSeek-R1,60,2992.5,12.063656801177977,2926.8,30.119244267187778,1198.5166666666667,35.617551586529174,1728.2833333333333,26.4833548237131,5919.3,16.259015249917756 -BookWriter-H_A2A,A2A_mix,outliner,DeepSeek-V3-1,60,1241.1333333333334,5.91788230813563,251.06666666666666,14.813802827737469,0.0,0,251.06666666666666,14.813802827737469,1492.2,5.505521448198405 -BookWriter-H_A2A,A2A_mix,outliner,GPT-4o-mini,60,1926.6666666666667,21.63478948870178,625.1,12.7879737622447,0.0,0,625.1,12.7879737622447,2551.766666666667,15.970008108257247 -BookWriter-H_A2A,A2A_mix,outliner,GPT-5,60,938.7166666666667,12.959519934134084,332.6333333333333,14.722141827773033,0.0,0,332.6333333333333,14.722141827773033,1271.35,10.773069198584695 -BookWriter-H_A2A,A2A_mix,outliner,Gemini-2.5-flash,60,1908.8333333333333,4.890570134143355,2996.3333333333335,17.280765109099594,1243.1,20.8572454481474,1753.2333333333333,14.912977492970612,4905.166666666667,11.143658299878792 -BookWriter-H_A2A,A2A_mix,outliner,Gemini-2.5-flash-nothinking,60,933.45,5.156886852338129,264.46666666666664,9.092066123025939,0.0,0,264.46666666666664,9.092066123025939,1197.9166666666667,4.658186141156322 -BookWriter-H_A2A,A2A_mix,outliner,Qwen3-235b,60,1122.5333333333333,29.879881973824574,284.43333333333334,16.925289405479266,0.0,0,284.43333333333334,16.925289405479266,1406.9666666666667,24.480996432582057 -BookWriter-H_A2A,A2A_mix,researcher,DeepSeek-R1,60,7442.4,25.9308543282053,6024.366666666667,37.9780407024597,2092.3,50.90198486914339,3932.0666666666666,31.923270508255776,13466.766666666666,27.016683037398103 -BookWriter-H_A2A,A2A_mix,researcher,DeepSeek-V3-1,60,5321.316666666667,18.032203402780166,682.9833333333333,18.89372396007343,0.0,0,682.9833333333333,18.89372396007343,6004.3,17.54556780450745 -BookWriter-H_A2A,A2A_mix,researcher,GPT-4o-mini,60,9907.533333333333,13.9291074969131,904.6,36.329777404853324,0.0,0,904.6,36.329777404853324,10812.133333333333,13.187180933299263 -BookWriter-H_A2A,A2A_mix,researcher,GPT-5,60,1044.4666666666667,13.418086087665015,67.0,189.8892689470975,0.0,0,67.0,189.8892689470975,1111.4666666666667,23.732725574778442 -BookWriter-H_A2A,A2A_mix,researcher,Gemini-2.5-flash,60,4753.466666666666,4.171386930383479,1019.9666666666667,17.95367673239852,339.1666666666667,24.614663152689012,680.8,15.050312464044325,5773.433333333333,4.45658753375819 -BookWriter-H_A2A,A2A_mix,researcher,Gemini-2.5-flash-nothinking,60,2078.85,4.358099653420941,116.15,12.981601254853354,0.0,0,116.15,12.981601254853354,2195.0,4.277602562174347 -BookWriter-H_A2A,A2A_mix,researcher,Qwen3-235b,60,5243.816666666667,11.125003098531488,371.96666666666664,87.81489395059756,0.0,0,371.96666666666664,87.81489395059756,5615.783333333334,11.464507588635843 EmailResponder,MCP,Expert Professional Email Response Composer and Communication Strategist,DeepSeek-R1,100,2303.81,56.41044978511383,1020.78,116.82506466700411,771.64,129.71278542845246,249.14,103.82346041955928,3324.59,68.35233003374778 EmailResponder,MCP,Expert Professional Email Response Composer and Communication Strategist,DeepSeek-V3-1,100,3955.07,75.83657846029752,291.7,75.69740281919655,0.0,0,291.7,75.69740281919655,4246.77,75.567608971621 EmailResponder,MCP,Expert Professional Email Response Composer and Communication Strategist,GPT-4o-mini,100,3864.18,54.24288388361641,223.91,80.02159242063959,0.0,0,223.91,80.02159242063959,4088.09,54.86963162839383 @@ -173,69 +69,6 @@ GameBuilder,Unknown,Software Quality Control Engineer,GPT-5,25,6006.36,21.990897 GameBuilder,Unknown,Software Quality Control Engineer,Gemini-2.5-flash,25,16442.24,92.27080121649286,18248.56,61.364583481253355,234.08,233.48011852240597,18014.48,62.46947375044051,34690.8,74.84680853817223 GameBuilder,Unknown,Software Quality Control Engineer,Gemini-2.5-flash-nothinking,27,12042.925925925925,116.25954283309503,16264.111111111111,84.52200401807481,0.0,0,16264.111111111111,84.52200401807481,28307.037037037036,93.67370333847249 GameBuilder,Unknown,Software Quality Control Engineer,Qwen3-235b,25,6539.08,45.642239203328636,3362.0,41.09504518841619,0.0,0,3362.0,41.09504518841619,9901.08,42.541815327827685 -LandingPageGenerator,A2A,Landing Page Content Generator,DeepSeek-R1,62,14728.661290322581,71.73666613415355,4634.709677419355,80.72426298120045,1596.6451612903227,77.48422461868437,3038.064516129032,117.51422586711409,19363.370967741936,70.43728447021738 -LandingPageGenerator,A2A,Landing Page Content Generator,DeepSeek-V3-1,60,19209.783333333333,120.68003545918936,5617.45,72.69682213492057,0.0,0,5617.45,72.69682213492057,24827.233333333334,109.63811204081809 -LandingPageGenerator,A2A,Landing Page Content Generator,GPT-4o-mini,60,10042.583333333334,32.422695034015604,1595.9333333333334,16.765906890814907,0.0,0,1595.9333333333334,16.765906890814907,11638.516666666666,27.763331465040984 -LandingPageGenerator,A2A,Landing Page Content Generator,GPT-5,60,10352.916666666666,52.936511547975215,1221.1166666666666,103.2537530274015,0.0,0,1221.1166666666666,103.2537530274015,11574.033333333333,55.90477880916874 -LandingPageGenerator,A2A,Landing Page Content Generator,Gemini-2.5-flash,60,3260.9333333333334,103.9497801908185,11073.716666666667,45.51564737491107,236.46666666666667,164.77993679116446,10837.25,46.7201178646246,14334.65,49.58859817725912 -LandingPageGenerator,A2A,Landing Page Content Generator,Gemini-2.5-flash-nothinking,61,4726.311475409836,188.66949721032242,12008.983606557376,146.02034984080748,0.0,0,12008.983606557376,146.02034984080748,16735.29508196721,153.07424118419434 -LandingPageGenerator,A2A,Landing Page Content Generator,Qwen3-235b,63,8954.285714285714,60.189899368627195,2732.809523809524,72.68063174874153,0.0,0,2732.809523809524,72.68063174874153,11687.095238095239,56.26636310152864 -LandingPageGenerator,A2A,Product Idea Analyst,DeepSeek-R1,62,1844.7903225806451,119.80673006643521,1801.241935483871,43.19314123416741,483.3709677419355,65.36725674801707,1317.8709677419354,52.424787120839426,3646.032258064516,72.40674669985887 -LandingPageGenerator,A2A,Product Idea Analyst,DeepSeek-V3-1,60,1650.7833333333333,27.680527626093017,349.3333333333333,33.263721482856575,0.0,0,349.3333333333333,33.263721482856575,2000.1166666666666,25.677670128375578 -LandingPageGenerator,A2A,Product Idea Analyst,GPT-4o-mini,60,6107.233333333334,72.95387335122724,633.1833333333333,63.759523949723516,0.0,0,633.1833333333333,63.759523949723516,6740.416666666667,71.4894050421477 -LandingPageGenerator,A2A,Product Idea Analyst,GPT-5,60,1570.35,16.80957559293467,188.68333333333334,22.24357634394371,0.0,0,188.68333333333334,22.24357634394371,1759.0333333333333,16.817616422034874 -LandingPageGenerator,A2A,Product Idea Analyst,Gemini-2.5-flash,60,1286.2666666666667,105.93327224949371,3617.0333333333333,29.600149146060062,211.48333333333332,58.30007706307098,3405.55,29.358918653904176,4903.3,40.81305719756927 -LandingPageGenerator,A2A,Product Idea Analyst,Gemini-2.5-flash-nothinking,64,820.078125,36.13701421037436,2849.40625,36.72459405637987,0.0,0,2849.40625,36.72459405637987,3669.484375,25.009358448264663 -LandingPageGenerator,A2A,Product Idea Analyst,Qwen3-235b,63,3432.3492063492063,58.428660320643345,352.23809523809524,65.14583264779615,0.0,0,352.23809523809524,65.14583264779615,3784.5873015873017,57.84897410461397 -LandingPageGenerator,A2A,Template Selection Specialist,DeepSeek-R1,62,3738.3870967741937,70.75026192725024,985.0806451612904,73.88285823041237,437.2096774193548,96.85333043296289,547.8709677419355,113.82829502608558,4723.467741935484,64.20542601438162 -LandingPageGenerator,A2A,Template Selection Specialist,DeepSeek-V3-1,60,1850.8,12.358608227671517,135.93333333333334,19.879003563344945,0.0,0,135.93333333333334,19.879003563344945,1986.7333333333333,11.540090838514216 -LandingPageGenerator,A2A,Template Selection Specialist,GPT-4o-mini,60,1918.3166666666666,17.382334211927798,42.31666666666667,37.073241237536905,0.0,0,42.31666666666667,37.073241237536905,1960.6333333333334,16.946492275581203 -LandingPageGenerator,A2A,Template Selection Specialist,GPT-5,60,1577.3166666666666,2.7940983866280873,54.46666666666667,9.136048487578972,0.0,0,54.46666666666667,9.136048487578972,1631.7833333333333,2.883544247719105 -LandingPageGenerator,A2A,Template Selection Specialist,Gemini-2.5-flash,60,1404.6333333333334,19.747566987605357,613.1,55.27811981493195,154.8,91.66692168153155,458.3,61.954784967917995,2017.7333333333333,16.125927201082096 -LandingPageGenerator,A2A,Template Selection Specialist,Gemini-2.5-flash-nothinking,61,1463.5573770491803,31.447499323738228,402.73770491803276,60.531683777093605,0.0,0,402.73770491803276,60.531683777093605,1866.295081967213,26.273150913167186 -LandingPageGenerator,A2A,Template Selection Specialist,Qwen3-235b,63,1729.952380952381,17.77305959865206,76.4920634920635,35.78784450289002,0.0,0,76.4920634920635,35.78784450289002,1806.4444444444443,17.620627703081507 -LandingPageGenerator,MCP,Landing Page Content Generator,DeepSeek-R1,68,11683.441176470587,62.945030569987395,4115.191176470588,49.504888693487274,724.9705882352941,67.18028551857408,3390.220588235294,58.94977333134136,15798.632352941177,55.375353588811905 -LandingPageGenerator,MCP,Landing Page Content Generator,DeepSeek-V3-1,60,9071.166666666666,88.27584690754149,2689.9,78.2390503193745,0.0,0,2689.9,78.2390503193745,11761.066666666668,84.43712964769003 -LandingPageGenerator,MCP,Landing Page Content Generator,GPT-4o-mini,60,7774.05,12.531953020207037,2837.25,21.348297327635617,0.0,0,2837.25,21.348297327635617,10611.3,12.578136563294894 -LandingPageGenerator,MCP,Landing Page Content Generator,GPT-5,60,6685.2,5.740536402407473,2512.4166666666665,9.98473744733994,0.0,0,2512.4166666666665,9.98473744733994,9197.616666666667,6.852531699993317 -LandingPageGenerator,MCP,Landing Page Content Generator,Gemini-2.5-flash,60,4236.65,133.9942296842048,15109.533333333333,56.48797001091671,69.76666666666667,365.32575328458216,15039.766666666666,56.74303170153399,19346.183333333334,63.8096333168769 -LandingPageGenerator,MCP,Landing Page Content Generator,Gemini-2.5-flash-nothinking,61,10884.295081967213,270.87968436313383,16455.360655737706,129.78980332711026,0.0,0,16455.360655737706,129.78980332711026,27339.655737704918,182.33479225007085 -LandingPageGenerator,MCP,Landing Page Content Generator,Qwen3-235b,60,7188.766666666666,21.305146165239176,2177.05,49.988494675499666,0.0,0,2177.05,49.988494675499666,9365.816666666668,23.374623181635666 -LandingPageGenerator,MCP,Product Idea Analyst,DeepSeek-R1,70,1948.2,96.44671147918734,1760.6285714285714,45.2160182214147,501.3285714285714,76.29958820801217,1259.3,52.45546881443336,3708.8285714285716,59.59127855723618 -LandingPageGenerator,MCP,Product Idea Analyst,DeepSeek-V3-1,60,1772.1833333333334,43.96939716171659,353.98333333333335,37.75920245108262,0.0,0,353.98333333333335,37.75920245108262,2126.1666666666665,41.16945293270239 -LandingPageGenerator,MCP,Product Idea Analyst,GPT-4o-mini,60,6073.183333333333,73.28934659343376,629.0666666666667,62.06246655163977,0.0,0,629.0666666666667,62.06246655163977,6702.25,71.41100197922994 -LandingPageGenerator,MCP,Product Idea Analyst,GPT-5,60,1600.6833333333334,18.735748968554,203.85,21.626941048713373,0.0,0,203.85,21.626941048713373,1804.5333333333333,18.22881114222054 -LandingPageGenerator,MCP,Product Idea Analyst,Gemini-2.5-flash,60,1220.05,111.23333180383258,3832.016666666667,18.294106135762124,200.1,43.58728448279186,3631.9166666666665,19.035668335781608,5052.066666666667,35.7569524138422 -LandingPageGenerator,MCP,Product Idea Analyst,Gemini-2.5-flash-nothinking,62,879.5967741935484,55.93946127500845,2792.3709677419356,38.5644920452205,0.0,0,2792.3709677419356,38.5644920452205,3671.967741935484,22.22875926194523 -LandingPageGenerator,MCP,Product Idea Analyst,Qwen3-235b,60,2938.0666666666666,58.27820700356096,342.48333333333335,63.029857894737816,0.0,0,342.48333333333335,63.029857894737816,3280.55,56.952697201088206 -LandingPageGenerator,MCP,Template Selection Specialist,DeepSeek-R1,70,4138.185714285714,81.07330737891581,1123.6571428571428,82.42044431517668,581.9428571428572,109.67091041997354,541.7142857142857,113.30913681761577,5261.842857142857,72.60873914559106 -LandingPageGenerator,MCP,Template Selection Specialist,DeepSeek-V3-1,60,1838.7333333333333,12.467833200941133,140.38333333333333,25.06508022473714,0.0,0,140.38333333333333,25.06508022473714,1979.1166666666666,11.700047591399645 -LandingPageGenerator,MCP,Template Selection Specialist,GPT-4o-mini,60,1892.5833333333333,18.15471145767483,43.38333333333333,35.89828286435497,0.0,0,43.38333333333333,35.89828286435497,1935.9666666666667,17.69343852132372 -LandingPageGenerator,MCP,Template Selection Specialist,GPT-5,60,1596.9333333333334,3.903914940425047,54.266666666666666,10.51689190017681,0.0,0,54.266666666666666,10.51689190017681,1651.2,3.8500084714617944 -LandingPageGenerator,MCP,Template Selection Specialist,Gemini-2.5-flash,60,1602.5,35.94243943591598,525.7666666666667,29.939179049969482,86.06666666666666,133.3048706248606,439.7,30.670206599436156,2128.266666666667,27.349275662677414 -LandingPageGenerator,MCP,Template Selection Specialist,Gemini-2.5-flash-nothinking,61,1401.377049180328,25.125634049770163,406.78688524590166,58.51726283064422,0.0,0,406.78688524590166,58.51726283064422,1808.1639344262296,20.857956401642568 -LandingPageGenerator,MCP,Template Selection Specialist,Qwen3-235b,60,1747.1833333333334,21.350183843304404,84.33333333333333,42.829128029519595,0.0,0,84.33333333333333,42.829128029519595,1831.5166666666667,21.66025862300292 -LandingPageGenerator-H_A2A,A2A_mix,Landing Page Content Generator,DeepSeek-R1,60,16717.916666666668,89.53227083529657,5064.933333333333,88.4506757211042,1219.7666666666667,81.5071251545445,3845.1666666666665,114.94996306188476,21782.85,85.2291522607396 -LandingPageGenerator-H_A2A,A2A_mix,Landing Page Content Generator,DeepSeek-V3-1,60,20386.783333333333,112.34401879200865,5825.416666666667,70.97041738426164,0.0,0,5825.416666666667,70.97041738426164,26212.2,102.7716719477648 -LandingPageGenerator-H_A2A,A2A_mix,Landing Page Content Generator,GPT-4o-mini,60,10334.65,22.00377159683142,1640.8,19.705315996056587,0.0,0,1640.8,19.705315996056587,11975.45,20.071011296275202 -LandingPageGenerator-H_A2A,A2A_mix,Landing Page Content Generator,GPT-5,60,15148.7,50.000385560451896,1886.85,78.2413785361121,0.0,0,1886.85,78.2413785361121,17035.55,51.35674975356549 -LandingPageGenerator-H_A2A,A2A_mix,Landing Page Content Generator,Gemini-2.5-flash,60,8845.233333333334,587.3133781735825,13499.5,199.0303924998546,366.1166666666667,107.73975444701223,13133.383333333333,204.14393140713298,22344.733333333334,351.1241498446218 -LandingPageGenerator-H_A2A,A2A_mix,Landing Page Content Generator,Gemini-2.5-flash-nothinking,60,1940.8166666666666,106.68237384945485,8035.483333333334,42.99390750618861,0.0,0,8035.483333333334,42.99390750618861,9976.3,47.112755569832395 -LandingPageGenerator-H_A2A,A2A_mix,Landing Page Content Generator,Qwen3-235b,63,8949.682539682539,58.94299534759435,2810.0793650793653,68.00697035244005,0.0,0,2810.0793650793653,68.00697035244005,11759.761904761905,54.68717990050247 -LandingPageGenerator-H_A2A,A2A_mix,agent,DeepSeek-R1,120,502.4,5.788299340819949,1142.025,69.94499510774723,654.2083333333334,81.74125584449665,487.81666666666666,87.28319032959581,1644.425,47.93941618796128 -LandingPageGenerator-H_A2A,A2A_mix,agent,DeepSeek-V3-1,123,863.8373983739838,4.245434938271793,177.9430894308943,27.925996941465907,0.0,0,177.9430894308943,27.925996941465907,1041.780487804878,5.183645413968699 -LandingPageGenerator-H_A2A,A2A_mix,agent,GPT-4o-mini,120,376.025,7.706468491424669,190.325,76.15488386724996,0.0,0,190.325,76.15488386724996,566.35,22.239195093430492 -LandingPageGenerator-H_A2A,A2A_mix,agent,GPT-5,119,343.96638655462186,22.60285154572534,242.2016806722689,94.47283788361518,0.0,0,242.2016806722689,94.47283788361518,586.1680672268908,32.77979477084056 -LandingPageGenerator-H_A2A,A2A_mix,agent,Gemini-2.5-flash,13,0.0,0,0.0,0,0.0,0,0.0,0,213.6153846153846,19.33041124736138 -LandingPageGenerator-H_A2A,A2A_mix,agent,Gemini-2.5-flash-nothinking,120,0.0,0,0.0,0,0.0,0,0.0,0,396.3,11.142896569615576 -LandingPageGenerator-H_A2A,A2A_mix,agent,Qwen3-235b,126,504.3809523809524,10.599951205238444,85.76190476190476,78.25096013855365,0.0,0,85.76190476190476,78.25096013855365,590.1428571428571,14.480739563958947 -LandingPageGenerator-H_A2A,A2A_mix,senior_react_engineer,DeepSeek-R1,60,30966.25,64.89098114876299,1164.8666666666666,147.79620249055736,506.28333333333336,171.75993772352726,658.5833333333334,133.0080977605029,32131.116666666665,66.46482966279385 -LandingPageGenerator-H_A2A,A2A_mix,senior_react_engineer,DeepSeek-V3-1,31,11861.548387096775,50.65154633618197,38.354838709677416,17.209691421729804,0.0,0,38.354838709677416,17.209691421729804,11899.90322580645,50.50502410226391 -LandingPageGenerator-H_A2A,A2A_mix,senior_react_engineer,GPT-4o-mini,60,30452.116666666665,40.25937588084281,28.333333333333332,33.36713630202549,0.0,0,28.333333333333332,33.36713630202549,30480.45,40.206150742118666 -LandingPageGenerator-H_A2A,A2A_mix,senior_react_engineer,GPT-5,56,7595.571428571428,62.501023488642794,9.285714285714286,110.77544660104559,0.0,0,9.285714285714286,110.77544660104559,7604.857142857143,62.39069344444811 -LandingPageGenerator-H_A2A,A2A_mix,senior_react_engineer,Gemini-2.5-flash,60,21172.266666666666,46.9914730259306,649.5,219.26283832783596,244.38333333333333,296.90946313579,405.1166666666667,174.54961407528685,21821.766666666666,43.84804100560929 -LandingPageGenerator-H_A2A,A2A_mix,senior_react_engineer,Gemini-2.5-flash-nothinking,30,8332.266666666666,54.12174087661239,28.433333333333334,71.20591212462624,0.0,0,28.433333333333334,71.20591212462624,8360.7,53.96060603441089 -LandingPageGenerator-H_A2A,A2A_mix,senior_react_engineer,Qwen3-235b,58,4179.3448275862065,48.400720123803445,0.6551724137931034,533.7717496287664,0.0,0,0.6551724137931034,533.7717496287664,4180.0,48.3687869654105 MarkdownValidator,MCP,Requirements Manager,DeepSeek-R1,60,1859.0333333333333,11.571333397286649,4026.2166666666667,39.10133252821658,3818.2166666666667,40.21647818711991,208.0,54.91773132075755,5885.25,28.059563621577578 MarkdownValidator,MCP,Requirements Manager,DeepSeek-V3-1,60,1825.9,6.902634218986391,237.26666666666668,56.86954915985149,0.0,0,237.26666666666668,56.86954915985149,2063.1666666666665,12.533909107840454 MarkdownValidator,MCP,Requirements Manager,GPT-4o-mini,60,1767.45,9.40812776011145,174.06666666666666,69.8921832500779,0.0,0,174.06666666666666,69.8921832500779,1941.5166666666667,13.554127972211544 @@ -250,236 +83,379 @@ MarkdownValidator,Unknown,Requirements Manager,GPT-5,60,1652.3666666666666,7.284 MarkdownValidator,Unknown,Requirements Manager,Gemini-2.5-flash,77,36822.96103896104,173.93565612185276,68546.7012987013,82.5088379053814,196.4025974025974,144.4344953411411,68350.2987012987,82.85458822801445,105369.66233766233,101.61903994433537 MarkdownValidator,Unknown,Requirements Manager,Gemini-2.5-flash-nothinking,64,9298.578125,234.62289079559625,20073.3125,214.49812955441197,0.0,0,20073.3125,214.49812955441197,29371.890625,218.28030878470227 MarkdownValidator,Unknown,Requirements Manager,Qwen3-235b,60,1657.4833333333333,12.81147185692845,196.48333333333332,64.92124356411036,0.0,0,196.48333333333332,64.92124356411036,1853.9666666666667,17.160459611445265 -RecruitmentAssistant,A2A,Senior Candidate Evaluator,DeepSeek-R1,61,30608.72131147541,85.47139199068391,9787.180327868853,55.55045080371037,4105.6557377049185,64.37780519768015,5681.524590163935,66.21544048252275,40395.901639344265,75.8785062287891 -RecruitmentAssistant,A2A,Senior Candidate Evaluator,DeepSeek-V3-1,60,44145.083333333336,26.656189318282337,5401.483333333334,30.26478943623152,0.0,0,5401.483333333334,30.26478943623152,49546.566666666666,25.906448943825584 -RecruitmentAssistant,A2A,Senior Candidate Evaluator,GPT-4o-mini,60,49297.583333333336,69.28305095261749,5020.75,56.50105679842509,0.0,0,5020.75,56.50105679842509,54318.333333333336,66.32346180171228 -RecruitmentAssistant,A2A,Senior Candidate Evaluator,GPT-5,60,13226.9,26.937408997029618,3046.366666666667,34.91904015952739,0.0,0,3046.366666666667,34.91904015952739,16273.266666666666,26.211711501751118 -RecruitmentAssistant,A2A,Senior Candidate Evaluator,Gemini-2.5-flash,60,23991.7,98.60139472801976,18181.15,60.07502185468543,1363.4166666666667,129.5350691271378,16817.733333333334,63.90155738026717,42172.85,80.03947786117939 -RecruitmentAssistant,A2A,Senior Candidate Evaluator,Gemini-2.5-flash-nothinking,60,18043.283333333333,62.15530627016903,15394.95,45.01412667451598,0.0,0,15394.95,45.01412667451598,33438.23333333333,45.278664292440304 -RecruitmentAssistant,A2A,Senior Candidate Evaluator,Qwen3-235b,84,45935.392857142855,66.74611734549899,6265.511904761905,39.63154296914339,0.0,0,6265.511904761905,39.63154296914339,52200.90476190476,62.47831267562696 -RecruitmentAssistant,A2A,Senior Interview Materials Coordinator,DeepSeek-R1,59,18707.949152542373,68.36245766496177,5320.033898305085,48.0507713963277,1768.3728813559321,61.25934178346806,3551.6610169491523,55.148105261817314,24027.98305084746,60.7617254527927 -RecruitmentAssistant,A2A,Senior Interview Materials Coordinator,DeepSeek-V3-1,60,8862.35,0.5983974201156282,2578.5,8.697786333336945,0.0,0,2578.5,8.697786333336945,11440.85,1.9061878614769379 -RecruitmentAssistant,A2A,Senior Interview Materials Coordinator,GPT-4o-mini,60,8221.183333333332,12.02311867550227,1176.1,23.20044827804071,0.0,0,1176.1,23.20044827804071,9397.283333333333,11.135432724587352 -RecruitmentAssistant,A2A,Senior Interview Materials Coordinator,GPT-5,60,8469.583333333334,0.7689309635478658,1370.8,10.047920187446989,0.0,0,1370.8,10.047920187446989,9840.383333333333,1.8482870428521505 -RecruitmentAssistant,A2A,Senior Interview Materials Coordinator,Gemini-2.5-flash,60,7108.916666666667,83.93356169245668,11081.683333333332,30.548869887530266,481.0,29.31099272322502,10600.683333333332,31.611278649130913,18190.6,50.674087024816096 -RecruitmentAssistant,A2A,Senior Interview Materials Coordinator,Gemini-2.5-flash-nothinking,60,5452.75,86.31883784211112,9163.1,34.03358049038389,0.0,0,9163.1,34.03358049038389,14615.85,49.32325587690237 -RecruitmentAssistant,A2A,Senior Interview Materials Coordinator,Qwen3-235b,59,8107.813559322034,23.29061450377309,2129.406779661017,20.99436664524487,0.0,0,2129.406779661017,20.99436664524487,10237.22033898305,20.10559815069613 -RecruitmentAssistant,A2A,Senior Job Requirements Analyst,DeepSeek-R1,66,4222.590909090909,79.7863104980631,3296.7272727272725,42.597515173813974,1062.3939393939395,50.42061555178468,2234.3333333333335,52.51257283166265,7519.318181818182,56.49389438079234 -RecruitmentAssistant,A2A,Senior Job Requirements Analyst,DeepSeek-V3-1,60,3159.1666666666665,22.515835800882613,1515.8333333333333,20.10252875846,0.0,0,1515.8333333333333,20.10252875846,4675.0,16.478007042122762 -RecruitmentAssistant,A2A,Senior Job Requirements Analyst,GPT-4o-mini,60,10355.333333333334,41.989902325782175,880.4166666666666,51.79656825701052,0.0,0,880.4166666666666,51.79656825701052,11235.75,41.30230749383285 -RecruitmentAssistant,A2A,Senior Job Requirements Analyst,GPT-5,60,4656.433333333333,34.12412461443836,1389.7333333333333,8.717407677450078,0.0,0,1389.7333333333333,8.717407677450078,6046.166666666667,27.175077919599154 -RecruitmentAssistant,A2A,Senior Job Requirements Analyst,Gemini-2.5-flash,60,1517.6666666666667,64.39184371070064,6837.916666666667,23.383652044270875,566.0333333333333,37.61981044399118,6271.883333333333,26.482438515841622,8355.583333333334,19.78596334410806 -RecruitmentAssistant,A2A,Senior Job Requirements Analyst,Gemini-2.5-flash-nothinking,60,2274.4166666666665,66.69388006633544,5331.016666666666,54.23476896873585,0.0,0,5331.016666666666,54.23476896873585,7605.433333333333,40.533818500039374 -RecruitmentAssistant,A2A,Senior Job Requirements Analyst,Qwen3-235b,84,8958.297619047618,72.57500811278435,1577.357142857143,54.55966809562383,0.0,0,1577.357142857143,54.55966809562383,10535.654761904761,68.08025374356997 -RecruitmentAssistant,MCP,Senior Candidate Evaluator,DeepSeek-R1,61,38807.311475409835,78.73494544590854,12910.786885245901,57.022484049971176,4886.180327868852,74.95704834287349,8024.606557377049,69.91624693704433,51718.098360655735,71.28101514199275 -RecruitmentAssistant,MCP,Senior Candidate Evaluator,DeepSeek-V3-1,60,44424.433333333334,27.58755956073861,5072.433333333333,19.274971461738403,0.0,0,5072.433333333333,19.274971461738403,49496.86666666667,25.91886123109842 -RecruitmentAssistant,MCP,Senior Candidate Evaluator,GPT-4o-mini,60,74780.53333333334,144.13623753722038,5728.333333333333,72.77211879153208,0.0,0,5728.333333333333,72.77211879153208,80508.86666666667,138.05802771551149 -RecruitmentAssistant,MCP,Senior Candidate Evaluator,GPT-5,60,13350.2,39.90093126335667,2746.75,39.56368979157489,0.0,0,2746.75,39.56368979157489,16096.95,37.079091655730004 -RecruitmentAssistant,MCP,Senior Candidate Evaluator,Gemini-2.5-flash,60,19091.533333333333,87.25615880545449,17908.9,54.37631654301038,1542.8833333333334,111.13738040639748,16366.016666666666,56.64012240687612,37000.433333333334,66.4002480138344 -RecruitmentAssistant,MCP,Senior Candidate Evaluator,Gemini-2.5-flash-nothinking,60,16751.75,82.36569176446726,14470.016666666666,42.96964763105335,0.0,0,14470.016666666666,42.96964763105335,31221.766666666666,59.127231557379275 -RecruitmentAssistant,MCP,Senior Candidate Evaluator,Qwen3-235b,84,39416.09523809524,76.08543895352956,5755.892857142857,40.12915816252277,0.0,0,5755.892857142857,40.12915816252277,45171.98809523809,70.44775341504183 -RecruitmentAssistant,MCP,Senior Interview Materials Coordinator,DeepSeek-R1,61,18264.245901639344,53.24768586357004,4752.950819672131,39.32765561330905,1643.8032786885246,50.95387785701018,3109.1475409836066,47.29028252235466,23017.196721311477,47.57243730472979 -RecruitmentAssistant,MCP,Senior Interview Materials Coordinator,DeepSeek-V3-1,60,8959.833333333334,7.668727542834645,2584.483333333333,11.448240434459004,0.0,0,2584.483333333333,11.448240434459004,11544.316666666668,6.718388210351916 -RecruitmentAssistant,MCP,Senior Interview Materials Coordinator,GPT-4o-mini,60,8504.616666666667,10.408856925823965,1179.25,19.476216654626356,0.0,0,1179.25,19.476216654626356,9683.866666666667,10.137405210863385 -RecruitmentAssistant,MCP,Senior Interview Materials Coordinator,GPT-5,60,8491.783333333333,0.7242967319190418,1376.1333333333334,10.680135021386754,0.0,0,1376.1333333333334,10.680135021386754,9867.916666666666,1.701654891817191 -RecruitmentAssistant,MCP,Senior Interview Materials Coordinator,Gemini-2.5-flash,60,8010.7,131.89158190861676,11293.383333333333,43.76823848501689,485.7,44.02501830455737,10807.683333333332,45.244822534482175,19304.083333333332,79.21854417291205 -RecruitmentAssistant,MCP,Senior Interview Materials Coordinator,Gemini-2.5-flash-nothinking,60,5751.583333333333,118.06996933816282,9880.2,64.43017315293808,0.0,0,9880.2,64.43017315293808,15631.783333333333,80.9700605456795 -RecruitmentAssistant,MCP,Senior Interview Materials Coordinator,Qwen3-235b,58,8229.810344827587,20.55410628071939,2098.0,10.34542072315204,0.0,0,2098.0,10.34542072315204,10327.810344827587,16.0823943424622 -RecruitmentAssistant,MCP,Senior Job Requirements Analyst,DeepSeek-R1,62,4921.887096774193,84.98465074338418,3502.3387096774195,40.35554726126912,1033.4193548387098,80.24353979986041,2468.9193548387098,44.22598451022549,8424.225806451614,58.964224180671195 -RecruitmentAssistant,MCP,Senior Job Requirements Analyst,DeepSeek-V3-1,60,3109.35,35.6004006139427,1597.0,27.876259852249895,0.0,0,1597.0,27.876259852249895,4706.35,27.398579242964498 -RecruitmentAssistant,MCP,Senior Job Requirements Analyst,GPT-4o-mini,60,8728.966666666667,47.57717517025726,1011.4166666666666,33.550194624871104,0.0,0,1011.4166666666666,33.550194624871104,9740.383333333333,44.9320298206683 -RecruitmentAssistant,MCP,Senior Job Requirements Analyst,GPT-5,60,5070.716666666666,35.261436302367315,1374.9333333333334,8.744002623867217,0.0,0,1374.9333333333334,8.744002623867217,6445.65,28.3911866947303 -RecruitmentAssistant,MCP,Senior Job Requirements Analyst,Gemini-2.5-flash,60,1680.2166666666667,101.64644967810707,6879.683333333333,30.8569458791522,518.9,41.58071346181834,6360.783333333334,33.251149353218565,8559.9,37.34470599837949 -RecruitmentAssistant,MCP,Senior Job Requirements Analyst,Gemini-2.5-flash-nothinking,60,2149.516666666667,62.67919843028152,5398.983333333334,60.82471807023979,0.0,0,5398.983333333334,60.82471807023979,7548.5,44.32553100200627 -RecruitmentAssistant,MCP,Senior Job Requirements Analyst,Qwen3-235b,84,8379.464285714286,68.90660294911564,1529.9404761904761,42.11551291574978,0.0,0,1529.9404761904761,42.11551291574978,9909.404761904761,62.952204701559445 -RecruitmentAssistant-H_A2A,A2A,Senior Candidate Evaluator,DeepSeek-R1,63,29212.015873015873,79.27146929490121,11420.603174603175,47.24641759225871,5145.9047619047615,49.24683521692371,6274.6984126984125,63.64900880359563,40632.619047619046,68.4778437274106 -RecruitmentAssistant-H_A2A,A2A,Senior Candidate Evaluator,DeepSeek-V3-1,60,43100.316666666666,27.329064409931114,5707.433333333333,26.09482352018231,0.0,0,5707.433333333333,26.09482352018231,48807.75,25.168122163175095 -RecruitmentAssistant-H_A2A,A2A,Senior Candidate Evaluator,GPT-4o-mini,60,63935.666666666664,119.49874595809436,5324.766666666666,51.57136654322137,0.0,0,5324.766666666666,51.57136654322137,69260.43333333333,112.44477165395266 -RecruitmentAssistant-H_A2A,A2A,Senior Candidate Evaluator,GPT-5,60,13564.35,51.20937603284846,3025.15,40.54669347605696,0.0,0,3025.15,40.54669347605696,16589.5,47.33385520471228 -RecruitmentAssistant-H_A2A,A2A,Senior Candidate Evaluator,Gemini-2.5-flash,60,10873.966666666667,81.87301065482046,11980.066666666668,47.274699349477814,1330.5,92.29164927271853,10649.566666666668,50.20842604988985,22854.033333333333,60.68425030125057 -RecruitmentAssistant-H_A2A,A2A,Senior Candidate Evaluator,Gemini-2.5-flash-nothinking,60,9651.766666666666,50.13753910794657,9547.483333333334,32.76839150573318,0.0,0,9547.483333333334,32.76839150573318,19199.25,37.06676090738178 -RecruitmentAssistant-H_A2A,A2A,Senior Candidate Evaluator,Qwen3-235b,72,37967.5,65.01412181487864,6343.402777777777,35.42409190987712,0.0,0,6343.402777777777,35.42409190987712,44310.90277777778,59.24528764645585 -RecruitmentAssistant-H_A2A,A2A,agent,DeepSeek-R1,128,1063.15625,26.42403567926495,1357.5859375,49.434409531264365,832.953125,62.726801620317715,524.6328125,57.7174748316211,2420.7421875,26.706040049711017 -RecruitmentAssistant-H_A2A,A2A,agent,DeepSeek-V3-1,120,1387.4333333333334,21.946992218344583,269.14166666666665,66.54040161033204,0.0,0,269.14166666666665,66.54040161033204,1656.575,23.09016790904449 -RecruitmentAssistant-H_A2A,A2A,agent,GPT-4o-mini,120,934.2916666666666,28.68114862468288,189.21666666666667,49.44636509477004,0.0,0,189.21666666666667,49.44636509477004,1123.5083333333334,27.333593467781448 -RecruitmentAssistant-H_A2A,A2A,agent,GPT-5,120,748.85,62.06122493006643,150.36666666666667,94.21232810491802,0.0,0,150.36666666666667,94.21232810491802,899.2166666666667,45.149670235582136 -RecruitmentAssistant-H_A2A,A2A,agent,Gemini-2.5-flash,2,0.0,0,0.0,0,0.0,0,0.0,0,348.5,34.29011363573215 -RecruitmentAssistant-H_A2A,A2A,agent,Gemini-2.5-flash-nothinking,120,0.0,0,0.0,0,0.0,0,0.0,0,1045.2916666666667,30.056942686789228 -RecruitmentAssistant-H_A2A,A2A,agent,Qwen3-235b,188,800.4414893617021,54.842883755676844,57.797872340425535,60.265614036407946,0.0,0,57.797872340425535,60.265614036407946,858.2393617021277,49.95606027156511 -RecruitmentAssistant-H_A2A,A2A,interview_coordinator,DeepSeek-R1,60,23383.933333333334,29.98697192032666,7815.833333333333,31.74836942555639,2267.266666666667,52.2742902137343,5548.566666666667,23.976983439745048,31199.766666666666,25.660897662427296 -RecruitmentAssistant-H_A2A,A2A,interview_coordinator,DeepSeek-V3-1,60,11089.15,14.891278657036603,1837.9,23.601221790479265,0.0,0,1837.9,23.601221790479265,12927.05,13.937643356473112 -RecruitmentAssistant-H_A2A,A2A,interview_coordinator,GPT-4o-mini,60,26998.766666666666,96.00411350515235,3429.9,12.102269139277249,0.0,0,3429.9,12.102269139277249,30428.666666666668,85.78990792859895 -RecruitmentAssistant-H_A2A,A2A,interview_coordinator,GPT-5,60,12807.55,19.05981063357447,1476.5166666666667,26.334829245601753,0.0,0,1476.5166666666667,26.334829245601753,14284.066666666668,18.613875644206377 -RecruitmentAssistant-H_A2A,A2A,interview_coordinator,Gemini-2.5-flash,60,21014.533333333333,30.68056310598397,10094.3,17.149592320307626,1407.0,47.549477027026875,8687.3,13.983733361060969,31108.833333333332,21.51990761818857 -RecruitmentAssistant-H_A2A,A2A,interview_coordinator,Gemini-2.5-flash-nothinking,60,12960.2,21.24605345577444,2637.1,41.942916349453334,0.0,0,2637.1,41.942916349453334,15597.3,18.968971603930353 -RecruitmentAssistant-H_A2A,A2A,interview_coordinator,Qwen3-235b,60,14951.783333333333,7.616467193430915,2445.2,18.925522410781245,0.0,0,2445.2,18.925522410781245,17396.983333333334,7.310596726245885 -SQLAssistant,A2A,Business Intelligence Analyst,DeepSeek-R1,8,5487.5,98.81809957550303,3639.0,90.43081778400133,1964.625,86.91507573055198,1674.375,110.45448593036183,9126.5,79.14673983669186 -SQLAssistant,A2A,Business Intelligence Analyst,GPT-4o-mini,60,8817.333333333334,20.729246532819793,604.3833333333333,20.15750402506632,0.0,0,604.3833333333333,20.15750402506632,9421.716666666667,20.40651946564459 -SQLAssistant,A2A,Business Intelligence Analyst,GPT-5,54,2863.5,78.08946438245844,337.55555555555554,45.70514988360644,0.0,0,337.55555555555554,45.70514988360644,3201.0555555555557,74.596612295914 -SQLAssistant,A2A,Business Intelligence Analyst,Gemini-2.5-flash,22,3589.7727272727275,43.88013189720562,3600.681818181818,41.30130637942791,671.1363636363636,75.6201779724386,2929.5454545454545,51.67340805624184,7190.454545454545,40.60073903279846 -SQLAssistant,A2A,Business Intelligence Analyst,Gemini-2.5-flash-nothinking,40,3086.0,108.50375935519052,3688.45,79.38337405110127,0.0,0,3688.45,79.38337405110127,6774.45,87.54024346276594 -SQLAssistant,A2A,Business Intelligence Analyst,Qwen3-235b,54,5520.444444444444,51.98433836245931,421.3888888888889,41.064079246409804,0.0,0,421.3888888888889,41.064079246409804,5941.833333333333,51.08655394668856 -SQLAssistant,A2A,Data Security and Compliance Auditor,DeepSeek-R1,177,852.0,34.12529211857415,1281.949152542373,100.92157360805396,1120.231638418079,112.68870886299736,161.71751412429379,89.40936532565787,2133.9491525423728,64.2059093325058 -SQLAssistant,A2A,Data Security and Compliance Auditor,DeepSeek-V3-1,195,852.9948717948718,30.81055051952714,88.51794871794871,36.86932645907556,0.0,0,88.51794871794871,36.86932645907556,941.5128205128206,30.73259720850392 -SQLAssistant,A2A,Data Security and Compliance Auditor,GPT-4o-mini,71,1664.380281690141,6.554787916547862,119.73239436619718,20.36134278278672,0.0,0,119.73239436619718,20.36134278278672,1784.112676056338,6.5303157408711705 -SQLAssistant,A2A,Data Security and Compliance Auditor,GPT-5,93,771.0,0.0,131.38709677419354,14.439311975801111,0.0,0,131.38709677419354,14.439311975801111,902.3870967741935,2.1023563908428513 -SQLAssistant,A2A,Data Security and Compliance Auditor,Gemini-2.5-flash,161,892.2298136645962,31.70961174051696,2535.0248447204967,161.2172546191433,1403.2173913043478,41.53203165271908,1131.807453416149,363.4155021792126,3427.254658385093,125.65502058578495 -SQLAssistant,A2A,Data Security and Compliance Auditor,Gemini-2.5-flash-nothinking,127,925.4724409448819,67.91264003500098,436.6062992125984,130.31337056469474,0.0,0,436.6062992125984,130.31337056469474,1362.0787401574803,82.18002041823304 -SQLAssistant,A2A,Data Security and Compliance Auditor,Qwen3-235b,90,1060.7333333333333,41.06962354863584,112.25555555555556,45.50873485305262,0.0,0,112.25555555555556,45.50873485305262,1172.9888888888888,40.07578362409585 -SQLAssistant,A2A,Expert SQL Query Generator,DeepSeek-R1,181,7744.900552486188,122.46200490882948,3808.0773480662983,81.42552270722112,1194.0441988950276,145.25665128053484,2614.033149171271,72.78607163997532,11552.977900552487,94.38852060267831 -SQLAssistant,A2A,Expert SQL Query Generator,DeepSeek-V3-1,209,6119.296650717703,66.13502754484202,275.8708133971292,47.34863575670719,0.0,0,275.8708133971292,47.34863575670719,6395.167464114833,64.6826708943063 -SQLAssistant,A2A,Expert SQL Query Generator,GPT-4o-mini,71,2658.4647887323945,17.75171231513613,56.80281690140845,50.78392179359812,0.0,0,56.80281690140845,50.78392179359812,2715.2676056338028,18.21380455405417 -SQLAssistant,A2A,Expert SQL Query Generator,GPT-5,93,2788.8387096774195,33.43668012491725,115.06451612903226,27.76374651552686,0.0,0,115.06451612903226,27.76374651552686,2903.9032258064517,33.008003389878475 -SQLAssistant,A2A,Expert SQL Query Generator,Gemini-2.5-flash,161,2268.3478260869565,70.45244642441207,2214.782608695652,173.52474504365,51.683229813664596,142.115603250684,2163.0993788819874,176.79695431951563,4483.130434782609,104.84098505699791 -SQLAssistant,A2A,Expert SQL Query Generator,Gemini-2.5-flash-nothinking,127,3343.1417322834645,203.4446093494842,2381.7244094488187,283.8084137156365,0.0,0,2381.7244094488187,283.8084137156365,5724.866141732284,232.9686154190881 -SQLAssistant,A2A,Expert SQL Query Generator,Qwen3-235b,90,3225.8333333333335,42.81182581085742,173.86666666666667,48.23444010399567,0.0,0,173.86666666666667,48.23444010399567,3399.7,42.58790636693682 -SQLAssistant,A2A,Senior SQL Code Reviewer,DeepSeek-R1,180,12656.705555555556,143.84091385372506,5378.45,83.15563294342726,3666.4555555555557,85.12788126866512,1711.9944444444445,137.15013710918555,18035.155555555557,110.71605154177261 -SQLAssistant,A2A,Senior SQL Code Reviewer,DeepSeek-V3-1,198,3221.09595959596,50.55559278940607,254.25757575757575,35.85703197524825,0.0,0,254.25757575757575,35.85703197524825,3475.3535353535353,49.073455398529894 -SQLAssistant,A2A,Senior SQL Code Reviewer,GPT-4o-mini,71,3176.056338028169,49.140239783326265,110.46478873239437,63.097526635068924,0.0,0,110.46478873239437,63.097526635068924,3286.521126760563,49.052339305245766 -SQLAssistant,A2A,Senior SQL Code Reviewer,GPT-5,93,1452.3118279569892,47.01798963292112,101.38709677419355,41.16215591422777,0.0,0,101.38709677419355,41.16215591422777,1553.6989247311828,46.16243173165622 -SQLAssistant,A2A,Senior SQL Code Reviewer,Gemini-2.5-flash,161,1743.1180124223602,68.39804070612475,1487.3788819875776,73.1046109985398,253.11180124223603,166.66100966439353,1234.2670807453417,80.38557297497421,3230.496894409938,58.579364946915604 -SQLAssistant,A2A,Senior SQL Code Reviewer,Gemini-2.5-flash-nothinking,127,1770.3464566929133,333.7792156005412,1189.6614173228347,211.67597972871567,0.0,0,1189.6614173228347,211.67597972871567,2960.0078740157483,227.92851916491966 -SQLAssistant,A2A,Senior SQL Code Reviewer,Qwen3-235b,90,3276.5666666666666,44.413985764824396,173.07777777777778,44.8287169689252,0.0,0,173.07777777777778,44.8287169689252,3449.6444444444446,44.007195711952775 -SQLAssistant,MCP,Business Intelligence Analyst,DeepSeek-R1,29,3474.3793103448274,130.29048826727126,2280.5172413793102,68.34584782682637,1431.1724137931035,83.5171733894097,849.3448275862069,95.28325823322227,5754.896551724138,92.05089498497685 -SQLAssistant,MCP,Business Intelligence Analyst,DeepSeek-V3-1,51,4698.176470588235,51.648903070617614,822.0,32.50167518234221,0.0,0,822.0,32.50167518234221,5520.176470588235,48.61279254109235 -SQLAssistant,MCP,Business Intelligence Analyst,GPT-4o-mini,34,6090.058823529412,51.06786904699376,602.5882352941177,33.026574965093985,0.0,0,602.5882352941177,33.026574965093985,6692.64705882353,48.951961697540455 -SQLAssistant,MCP,Business Intelligence Analyst,GPT-5,35,686.9142857142857,5.199558116159608,319.85714285714283,12.486751183755516,0.0,0,319.85714285714283,12.486751183755516,1006.7714285714286,4.288644702652085 -SQLAssistant,MCP,Business Intelligence Analyst,Gemini-2.5-flash,55,1018.1272727272727,36.562931944417784,1129.9818181818182,48.88332428065035,79.74545454545455,273.0614910745346,1050.2363636363636,52.3138445348215,2148.109090909091,32.217366916268865 -SQLAssistant,MCP,Business Intelligence Analyst,Gemini-2.5-flash-nothinking,59,1182.0,144.0226420054809,902.9830508474577,178.0302608474315,0.0,0,902.9830508474577,178.0302608474315,2084.9830508474574,156.77252707967972 -SQLAssistant,MCP,Business Intelligence Analyst,Qwen3-235b,56,2416.375,113.71931212844764,439.5357142857143,53.726067151208134,0.0,0,439.5357142857143,53.726067151208134,2855.910714285714,103.77053220896117 -SQLAssistant,MCP,Data Security and Compliance Auditor,DeepSeek-R1,142,1253.3239436619717,82.00583239482967,1596.5070422535211,75.7237078134924,1007.387323943662,81.98776868968957,589.1197183098592,104.34377652987085,2849.830985915493,67.59304007204267 -SQLAssistant,MCP,Data Security and Compliance Auditor,DeepSeek-V3-1,110,1476.5636363636363,63.22510206154289,390.59090909090907,41.23568288986837,0.0,0,390.59090909090907,41.23568288986837,1867.1545454545455,56.36916656439135 -SQLAssistant,MCP,Data Security and Compliance Auditor,GPT-4o-mini,130,1669.4615384615386,72.22045922793757,314.1923076923077,32.44563810647648,0.0,0,314.1923076923077,32.44563810647648,1983.6538461538462,63.200808861705625 -SQLAssistant,MCP,Data Security and Compliance Auditor,GPT-5,129,988.2713178294574,45.23097998718646,237.8139534883721,45.98621057509186,0.0,0,237.8139534883721,45.98621057509186,1226.0852713178294,43.72968814849746 -SQLAssistant,MCP,Data Security and Compliance Auditor,Gemini-2.5-flash,86,810.8372093023256,76.22680780390911,2100.3953488372094,62.351308794280946,409.8255813953488,86.51517268550462,1690.5697674418604,73.23372521084816,2911.232558139535,62.46915454692215 -SQLAssistant,MCP,Data Security and Compliance Auditor,Gemini-2.5-flash-nothinking,84,905.702380952381,49.98036895728147,2581.4166666666665,255.20723979344925,0.0,0,2581.4166666666665,255.20723979344925,3487.1190476190477,191.9819290678375 -SQLAssistant,MCP,Data Security and Compliance Auditor,Qwen3-235b,93,1453.0752688172042,26.175735030426463,326.5806451612903,35.70466550626547,0.0,0,326.5806451612903,35.70466550626547,1779.6559139784947,25.78137229565623 -SQLAssistant,MCP,Expert SQL Query Generator,DeepSeek-R1,155,6817.754838709678,125.89027317685193,3042.2193548387095,85.0793932374139,1074.141935483871,149.1207087425162,1968.0774193548386,85.74403552217589,9859.974193548387,101.07170398338677 -SQLAssistant,MCP,Expert SQL Query Generator,DeepSeek-V3-1,115,5694.365217391304,43.48266107607825,275.8695652173913,43.55108059064449,0.0,0,275.8695652173913,43.55108059064449,5970.234782608695,43.22582798347546 -SQLAssistant,MCP,Expert SQL Query Generator,GPT-4o-mini,130,3075.2846153846153,34.74129815129366,59.83076923076923,65.66697735756406,0.0,0,59.83076923076923,65.66697735756406,3135.1153846153848,35.01416629785569 -SQLAssistant,MCP,Expert SQL Query Generator,GPT-5,129,3142.4496124031007,27.53911907023105,121.29457364341086,30.3496944553224,0.0,0,121.29457364341086,30.3496944553224,3263.7441860465115,27.30167652233267 -SQLAssistant,MCP,Expert SQL Query Generator,Gemini-2.5-flash,87,2747.1609195402298,75.58000940564662,2648.206896551724,122.47886673789839,68.36781609195403,124.59376620360645,2579.8390804597702,124.49667933915079,5395.367816091954,84.69984737132195 -SQLAssistant,MCP,Expert SQL Query Generator,Gemini-2.5-flash-nothinking,84,2178.964285714286,63.278966903552025,1367.3214285714287,100.74060867647496,0.0,0,1367.3214285714287,100.74060867647496,3546.285714285714,61.65855996059554 -SQLAssistant,MCP,Expert SQL Query Generator,Qwen3-235b,93,3430.4086021505377,36.89066405362386,167.49462365591398,38.34860586557435,0.0,0,167.49462365591398,38.34860586557435,3597.9032258064517,36.5664931705788 -SQLAssistant,MCP,Senior SQL Code Reviewer,DeepSeek-R1,155,12028.23870967742,127.12190039733528,5594.096774193548,82.47458960944094,4114.348387096774,86.3141557897285,1479.7483870967742,132.60678616158498,17622.335483870967,99.71614804703174 -SQLAssistant,MCP,Senior SQL Code Reviewer,DeepSeek-V3-1,111,3044.009009009009,43.04931230645842,241.57657657657657,34.35303565569292,0.0,0,241.57657657657657,34.35303565569292,3285.5855855855857,41.78719748769591 -SQLAssistant,MCP,Senior SQL Code Reviewer,GPT-4o-mini,130,4187.369230769231,70.53992601845951,131.76153846153846,77.09785926352211,0.0,0,131.76153846153846,77.09785926352211,4319.130769230769,70.49492185121261 -SQLAssistant,MCP,Senior SQL Code Reviewer,GPT-5,129,1589.860465116279,56.57819393362643,103.51937984496124,48.9427944632686,0.0,0,103.51937984496124,48.9427944632686,1693.3798449612402,55.49874246939609 -SQLAssistant,MCP,Senior SQL Code Reviewer,Gemini-2.5-flash,87,1935.1724137931035,74.68057718154739,1612.7356321839081,122.68188242580645,264.44827586206895,191.25488907626416,1348.287356321839,147.211824788055,3547.9080459770116,87.93511836786504 -SQLAssistant,MCP,Senior SQL Code Reviewer,Gemini-2.5-flash-nothinking,84,1212.452380952381,33.43982622965611,1013.7380952380952,100.11769883492005,0.0,0,1013.7380952380952,100.11769883492005,2226.190476190476,52.389496567984786 -SQLAssistant,MCP,Senior SQL Code Reviewer,Qwen3-235b,93,3832.989247311828,42.38508622203441,187.0215053763441,45.66398853296014,0.0,0,187.0215053763441,45.66398853296014,4020.010752688172,41.81423823450659 -SQLAssistant-H_A2A,A2A,Expert SQL Query Generator,DeepSeek-R1,124,6848.548387096775,134.47031705827283,3875.5887096774195,81.82713090431625,1383.782258064516,142.22188189031823,2491.8064516129034,75.47003470410645,10724.137096774193,98.06430878514263 -SQLAssistant-H_A2A,A2A,Expert SQL Query Generator,DeepSeek-V3-1,81,6381.925925925926,84.01749617307786,273.7037037037037,46.88881296644141,0.0,0,273.7037037037037,46.88881296644141,6655.62962962963,82.14590122682107 -SQLAssistant-H_A2A,A2A,Expert SQL Query Generator,GPT-4o-mini,83,2876.6867469879517,27.574527005627974,54.34939759036145,41.30613716215725,0.0,0,54.34939759036145,41.30613716215725,2931.0361445783133,27.532594795035013 -SQLAssistant-H_A2A,A2A,Expert SQL Query Generator,GPT-5,75,2665.8933333333334,8.50067376058639,111.38666666666667,19.239757599028962,0.0,0,111.38666666666667,19.239757599028962,2777.28,8.280080390626908 -SQLAssistant-H_A2A,A2A,Expert SQL Query Generator,Gemini-2.5-flash,93,4071.0860215053763,86.21834623921374,2603.3763440860216,157.23841414362738,124.63440860215054,63.408319362369326,2478.7419354838707,164.83433627824266,6674.462365591397,111.0590590123281 -SQLAssistant-H_A2A,A2A,Expert SQL Query Generator,Gemini-2.5-flash-nothinking,86,3087.5,237.3400921039416,2226.0581395348836,314.3558328831339,0.0,0,2226.0581395348836,314.3558328831339,5313.558139534884,268.6182075615013 -SQLAssistant-H_A2A,A2A,Expert SQL Query Generator,Qwen3-235b,88,3520.465909090909,58.68394950059209,172.2159090909091,48.51525999064374,0.0,0,172.2159090909091,48.51525999064374,3692.681818181818,57.9727421926901 -SQLAssistant-H_A2A,A2A,Senior SQL Code Reviewer,DeepSeek-R1,124,10224.66935483871,135.32396029128248,5630.862903225807,89.17952250909765,4032.0806451612902,86.79004739599318,1598.782258064516,151.84088805137114,15855.532258064517,108.76307215821974 -SQLAssistant-H_A2A,A2A,Senior SQL Code Reviewer,DeepSeek-V3-1,78,2964.0641025641025,50.69372699852589,229.74358974358975,29.062281914723414,0.0,0,229.74358974358975,29.062281914723414,3193.8076923076924,48.718277159651805 -SQLAssistant-H_A2A,A2A,Senior SQL Code Reviewer,GPT-4o-mini,83,3532.3012048192772,52.437943182669066,110.50602409638554,53.273230160446005,0.0,0,110.50602409638554,53.273230160446005,3642.8072289156626,52.170063006993914 -SQLAssistant-H_A2A,A2A,Senior SQL Code Reviewer,GPT-5,75,1587.4933333333333,51.84062668554452,103.76,37.64341295752474,0.0,0,103.76,37.64341295752474,1691.2533333333333,50.63881346075958 -SQLAssistant-H_A2A,A2A,Senior SQL Code Reviewer,Gemini-2.5-flash,93,1993.268817204301,59.10763969335386,1738.9247311827958,135.96190923164673,495.76344086021504,200.2828364560512,1243.1612903225807,156.79958589764996,3732.1935483870966,78.5789442232388 -SQLAssistant-H_A2A,A2A,Senior SQL Code Reviewer,Gemini-2.5-flash-nothinking,86,1907.953488372093,365.43035756977724,1650.4186046511627,420.817327375762,0.0,0,1650.4186046511627,420.817327375762,3558.3720930232557,389.8930954303707 -SQLAssistant-H_A2A,A2A,Senior SQL Code Reviewer,Qwen3-235b,88,3802.2272727272725,44.78708807153315,201.375,37.927552874843244,0.0,0,201.375,37.927552874843244,4003.6022727272725,43.78805349760636 -SQLAssistant-H_A2A,A2A,agent,DeepSeek-R1,271,361.7158671586716,57.726686198407705,73.17343173431735,147.40839767744063,0.0,0,73.17343173431735,147.40839767744063,434.8892988929889,57.65824064853029 -SQLAssistant-H_A2A,A2A,agent,DeepSeek-V3-1,174,359.63793103448273,31.678917055123023,120.77011494252874,129.19098230387212,0.0,0,120.77011494252874,129.19098230387212,480.4080459770115,43.860203281544884 -SQLAssistant-H_A2A,A2A,agent,GPT-4o-mini,231,332.45454545454544,37.83009038464835,106.21212121212122,143.8244350737371,0.0,0,106.21212121212122,143.8244350737371,438.6666666666667,52.61237162742572 -SQLAssistant-H_A2A,A2A,agent,GPT-5,161,376.41614906832297,27.42208483153654,147.61490683229815,120.01241769347808,0.0,0,147.61490683229815,120.01241769347808,524.0310559006211,41.6044470784623 -SQLAssistant-H_A2A,A2A,agent,Gemini-2.5-flash,222,371.84684684684686,68.987549034481,101.54504504504504,140.98854470315317,0.0,0,101.54504504504504,140.98854470315317,473.3918918918919,66.3050887498975 -SQLAssistant-H_A2A,A2A,agent,Gemini-2.5-flash-nothinking,239,352.7573221757322,41.25125198985155,101.49372384937239,144.92364601958147,0.0,0,101.49372384937239,144.92364601958147,454.2510460251046,51.26709235276514 -SQLAssistant-H_A2A,A2A,agent,Qwen3-235b,249,322.03212851405624,41.45602439159568,94.65863453815261,152.78035898678868,0.0,0,94.65863453815261,152.78035898678868,416.6907630522088,56.02277314204641 -SQLAssistant-H_A2A,A2A,result_interpreter,DeepSeek-R1,39,1146.923076923077,17.81714532424687,1527.128205128205,68.47776942822948,567.0769230769231,91.88804180622557,960.0512820512821,54.76633741090723,2674.051282051282,41.651140858589876 -SQLAssistant-H_A2A,A2A,result_interpreter,DeepSeek-V3-1,56,519.0714285714286,3.749693133212951,249.58928571428572,29.41237025733157,0.0,0,249.58928571428572,29.41237025733157,768.6607142857143,10.301535240978023 -SQLAssistant-H_A2A,A2A,result_interpreter,GPT-4o-mini,54,1058.0740740740741,5.168514030102506,375.3703703703704,10.682661010677377,0.0,0,375.3703703703704,10.682661010677377,1433.4444444444443,5.162384583239296 -SQLAssistant-H_A2A,A2A,result_interpreter,GPT-5,59,512.7966101694915,2.913182663836602,238.89830508474577,10.691939040905517,0.0,0,238.89830508474577,10.691939040905517,751.6949152542373,4.604297563607705 -SQLAssistant-H_A2A,A2A,result_interpreter,Gemini-2.5-flash,50,1732.24,168.6133013833691,1940.6,38.0926283729672,592.12,54.557063840985045,1348.48,32.69130235167286,3672.84,78.82623406294056 -SQLAssistant-H_A2A,A2A,result_interpreter,Gemini-2.5-flash-nothinking,50,678.72,18.240126041301696,180.7,54.87809954143024,0.0,0,180.7,54.87809954143024,859.42,18.181057028917245 -SQLAssistant-H_A2A,A2A,result_interpreter,Qwen3-235b,56,588.6428571428571,10.936343671556251,223.51785714285714,12.508354018072623,0.0,0,223.51785714285714,12.508354018072623,812.1607142857143,10.272093999084142 -SQLAssistant-H_A2A,A2A_mix,Expert SQL Query Generator,DeepSeek-R1,14,15390.714285714286,162.83999437382508,3136.0,71.40219345723854,900.5714285714286,143.20384225465702,2235.4285714285716,90.99495937862903,18526.714285714286,143.1015011976672 -SQLAssistant-H_A2A,A2A_mix,Expert SQL Query Generator,DeepSeek-V3-1,2,3466.5,27.435702313454673,209.0,31.80288872322271,0.0,0,209.0,31.80288872322271,3675.5,27.684033685959513 -SQLAssistant-H_A2A,A2A_mix,Expert SQL Query Generator,GPT-4o-mini,3,5351.0,51.68613756957428,133.33333333333334,63.32653472281584,0.0,0,133.33333333333334,63.32653472281584,5484.333333333333,51.90369242773092 -SQLAssistant-H_A2A,A2A_mix,Expert SQL Query Generator,GPT-5,2,2595.5,7.165058117975804,92.5,6.879957871004246,0.0,0,92.5,6.879957871004246,2688.0,7.15524719057816 -SQLAssistant-H_A2A,A2A_mix,Expert SQL Query Generator,Gemini-2.5-flash,8,4298.875,60.48891404465944,3716.375,148.88136457535953,82.125,29.284835037521407,3634.25,152.37478097404176,8015.25,90.6722951534848 -SQLAssistant-H_A2A,A2A_mix,Expert SQL Query Generator,Gemini-2.5-flash-nothinking,9,4729.888888888889,158.86448436781876,4019.1111111111113,171.48726752859972,0.0,0,4019.1111111111113,171.48726752859972,8749.0,164.1917313972726 -SQLAssistant-H_A2A,A2A_mix,Senior SQL Code Reviewer,DeepSeek-R1,14,13844.357142857143,89.55917032097541,7098.571428571428,73.15174091955346,4841.5,91.48184258411311,2257.0714285714284,99.45678514205082,20942.928571428572,68.29001424210092 -SQLAssistant-H_A2A,A2A_mix,Senior SQL Code Reviewer,DeepSeek-V3-1,2,3279.5,1.1427552798562894,191.5,5.538695413993845,0.0,0,191.5,5.538695413993845,3471.0,0.7741301551451687 -SQLAssistant-H_A2A,A2A_mix,Senior SQL Code Reviewer,GPT-4o-mini,3,3862.6666666666665,85.09289996107712,84.66666666666667,40.23267623880463,0.0,0,84.66666666666667,40.23267623880463,3947.3333333333335,84.13068945394512 -SQLAssistant-H_A2A,A2A_mix,Senior SQL Code Reviewer,GPT-5,2,951.0,0.0,69.5,5.0870991452269605,0.0,0,69.5,5.0870991452269605,1020.5,0.3464511421786122 -SQLAssistant-H_A2A,A2A_mix,Senior SQL Code Reviewer,Gemini-2.5-flash,8,6224.875,150.41261769537618,2184.75,61.34013221701014,119.25,16.70998005297483,2065.5,64.82841376097402,8409.625,105.8677709289928 -SQLAssistant-H_A2A,A2A_mix,Senior SQL Code Reviewer,Gemini-2.5-flash-nothinking,9,1407.888888888889,36.36571621226846,2060.8888888888887,200.88552182094185,0.0,0,2060.8888888888887,200.88552182094185,3468.777777777778,116.8476108098637 -SQLAssistant-H_A2A,A2A_mix,agent,DeepSeek-R1,62,327.9032258064516,38.671656624998626,43.225806451612904,186.49065277226617,0.0,0,43.225806451612904,186.49065277226617,371.1290322580645,46.48332139225827 -SQLAssistant-H_A2A,A2A_mix,agent,DeepSeek-V3-1,10,294.0,38.475320214114426,19.5,8.108404256841999,0.0,0,19.5,8.108404256841999,313.5,36.30895528393413 -SQLAssistant-H_A2A,A2A_mix,agent,GPT-4o-mini,12,330.1666666666667,33.72843495706268,88.41666666666667,190.82682690586395,0.0,0,88.41666666666667,190.82682690586395,418.5833333333333,57.33095538962553 -SQLAssistant-H_A2A,A2A_mix,agent,GPT-5,10,296.6,34.107335049422375,33.4,145.98311708489723,0.0,0,33.4,145.98311708489723,330.0,30.813213680707264 -SQLAssistant-H_A2A,A2A_mix,agent,Gemini-2.5-flash,40,300.45,33.62075836993062,38.925,184.9723676871833,0.0,0,38.925,184.9723676871833,339.375,41.59243943489807 -SQLAssistant-H_A2A,A2A_mix,agent,Gemini-2.5-flash-nothinking,46,304.89130434782606,41.93535528040746,54.108695652173914,216.1252881100585,0.0,0,54.108695652173914,216.1252881100585,359.0,59.17607150034455 -SQLAssistant-H_A2A,A2A_mix,result_interpreter,DeepSeek-R1,3,1097.3333333333333,1.8616711356068727,1178.6666666666667,5.094267081084933,395.6666666666667,11.11568404250079,783.0,2.892656871751245,2276.0,3.083093988198389 -SQLAssistant-H_A2A,A2A_mix,result_interpreter,DeepSeek-V3-1,1,488.0,0.0,257.0,0.0,0.0,0,257.0,0.0,745.0,0.0 -SQLAssistant-H_A2A,A2A_mix,result_interpreter,GPT-4o-mini,1,1014.0,0.0,300.0,0.0,0.0,0,300.0,0.0,1314.0,0.0 -SQLAssistant-H_A2A,A2A_mix,result_interpreter,GPT-5,1,505.0,0.0,240.0,0.0,0.0,0,240.0,0.0,745.0,0.0 -SQLAssistant-H_A2A,A2A_mix,result_interpreter,Gemini-2.5-flash,2,1051.0,4.171324494154705,1340.0,18.363668645740187,328.0,33.63068837350653,1012.0,13.415464623302087,2391.0,8.458073585083756 -SQLAssistant-H_A2A,A2A_mix,result_interpreter,Gemini-2.5-flash-nothinking,1,537.0,0.0,118.0,0.0,0.0,0,118.0,0.0,655.0,0.0 -SocialMediaManager,A2A,Shakespearean Bard,DeepSeek-R1,158,24517.09493670886,120.09427444513865,1899.5949367088608,86.23864247020589,428.7278481012658,182.78115064772732,1470.867088607595,92.40813900321217,26416.689873417723,116.16724296422109 -SocialMediaManager,A2A,Shakespearean Bard,DeepSeek-V3-1,77,49076.61038961039,77.11425263971311,1152.2077922077922,60.931864289308756,0.0,0,1152.2077922077922,60.931864289308756,50228.818181818184,76.70276124936257 -SocialMediaManager,A2A,Shakespearean Bard,GPT-4o-mini,136,27476.66911764706,76.63006242221073,735.9191176470588,68.80033923273487,0.0,0,735.9191176470588,68.80033923273487,28212.58823529412,76.17572476355774 -SocialMediaManager,A2A,Shakespearean Bard,GPT-5,112,5145.044642857143,85.43595033848925,163.60714285714286,75.1576530699694,0.0,0,163.60714285714286,75.1576530699694,5308.651785714285,85.09358454377161 -SocialMediaManager,A2A,Shakespearean Bard,Gemini-2.5-flash,64,3019.796875,32.91325930028227,1843.203125,22.52697500217696,36.640625,268.6541695891178,1806.5625,23.488613847183228,4863.0,21.49934067598476 -SocialMediaManager,A2A,Shakespearean Bard,Gemini-2.5-flash-nothinking,67,3091.7611940298507,36.773773252329384,1475.2835820895523,23.490087905616058,0.0,0,1475.2835820895523,23.490087905616058,4567.044776119403,26.430268017630397 -SocialMediaManager,A2A,Shakespearean Bard,Qwen3-235b,109,14591.669724770642,151.27495079787744,437.44954128440367,118.65361448950442,0.0,0,437.44954128440367,118.65361448950442,15029.119266055046,150.13426662447102 -SocialMediaManager,A2A,Topic Analysis Expert,DeepSeek-R1,75,2853.5066666666667,112.10462330650299,1320.2666666666667,52.78911430996324,237.85333333333332,128.19450608227714,1082.4133333333334,48.95648015454556,4173.7733333333335,88.16972731114994 -SocialMediaManager,A2A,Topic Analysis Expert,DeepSeek-V3-1,60,3048.4333333333334,24.8858117774886,716.0166666666667,13.039534067976803,0.0,0,716.0166666666667,13.039534067976803,3764.45,20.75572548154977 -SocialMediaManager,A2A,Topic Analysis Expert,GPT-4o-mini,60,2870.0833333333335,1.5956037104479892,591.6166666666667,14.27871673690874,0.0,0,591.6166666666667,14.27871673690874,3461.7,2.97461228732318 -SocialMediaManager,A2A,Topic Analysis Expert,GPT-5,60,2918.5833333333335,1.568517771901031,575.7166666666667,6.9743145598233856,0.0,0,575.7166666666667,6.9743145598233856,3494.3,1.7748446127345268 -SocialMediaManager,A2A,Topic Analysis Expert,Gemini-2.5-flash,60,909.25,52.94627116033163,2217.9166666666665,26.993991231680926,123.21666666666667,34.78977304226658,2094.7,28.50650321223646,3127.1666666666665,28.900352701666716 -SocialMediaManager,A2A,Topic Analysis Expert,Gemini-2.5-flash-nothinking,60,935.8333333333334,62.945609887703924,2397.9333333333334,23.83170132775743,0.0,0,2397.9333333333334,23.83170132775743,3333.766666666667,30.275649518610965 -SocialMediaManager,A2A,Topic Analysis Expert,Qwen3-235b,60,2951.05,1.6054687168016675,616.7166666666667,19.19529956377522,0.0,0,616.7166666666667,19.19529956377522,3567.766666666667,3.5980865330336345 -SocialMediaManager,A2A,X Post Verifier,DeepSeek-R1,151,16152.46357615894,109.74517351771962,3797.337748344371,66.8129206284043,2654.251655629139,82.90366422835068,1143.0860927152319,106.60813198855963,19949.80132450331,95.36596303008498 -SocialMediaManager,A2A,X Post Verifier,DeepSeek-V3-1,77,14410.90909090909,3.140173553292449,611.5324675324675,14.814643553563812,0.0,0,611.5324675324675,14.814643553563812,15022.441558441558,3.384565084640862 -SocialMediaManager,A2A,X Post Verifier,GPT-4o-mini,136,12964.573529411764,20.4003893235752,448.69117647058823,19.902393896960877,0.0,0,448.69117647058823,19.902393896960877,13413.264705882353,20.297105108977217 -SocialMediaManager,A2A,X Post Verifier,GPT-5,112,13965.625,1.2617032169478026,587.5446428571429,8.296350729464171,0.0,0,587.5446428571429,8.296350729464171,14553.169642857143,1.5240384451905256 -SocialMediaManager,A2A,X Post Verifier,Gemini-2.5-flash,64,2751.03125,51.610839647669906,1599.890625,21.8129752053128,275.734375,31.84576552391943,1324.15625,24.644803287300515,4350.921875,37.141629072111506 -SocialMediaManager,A2A,X Post Verifier,Gemini-2.5-flash-nothinking,67,1641.4925373134329,25.59462544214056,1041.0746268656717,22.881862272179642,0.0,0,1041.0746268656717,22.881862272179642,2682.5671641791046,20.292899045577297 -SocialMediaManager,A2A,X Post Verifier,Qwen3-235b,109,13131.834862385322,24.700824006080317,626.9908256880734,26.443794253113417,0.0,0,626.9908256880734,26.443794253113417,13758.825688073395,24.565892978658457 -SocialMediaManager,MCP,Shakespearean Bard,DeepSeek-R1,153,20707.07843137255,120.99077001976293,1706.9477124183006,87.56719942529803,410.7516339869281,169.6524520166698,1296.1960784313726,97.61114981754007,22414.02614379085,116.89205758346732 -SocialMediaManager,MCP,Shakespearean Bard,DeepSeek-V3-1,90,45081.8,88.35208565716653,1038.388888888889,65.87471354009739,0.0,0,1038.388888888889,65.87471354009739,46120.188888888886,87.78207040328961 -SocialMediaManager,MCP,Shakespearean Bard,GPT-4o-mini,148,28276.83108108108,78.09200069995009,754.9324324324324,61.442849408849284,0.0,0,754.9324324324324,61.442849408849284,29031.763513513513,77.58244632357636 -SocialMediaManager,MCP,Shakespearean Bard,GPT-5,112,4346.214285714285,52.107298199727424,141.53571428571428,48.30429933320124,0.0,0,141.53571428571428,48.30429933320124,4487.75,51.95762477326132 -SocialMediaManager,MCP,Shakespearean Bard,Gemini-2.5-flash,70,2779.7714285714287,5.496277104993093,1810.8142857142857,17.191081308782824,24.114285714285714,262.64412312049734,1786.7,17.533136376872832,4590.585714285714,7.840426718294427 -SocialMediaManager,MCP,Shakespearean Bard,Gemini-2.5-flash-nothinking,65,3295.8615384615387,42.320471973983175,1478.1076923076923,20.213394402916133,0.0,0,1478.1076923076923,20.213394402916133,4773.969230769231,28.963715539668016 -SocialMediaManager,MCP,Shakespearean Bard,Qwen3-235b,101,11189.524752475247,147.85386765545397,361.4059405940594,112.04480110883823,0.0,0,361.4059405940594,112.04480110883823,11550.930693069307,146.60710112314752 -SocialMediaManager,MCP,Topic Analysis Expert,DeepSeek-R1,76,2163.6710526315787,93.82164797465249,1157.9868421052631,43.153573579792386,226.17105263157896,125.4768607056072,931.8157894736842,36.49022878620645,3321.657894736842,69.27550998937176 -SocialMediaManager,MCP,Topic Analysis Expert,DeepSeek-V3-1,60,2953.133333333333,1.8179758511125341,737.3833333333333,18.35291939378041,0.0,0,737.3833333333333,18.35291939378041,3690.516666666667,4.202646399564699 -SocialMediaManager,MCP,Topic Analysis Expert,GPT-4o-mini,61,2871.8852459016393,1.8941759461081658,634.5245901639345,18.370708502901763,0.0,0,634.5245901639345,18.370708502901763,3506.409836065574,3.778477733140932 -SocialMediaManager,MCP,Topic Analysis Expert,GPT-5,60,2913.4333333333334,1.5169127788739678,588.0833333333334,7.942781538693319,0.0,0,588.0833333333334,7.942781538693319,3501.516666666667,1.9366301188280657 -SocialMediaManager,MCP,Topic Analysis Expert,Gemini-2.5-flash,60,832.2333333333333,0.20440336792975777,2232.45,23.183552217452807,133.91666666666666,23.660669673900898,2098.5333333333333,24.41471163325217,3064.6833333333334,16.870509770272296 -SocialMediaManager,MCP,Topic Analysis Expert,Gemini-2.5-flash-nothinking,60,884.9833333333333,38.37583029656196,2464.0833333333335,19.71378658217498,0.0,0,2464.0833333333335,19.71378658217498,3349.0666666666666,18.00238529560151 -SocialMediaManager,MCP,Topic Analysis Expert,Qwen3-235b,60,2955.9666666666667,1.6714322597226081,616.2,22.02637449649295,0.0,0,616.2,22.02637449649295,3572.1666666666665,4.449574854466486 -SocialMediaManager,MCP,X Post Verifier,DeepSeek-R1,145,15475.731034482758,107.43872636109451,4296.4068965517245,66.04626777556719,3078.9241379310347,79.00737038144614,1217.4827586206898,100.51493432544257,19772.137931034482,91.90146942420998 -SocialMediaManager,MCP,X Post Verifier,DeepSeek-V3-1,90,14614.666666666666,5.267022661188486,618.9777777777778,14.629199030602305,0.0,0,618.9777777777778,14.629199030602305,15233.644444444444,5.383190765676044 -SocialMediaManager,MCP,X Post Verifier,GPT-4o-mini,147,12980.027210884355,19.63968972771007,453.5442176870748,18.388800827858624,0.0,0,453.5442176870748,18.388800827858624,13433.57142857143,19.51353265362196 -SocialMediaManager,MCP,X Post Verifier,GPT-5,112,13907.125,7.5108830049564475,589.8303571428571,10.139853496044054,0.0,0,589.8303571428571,10.139853496044054,14496.955357142857,7.50948299986431 -SocialMediaManager,MCP,X Post Verifier,Gemini-2.5-flash,70,2134.885714285714,51.3184612099798,1575.2,18.869826259627718,268.34285714285716,25.049131124929954,1306.857142857143,22.150945188644048,3710.0857142857144,30.599970211390026 -SocialMediaManager,MCP,X Post Verifier,Gemini-2.5-flash-nothinking,65,1671.9538461538461,28.322296025966164,1059.5692307692307,15.954205163295597,0.0,0,1059.5692307692307,15.954205163295597,2731.523076923077,19.03236658821829 -SocialMediaManager,MCP,X Post Verifier,Qwen3-235b,101,13331.227722772277,22.672997113003706,653.6336633663366,26.410941415064993,0.0,0,653.6336633663366,26.410941415064993,13984.861386138615,22.672550112231082 -SocialMediaManager-H_A2A,A2A_mix,Shakespearean Bard,DeepSeek-R1,124,28973.00806451613,103.52697195827325,1846.008064516129,77.03374331978856,319.75,149.82084672485206,1526.258064516129,80.54626891011155,30819.016129032258,101.1262864187143 -SocialMediaManager-H_A2A,A2A_mix,Shakespearean Bard,DeepSeek-V3-1,71,40320.05633802817,84.08719825779922,1055.9718309859154,64.46922404612,0.0,0,1055.9718309859154,64.46922404612,41376.028169014084,83.54172736353117 -SocialMediaManager-H_A2A,A2A_mix,Shakespearean Bard,GPT-4o-mini,72,27680.63888888889,91.06780516927915,748.0694444444445,70.4677404952296,0.0,0,748.0694444444445,70.4677404952296,28428.708333333332,90.41849506019624 -SocialMediaManager-H_A2A,A2A_mix,Shakespearean Bard,GPT-5,66,6135.742424242424,107.52245070564688,208.07575757575756,92.71662199570262,0.0,0,208.07575757575756,92.71662199570262,6343.818181818182,107.01593692157796 -SocialMediaManager-H_A2A,A2A_mix,Shakespearean Bard,Gemini-2.5-flash,94,1911.0425531914893,34.98125642410906,1705.4574468085107,18.449845384949295,169.89361702127658,130.0243129931844,1535.563829787234,16.623772392380136,3616.5,21.435022587496608 -SocialMediaManager-H_A2A,A2A_mix,Shakespearean Bard,Gemini-2.5-flash-nothinking,80,2190.825,41.36128907508648,1347.275,19.3314910759684,0.0,0,1347.275,19.3314910759684,3538.1,28.127013749783313 -SocialMediaManager-H_A2A,A2A_mix,Shakespearean Bard,Qwen3-235b,106,10792.88679245283,159.323567704186,354.92452830188677,109.2214361839175,0.0,0,354.92452830188677,109.2214361839175,11147.811320754718,157.60129126020524 -SocialMediaManager-H_A2A,A2A_mix,agent,DeepSeek-R1,133,616.3308270676691,5.443759969635356,1002.4812030075188,88.99107565528091,627.0526315789474,96.22896291949125,375.42857142857144,97.0777013853438,1618.812030075188,55.31359486441886 -SocialMediaManager-H_A2A,A2A_mix,agent,DeepSeek-V3-1,129,1078.2248062015503,39.50727211240121,130.7906976744186,49.543527972117666,0.0,0,130.7906976744186,49.543527972117666,1209.015503875969,34.74163785299805 -SocialMediaManager-H_A2A,A2A_mix,agent,GPT-4o-mini,120,461.84166666666664,4.433254999134604,155.875,64.061799916006,0.0,0,155.875,64.061799916006,617.7166666666667,16.420156339313756 -SocialMediaManager-H_A2A,A2A_mix,agent,GPT-5,179,426.0223463687151,15.647491993000953,88.83798882681565,96.2546588908543,0.0,0,88.83798882681565,96.2546588908543,514.8603351955308,25.393982126254667 -SocialMediaManager-H_A2A,A2A_mix,agent,Gemini-2.5-flash,24,0.0,0,0.0,0,0.0,0,0.0,0,454.5416666666667,8.818700966266045 -SocialMediaManager-H_A2A,A2A_mix,agent,Gemini-2.5-flash-nothinking,120,0.0,0,0.0,0,0.0,0,0.0,0,476.1,8.590425289200535 -SocialMediaManager-H_A2A,A2A_mix,agent,Qwen3-235b,139,601.6402877697842,9.49754667339206,122.22302158273381,85.2023017972016,0.0,0,122.22302158273381,85.2023017972016,723.863309352518,17.367732266959138 -SocialMediaManager-H_A2A,A2A_mix,x_post_verifier,DeepSeek-R1,116,9011.103448275862,62.13042338862995,6994.5,53.99816144031928,2674.689655172414,66.99302714893922,4319.810344827586,47.51365623058597,16005.603448275862,46.64810729446687 -SocialMediaManager-H_A2A,A2A_mix,x_post_verifier,DeepSeek-V3-1,71,4390.394366197183,7.702914005478295,585.4225352112676,13.952371755629384,0.0,0,585.4225352112676,13.952371755629384,4975.816901408451,8.144406642204014 -SocialMediaManager-H_A2A,A2A_mix,x_post_verifier,GPT-4o-mini,72,5436.5,2.0615248591280246,1207.0,14.11452620976982,0.0,0,1207.0,14.11452620976982,6643.5,3.6835202181652935 -SocialMediaManager-H_A2A,A2A_mix,x_post_verifier,GPT-5,66,6835.227272727273,5.2132302121481935,694.0909090909091,12.267838837387265,0.0,0,694.0909090909091,12.267838837387265,7529.318181818182,4.834402618288319 -SocialMediaManager-H_A2A,A2A_mix,x_post_verifier,Gemini-2.5-flash,94,6081.148936170212,20.387984376868197,2483.021276595745,25.213585399853155,737.6276595744681,38.811606119803,1745.3936170212767,20.06589798694245,8564.170212765957,16.57280001862982 -SocialMediaManager-H_A2A,A2A_mix,x_post_verifier,Gemini-2.5-flash-nothinking,80,2561.55,2.847875933148397,417.8625,15.398075050106264,0.0,0,417.8625,15.398075050106264,2979.4125,4.195791729926996 -SocialMediaManager-H_A2A,A2A_mix,x_post_verifier,Qwen3-235b,106,3797.433962264151,33.09619459108685,769.7547169811321,16.014808817685545,0.0,0,769.7547169811321,16.014808817685545,4567.188679245283,27.926850174629703 +SQL_assistant,A2A,Business Intelligence Analyst,DeepSeek-R1,8,5487.5,98.81809957550303,3639.0,90.43081778400133,1964.625,86.91507573055198,1674.375,110.45448593036183,9126.5,79.14673983669186 +SQL_assistant,A2A,Business Intelligence Analyst,GPT-4o-mini,60,8817.333333333334,20.729246532819793,604.3833333333333,20.15750402506632,0.0,0,604.3833333333333,20.15750402506632,9421.716666666667,20.40651946564459 +SQL_assistant,A2A,Business Intelligence Analyst,GPT-5,54,2863.5,78.08946438245844,337.55555555555554,45.70514988360644,0.0,0,337.55555555555554,45.70514988360644,3201.0555555555557,74.596612295914 +SQL_assistant,A2A,Business Intelligence Analyst,Gemini-2.5-flash,22,3589.7727272727275,43.88013189720562,3600.681818181818,41.30130637942791,671.1363636363636,75.6201779724386,2929.5454545454545,51.67340805624184,7190.454545454545,40.60073903279846 +SQL_assistant,A2A,Business Intelligence Analyst,Gemini-2.5-flash-nothinking,40,3086.0,108.50375935519052,3688.45,79.38337405110127,0.0,0,3688.45,79.38337405110127,6774.45,87.54024346276594 +SQL_assistant,A2A,Business Intelligence Analyst,Qwen3-235b,54,5520.444444444444,51.98433836245931,421.3888888888889,41.064079246409804,0.0,0,421.3888888888889,41.064079246409804,5941.833333333333,51.08655394668856 +SQL_assistant,A2A,Data Security and Compliance Auditor,DeepSeek-R1,177,852.0,34.12529211857415,1281.949152542373,100.92157360805396,1120.231638418079,112.68870886299736,161.71751412429379,89.40936532565787,2133.9491525423728,64.2059093325058 +SQL_assistant,A2A,Data Security and Compliance Auditor,DeepSeek-V3-1,195,852.9948717948718,30.81055051952714,88.51794871794871,36.86932645907556,0.0,0,88.51794871794871,36.86932645907556,941.5128205128206,30.73259720850392 +SQL_assistant,A2A,Data Security and Compliance Auditor,GPT-4o-mini,71,1664.380281690141,6.554787916547862,119.73239436619718,20.36134278278672,0.0,0,119.73239436619718,20.36134278278672,1784.112676056338,6.5303157408711705 +SQL_assistant,A2A,Data Security and Compliance Auditor,GPT-5,93,771.0,0.0,131.38709677419354,14.439311975801111,0.0,0,131.38709677419354,14.439311975801111,902.3870967741935,2.1023563908428513 +SQL_assistant,A2A,Data Security and Compliance Auditor,Gemini-2.5-flash,161,892.2298136645962,31.70961174051696,2535.0248447204967,161.2172546191433,1403.2173913043478,41.53203165271908,1131.807453416149,363.4155021792126,3427.254658385093,125.65502058578495 +SQL_assistant,A2A,Data Security and Compliance Auditor,Gemini-2.5-flash-nothinking,127,925.4724409448819,67.91264003500098,436.6062992125984,130.31337056469474,0.0,0,436.6062992125984,130.31337056469474,1362.0787401574803,82.18002041823304 +SQL_assistant,A2A,Data Security and Compliance Auditor,Qwen3-235b,90,1060.7333333333333,41.06962354863584,112.25555555555556,45.50873485305262,0.0,0,112.25555555555556,45.50873485305262,1172.9888888888888,40.07578362409585 +SQL_assistant,A2A,Expert SQL Query Generator,DeepSeek-R1,181,7744.900552486188,122.46200490882948,3808.0773480662983,81.42552270722112,1194.0441988950276,145.25665128053484,2614.033149171271,72.78607163997532,11552.977900552487,94.38852060267831 +SQL_assistant,A2A,Expert SQL Query Generator,DeepSeek-V3-1,209,6119.296650717703,66.13502754484202,275.8708133971292,47.34863575670719,0.0,0,275.8708133971292,47.34863575670719,6395.167464114833,64.6826708943063 +SQL_assistant,A2A,Expert SQL Query Generator,GPT-4o-mini,71,2658.4647887323945,17.75171231513613,56.80281690140845,50.78392179359812,0.0,0,56.80281690140845,50.78392179359812,2715.2676056338028,18.21380455405417 +SQL_assistant,A2A,Expert SQL Query Generator,GPT-5,93,2788.8387096774195,33.43668012491725,115.06451612903226,27.76374651552686,0.0,0,115.06451612903226,27.76374651552686,2903.9032258064517,33.008003389878475 +SQL_assistant,A2A,Expert SQL Query Generator,Gemini-2.5-flash,161,2268.3478260869565,70.45244642441207,2214.782608695652,173.52474504365,51.683229813664596,142.115603250684,2163.0993788819874,176.79695431951563,4483.130434782609,104.84098505699791 +SQL_assistant,A2A,Expert SQL Query Generator,Gemini-2.5-flash-nothinking,127,3343.1417322834645,203.4446093494842,2381.7244094488187,283.8084137156365,0.0,0,2381.7244094488187,283.8084137156365,5724.866141732284,232.9686154190881 +SQL_assistant,A2A,Expert SQL Query Generator,Qwen3-235b,90,3225.8333333333335,42.81182581085742,173.86666666666667,48.23444010399567,0.0,0,173.86666666666667,48.23444010399567,3399.7,42.58790636693682 +SQL_assistant,A2A,Senior SQL Code Reviewer,DeepSeek-R1,180,12656.705555555556,143.84091385372506,5378.45,83.15563294342726,3666.4555555555557,85.12788126866512,1711.9944444444445,137.15013710918555,18035.155555555557,110.71605154177261 +SQL_assistant,A2A,Senior SQL Code Reviewer,DeepSeek-V3-1,198,3221.09595959596,50.55559278940607,254.25757575757575,35.85703197524825,0.0,0,254.25757575757575,35.85703197524825,3475.3535353535353,49.073455398529894 +SQL_assistant,A2A,Senior SQL Code Reviewer,GPT-4o-mini,71,3176.056338028169,49.140239783326265,110.46478873239437,63.097526635068924,0.0,0,110.46478873239437,63.097526635068924,3286.521126760563,49.052339305245766 +SQL_assistant,A2A,Senior SQL Code Reviewer,GPT-5,93,1452.3118279569892,47.01798963292112,101.38709677419355,41.16215591422777,0.0,0,101.38709677419355,41.16215591422777,1553.6989247311828,46.16243173165622 +SQL_assistant,A2A,Senior SQL Code Reviewer,Gemini-2.5-flash,161,1743.1180124223602,68.39804070612475,1487.3788819875776,73.1046109985398,253.11180124223603,166.66100966439353,1234.2670807453417,80.38557297497421,3230.496894409938,58.579364946915604 +SQL_assistant,A2A,Senior SQL Code Reviewer,Gemini-2.5-flash-nothinking,127,1770.3464566929133,333.7792156005412,1189.6614173228347,211.67597972871567,0.0,0,1189.6614173228347,211.67597972871567,2960.0078740157483,227.92851916491966 +SQL_assistant,A2A,Senior SQL Code Reviewer,Qwen3-235b,90,3276.5666666666666,44.413985764824396,173.07777777777778,44.8287169689252,0.0,0,173.07777777777778,44.8287169689252,3449.6444444444446,44.007195711952775 +SQL_assistant,A2A_mix,Expert SQL Query Generator,DeepSeek-R1,138,7715.144927536232,154.69679811661777,3800.557971014493,81.33453041841516,1334.7608695652175,143.2643480557206,2465.7971014492755,76.66684590142923,11515.702898550724,113.74886628464455 +SQL_assistant,A2A_mix,Expert SQL Query Generator,DeepSeek-V3-1,83,6311.674698795181,84.22874973612699,272.144578313253,46.800792590036956,0.0,0,272.144578313253,46.800792590036956,6583.819277108434,82.33735554713274 +SQL_assistant,A2A_mix,Expert SQL Query Generator,GPT-4o-mini,86,2963.0,33.6741222365596,57.104651162790695,51.54656199950498,0.0,0,57.104651162790695,51.54656199950498,3020.1046511627906,33.782980990953405 +SQL_assistant,A2A_mix,Expert SQL Query Generator,GPT-5,77,2664.064935064935,8.442546312566805,110.8961038961039,19.27409892814165,0.0,0,110.8961038961039,19.27409892814165,2774.961038961039,8.231923550064945 +SQL_assistant,A2A_mix,Expert SQL Query Generator,Gemini-2.5-flash,101,4089.128712871287,84.04793309202067,2691.5346534653463,156.09119886916554,121.26732673267327,63.445072057495445,2570.267326732673,163.23734782210934,6780.663366336634,108.75508375559791 +SQL_assistant,A2A_mix,Expert SQL Query Generator,Gemini-2.5-flash-nothinking,95,3243.0947368421052,225.73802822374188,2395.926315789474,290.9719559423397,0.0,0,2395.926315789474,290.9719559423397,5639.021052631579,252.54228098544283 +SQL_assistant,A2A_mix,Expert SQL Query Generator,Qwen3-235b,88,3520.465909090909,58.68394950059209,172.2159090909091,48.51525999064374,0.0,0,172.2159090909091,48.51525999064374,3692.681818181818,57.9727421926901 +SQL_assistant,A2A_mix,Senior SQL Code Reviewer,DeepSeek-R1,138,10591.884057971014,129.33868470823282,5779.760869565217,87.19098815961551,4114.195652173913,87.35426235192728,1665.5652173913043,144.70678558112905,16371.644927536232,103.79936579370481 +SQL_assistant,A2A_mix,Senior SQL Code Reviewer,DeepSeek-V3-1,80,2971.95,49.94316896327415,228.7875,28.936092788416858,0.0,0,228.7875,28.936092788416858,3200.7375,48.01287796850852 +SQL_assistant,A2A_mix,Senior SQL Code Reviewer,GPT-4o-mini,86,3543.8255813953488,53.29945267862964,109.6046511627907,53.148341467016614,0.0,0,109.6046511627907,53.148341467016614,3653.4302325581393,52.98290204379742 +SQL_assistant,A2A_mix,Senior SQL Code Reviewer,GPT-5,77,1570.9610389610389,52.09769792002174,102.87012987012987,37.84568733579878,0.0,0,102.87012987012987,37.84568733579878,1673.8311688311687,50.89417322546653 +SQL_assistant,A2A_mix,Senior SQL Code Reviewer,Gemini-2.5-flash,101,2328.4455445544554,126.91360373871927,1774.2376237623762,129.54699289501968,465.94059405940595,205.57670770795488,1308.2970297029703,146.4490457315198,4102.683168316832,94.63048796675108 +SQL_assistant,A2A_mix,Senior SQL Code Reviewer,Gemini-2.5-flash-nothinking,95,1860.578947368421,356.5225501474367,1689.3052631578948,397.50141120923007,0.0,0,1689.3052631578948,397.50141120923007,3549.8842105263157,373.1354032648248 +SQL_assistant,A2A_mix,Senior SQL Code Reviewer,Qwen3-235b,88,3802.2272727272725,44.78708807153315,201.375,37.927552874843244,0.0,0,201.375,37.927552874843244,4003.6022727272725,43.78805349760636 +SQL_assistant,A2A_mix,agent,DeepSeek-R1,333,355.42042042042044,55.26793891260944,67.5975975975976,153.68182153493282,0.0,0,67.5975975975976,153.68182153493282,423.01801801801804,56.547458864920486 +SQL_assistant,A2A_mix,agent,DeepSeek-V3-1,184,356.07065217391306,32.17161676762111,115.2663043478261,133.11647410335686,0.0,0,115.2663043478261,133.11647410335686,471.3369565217391,44.52810568836708 +SQL_assistant,A2A_mix,agent,GPT-4o-mini,243,332.34156378600824,37.57836413823401,105.33333333333333,145.49521481827406,0.0,0,105.33333333333333,145.49521481827406,437.67489711934155,52.72933987720345 +SQL_assistant,A2A_mix,agent,GPT-5,171,371.7485380116959,28.113274202535493,140.93567251461988,123.68573954597382,0.0,0,140.93567251461988,123.68573954597382,512.6842105263158,42.452011189240295 +SQL_assistant,A2A_mix,agent,Gemini-2.5-flash,262,360.9465648854962,66.66935797672376,91.98473282442748,148.4223944142234,0.0,0,91.98473282442748,148.4223944142234,452.9312977099237,65.76726756415341 +SQL_assistant,A2A_mix,agent,Gemini-2.5-flash-nothinking,285,345.0315789473684,41.64548807744146,93.84561403508772,152.9492274687455,0.0,0,93.84561403508772,152.9492274687455,438.87719298245617,52.866516209899864 +SQL_assistant,A2A_mix,agent,Qwen3-235b,249,322.03212851405624,41.45602439159568,94.65863453815261,152.78035898678868,0.0,0,94.65863453815261,152.78035898678868,416.6907630522088,56.02277314204641 +SQL_assistant,A2A_mix,result_interpreter,DeepSeek-R1,42,1143.3809523809523,17.247670901654732,1502.2380952380952,67.29517843147693,554.8333333333334,90.7893363872332,947.4047619047619,53.65263517039461,2645.6190476190477,40.72282804524033 +SQL_assistant,A2A_mix,result_interpreter,DeepSeek-V3-1,57,518.5263157894736,3.803698602565933,249.71929824561403,29.136052646015077,0.0,0,249.71929824561403,29.136052646015077,768.2456140350877,10.222801576179101 +SQL_assistant,A2A_mix,result_interpreter,GPT-4o-mini,55,1057.2727272727273,5.155051882593489,374.0,10.96413671256443,0.0,0,374.0,10.96413671256443,1431.2727272727273,5.244272458790303 +SQL_assistant,A2A_mix,result_interpreter,GPT-5,60,512.6666666666666,2.895784647056012,238.91666666666666,10.600294601348057,0.0,0,238.91666666666666,10.600294601348057,751.5833333333334,4.567237092103713 +SQL_assistant,A2A_mix,result_interpreter,Gemini-2.5-flash,52,1706.0384615384614,167.99182326856717,1917.5,38.316574442442395,581.9615384615385,55.183054540856645,1335.5384615384614,32.753137951355015,3623.5384615384614,78.62099667208034 +SQL_assistant,A2A_mix,result_interpreter,Gemini-2.5-flash-nothinking,51,675.9411764705883,18.36719375735339,179.47058823529412,54.91702129807776,0.0,0,179.47058823529412,54.91702129807776,855.4117647058823,18.389680683903972 +SQL_assistant,A2A_mix,result_interpreter,Qwen3-235b,56,588.6428571428571,10.936343671556251,223.51785714285714,12.508354018072623,0.0,0,223.51785714285714,12.508354018072623,812.1607142857143,10.272093999084142 +SQL_assistant,MCP,Business Intelligence Analyst,DeepSeek-R1,29,3474.3793103448274,130.29048826727126,2280.5172413793102,68.34584782682637,1431.1724137931035,83.5171733894097,849.3448275862069,95.28325823322227,5754.896551724138,92.05089498497685 +SQL_assistant,MCP,Business Intelligence Analyst,DeepSeek-V3-1,51,4698.176470588235,51.648903070617614,822.0,32.50167518234221,0.0,0,822.0,32.50167518234221,5520.176470588235,48.61279254109235 +SQL_assistant,MCP,Business Intelligence Analyst,GPT-4o-mini,34,6090.058823529412,51.06786904699376,602.5882352941177,33.026574965093985,0.0,0,602.5882352941177,33.026574965093985,6692.64705882353,48.951961697540455 +SQL_assistant,MCP,Business Intelligence Analyst,GPT-5,35,686.9142857142857,5.199558116159608,319.85714285714283,12.486751183755516,0.0,0,319.85714285714283,12.486751183755516,1006.7714285714286,4.288644702652085 +SQL_assistant,MCP,Business Intelligence Analyst,Gemini-2.5-flash,55,1018.1272727272727,36.562931944417784,1129.9818181818182,48.88332428065035,79.74545454545455,273.0614910745346,1050.2363636363636,52.3138445348215,2148.109090909091,32.217366916268865 +SQL_assistant,MCP,Business Intelligence Analyst,Gemini-2.5-flash-nothinking,59,1182.0,144.0226420054809,902.9830508474577,178.0302608474315,0.0,0,902.9830508474577,178.0302608474315,2084.9830508474574,156.77252707967972 +SQL_assistant,MCP,Business Intelligence Analyst,Qwen3-235b,56,2416.375,113.71931212844764,439.5357142857143,53.726067151208134,0.0,0,439.5357142857143,53.726067151208134,2855.910714285714,103.77053220896117 +SQL_assistant,MCP,Data Security and Compliance Auditor,DeepSeek-R1,142,1253.3239436619717,82.00583239482967,1596.5070422535211,75.7237078134924,1007.387323943662,81.98776868968957,589.1197183098592,104.34377652987085,2849.830985915493,67.59304007204267 +SQL_assistant,MCP,Data Security and Compliance Auditor,DeepSeek-V3-1,110,1476.5636363636363,63.22510206154289,390.59090909090907,41.23568288986837,0.0,0,390.59090909090907,41.23568288986837,1867.1545454545455,56.36916656439135 +SQL_assistant,MCP,Data Security and Compliance Auditor,GPT-4o-mini,130,1669.4615384615386,72.22045922793757,314.1923076923077,32.44563810647648,0.0,0,314.1923076923077,32.44563810647648,1983.6538461538462,63.200808861705625 +SQL_assistant,MCP,Data Security and Compliance Auditor,GPT-5,129,988.2713178294574,45.23097998718646,237.8139534883721,45.98621057509186,0.0,0,237.8139534883721,45.98621057509186,1226.0852713178294,43.72968814849746 +SQL_assistant,MCP,Data Security and Compliance Auditor,Gemini-2.5-flash,86,810.8372093023256,76.22680780390911,2100.3953488372094,62.351308794280946,409.8255813953488,86.51517268550462,1690.5697674418604,73.23372521084816,2911.232558139535,62.46915454692215 +SQL_assistant,MCP,Data Security and Compliance Auditor,Gemini-2.5-flash-nothinking,84,905.702380952381,49.98036895728147,2581.4166666666665,255.20723979344925,0.0,0,2581.4166666666665,255.20723979344925,3487.1190476190477,191.9819290678375 +SQL_assistant,MCP,Data Security and Compliance Auditor,Qwen3-235b,93,1453.0752688172042,26.175735030426463,326.5806451612903,35.70466550626547,0.0,0,326.5806451612903,35.70466550626547,1779.6559139784947,25.78137229565623 +SQL_assistant,MCP,Expert SQL Query Generator,DeepSeek-R1,155,6817.754838709678,125.89027317685193,3042.2193548387095,85.0793932374139,1074.141935483871,149.1207087425162,1968.0774193548386,85.74403552217589,9859.974193548387,101.07170398338677 +SQL_assistant,MCP,Expert SQL Query Generator,DeepSeek-V3-1,115,5694.365217391304,43.48266107607825,275.8695652173913,43.55108059064449,0.0,0,275.8695652173913,43.55108059064449,5970.234782608695,43.22582798347546 +SQL_assistant,MCP,Expert SQL Query Generator,GPT-4o-mini,130,3075.2846153846153,34.74129815129366,59.83076923076923,65.66697735756406,0.0,0,59.83076923076923,65.66697735756406,3135.1153846153848,35.01416629785569 +SQL_assistant,MCP,Expert SQL Query Generator,GPT-5,129,3142.4496124031007,27.53911907023105,121.29457364341086,30.3496944553224,0.0,0,121.29457364341086,30.3496944553224,3263.7441860465115,27.30167652233267 +SQL_assistant,MCP,Expert SQL Query Generator,Gemini-2.5-flash,87,2747.1609195402298,75.58000940564662,2648.206896551724,122.47886673789839,68.36781609195403,124.59376620360645,2579.8390804597702,124.49667933915079,5395.367816091954,84.69984737132195 +SQL_assistant,MCP,Expert SQL Query Generator,Gemini-2.5-flash-nothinking,84,2178.964285714286,63.278966903552025,1367.3214285714287,100.74060867647496,0.0,0,1367.3214285714287,100.74060867647496,3546.285714285714,61.65855996059554 +SQL_assistant,MCP,Expert SQL Query Generator,Qwen3-235b,93,3430.4086021505377,36.89066405362386,167.49462365591398,38.34860586557435,0.0,0,167.49462365591398,38.34860586557435,3597.9032258064517,36.5664931705788 +SQL_assistant,MCP,Senior SQL Code Reviewer,DeepSeek-R1,155,12028.23870967742,127.12190039733528,5594.096774193548,82.47458960944094,4114.348387096774,86.3141557897285,1479.7483870967742,132.60678616158498,17622.335483870967,99.71614804703174 +SQL_assistant,MCP,Senior SQL Code Reviewer,DeepSeek-V3-1,111,3044.009009009009,43.04931230645842,241.57657657657657,34.35303565569292,0.0,0,241.57657657657657,34.35303565569292,3285.5855855855857,41.78719748769591 +SQL_assistant,MCP,Senior SQL Code Reviewer,GPT-4o-mini,130,4187.369230769231,70.53992601845951,131.76153846153846,77.09785926352211,0.0,0,131.76153846153846,77.09785926352211,4319.130769230769,70.49492185121261 +SQL_assistant,MCP,Senior SQL Code Reviewer,GPT-5,129,1589.860465116279,56.57819393362643,103.51937984496124,48.9427944632686,0.0,0,103.51937984496124,48.9427944632686,1693.3798449612402,55.49874246939609 +SQL_assistant,MCP,Senior SQL Code Reviewer,Gemini-2.5-flash,87,1935.1724137931035,74.68057718154739,1612.7356321839081,122.68188242580645,264.44827586206895,191.25488907626416,1348.287356321839,147.211824788055,3547.9080459770116,87.93511836786504 +SQL_assistant,MCP,Senior SQL Code Reviewer,Gemini-2.5-flash-nothinking,84,1212.452380952381,33.43982622965611,1013.7380952380952,100.11769883492005,0.0,0,1013.7380952380952,100.11769883492005,2226.190476190476,52.389496567984786 +SQL_assistant,MCP,Senior SQL Code Reviewer,Qwen3-235b,93,3832.989247311828,42.38508622203441,187.0215053763441,45.66398853296014,0.0,0,187.0215053763441,45.66398853296014,4020.010752688172,41.81423823450659 +intelligent_recruitment_platform,A2A,Senior Candidate Evaluator,DeepSeek-R1,61,30608.72131147541,85.47139199068391,9787.180327868853,55.55045080371037,4105.6557377049185,64.37780519768015,5681.524590163935,66.21544048252275,40395.901639344265,75.8785062287891 +intelligent_recruitment_platform,A2A,Senior Candidate Evaluator,DeepSeek-V3-1,60,44145.083333333336,26.656189318282337,5401.483333333334,30.26478943623152,0.0,0,5401.483333333334,30.26478943623152,49546.566666666666,25.906448943825584 +intelligent_recruitment_platform,A2A,Senior Candidate Evaluator,GPT-4o-mini,60,49297.583333333336,69.28305095261749,5020.75,56.50105679842509,0.0,0,5020.75,56.50105679842509,54318.333333333336,66.32346180171228 +intelligent_recruitment_platform,A2A,Senior Candidate Evaluator,GPT-5,60,13226.9,26.937408997029618,3046.366666666667,34.91904015952739,0.0,0,3046.366666666667,34.91904015952739,16273.266666666666,26.211711501751118 +intelligent_recruitment_platform,A2A,Senior Candidate Evaluator,Gemini-2.5-flash,60,23991.7,98.60139472801976,18181.15,60.07502185468543,1363.4166666666667,129.5350691271378,16817.733333333334,63.90155738026717,42172.85,80.03947786117939 +intelligent_recruitment_platform,A2A,Senior Candidate Evaluator,Gemini-2.5-flash-nothinking,60,18043.283333333333,62.15530627016903,15394.95,45.01412667451598,0.0,0,15394.95,45.01412667451598,33438.23333333333,45.278664292440304 +intelligent_recruitment_platform,A2A,Senior Candidate Evaluator,Qwen3-235b,84,45935.392857142855,66.74611734549899,6265.511904761905,39.63154296914339,0.0,0,6265.511904761905,39.63154296914339,52200.90476190476,62.47831267562696 +intelligent_recruitment_platform,A2A,Senior Interview Materials Coordinator,DeepSeek-R1,59,18707.949152542373,68.36245766496177,5320.033898305085,48.0507713963277,1768.3728813559321,61.25934178346806,3551.6610169491523,55.148105261817314,24027.98305084746,60.7617254527927 +intelligent_recruitment_platform,A2A,Senior Interview Materials Coordinator,DeepSeek-V3-1,60,8862.35,0.5983974201156282,2578.5,8.697786333336945,0.0,0,2578.5,8.697786333336945,11440.85,1.9061878614769379 +intelligent_recruitment_platform,A2A,Senior Interview Materials Coordinator,GPT-4o-mini,60,8221.183333333332,12.02311867550227,1176.1,23.20044827804071,0.0,0,1176.1,23.20044827804071,9397.283333333333,11.135432724587352 +intelligent_recruitment_platform,A2A,Senior Interview Materials Coordinator,GPT-5,60,8469.583333333334,0.7689309635478658,1370.8,10.047920187446989,0.0,0,1370.8,10.047920187446989,9840.383333333333,1.8482870428521505 +intelligent_recruitment_platform,A2A,Senior Interview Materials Coordinator,Gemini-2.5-flash,60,7108.916666666667,83.93356169245668,11081.683333333332,30.548869887530266,481.0,29.31099272322502,10600.683333333332,31.611278649130913,18190.6,50.674087024816096 +intelligent_recruitment_platform,A2A,Senior Interview Materials Coordinator,Gemini-2.5-flash-nothinking,60,5452.75,86.31883784211112,9163.1,34.03358049038389,0.0,0,9163.1,34.03358049038389,14615.85,49.32325587690237 +intelligent_recruitment_platform,A2A,Senior Interview Materials Coordinator,Qwen3-235b,59,8107.813559322034,23.29061450377309,2129.406779661017,20.99436664524487,0.0,0,2129.406779661017,20.99436664524487,10237.22033898305,20.10559815069613 +intelligent_recruitment_platform,A2A,Senior Job Requirements Analyst,DeepSeek-R1,66,4222.590909090909,79.7863104980631,3296.7272727272725,42.597515173813974,1062.3939393939395,50.42061555178468,2234.3333333333335,52.51257283166265,7519.318181818182,56.49389438079234 +intelligent_recruitment_platform,A2A,Senior Job Requirements Analyst,DeepSeek-V3-1,60,3159.1666666666665,22.515835800882613,1515.8333333333333,20.10252875846,0.0,0,1515.8333333333333,20.10252875846,4675.0,16.478007042122762 +intelligent_recruitment_platform,A2A,Senior Job Requirements Analyst,GPT-4o-mini,60,10355.333333333334,41.989902325782175,880.4166666666666,51.79656825701052,0.0,0,880.4166666666666,51.79656825701052,11235.75,41.30230749383285 +intelligent_recruitment_platform,A2A,Senior Job Requirements Analyst,GPT-5,60,4656.433333333333,34.12412461443836,1389.7333333333333,8.717407677450078,0.0,0,1389.7333333333333,8.717407677450078,6046.166666666667,27.175077919599154 +intelligent_recruitment_platform,A2A,Senior Job Requirements Analyst,Gemini-2.5-flash,60,1517.6666666666667,64.39184371070064,6837.916666666667,23.383652044270875,566.0333333333333,37.61981044399118,6271.883333333333,26.482438515841622,8355.583333333334,19.78596334410806 +intelligent_recruitment_platform,A2A,Senior Job Requirements Analyst,Gemini-2.5-flash-nothinking,60,2274.4166666666665,66.69388006633544,5331.016666666666,54.23476896873585,0.0,0,5331.016666666666,54.23476896873585,7605.433333333333,40.533818500039374 +intelligent_recruitment_platform,A2A,Senior Job Requirements Analyst,Qwen3-235b,84,8958.297619047618,72.57500811278435,1577.357142857143,54.55966809562383,0.0,0,1577.357142857143,54.55966809562383,10535.654761904761,68.08025374356997 +intelligent_recruitment_platform,A2A_mix,Senior Candidate Evaluator,DeepSeek-R1,63,29212.015873015873,79.27146929490121,11420.603174603175,47.24641759225871,5145.9047619047615,49.24683521692371,6274.6984126984125,63.64900880359563,40632.619047619046,68.4778437274106 +intelligent_recruitment_platform,A2A_mix,Senior Candidate Evaluator,DeepSeek-V3-1,60,43100.316666666666,27.329064409931114,5707.433333333333,26.09482352018231,0.0,0,5707.433333333333,26.09482352018231,48807.75,25.168122163175095 +intelligent_recruitment_platform,A2A_mix,Senior Candidate Evaluator,GPT-4o-mini,60,63935.666666666664,119.49874595809436,5324.766666666666,51.57136654322137,0.0,0,5324.766666666666,51.57136654322137,69260.43333333333,112.44477165395266 +intelligent_recruitment_platform,A2A_mix,Senior Candidate Evaluator,GPT-5,60,13564.35,51.20937603284846,3025.15,40.54669347605696,0.0,0,3025.15,40.54669347605696,16589.5,47.33385520471228 +intelligent_recruitment_platform,A2A_mix,Senior Candidate Evaluator,Gemini-2.5-flash,60,10873.966666666667,81.87301065482046,11980.066666666668,47.274699349477814,1330.5,92.29164927271853,10649.566666666668,50.20842604988985,22854.033333333333,60.68425030125057 +intelligent_recruitment_platform,A2A_mix,Senior Candidate Evaluator,Gemini-2.5-flash-nothinking,60,9651.766666666666,50.13753910794657,9547.483333333334,32.76839150573318,0.0,0,9547.483333333334,32.76839150573318,19199.25,37.06676090738178 +intelligent_recruitment_platform,A2A_mix,Senior Candidate Evaluator,Qwen3-235b,72,37967.5,65.01412181487864,6343.402777777777,35.42409190987712,0.0,0,6343.402777777777,35.42409190987712,44310.90277777778,59.24528764645585 +intelligent_recruitment_platform,A2A_mix,agent,DeepSeek-R1,128,1063.15625,26.42403567926495,1357.5859375,49.434409531264365,832.953125,62.726801620317715,524.6328125,57.7174748316211,2420.7421875,26.706040049711017 +intelligent_recruitment_platform,A2A_mix,agent,DeepSeek-V3-1,120,1387.4333333333334,21.946992218344583,269.14166666666665,66.54040161033204,0.0,0,269.14166666666665,66.54040161033204,1656.575,23.09016790904449 +intelligent_recruitment_platform,A2A_mix,agent,GPT-4o-mini,120,934.2916666666666,28.68114862468288,189.21666666666667,49.44636509477004,0.0,0,189.21666666666667,49.44636509477004,1123.5083333333334,27.333593467781448 +intelligent_recruitment_platform,A2A_mix,agent,GPT-5,120,748.85,62.06122493006643,150.36666666666667,94.21232810491802,0.0,0,150.36666666666667,94.21232810491802,899.2166666666667,45.149670235582136 +intelligent_recruitment_platform,A2A_mix,agent,Gemini-2.5-flash,2,0.0,0,0.0,0,0.0,0,0.0,0,348.5,34.29011363573215 +intelligent_recruitment_platform,A2A_mix,agent,Gemini-2.5-flash-nothinking,120,0.0,0,0.0,0,0.0,0,0.0,0,1045.2916666666667,30.056942686789228 +intelligent_recruitment_platform,A2A_mix,agent,Qwen3-235b,188,800.4414893617021,54.842883755676844,57.797872340425535,60.265614036407946,0.0,0,57.797872340425535,60.265614036407946,858.2393617021277,49.95606027156511 +intelligent_recruitment_platform,A2A_mix,interview_coordinator,DeepSeek-R1,60,23383.933333333334,29.98697192032666,7815.833333333333,31.74836942555639,2267.266666666667,52.2742902137343,5548.566666666667,23.976983439745048,31199.766666666666,25.660897662427296 +intelligent_recruitment_platform,A2A_mix,interview_coordinator,DeepSeek-V3-1,60,11089.15,14.891278657036603,1837.9,23.601221790479265,0.0,0,1837.9,23.601221790479265,12927.05,13.937643356473112 +intelligent_recruitment_platform,A2A_mix,interview_coordinator,GPT-4o-mini,60,26998.766666666666,96.00411350515235,3429.9,12.102269139277249,0.0,0,3429.9,12.102269139277249,30428.666666666668,85.78990792859895 +intelligent_recruitment_platform,A2A_mix,interview_coordinator,GPT-5,60,12807.55,19.05981063357447,1476.5166666666667,26.334829245601753,0.0,0,1476.5166666666667,26.334829245601753,14284.066666666668,18.613875644206377 +intelligent_recruitment_platform,A2A_mix,interview_coordinator,Gemini-2.5-flash,60,21014.533333333333,30.68056310598397,10094.3,17.149592320307626,1407.0,47.549477027026875,8687.3,13.983733361060969,31108.833333333332,21.51990761818857 +intelligent_recruitment_platform,A2A_mix,interview_coordinator,Gemini-2.5-flash-nothinking,60,12960.2,21.24605345577444,2637.1,41.942916349453334,0.0,0,2637.1,41.942916349453334,15597.3,18.968971603930353 +intelligent_recruitment_platform,A2A_mix,interview_coordinator,Qwen3-235b,60,14951.783333333333,7.616467193430915,2445.2,18.925522410781245,0.0,0,2445.2,18.925522410781245,17396.983333333334,7.310596726245885 +intelligent_recruitment_platform,MCP,Senior Candidate Evaluator,DeepSeek-R1,61,38807.311475409835,78.73494544590854,12910.786885245901,57.022484049971176,4886.180327868852,74.95704834287349,8024.606557377049,69.91624693704433,51718.098360655735,71.28101514199275 +intelligent_recruitment_platform,MCP,Senior Candidate Evaluator,DeepSeek-V3-1,60,44424.433333333334,27.58755956073861,5072.433333333333,19.274971461738403,0.0,0,5072.433333333333,19.274971461738403,49496.86666666667,25.91886123109842 +intelligent_recruitment_platform,MCP,Senior Candidate Evaluator,GPT-4o-mini,60,74780.53333333334,144.13623753722038,5728.333333333333,72.77211879153208,0.0,0,5728.333333333333,72.77211879153208,80508.86666666667,138.05802771551149 +intelligent_recruitment_platform,MCP,Senior Candidate Evaluator,GPT-5,60,13350.2,39.90093126335667,2746.75,39.56368979157489,0.0,0,2746.75,39.56368979157489,16096.95,37.079091655730004 +intelligent_recruitment_platform,MCP,Senior Candidate Evaluator,Gemini-2.5-flash,60,19091.533333333333,87.25615880545449,17908.9,54.37631654301038,1542.8833333333334,111.13738040639748,16366.016666666666,56.64012240687612,37000.433333333334,66.4002480138344 +intelligent_recruitment_platform,MCP,Senior Candidate Evaluator,Gemini-2.5-flash-nothinking,60,16751.75,82.36569176446726,14470.016666666666,42.96964763105335,0.0,0,14470.016666666666,42.96964763105335,31221.766666666666,59.127231557379275 +intelligent_recruitment_platform,MCP,Senior Candidate Evaluator,Qwen3-235b,84,39416.09523809524,76.08543895352956,5755.892857142857,40.12915816252277,0.0,0,5755.892857142857,40.12915816252277,45171.98809523809,70.44775341504183 +intelligent_recruitment_platform,MCP,Senior Interview Materials Coordinator,DeepSeek-R1,61,18264.245901639344,53.24768586357004,4752.950819672131,39.32765561330905,1643.8032786885246,50.95387785701018,3109.1475409836066,47.29028252235466,23017.196721311477,47.57243730472979 +intelligent_recruitment_platform,MCP,Senior Interview Materials Coordinator,DeepSeek-V3-1,60,8959.833333333334,7.668727542834645,2584.483333333333,11.448240434459004,0.0,0,2584.483333333333,11.448240434459004,11544.316666666668,6.718388210351916 +intelligent_recruitment_platform,MCP,Senior Interview Materials Coordinator,GPT-4o-mini,60,8504.616666666667,10.408856925823965,1179.25,19.476216654626356,0.0,0,1179.25,19.476216654626356,9683.866666666667,10.137405210863385 +intelligent_recruitment_platform,MCP,Senior Interview Materials Coordinator,GPT-5,60,8491.783333333333,0.7242967319190418,1376.1333333333334,10.680135021386754,0.0,0,1376.1333333333334,10.680135021386754,9867.916666666666,1.701654891817191 +intelligent_recruitment_platform,MCP,Senior Interview Materials Coordinator,Gemini-2.5-flash,60,8010.7,131.89158190861676,11293.383333333333,43.76823848501689,485.7,44.02501830455737,10807.683333333332,45.244822534482175,19304.083333333332,79.21854417291205 +intelligent_recruitment_platform,MCP,Senior Interview Materials Coordinator,Gemini-2.5-flash-nothinking,60,5751.583333333333,118.06996933816282,9880.2,64.43017315293808,0.0,0,9880.2,64.43017315293808,15631.783333333333,80.9700605456795 +intelligent_recruitment_platform,MCP,Senior Interview Materials Coordinator,Qwen3-235b,58,8229.810344827587,20.55410628071939,2098.0,10.34542072315204,0.0,0,2098.0,10.34542072315204,10327.810344827587,16.0823943424622 +intelligent_recruitment_platform,MCP,Senior Job Requirements Analyst,DeepSeek-R1,62,4921.887096774193,84.98465074338418,3502.3387096774195,40.35554726126912,1033.4193548387098,80.24353979986041,2468.9193548387098,44.22598451022549,8424.225806451614,58.964224180671195 +intelligent_recruitment_platform,MCP,Senior Job Requirements Analyst,DeepSeek-V3-1,60,3109.35,35.6004006139427,1597.0,27.876259852249895,0.0,0,1597.0,27.876259852249895,4706.35,27.398579242964498 +intelligent_recruitment_platform,MCP,Senior Job Requirements Analyst,GPT-4o-mini,60,8728.966666666667,47.57717517025726,1011.4166666666666,33.550194624871104,0.0,0,1011.4166666666666,33.550194624871104,9740.383333333333,44.9320298206683 +intelligent_recruitment_platform,MCP,Senior Job Requirements Analyst,GPT-5,60,5070.716666666666,35.261436302367315,1374.9333333333334,8.744002623867217,0.0,0,1374.9333333333334,8.744002623867217,6445.65,28.3911866947303 +intelligent_recruitment_platform,MCP,Senior Job Requirements Analyst,Gemini-2.5-flash,60,1680.2166666666667,101.64644967810707,6879.683333333333,30.8569458791522,518.9,41.58071346181834,6360.783333333334,33.251149353218565,8559.9,37.34470599837949 +intelligent_recruitment_platform,MCP,Senior Job Requirements Analyst,Gemini-2.5-flash-nothinking,60,2149.516666666667,62.67919843028152,5398.983333333334,60.82471807023979,0.0,0,5398.983333333334,60.82471807023979,7548.5,44.32553100200627 +intelligent_recruitment_platform,MCP,Senior Job Requirements Analyst,Qwen3-235b,84,8379.464285714286,68.90660294911564,1529.9404761904761,42.11551291574978,0.0,0,1529.9404761904761,42.11551291574978,9909.404761904761,62.952204701559445 +landing_page_generator,A2A,Landing Page Content Generator,DeepSeek-R1,62,14728.661290322581,71.73666613415355,4634.709677419355,80.72426298120045,1596.6451612903227,77.48422461868437,3038.064516129032,117.51422586711409,19363.370967741936,70.43728447021738 +landing_page_generator,A2A,Landing Page Content Generator,DeepSeek-V3-1,60,19209.783333333333,120.68003545918936,5617.45,72.69682213492057,0.0,0,5617.45,72.69682213492057,24827.233333333334,109.63811204081809 +landing_page_generator,A2A,Landing Page Content Generator,GPT-4o-mini,60,10042.583333333334,32.422695034015604,1595.9333333333334,16.765906890814907,0.0,0,1595.9333333333334,16.765906890814907,11638.516666666666,27.763331465040984 +landing_page_generator,A2A,Landing Page Content Generator,GPT-5,60,10352.916666666666,52.936511547975215,1221.1166666666666,103.2537530274015,0.0,0,1221.1166666666666,103.2537530274015,11574.033333333333,55.90477880916874 +landing_page_generator,A2A,Landing Page Content Generator,Gemini-2.5-flash,60,3260.9333333333334,103.9497801908185,11073.716666666667,45.51564737491107,236.46666666666667,164.77993679116446,10837.25,46.7201178646246,14334.65,49.58859817725912 +landing_page_generator,A2A,Landing Page Content Generator,Gemini-2.5-flash-nothinking,61,4726.311475409836,188.66949721032242,12008.983606557376,146.02034984080748,0.0,0,12008.983606557376,146.02034984080748,16735.29508196721,153.07424118419434 +landing_page_generator,A2A,Landing Page Content Generator,Qwen3-235b,63,8954.285714285714,60.189899368627195,2732.809523809524,72.68063174874153,0.0,0,2732.809523809524,72.68063174874153,11687.095238095239,56.26636310152864 +landing_page_generator,A2A,Product Idea Analyst,DeepSeek-R1,62,1844.7903225806451,119.80673006643521,1801.241935483871,43.19314123416741,483.3709677419355,65.36725674801707,1317.8709677419354,52.424787120839426,3646.032258064516,72.40674669985887 +landing_page_generator,A2A,Product Idea Analyst,DeepSeek-V3-1,60,1650.7833333333333,27.680527626093017,349.3333333333333,33.263721482856575,0.0,0,349.3333333333333,33.263721482856575,2000.1166666666666,25.677670128375578 +landing_page_generator,A2A,Product Idea Analyst,GPT-4o-mini,60,6107.233333333334,72.95387335122724,633.1833333333333,63.759523949723516,0.0,0,633.1833333333333,63.759523949723516,6740.416666666667,71.4894050421477 +landing_page_generator,A2A,Product Idea Analyst,GPT-5,60,1570.35,16.80957559293467,188.68333333333334,22.24357634394371,0.0,0,188.68333333333334,22.24357634394371,1759.0333333333333,16.817616422034874 +landing_page_generator,A2A,Product Idea Analyst,Gemini-2.5-flash,60,1286.2666666666667,105.93327224949371,3617.0333333333333,29.600149146060062,211.48333333333332,58.30007706307098,3405.55,29.358918653904176,4903.3,40.81305719756927 +landing_page_generator,A2A,Product Idea Analyst,Gemini-2.5-flash-nothinking,64,820.078125,36.13701421037436,2849.40625,36.72459405637987,0.0,0,2849.40625,36.72459405637987,3669.484375,25.009358448264663 +landing_page_generator,A2A,Product Idea Analyst,Qwen3-235b,63,3432.3492063492063,58.428660320643345,352.23809523809524,65.14583264779615,0.0,0,352.23809523809524,65.14583264779615,3784.5873015873017,57.84897410461397 +landing_page_generator,A2A,Template Selection Specialist,DeepSeek-R1,62,3738.3870967741937,70.75026192725024,985.0806451612904,73.88285823041237,437.2096774193548,96.85333043296289,547.8709677419355,113.82829502608558,4723.467741935484,64.20542601438162 +landing_page_generator,A2A,Template Selection Specialist,DeepSeek-V3-1,60,1850.8,12.358608227671517,135.93333333333334,19.879003563344945,0.0,0,135.93333333333334,19.879003563344945,1986.7333333333333,11.540090838514216 +landing_page_generator,A2A,Template Selection Specialist,GPT-4o-mini,60,1918.3166666666666,17.382334211927798,42.31666666666667,37.073241237536905,0.0,0,42.31666666666667,37.073241237536905,1960.6333333333334,16.946492275581203 +landing_page_generator,A2A,Template Selection Specialist,GPT-5,60,1577.3166666666666,2.7940983866280873,54.46666666666667,9.136048487578972,0.0,0,54.46666666666667,9.136048487578972,1631.7833333333333,2.883544247719105 +landing_page_generator,A2A,Template Selection Specialist,Gemini-2.5-flash,60,1404.6333333333334,19.747566987605357,613.1,55.27811981493195,154.8,91.66692168153155,458.3,61.954784967917995,2017.7333333333333,16.125927201082096 +landing_page_generator,A2A,Template Selection Specialist,Gemini-2.5-flash-nothinking,61,1463.5573770491803,31.447499323738228,402.73770491803276,60.531683777093605,0.0,0,402.73770491803276,60.531683777093605,1866.295081967213,26.273150913167186 +landing_page_generator,A2A,Template Selection Specialist,Qwen3-235b,63,1729.952380952381,17.77305959865206,76.4920634920635,35.78784450289002,0.0,0,76.4920634920635,35.78784450289002,1806.4444444444443,17.620627703081507 +landing_page_generator,A2A_mix,Landing Page Content Generator,DeepSeek-R1,60,16717.916666666668,89.53227083529657,5064.933333333333,88.4506757211042,1219.7666666666667,81.5071251545445,3845.1666666666665,114.94996306188476,21782.85,85.2291522607396 +landing_page_generator,A2A_mix,Landing Page Content Generator,DeepSeek-V3-1,60,20386.783333333333,112.34401879200865,5825.416666666667,70.97041738426164,0.0,0,5825.416666666667,70.97041738426164,26212.2,102.7716719477648 +landing_page_generator,A2A_mix,Landing Page Content Generator,GPT-4o-mini,60,10334.65,22.00377159683142,1640.8,19.705315996056587,0.0,0,1640.8,19.705315996056587,11975.45,20.071011296275202 +landing_page_generator,A2A_mix,Landing Page Content Generator,GPT-5,60,15148.7,50.000385560451896,1886.85,78.2413785361121,0.0,0,1886.85,78.2413785361121,17035.55,51.35674975356549 +landing_page_generator,A2A_mix,Landing Page Content Generator,Gemini-2.5-flash,60,8845.233333333334,587.3133781735825,13499.5,199.0303924998546,366.1166666666667,107.73975444701223,13133.383333333333,204.14393140713298,22344.733333333334,351.1241498446218 +landing_page_generator,A2A_mix,Landing Page Content Generator,Gemini-2.5-flash-nothinking,60,1940.8166666666666,106.68237384945485,8035.483333333334,42.99390750618861,0.0,0,8035.483333333334,42.99390750618861,9976.3,47.112755569832395 +landing_page_generator,A2A_mix,Landing Page Content Generator,Qwen3-235b,63,8949.682539682539,58.94299534759435,2810.0793650793653,68.00697035244005,0.0,0,2810.0793650793653,68.00697035244005,11759.761904761905,54.68717990050247 +landing_page_generator,A2A_mix,agent,DeepSeek-R1,120,502.4,5.788299340819949,1142.025,69.94499510774723,654.2083333333334,81.74125584449665,487.81666666666666,87.28319032959581,1644.425,47.93941618796128 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Bard,Qwen3-235b,101,11189.524752475247,147.85386765545397,361.4059405940594,112.04480110883823,0.0,0,361.4059405940594,112.04480110883823,11550.930693069307,146.60710112314752 +self_evaluation_loop_flow,MCP,Topic Analysis Expert,DeepSeek-R1,76,2163.6710526315787,93.82164797465249,1157.9868421052631,43.153573579792386,226.17105263157896,125.4768607056072,931.8157894736842,36.49022878620645,3321.657894736842,69.27550998937176 +self_evaluation_loop_flow,MCP,Topic Analysis Expert,DeepSeek-V3-1,60,2953.133333333333,1.8179758511125341,737.3833333333333,18.35291939378041,0.0,0,737.3833333333333,18.35291939378041,3690.516666666667,4.202646399564699 +self_evaluation_loop_flow,MCP,Topic Analysis Expert,GPT-4o-mini,61,2871.8852459016393,1.8941759461081658,634.5245901639345,18.370708502901763,0.0,0,634.5245901639345,18.370708502901763,3506.409836065574,3.778477733140932 +self_evaluation_loop_flow,MCP,Topic Analysis Expert,GPT-5,60,2913.4333333333334,1.5169127788739678,588.0833333333334,7.942781538693319,0.0,0,588.0833333333334,7.942781538693319,3501.516666666667,1.9366301188280657 +self_evaluation_loop_flow,MCP,Topic Analysis Expert,Gemini-2.5-flash,60,832.2333333333333,0.20440336792975777,2232.45,23.183552217452807,133.91666666666666,23.660669673900898,2098.5333333333333,24.41471163325217,3064.6833333333334,16.870509770272296 +self_evaluation_loop_flow,MCP,Topic Analysis Expert,Gemini-2.5-flash-nothinking,60,884.9833333333333,38.37583029656196,2464.0833333333335,19.71378658217498,0.0,0,2464.0833333333335,19.71378658217498,3349.0666666666666,18.00238529560151 +self_evaluation_loop_flow,MCP,Topic Analysis Expert,Qwen3-235b,60,2955.9666666666667,1.6714322597226081,616.2,22.02637449649295,0.0,0,616.2,22.02637449649295,3572.1666666666665,4.449574854466486 +self_evaluation_loop_flow,MCP,X Post Verifier,DeepSeek-R1,145,15475.731034482758,107.43872636109451,4296.4068965517245,66.04626777556719,3078.9241379310347,79.00737038144614,1217.4827586206898,100.51493432544257,19772.137931034482,91.90146942420998 +self_evaluation_loop_flow,MCP,X Post Verifier,DeepSeek-V3-1,90,14614.666666666666,5.267022661188486,618.9777777777778,14.629199030602305,0.0,0,618.9777777777778,14.629199030602305,15233.644444444444,5.383190765676044 +self_evaluation_loop_flow,MCP,X Post Verifier,GPT-4o-mini,147,12980.027210884355,19.63968972771007,453.5442176870748,18.388800827858624,0.0,0,453.5442176870748,18.388800827858624,13433.57142857143,19.51353265362196 +self_evaluation_loop_flow,MCP,X Post Verifier,GPT-5,112,13907.125,7.5108830049564475,589.8303571428571,10.139853496044054,0.0,0,589.8303571428571,10.139853496044054,14496.955357142857,7.50948299986431 +self_evaluation_loop_flow,MCP,X Post Verifier,Gemini-2.5-flash,70,2134.885714285714,51.3184612099798,1575.2,18.869826259627718,268.34285714285716,25.049131124929954,1306.857142857143,22.150945188644048,3710.0857142857144,30.599970211390026 +self_evaluation_loop_flow,MCP,X Post Verifier,Gemini-2.5-flash-nothinking,65,1671.9538461538461,28.322296025966164,1059.5692307692307,15.954205163295597,0.0,0,1059.5692307692307,15.954205163295597,2731.523076923077,19.03236658821829 +self_evaluation_loop_flow,MCP,X Post Verifier,Qwen3-235b,101,13331.227722772277,22.672997113003706,653.6336633663366,26.410941415064993,0.0,0,653.6336633663366,26.410941415064993,13984.861386138615,22.672550112231082 +write_a_book_with_flows,A2A,Expert Research Agent for Book Outline Planning,DeepSeek-R1,64,10361.828125,67.41484742410444,4074.546875,40.631701633413535,1382.5,47.02664582784801,2692.046875,51.46832836598371,14436.375,56.87006953441834 +write_a_book_with_flows,A2A,Expert Research Agent for Book Outline Planning,DeepSeek-V3-1,61,25631.06557377049,33.27355144223436,2774.590163934426,23.572391877858305,0.0,0,2774.590163934426,23.572391877858305,28405.655737704918,31.89944392601143 +write_a_book_with_flows,A2A,Expert Research Agent for Book Outline Planning,GPT-4o-mini,60,19805.533333333333,33.20189292289845,1952.2833333333333,20.597993332066984,0.0,0,1952.2833333333333,20.597993332066984,21757.816666666666,31.454504360058767 +write_a_book_with_flows,A2A,Expert Research Agent for Book Outline Planning,GPT-5,60,4533.366666666667,27.912885200417875,1686.7333333333333,10.60507657111148,0.0,0,1686.7333333333333,10.60507657111148,6220.1,22.1231284354429 +write_a_book_with_flows,A2A,Expert Research Agent for Book Outline Planning,Gemini-2.5-flash,60,5730.483333333334,86.02309097219712,8714.3,29.50697992698816,229.95,45.89958951040056,8484.35,29.90969349304268,14444.783333333333,48.97116979491752 +write_a_book_with_flows,A2A,Expert Research Agent for Book Outline Planning,Gemini-2.5-flash-nothinking,60,2974.05,79.3090412190428,7487.816666666667,58.40888380657488,0.0,0,7487.816666666667,58.40888380657488,10461.866666666667,51.23980632020449 +write_a_book_with_flows,A2A,Expert Research Agent for Book Outline Planning,Qwen3-235b,60,5315.383333333333,64.819917752919,1434.6333333333334,55.22283284745203,0.0,0,1434.6333333333334,55.22283284745203,6750.016666666666,58.740386653593546 +write_a_book_with_flows,A2A,Professional Chapter Content Writer,DeepSeek-R1,220,13251.077272727272,170.87945133276364,6097.9,69.49089835907527,2310.190909090909,116.90409227404545,3787.7090909090907,76.54972329983892,19348.977272727272,131.31889733925595 +write_a_book_with_flows,A2A,Professional Chapter Content Writer,DeepSeek-V3-1,232,19916.219827586207,17.05662106685926,4704.788793103448,16.125016345238805,0.0,0,4704.788793103448,16.125016345238805,24621.008620689656,16.468822539622117 +write_a_book_with_flows,A2A,Professional Chapter Content Writer,GPT-4o-mini,212,3213.132075471698,11.604311922448723,1046.5094339622642,33.28575130189195,0.0,0,1046.5094339622642,33.28575130189195,4259.641509433963,16.111933666855474 +write_a_book_with_flows,A2A,Professional Chapter Content Writer,GPT-5,290,12555.675862068965,87.06887285435508,3369.0172413793102,67.41281123586998,0.0,0,3369.0172413793102,67.41281123586998,15924.693103448275,82.70461505845509 +write_a_book_with_flows,A2A,Professional Chapter Content Writer,Gemini-2.5-flash,193,5504.357512953368,50.089913895180096,7455.4715025906735,26.87358738857965,50.19170984455958,504.60698276759234,7405.279792746114,27.28072162940356,12959.82901554404,27.647809826832788 +write_a_book_with_flows,A2A,Professional Chapter Content Writer,Gemini-2.5-flash-nothinking,184,5174.358695652174,64.88519649529152,4875.554347826087,57.46345896533697,0.0,0,4875.554347826087,57.46345896533697,10049.91304347826,50.10193153680501 +write_a_book_with_flows,A2A,Professional Chapter Content Writer,Qwen3-235b,228,6421.350877192983,134.86817766326695,1619.5219298245613,90.0902942125588,0.0,0,1619.5219298245613,90.0902942125588,8040.872807017544,124.76157621832951 +write_a_book_with_flows,A2A,Senior Book Editor and Quality Assurance Specialist,DeepSeek-R1,61,32441.344262295082,74.10836386373715,2517.245901639344,48.85449588605152,357.27868852459017,93.23302885720148,2159.967213114754,54.02550403397143,34958.59016393442,70.72060870430141 +write_a_book_with_flows,A2A,Senior Book Editor and Quality Assurance Specialist,DeepSeek-V3-1,61,41474.72131147541,14.09491083122219,1362.672131147541,13.286892370515178,0.0,0,1362.672131147541,13.286892370515178,42837.39344262295,14.001781747912059 +write_a_book_with_flows,A2A,Senior Book Editor and Quality Assurance Specialist,GPT-4o-mini,60,35133.166666666664,9.626733866429644,1159.0833333333333,5.351348731388179,0.0,0,1159.0833333333333,5.351348731388179,36292.25,9.404969927388478 +write_a_book_with_flows,A2A,Senior Book Editor and Quality Assurance Specialist,GPT-5,60,53322.083333333336,7.493212686915587,1574.75,6.508877797168585,0.0,0,1574.75,6.508877797168585,54896.833333333336,7.319130240856827 +write_a_book_with_flows,A2A,Senior Book Editor and Quality Assurance Specialist,Gemini-2.5-flash,60,10498.366666666667,57.06202359756707,3808.2166666666667,33.923714627743465,84.71666666666667,121.87810348538024,3723.5,34.75435505241889,14306.583333333334,49.34886718140945 +write_a_book_with_flows,A2A,Senior Book Editor and Quality Assurance Specialist,Gemini-2.5-flash-nothinking,60,9365.0,37.159155723119305,2715.883333333333,23.610703563664593,0.0,0,2715.883333333333,23.610703563664593,12080.883333333333,29.84736764628768 +write_a_book_with_flows,A2A,Senior Book Editor and Quality Assurance Specialist,Qwen3-235b,60,37660.76666666667,14.190516763367937,1530.1666666666667,8.2305296376385,0.0,0,1530.1666666666667,8.2305296376385,39190.933333333334,13.670221209107481 +write_a_book_with_flows,A2A,Specialized Chapter Research Agent,DeepSeek-R1,224,12597.763392857143,73.35817538473954,3589.2276785714284,48.37796267291225,1641.4107142857142,61.239304704799444,1947.8169642857142,56.16262938093516,16186.99107142857,65.38175321830074 +write_a_book_with_flows,A2A,Specialized Chapter Research Agent,DeepSeek-V3-1,232,24926.55172413793,37.70732211664461,1381.3706896551723,28.39652209378268,0.0,0,1381.3706896551723,28.39652209378268,26307.922413793105,36.96250503107319 +write_a_book_with_flows,A2A,Specialized Chapter Research Agent,GPT-4o-mini,212,16700.622641509435,37.290749744699944,1190.188679245283,35.778480316918994,0.0,0,1190.188679245283,35.778480316918994,17890.811320754718,36.38847848140847 +write_a_book_with_flows,A2A,Specialized Chapter Research Agent,GPT-5,290,6950.841379310345,23.204457337303637,1251.1172413793104,8.746699529886095,0.0,0,1251.1172413793104,8.746699529886095,8201.958620689655,20.136869563835546 +write_a_book_with_flows,A2A,Specialized Chapter Research Agent,Gemini-2.5-flash,193,5151.782383419689,68.5943159599579,5461.119170984456,36.25927373330967,206.00518134715026,106.37406033951197,5255.113989637306,36.64600286783082,10612.901554404145,47.636940142953584 +write_a_book_with_flows,A2A,Specialized Chapter Research Agent,Gemini-2.5-flash-nothinking,184,4795.494565217391,310.90615041399536,4733.271739130435,211.6385840316679,0.0,0,4733.271739130435,211.6385840316679,9528.766304347826,260.6393982095932 +write_a_book_with_flows,A2A,Specialized Chapter Research Agent,Qwen3-235b,228,9180.986842105263,37.25133703027434,1079.0263157894738,24.464136133509008,0.0,0,1079.0263157894738,24.464136133509008,10260.013157894737,34.99561424522148 +write_a_book_with_flows,A2A,Strategic Book Structure Architect,DeepSeek-R1,63,2448.8888888888887,24.200949595804886,1703.6507936507937,71.03128216379778,1344.888888888889,90.2434601302901,358.76190476190476,35.86258975687904,4152.539682539683,30.69951388751204 +write_a_book_with_flows,A2A,Strategic Book Structure Architect,DeepSeek-V3-1,61,2531.2131147540986,8.08356126118702,340.39344262295083,20.485616431777252,0.0,0,340.39344262295083,20.485616431777252,2871.6065573770493,7.732603188966533 +write_a_book_with_flows,A2A,Strategic Book Structure Architect,GPT-4o-mini,60,2668.766666666667,8.133633421035109,681.5666666666667,16.90451632390839,0.0,0,681.5666666666667,16.90451632390839,3350.3333333333335,8.97625121841687 +write_a_book_with_flows,A2A,Strategic Book Structure Architect,GPT-5,60,2683.516666666667,22.03673317449624,609.3666666666667,29.66779681474016,0.0,0,609.3666666666667,29.66779681474016,3292.883333333333,18.918660209559455 +write_a_book_with_flows,A2A,Strategic Book Structure Architect,Gemini-2.5-flash,60,3658.35,9.457898466400911,1891.1,24.39511396565831,1590.55,28.486385000020924,300.55,16.018984529560683,5549.45,10.684571806915105 +write_a_book_with_flows,A2A,Strategic Book Structure Architect,Gemini-2.5-flash-nothinking,60,3342.9166666666665,16.61776481046472,321.7,33.083055197012705,0.0,0,321.7,33.083055197012705,3664.616666666667,15.131922403413123 +write_a_book_with_flows,A2A,Strategic Book Structure Architect,Qwen3-235b,60,2567.9333333333334,22.717436257764277,353.3333333333333,22.569119967305273,0.0,0,353.3333333333333,22.569119967305273,2921.266666666667,21.12221837643799 +write_a_book_with_flows,A2A_mix,Professional Chapter Content Writer,DeepSeek-R1,210,9071.180952380952,159.4476392435725,5769.104761904762,69.7468532283938,2383.842857142857,105.89301771470554,3385.2619047619046,76.05722674501209,14840.285714285714,116.16493530130842 +write_a_book_with_flows,A2A_mix,Professional Chapter Content Writer,DeepSeek-V3-1,217,20275.764976958526,26.570354508540127,4817.944700460829,16.388349228630023,0.0,0,4817.944700460829,16.388349228630023,25093.709677419356,24.0535729589754 +write_a_book_with_flows,A2A_mix,Professional Chapter Content Writer,GPT-4o-mini,234,3136.905982905983,12.207799354958706,1063.0726495726497,30.46021146887555,0.0,0,1063.0726495726497,30.46021146887555,4199.978632478633,15.93171824023823 +write_a_book_with_flows,A2A_mix,Professional Chapter Content Writer,GPT-5,255,13306.76862745098,79.25983385740174,3657.5803921568627,63.092431655477654,0.0,0,3657.5803921568627,63.092431655477654,16964.349019607842,75.5766558046713 +write_a_book_with_flows,A2A_mix,Professional Chapter Content Writer,Gemini-2.5-flash,180,5921.833333333333,58.957875258646276,7608.366666666667,29.33137125755558,119.16111111111111,883.0222187127703,7489.205555555555,24.09642775870188,13530.2,33.244320637553244 +write_a_book_with_flows,A2A_mix,Professional Chapter Content Writer,Gemini-2.5-flash-nothinking,183,5429.7213114754095,61.5173420690852,5222.934426229508,50.235496479400574,0.0,0,5222.934426229508,50.235496479400574,10652.655737704918,46.493942855483155 +write_a_book_with_flows,A2A_mix,Professional Chapter Content Writer,Qwen3-235b,211,5217.028436018957,136.38496546266526,1396.3886255924172,86.66999858073363,0.0,0,1396.3886255924172,86.66999858073363,6613.417061611374,124.89148439302883 +write_a_book_with_flows,A2A_mix,Specialized Chapter Research Agent,DeepSeek-R1,210,10772.07142857143,69.56432258814573,3136.057142857143,39.219233154808435,1427.2666666666667,52.53659665474794,1708.7904761904763,50.55953851271391,13908.128571428571,59.8900135130332 +write_a_book_with_flows,A2A_mix,Specialized Chapter Research Agent,DeepSeek-V3-1,217,8022.198156682028,25.7610305503158,961.7880184331797,14.088123711597142,0.0,0,961.7880184331797,14.088123711597142,8983.986175115208,23.874000982327235 +write_a_book_with_flows,A2A_mix,Specialized Chapter Research Agent,GPT-4o-mini,234,15370.423076923076,32.06052304643358,1309.320512820513,39.77874345446647,0.0,0,1309.320512820513,39.77874345446647,16679.74358974359,32.3348707636347 +write_a_book_with_flows,A2A_mix,Specialized Chapter Research Agent,GPT-5,255,6721.733333333334,23.134099978057705,1265.9058823529413,9.880959928840587,0.0,0,1265.9058823529413,9.880959928840587,7987.639215686275,20.088497541958926 +write_a_book_with_flows,A2A_mix,Specialized Chapter Research Agent,Gemini-2.5-flash,180,5060.083333333333,66.7710537885546,5479.233333333334,33.31501405607243,242.63888888888889,115.63795504297664,5236.594444444445,32.95815653780201,10539.316666666668,44.4261774284995 +write_a_book_with_flows,A2A_mix,Specialized Chapter Research Agent,Gemini-2.5-flash-nothinking,183,3571.2677595628415,61.462661519653416,3730.9836065573772,45.25080488866611,0.0,0,3730.9836065573772,45.25080488866611,7302.251366120218,41.590996073984286 +write_a_book_with_flows,A2A_mix,Specialized Chapter Research Agent,Qwen3-235b,211,8973.189573459716,38.82516798028017,1055.1279620853081,28.431656831529136,0.0,0,1055.1279620853081,28.431656831529136,10028.317535545024,36.392040299385656 +write_a_book_with_flows,A2A_mix,agent,DeepSeek-R1,172,2572.9127906976746,81.19791164085946,872.6802325581396,101.56053368567764,510.50581395348837,148.869515269278,362.1744186046512,74.33653890919224,3445.593023255814,64.09432956117521 +write_a_book_with_flows,A2A_mix,agent,DeepSeek-V3-1,135,4360.688888888889,69.77909467105562,288.2,24.724672218170745,0.0,0,288.2,24.724672218170745,4648.888888888889,65.94832924387029 +write_a_book_with_flows,A2A_mix,agent,GPT-4o-mini,120,3600.375,78.84676678931586,284.35833333333335,14.037530428841317,0.0,0,284.35833333333335,14.037530428841317,3884.733333333333,72.14224473692114 +write_a_book_with_flows,A2A_mix,agent,GPT-5,75,7052.4,49.01817334719725,90.89333333333333,24.652535877815076,0.0,0,90.89333333333333,24.652535877815076,7143.293333333333,48.63205705951344 +write_a_book_with_flows,A2A_mix,agent,Gemini-2.5-flash-nothinking,120,0.0,0,0.0,0,0.0,0,0.0,0,3835.4,71.20887828304095 +write_a_book_with_flows,A2A_mix,agent,Qwen3-235b,300,2069.7566666666667,107.75567936388693,123.49666666666667,82.86503172701403,0.0,0,123.49666666666667,82.86503172701403,2193.2533333333336,100.86132313520369 +write_a_book_with_flows,A2A_mix,outliner,DeepSeek-R1,60,2992.5,12.063656801177977,2926.8,30.119244267187778,1198.5166666666667,35.617551586529174,1728.2833333333333,26.4833548237131,5919.3,16.259015249917756 +write_a_book_with_flows,A2A_mix,outliner,DeepSeek-V3-1,60,1241.1333333333334,5.91788230813563,251.06666666666666,14.813802827737469,0.0,0,251.06666666666666,14.813802827737469,1492.2,5.505521448198405 +write_a_book_with_flows,A2A_mix,outliner,GPT-4o-mini,60,1926.6666666666667,21.63478948870178,625.1,12.7879737622447,0.0,0,625.1,12.7879737622447,2551.766666666667,15.970008108257247 +write_a_book_with_flows,A2A_mix,outliner,GPT-5,60,938.7166666666667,12.959519934134084,332.6333333333333,14.722141827773033,0.0,0,332.6333333333333,14.722141827773033,1271.35,10.773069198584695 +write_a_book_with_flows,A2A_mix,outliner,Gemini-2.5-flash,60,1908.8333333333333,4.890570134143355,2996.3333333333335,17.280765109099594,1243.1,20.8572454481474,1753.2333333333333,14.912977492970612,4905.166666666667,11.143658299878792 +write_a_book_with_flows,A2A_mix,outliner,Gemini-2.5-flash-nothinking,60,933.45,5.156886852338129,264.46666666666664,9.092066123025939,0.0,0,264.46666666666664,9.092066123025939,1197.9166666666667,4.658186141156322 +write_a_book_with_flows,A2A_mix,outliner,Qwen3-235b,60,1122.5333333333333,29.879881973824574,284.43333333333334,16.925289405479266,0.0,0,284.43333333333334,16.925289405479266,1406.9666666666667,24.480996432582057 +write_a_book_with_flows,A2A_mix,researcher,DeepSeek-R1,60,7442.4,25.9308543282053,6024.366666666667,37.9780407024597,2092.3,50.90198486914339,3932.0666666666666,31.923270508255776,13466.766666666666,27.016683037398103 +write_a_book_with_flows,A2A_mix,researcher,DeepSeek-V3-1,60,5321.316666666667,18.032203402780166,682.9833333333333,18.89372396007343,0.0,0,682.9833333333333,18.89372396007343,6004.3,17.54556780450745 +write_a_book_with_flows,A2A_mix,researcher,GPT-4o-mini,60,9907.533333333333,13.9291074969131,904.6,36.329777404853324,0.0,0,904.6,36.329777404853324,10812.133333333333,13.187180933299263 +write_a_book_with_flows,A2A_mix,researcher,GPT-5,60,1044.4666666666667,13.418086087665015,67.0,189.8892689470975,0.0,0,67.0,189.8892689470975,1111.4666666666667,23.732725574778442 +write_a_book_with_flows,A2A_mix,researcher,Gemini-2.5-flash,60,4753.466666666666,4.171386930383479,1019.9666666666667,17.95367673239852,339.1666666666667,24.614663152689012,680.8,15.050312464044325,5773.433333333333,4.45658753375819 +write_a_book_with_flows,A2A_mix,researcher,Gemini-2.5-flash-nothinking,60,2078.85,4.358099653420941,116.15,12.981601254853354,0.0,0,116.15,12.981601254853354,2195.0,4.277602562174347 +write_a_book_with_flows,A2A_mix,researcher,Qwen3-235b,60,5243.816666666667,11.125003098531488,371.96666666666664,87.81489395059756,0.0,0,371.96666666666664,87.81489395059756,5615.783333333334,11.464507588635843 +write_a_book_with_flows,MCP,Expert Research Agent for Book Outline Planning,DeepSeek-R1,64,9690.5,70.6309859765369,4043.484375,39.49519809386777,1542.6875,48.26674337729842,2500.796875,56.5232594633389,13733.984375,59.46444934598388 +write_a_book_with_flows,MCP,Expert Research Agent for Book Outline Planning,DeepSeek-V3-1,60,6036.9,40.95889863873784,1314.1333333333334,49.21024336961946,0.0,0,1314.1333333333334,49.21024336961946,7351.033333333334,41.39816107146624 +write_a_book_with_flows,MCP,Expert Research Agent for Book Outline Planning,GPT-4o-mini,60,14101.266666666666,22.95184305671251,1880.4833333333333,22.897811268707535,0.0,0,1880.4833333333333,22.897811268707535,15981.75,22.661491614000475 +write_a_book_with_flows,MCP,Expert Research Agent for Book Outline Planning,GPT-5,60,4443.75,25.383394664466213,1681.4,10.351549713740196,0.0,0,1681.4,10.351549713740196,6125.15,20.047187880496857 +write_a_book_with_flows,MCP,Expert Research Agent for Book Outline Planning,Gemini-2.5-flash,60,5944.066666666667,74.7741125659151,8193.75,35.17640830104612,214.71666666666667,65.05115165511039,7979.033333333334,35.50664700581903,14137.816666666668,45.49712439383524 +write_a_book_with_flows,MCP,Expert Research Agent for Book Outline Planning,Gemini-2.5-flash-nothinking,61,2843.688524590164,75.28619373545106,5881.213114754099,54.66352985374048,0.0,0,5881.213114754099,54.66352985374048,8724.901639344262,44.14936961898731 +write_a_book_with_flows,MCP,Expert Research Agent for Book Outline Planning,Qwen3-235b,60,5422.5,65.11952979269911,1551.5333333333333,44.42240323058732,0.0,0,1551.5333333333333,44.42240323058732,6974.033333333334,56.694464390944646 +write_a_book_with_flows,MCP,Professional Chapter Content Writer,DeepSeek-R1,229,12548.480349344978,184.83096081083187,6378.397379912663,77.00706380422628,2619.2489082969432,111.7847257486081,3759.1484716157206,87.32363767424455,18926.87772925764,142.04048656403967 +write_a_book_with_flows,MCP,Professional Chapter Content Writer,DeepSeek-V3-1,230,19659.934782608696,22.530306178883063,4722.186956521739,16.230416989817112,0.0,0,4722.186956521739,16.230416989817112,24382.121739130434,20.737820172104524 +write_a_book_with_flows,MCP,Professional Chapter Content Writer,GPT-4o-mini,221,3201.004524886878,13.361628402715361,1079.158371040724,34.090132234701045,0.0,0,1079.158371040724,34.090132234701045,4280.162895927602,17.76915570559501 +write_a_book_with_flows,MCP,Professional Chapter Content Writer,GPT-5,293,13712.296928327645,76.03412960576338,3657.4709897610924,60.59801464864714,0.0,0,3657.4709897610924,60.59801464864714,17369.76791808874,72.65533929238057 +write_a_book_with_flows,MCP,Professional Chapter Content Writer,Gemini-2.5-flash,188,5905.537234042553,59.71052626977882,7667.904255319149,25.97328230885238,21.154255319148938,737.1750086528276,7646.75,26.276605081484245,13573.441489361701,32.56556756109358 +write_a_book_with_flows,MCP,Professional Chapter Content Writer,Gemini-2.5-flash-nothinking,187,5456.0695187165775,63.27989557371774,5312.117647058823,47.83084038812565,0.0,0,5312.117647058823,47.83084038812565,10768.187165775402,46.69514281144027 +write_a_book_with_flows,MCP,Professional Chapter Content Writer,Qwen3-235b,225,6007.155555555555,140.3099156762884,1506.951111111111,103.13962039638731,0.0,0,1506.951111111111,103.13962039638731,7514.106666666667,131.97875673145052 +write_a_book_with_flows,MCP,Senior Book Editor and Quality Assurance Specialist,DeepSeek-R1,64,37983.703125,89.38729485464695,2570.0625,54.51676922521056,315.21875,68.69598848507043,2254.84375,59.80620578450752,40553.765625,85.82510583840293 +write_a_book_with_flows,MCP,Senior Book Editor and Quality Assurance Specialist,DeepSeek-V3-1,60,42369.46666666667,8.568909501861329,1376.0333333333333,5.525114591622007,0.0,0,1376.0333333333333,5.525114591622007,43745.5,8.375875183240945 +write_a_book_with_flows,MCP,Senior Book Editor and Quality Assurance Specialist,GPT-4o-mini,60,36034.53333333333,10.335092642706027,1172.6833333333334,4.905800395733966,0.0,0,1172.6833333333334,4.905800395733966,37207.21666666667,10.06454563778402 +write_a_book_with_flows,MCP,Senior Book Editor and Quality Assurance Specialist,GPT-5,60,53949.3,6.034502662418836,1552.1666666666667,7.004392744486282,0.0,0,1552.1666666666667,7.004392744486282,55501.46666666667,5.8897666034458345 +write_a_book_with_flows,MCP,Senior Book Editor and Quality Assurance Specialist,Gemini-2.5-flash,60,9983.65,47.90951715147879,3618.733333333333,25.22332833931965,114.78333333333333,101.95207135521986,3503.95,26.50394318395112,13602.383333333333,40.54868271458845 +write_a_book_with_flows,MCP,Senior Book Editor and Quality Assurance Specialist,Gemini-2.5-flash-nothinking,61,10084.0,37.537841139544774,2804.6721311475408,22.503192147055636,0.0,0,2804.6721311475408,22.503192147055636,12888.672131147541,30.582871810102674 +write_a_book_with_flows,MCP,Senior Book Editor and Quality Assurance Specialist,Qwen3-235b,60,35864.15,29.027600833894223,1638.0666666666666,50.229707675563006,5.75,774.5966692414834,1632.3166666666666,50.11614653162201,37502.21666666667,29.096080121470518 +write_a_book_with_flows,MCP,Specialized Chapter Research Agent,DeepSeek-R1,233,11169.32618025751,71.81641939349647,3330.2317596566522,40.821277187235296,1488.961373390558,59.780311942998,1841.2703862660944,53.04422883770449,14499.557939914162,62.301815975855234 +write_a_book_with_flows,MCP,Specialized Chapter Research Agent,DeepSeek-V3-1,230,8707.804347826086,28.74686906808646,984.1130434782609,18.330839293032145,0.0,0,984.1130434782609,18.330839293032145,9691.917391304349,27.186667918876356 +write_a_book_with_flows,MCP,Specialized Chapter Research Agent,GPT-4o-mini,221,15300.171945701357,33.91417736988102,1285.9411764705883,39.91245427617763,0.0,0,1285.9411764705883,39.91245427617763,16586.113122171944,34.04705317144145 +write_a_book_with_flows,MCP,Specialized Chapter Research Agent,GPT-5,293,7152.791808873721,22.358991739542038,1258.1399317406144,9.107124734788682,0.0,0,1258.1399317406144,9.107124734788682,8410.931740614335,19.568423530656094 +write_a_book_with_flows,MCP,Specialized Chapter Research Agent,Gemini-2.5-flash,188,4907.882978723404,66.63652284383063,5129.765957446809,36.268789495727866,205.13829787234042,107.96315347732997,4924.627659574468,36.90481925508839,10037.648936170213,46.2611747258881 +write_a_book_with_flows,MCP,Specialized Chapter Research Agent,Gemini-2.5-flash-nothinking,187,3766.9732620320856,140.91650625834046,3790.9786096256685,135.18922556290212,0.0,0,3790.9786096256685,135.18922556290212,7557.951871657754,134.46872978651447 +write_a_book_with_flows,MCP,Specialized Chapter Research Agent,Qwen3-235b,225,8218.684444444445,43.9977953540838,1132.3733333333332,21.600803725632854,0.0,0,1132.3733333333332,21.600803725632854,9351.057777777778,39.593743828780255 +write_a_book_with_flows,MCP,Strategic Book Structure Architect,DeepSeek-R1,64,2616.96875,24.63477238866856,1371.109375,53.60677107835645,1035.890625,70.90269540440075,335.21875,47.518682515158545,3988.078125,22.858356134329927 +write_a_book_with_flows,MCP,Strategic Book Structure Architect,DeepSeek-V3-1,60,2259.4166666666665,9.055156270452724,341.51666666666665,14.04869336949735,0.0,0,341.51666666666665,14.04869336949735,2600.9333333333334,8.044446973935177 +write_a_book_with_flows,MCP,Strategic Book Structure Architect,GPT-4o-mini,60,2599.65,8.67574923358922,693.3,17.40172853337561,0.0,0,693.3,17.40172853337561,3292.95,8.839321052530904 +write_a_book_with_flows,MCP,Strategic Book Structure Architect,GPT-5,60,2664.7,19.779759778241647,655.25,31.09195210638837,0.0,0,655.25,31.09195210638837,3319.95,15.513018262513661 +write_a_book_with_flows,MCP,Strategic Book Structure Architect,Gemini-2.5-flash,60,3545.6666666666665,16.600005061442097,1886.3166666666666,22.91616679099501,1564.5333333333333,30.799356442377118,321.78333333333336,61.344709943458575,5431.983333333334,14.509904048748012 +write_a_book_with_flows,MCP,Strategic Book Structure Architect,Gemini-2.5-flash-nothinking,61,3077.7377049180327,26.963651532723226,321.1803278688525,30.655800176560653,0.0,0,321.1803278688525,30.655800176560653,3398.9180327868853,24.05565493001902 +write_a_book_with_flows,MCP,Strategic Book Structure Architect,Qwen3-235b,60,2649.266666666667,20.596948188858573,346.1166666666667,27.9353069036048,0.0,0,346.1166666666667,27.9353069036048,2995.383333333333,19.434697949333124 diff --git a/data/processed/RQ3/performance_reports/task_time_statistics.csv b/data/processed/RQ3/performance_reports/task_time_statistics.csv index f16a2bdd63da256e631f360a38ab13ff736243b2..edc85f3b0d807d722c45eb357092ae22ab82f7d8 100644 --- a/data/processed/RQ3/performance_reports/task_time_statistics.csv +++ b/data/processed/RQ3/performance_reports/task_time_statistics.csv @@ -1,25 +1,4 @@ task,architecture,model,count,mean_time,p90_time,p99_time,cv_time,throughput_tasks_per_hour -BookWriter,A2A,DeepSeek-R1,60,962.7981666666664,1250.4640000000002,2422.4851,38.321925413338604,3.7391014281463297 -BookWriter,A2A,DeepSeek-V3-1,60,444.64733333333334,511.176,779.2511999999997,19.595425438372164,8.096304037207014 -BookWriter,A2A,GPT-4o-mini,60,102.09533333333336,127.111,158.4478999999999,17.866487066513244,35.26116113697654 -BookWriter,A2A,GPT-5,60,194.83466666666664,228.953,243.75689999999997,15.02312771506408,18.477204604245653 -BookWriter,A2A,Gemini-2.5-flash,60,242.21800000000002,294.747,406.4494999999995,20.570771192489357,14.862644394718806 -BookWriter,A2A,Gemini-2.5-flash-nothinking,60,134.9315,155.095,338.96829999999875,44.29162071845992,26.680204400010375 -BookWriter,A2A,Qwen3-235b,60,459.7638333333334,727.059,1092.5847999999999,46.1011698331875,7.8301069788365965 -BookWriter,MCP,DeepSeek-R1,60,963.3698333333334,1319.303,2560.2510999999995,50.79706909358479,3.736882633685678 -BookWriter,MCP,DeepSeek-V3-1,60,397.91133333333335,487.757,768.1421999999999,26.12924541430018,9.047241680307337 -BookWriter,MCP,GPT-4o-mini,60,115.59383333333334,169.083,186.93979999999993,23.78304668193222,31.143529859580166 -BookWriter,MCP,GPT-5,60,143.5825,164.83200000000002,181.57829999999993,14.014593952276009,25.07269339926523 -BookWriter,MCP,Gemini-2.5-flash,60,186.7251666666667,214.264,237.30279999999996,13.50849151676382,19.279672174166844 -BookWriter,MCP,Gemini-2.5-flash-nothinking,60,117.68033333333332,140.872,278.4979999999991,39.06430240597386,30.59134774714552 -BookWriter,MCP,Qwen3-235b,60,445.1478333333333,685.778,931.0739999999992,40.410880889005185,8.087200993527619 -BookWriter-H_A2A,A2A_mix,DeepSeek-R1,60,817.5155,1186.2269999999999,1590.8631999999998,32.07395141308888,4.403586231698359 -BookWriter-H_A2A,A2A_mix,DeepSeek-V3-1,60,294.0398333333334,348.757,427.02139999999974,13.179759299944442,12.243239152971903 -BookWriter-H_A2A,A2A_mix,GPT-4o-mini,60,92.01266666666666,123.262,148.82899999999998,21.49474166787132,39.1250480006376 -BookWriter-H_A2A,A2A_mix,GPT-5,60,112.35966666666668,162.197,190.81709999999995,31.500483836233062,32.03996689203421 -BookWriter-H_A2A,A2A_mix,Gemini-2.5-flash,60,169.7746666666667,190.988,432.9112999999998,32.124090144510404,21.204577047223374 -BookWriter-H_A2A,A2A_mix,Gemini-2.5-flash-nothinking,60,80.31433333333332,92.788,103.88859999999995,12.237723685366692,44.82387950677132 -BookWriter-H_A2A,A2A_mix,Qwen3-235b,60,257.65616666666665,401.849,692.1228999999995,48.47814686190344,13.972108824615752 EmailResponder,MCP,DeepSeek-R1,100,136.5648,205.572,318.1932000000006,44.00119404669094,26.361112087448596 EmailResponder,MCP,DeepSeek-V3-1,100,44.00509999999999,64.554,69.93200000000007,27.176905521510097,81.80869944620056 EmailResponder,MCP,GPT-4o-mini,100,18.221700000000002,23.904999999999998,82.2790000000004,90.52065155030824,197.56663758046722 @@ -48,27 +27,6 @@ GameBuilder,Unknown,GPT-5,25,45.632400000000004,59.392,62.025999999999996,24.443 GameBuilder,Unknown,Gemini-2.5-flash,25,164.1072,284.74800000000005,347.1471999999999,48.53216489060525,21.936880283132002 GameBuilder,Unknown,Gemini-2.5-flash-nothinking,25,169.558,249.56600000000006,867.0063999999986,114.65053059564623,21.231672937873768 GameBuilder,Unknown,Qwen3-235b,25,317.81,501.47400000000016,1131.922399999999,73.75706505052094,11.32752273370882 -LandingPageGenerator,A2A,DeepSeek-R1,60,302.4803333333333,472.151,813.3017999999992,52.2035082060071,11.901600214228806 -LandingPageGenerator,A2A,DeepSeek-V3-1,60,215.62249999999997,220.464,849.1174999999998,71.7494290476987,16.69584574893622 -LandingPageGenerator,A2A,GPT-4o-mini,60,45.10933333333333,57.63,70.05839999999999,23.910847131765998,79.80610073303382 -LandingPageGenerator,A2A,GPT-5,60,23.356499999999997,35.82000000000001,49.06869999999997,41.50530287331286,154.1326825508959 -LandingPageGenerator,A2A,Gemini-2.5-flash,60,77.21616666666667,102.772,147.37399999999997,27.39013599258367,46.6223610340667 -LandingPageGenerator,A2A,Gemini-2.5-flash-nothinking,60,98.23066666666665,108.661,791.413599999996,189.02849701888354,36.64843294015447 -LandingPageGenerator,A2A,Qwen3-235b,60,209.10800000000006,481.05300000000005,1213.4666999999997,125.28553951511981,17.21598408477915 -LandingPageGenerator,MCP,DeepSeek-R1,60,275.0818333333334,438.15500000000003,632.8926999999999,47.941278547763005,13.087014712591582 -LandingPageGenerator,MCP,DeepSeek-V3-1,60,136.2995,187.21,404.33639999999855,57.12655083132081,26.41242264278299 -LandingPageGenerator,MCP,GPT-4o-mini,60,56.565333333333335,74.60300000000001,90.62649999999998,26.237064678071082,63.64322081840467 -LandingPageGenerator,MCP,GPT-5,60,24.435333333333332,26.992,30.833299999999998,8.473922343338693,147.3276403022945 -LandingPageGenerator,MCP,Gemini-2.5-flash,60,92.31383333333335,125.32800000000002,210.58699999999976,39.89791327644906,38.99740558926704 -LandingPageGenerator,MCP,Gemini-2.5-flash-nothinking,60,97.85800000000002,141.395,525.7189999999993,102.52194751133973,36.78799893723558 -LandingPageGenerator,MCP,Qwen3-235b,60,92.53350000000002,118.519,256.0165999999991,52.54943569289582,38.9048290619073 -LandingPageGenerator-H_A2A,A2A_mix,DeepSeek-R1,60,280.28266666666667,467.899,763.8529999999996,57.513220651891736,12.84417635529846 -LandingPageGenerator-H_A2A,A2A_mix,DeepSeek-V3-1,60,242.6,285.4380000000001,798.3385999999994,63.851757549309376,14.83924154987634 -LandingPageGenerator-H_A2A,A2A_mix,GPT-4o-mini,60,41.24383333333333,51.531,58.4616,17.870088883967096,87.28577605541031 -LandingPageGenerator-H_A2A,A2A_mix,GPT-5,60,30.51333333333333,46.719,56.49599999999998,40.46277618854518,117.98121039982523 -LandingPageGenerator-H_A2A,A2A_mix,Gemini-2.5-flash,60,73.31966666666666,86.244,480.9071999999984,132.28751674718123,49.100059556553724 -LandingPageGenerator-H_A2A,A2A_mix,Gemini-2.5-flash-nothinking,60,51.60166666666666,71.124,105.30529999999999,30.4389530522223,69.76518846290496 -LandingPageGenerator-H_A2A,A2A_mix,Qwen3-235b,60,196.78083333333333,434.576,1293.3333999999988,146.52082353895904,18.294464653993234 MarkdownValidator,MCP,DeepSeek-R1,60,129.2225,208.65,245.06149999999982,38.223506675744034,27.858925496720772 MarkdownValidator,MCP,DeepSeek-V3-1,60,10.73466666666667,16.768,28.483899999999995,42.24625638321171,335.36206682399694 MarkdownValidator,MCP,GPT-4o-mini,60,3.5965000000000007,5.154,10.717099999999974,51.91353868777021,1000.9731683581258 @@ -83,72 +41,108 @@ MarkdownValidator,Unknown,GPT-5,60,7.526000000000001,4.41,115.62919999999909,461 MarkdownValidator,Unknown,Gemini-2.5-flash,60,562.4020000000002,1747.882,2893.9866999999986,141.07243007721485,6.40111521651772 MarkdownValidator,Unknown,Gemini-2.5-flash-nothinking,60,133.80516666666668,249.01700000000022,2021.5269999999946,337.28610661271756,26.904790672009423 MarkdownValidator,Unknown,Qwen3-235b,60,9.745499999999998,17.765,26.693399999999997,59.34803053490194,369.40126212097897 -RecruitmentAssistant,A2A,DeepSeek-R1,60,922.5811666666667,1313.0190000000002,4454.7388999999985,94.7602913691143,3.9020956963678173 -RecruitmentAssistant,A2A,DeepSeek-V3-1,60,312.50899999999996,374.51500000000004,443.49659999999983,14.612553506872464,11.519668233554874 -RecruitmentAssistant,A2A,GPT-4o-mini,60,178.89833333333334,253.334,453.5200999999995,43.00150024111714,20.123161199564 -RecruitmentAssistant,A2A,GPT-5,60,67.78416666666666,78.419,82.58719999999998,11.010205253753197,53.10974785163694 -RecruitmentAssistant,A2A,Gemini-2.5-flash,60,359.24133333333333,579.4580000000001,853.4880999999999,48.35810421581256,10.021118579525 -RecruitmentAssistant,A2A,Gemini-2.5-flash-nothinking,60,195.79049999999998,272.944,428.7082999999999,34.65382037921111,18.38700039072376 -RecruitmentAssistant,A2A,Qwen3-235b,60,1478.4976666666664,3075.336,4723.5041,79.4984062881123,2.434904079433786 -RecruitmentAssistant,MCP,DeepSeek-R1,60,840.7478333333332,1209.0780000000002,1999.1607999999987,43.5799091946173,4.281902203336038 -RecruitmentAssistant,MCP,DeepSeek-V3-1,60,339.9911666666667,423.014,657.8591999999995,25.94808297287897,10.588510387769878 -RecruitmentAssistant,MCP,GPT-4o-mini,60,129.95566666666667,202.654,397.8909999999991,61.098294142652854,27.701754700962123 -RecruitmentAssistant,MCP,GPT-5,60,60.02516666666667,71.82300000000001,75.4914,13.45706801219425,59.97484388492604 -RecruitmentAssistant,MCP,Gemini-2.5-flash,60,204.43866666666665,284.635,442.3269,35.27979461566784,17.609193303289008 -RecruitmentAssistant,MCP,Gemini-2.5-flash-nothinking,60,163.95133333333334,233.54899999999998,318.05609999999945,34.28661609907417,21.95773542555311 -RecruitmentAssistant,MCP,Qwen3-235b,60,1382.4455,2643.61,4021.4524999999994,67.62743222656377,2.6040809565368037 -RecruitmentAssistant-H_A2A,A2A,DeepSeek-R1,60,756.6999999999999,1082.495,2681.9859999999926,69.30835872568592,4.757499669618079 -RecruitmentAssistant-H_A2A,A2A,DeepSeek-V3-1,60,245.57133333333334,293.204,305.4792,15.716277611735535,14.659691549230773 -RecruitmentAssistant-H_A2A,A2A,GPT-4o-mini,60,142.1095,225.036,354.0654999999997,45.883022940300066,25.332578047210074 -RecruitmentAssistant-H_A2A,A2A,GPT-5,60,53.043499999999995,69.426,80.88439999999999,20.65351328411091,67.86882464392433 -RecruitmentAssistant-H_A2A,A2A,Gemini-2.5-flash,60,122.00999999999999,174.93400000000003,227.74009999999996,29.66479249005978,29.50577821490042 -RecruitmentAssistant-H_A2A,A2A,Gemini-2.5-flash-nothinking,60,94.54583333333333,120.921,150.83539999999994,25.09845832256317,38.07677052575911 -RecruitmentAssistant-H_A2A,A2A,Qwen3-235b,60,973.2838333333333,1806.844,2532.1452999999983,59.566113029322395,3.6988182446949787 -SQLAssistant,A2A,DeepSeek-R1,60,1131.0566666666666,1725.898,2996.832399999998,46.963796657508915,3.182864401135222 -SQLAssistant,A2A,DeepSeek-V3-1,60,162.0703333333333,246.17499999999998,357.10889999999955,37.867000464506106,22.212578489585802 -SQLAssistant,A2A,GPT-4o-mini,60,34.12733333333333,47.93,66.04569999999998,32.00832991166617,105.48729268816784 -SQLAssistant,A2A,GPT-5,60,21.214166666666664,29.54,34.09349999999999,30.25240328038727,169.69792198609423 -SQLAssistant,A2A,Gemini-2.5-flash,60,151.32066666666665,282.42300000000006,492.8688999999995,65.73813199807482,23.79053753397862 -SQLAssistant,A2A,Gemini-2.5-flash-nothinking,60,68.71716666666666,120.34800000000001,245.24519999999947,72.32782810939213,52.388655915673674 -SQLAssistant,A2A,Qwen3-235b,60,82.47083333333333,125.896,139.14589999999998,34.925156622131496,43.6517960895266 -SQLAssistant,MCP,DeepSeek-R1,60,941.9503333333334,1400.404,2493.3644999999997,52.545021887084644,3.8218575572455866 -SQLAssistant,MCP,DeepSeek-V3-1,60,112.7195,161.99,223.2645999999999,37.312978680217924,31.937686025931626 -SQLAssistant,MCP,GPT-4o-mini,60,49.404333333333334,70.93,92.36559999999999,37.194589570836584,72.8681019883546 -SQLAssistant,MCP,GPT-5,60,26.214333333333332,35.848000000000006,47.37849999999998,36.29117266307374,137.32945080935366 -SQLAssistant,MCP,Gemini-2.5-flash,60,105.03099999999999,182.41200000000003,726.5971999999967,159.873433539799,34.27559482438519 -SQLAssistant,MCP,Gemini-2.5-flash-nothinking,60,47.04783333333333,81.49000000000001,243.9763999999995,101.25091031187496,76.5178701109155 -SQLAssistant,MCP,Qwen3-235b,60,89.16966666666667,141.445,190.77879999999988,43.98448549193786,40.372473449491416 -SQLAssistant-H_A2A,A2A,DeepSeek-R1,55,867.8838181818182,1909.8200000000008,2989.2940000000003,80.86078809197662,4.148020650438967 -SQLAssistant-H_A2A,A2A,DeepSeek-V3-1,59,68.45118644067796,113.96000000000001,141.87640000000002,41.81304408079363,52.59222209566635 -SQLAssistant-H_A2A,A2A,GPT-4o-mini,59,28.81813559322034,52.098,69.40360000000004,51.24064943186208,124.92133602310221 -SQLAssistant-H_A2A,A2A,GPT-5,59,28.735762711864403,44.754000000000005,71.94880000000006,46.12916354305166,125.27943093411035 -SQLAssistant-H_A2A,A2A,Gemini-2.5-flash,57,80.39736842105262,129.68,384.8571999999997,87.7641986162553,44.77758502176689 -SQLAssistant-H_A2A,A2A,Gemini-2.5-flash-nothinking,57,50.776666666666664,78.496,291.647999999999,136.6877250273173,70.89870675507123 -SQLAssistant-H_A2A,A2A,Qwen3-235b,60,70.17833333333331,123.25100000000002,163.00779999999995,48.392158961654665,51.29788396228657 -SQLAssistant-H_A2A,A2A_mix,DeepSeek-R1,5,1225.616,2080.4300000000003,2459.8700000000003,66.60131469044637,2.9372984686883985 -SQLAssistant-H_A2A,A2A_mix,DeepSeek-V3-1,1,79.92,79.92,79.92,0.0,45.04504504504504 -SQLAssistant-H_A2A,A2A_mix,GPT-4o-mini,1,65.57,65.57,65.57,0.0,54.90315693152357 -SQLAssistant-H_A2A,A2A_mix,GPT-5,1,44.91,44.91,44.91,0.0,80.16032064128257 -SQLAssistant-H_A2A,A2A_mix,Gemini-2.5-flash,3,135.46666666666667,166.472,171.6452,30.521572209897545,26.5748031496063 -SQLAssistant-H_A2A,A2A_mix,Gemini-2.5-flash-nothinking,3,125.25666666666666,193.88,219.422,67.12989264436516,28.740985177103017 -SocialMediaManager,A2A,DeepSeek-R1,60,562.5068333333332,1035.443,1602.6040999999996,65.39710690087655,6.399922252796338 -SocialMediaManager,A2A,DeepSeek-V3-1,60,159.77683333333331,222.53700000000003,382.6891999999998,42.37266287268792,22.531426646124128 -SocialMediaManager,A2A,GPT-4o-mini,60,88.21666666666667,167.82500000000002,203.17159999999998,56.37128018296188,40.80861515208766 -SocialMediaManager,A2A,GPT-5,60,41.695166666666665,67.89800000000001,89.44589999999997,43.74115514643477,86.34094279512813 -SocialMediaManager,A2A,Gemini-2.5-flash,60,65.57133333333333,99.568,116.1951,30.346123605554233,54.902040525839546 -SocialMediaManager,A2A,Gemini-2.5-flash-nothinking,60,40.76433333333333,52.101,73.50559999999997,24.368465337066315,88.31249540039086 -SocialMediaManager,A2A,Qwen3-235b,60,135.22466666666668,243.574,374.2076999999998,54.79432343093298,26.622361797896833 -SocialMediaManager,MCP,DeepSeek-R1,60,736.3194999999998,1266.1240000000003,2256.6313999999998,67.64817339601946,4.889181938003816 -SocialMediaManager,MCP,DeepSeek-V3-1,60,220.1423333333333,346.65100000000007,466.11139999999983,42.39555705341282,16.353056431672236 -SocialMediaManager,MCP,GPT-4o-mini,60,145.0385,216.085,779.4937999999975,115.64732969214104,24.820995804562237 -SocialMediaManager,MCP,GPT-5,60,40.342999999999996,64.00200000000001,85.80959999999999,44.57128269167129,89.23481149146073 -SocialMediaManager,MCP,Gemini-2.5-flash,60,60.458,81.791,115.22199999999998,27.565436521143372,59.54546958218929 -SocialMediaManager,MCP,Gemini-2.5-flash-nothinking,60,39.112833333333334,46.307,60.93209999999996,16.972572512070574,92.0414015860097 -SocialMediaManager,MCP,Qwen3-235b,60,127.30300000000001,232.203,354.44329999999997,59.44081616824456,28.278987926443207 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a/data/processed/RQ3/performance_reports/task_token_statistics.csv b/data/processed/RQ3/performance_reports/task_token_statistics.csv index 058fabe84f145eba8a2272177d4b85d6d8af6c62..11fc3445552aa5ed612c43e0831bc5d9d7ee9f45 100644 --- a/data/processed/RQ3/performance_reports/task_token_statistics.csv +++ b/data/processed/RQ3/performance_reports/task_token_statistics.csv @@ -1,25 +1,4 @@ task,architecture,model,count,mean_input,cv_input,mean_output,cv_output,mean_reasoning,cv_reasoning,mean_result,cv_result,mean_total,cv_total -BookWriter,A2A,DeepSeek-R1,60,142224.91666666666,53.19294869847499,44452.96666666667,32.263151026613635,17848.666666666668,37.84974990429266,26604.3,34.96455940847422,186677.88333333333,46.92635370325584 -BookWriter,A2A,DeepSeek-V3-1,60,244189.66666666666,17.97678582087039,28085.433333333334,18.092347247354923,0.0,0,28085.433333333334,18.092347247354923,272275.1,17.737930359522892 -BookWriter,A2A,GPT-4o-mini,60,127969.4,18.564775436123924,11695.933333333332,16.347746564807554,0.0,0,11695.933333333332,16.347746564807554,139665.33333333334,18.065178001782918 -BookWriter,A2A,GPT-5,60,154820.46666666667,17.948791258779174,26201.5,22.326136507926584,0.0,0,26201.5,22.326136507926584,181021.96666666667,18.48392479736698 -BookWriter,A2A,Gemini-2.5-flash,60,54164.45,23.75611561241704,55961.98333333333,12.465604721685601,2729.3166666666666,31.755523840518002,53232.666666666664,13.10909741530473,110126.43333333333,16.476279031103765 -BookWriter,A2A,Gemini-2.5-flash-nothinking,60,46256.183333333334,59.947426976023245,39992.46666666667,49.60134056131592,0.0,0,39992.46666666667,49.60134056131592,86248.65,54.37684695443079 -BookWriter,A2A,Qwen3-235b,60,104832.96666666666,25.34035766357372,13572.616666666667,31.295872953964093,0.0,0,13572.616666666667,31.295872953964093,118405.58333333333,25.793182569403594 -BookWriter,MCP,DeepSeek-R1,60,144911.5,63.77696179414857,45793.583333333336,43.19835125822024,18865.65,48.44364930229119,26927.933333333334,45.727890899036936,190705.08333333334,57.972771281345224 -BookWriter,MCP,DeepSeek-V3-1,60,159408.78333333333,12.915302614151484,24905.833333333332,12.775356660272932,0.0,0,24905.833333333332,12.775356660272932,184314.61666666667,12.809420193476036 -BookWriter,MCP,GPT-4o-mini,60,120881.45,18.275704153554422,12457.916666666666,18.69937539086685,0.0,0,12457.916666666666,18.69937539086685,133339.36666666667,18.22067806739296 -BookWriter,MCP,GPT-5,60,162948.93333333332,13.645326994313853,27893.383333333335,16.45788358236414,0.0,0,27893.383333333335,16.45788358236414,190842.31666666668,13.987282390065433 -BookWriter,MCP,Gemini-2.5-flash,60,53355.433333333334,20.76295489431966,53798.166666666664,13.60318461788276,2603.0833333333335,27.24219942061455,51195.083333333336,14.306085305053815,107153.6,15.01159020116359 -BookWriter,MCP,Gemini-2.5-flash-nothinking,60,45017.333333333336,26.780250955756813,37528.5,34.72733225429748,0.0,0,37528.5,34.72733225429748,82545.83333333333,28.02094064958542 -BookWriter,MCP,Qwen3-235b,60,97282.81666666667,24.274522742883743,13433.183333333332,24.603546618842866,5.75,774.5966692414834,13427.433333333332,24.534748705734984,110716.0,23.66663496913068 -BookWriter-H_A2A,A2A_mix,DeepSeek-R1,60,87261.96666666666,36.404573606166146,42620.916666666664,23.80591359294649,18093.15,29.889161283437666,24527.766666666666,26.585869292945407,129882.88333333333,30.55969275212742 -BookWriter-H_A2A,A2A_mix,DeepSeek-V3-1,60,118718.3,14.97705031191218,22485.866666666665,13.334953458695942,0.0,0,22485.866666666665,13.334953458695942,141204.16666666666,14.544639838891246 -BookWriter-H_A2A,A2A_mix,GPT-4o-mini,60,91213.53333333334,16.19846808666701,11350.75,15.223863474394292,0.0,0,11350.75,15.223863474394292,102564.28333333334,15.897004325980088 -BookWriter-H_A2A,A2A_mix,GPT-5,60,95919.81666666667,28.90497431878102,21438.066666666666,28.088814390066513,0.0,0,21438.066666666666,28.088814390066513,117357.88333333333,28.65795828707487 -BookWriter-H_A2A,A2A_mix,Gemini-2.5-flash,60,39608.05,23.432872942188325,43279.1,10.66253196335402,3172.6,60.01869440886143,40106.5,10.144287546275583,91585.33333333333,12.975772430093405 -BookWriter-H_A2A,A2A_mix,Gemini-2.5-flash-nothinking,60,30465.316666666666,21.970653939375463,27690.066666666666,18.785588489135222,0.0,0,27690.066666666666,18.785588489135222,65826.18333333333,14.81605227812274 -BookWriter-H_A2A,A2A_mix,Qwen3-235b,60,66617.4,26.989655099130545,9895.05,29.155615294342198,0.0,0,9895.05,29.155615294342198,76512.45,26.952195619621666 EmailResponder,MCP,DeepSeek-R1,100,10807.41,44.26120171683066,4595.76,44.41084187037948,3271.51,52.910461315901415,1324.25,32.863464384918416,15403.17,41.23497320804384 EmailResponder,MCP,DeepSeek-V3-1,100,8580.78,35.918440848002135,999.69,25.408905511847106,0.0,0,999.69,25.408905511847106,9580.47,34.58077760583236 EmailResponder,MCP,GPT-4o-mini,100,8732.75,25.99251410389405,704.23,26.888904506741024,0.0,0,704.23,26.888904506741024,9436.98,25.62756072549295 @@ -48,27 +27,6 @@ GameBuilder,Unknown,GPT-5,25,15202.44,22.82502495389533,7701.72,26.6629750285484 GameBuilder,Unknown,Gemini-2.5-flash,25,28885.56,73.54377394385536,43886.44,52.15298603380898,9173.44,61.18885182625362,34713.0,54.23829573077429,72772.0,59.33849007189289 GameBuilder,Unknown,Gemini-2.5-flash-nothinking,25,31678.56,85.05047838272631,36285.32,101.87952235240189,0.0,0,36285.32,101.87952235240189,67963.88,92.21279770826408 GameBuilder,Unknown,Qwen3-235b,25,16060.32,33.35544798079702,8151.72,33.347346998661514,0.0,0,8151.72,33.347346998661514,24212.04,32.8737571914701 -LandingPageGenerator,A2A,DeepSeek-R1,60,20988.9,56.33243309100763,7668.4,52.45761045724232,2601.133333333333,57.68662591314938,5067.266666666666,73.96747616434195,28657.3,53.34662868715279 -LandingPageGenerator,A2A,DeepSeek-V3-1,60,22711.366666666665,101.8567300540781,6102.716666666666,66.66044737099499,0.0,0,6102.716666666666,66.66044737099499,28814.083333333332,94.24364518061739 -LandingPageGenerator,A2A,GPT-4o-mini,60,18068.133333333335,32.71283080053945,2271.4333333333334,19.042322536540148,0.0,0,2271.4333333333334,19.042322536540148,20339.566666666666,30.52501073614433 -LandingPageGenerator,A2A,GPT-5,60,13500.583333333334,40.55665133188484,1464.2666666666667,86.3632691661363,0.0,0,1464.2666666666667,86.3632691661363,14964.85,43.26682078131815 -LandingPageGenerator,A2A,Gemini-2.5-flash,60,5951.833333333333,58.216404502706354,15303.85,32.9555813533507,602.75,72.34367328092584,14701.1,34.36063459349684,21255.683333333334,33.150480782625394 -LandingPageGenerator,A2A,Gemini-2.5-flash-nothinking,60,7167.783333333334,132.790494855184,15657.95,121.88407205346206,0.0,0,15657.95,121.88407205346206,22825.733333333334,121.77397279303541 -LandingPageGenerator,A2A,Qwen3-235b,60,14822.416666666666,46.4010155369555,3319.616666666667,70.00736091563185,0.0,0,3319.616666666667,70.00736091563185,18142.033333333333,46.75496817931351 -LandingPageGenerator,MCP,DeepSeek-R1,60,20342.016666666666,66.8090505909146,8028.883333333333,48.97035995021221,2085.45,67.32398499557343,5943.433333333333,52.67478565510157,28370.9,60.51628399593007 -LandingPageGenerator,MCP,DeepSeek-V3-1,60,12682.083333333334,67.35866661410863,3184.266666666667,67.835692617547,0.0,0,3184.266666666667,67.835692617547,15866.35,66.32563124512629 -LandingPageGenerator,MCP,GPT-4o-mini,60,15739.816666666668,31.21169932155824,3509.7,18.07876273451736,0.0,0,3509.7,18.07876273451736,19249.516666666666,27.212907232539653 -LandingPageGenerator,MCP,GPT-5,60,9882.816666666668,5.451455398329568,2770.5333333333333,9.495473532141483,0.0,0,2770.5333333333333,9.495473532141483,12653.35,6.116544372562067 -LandingPageGenerator,MCP,Gemini-2.5-flash,60,7059.2,85.88470517537033,19467.316666666666,44.557165893468614,355.93333333333334,84.03583768734165,19111.383333333335,45.3920570204386,26526.516666666666,47.345298186429225 -LandingPageGenerator,MCP,Gemini-2.5-flash-nothinking,60,13399.35,221.27022007394038,20028.633333333335,107.70836171358569,0.0,0,20028.633333333335,107.70836171358569,33427.98333333333,150.19808750683447 -LandingPageGenerator,MCP,Qwen3-235b,60,11874.016666666666,21.234934129184087,2603.866666666667,40.50431474871268,0.0,0,2603.866666666667,40.50431474871268,14477.883333333333,20.300893498484495 -LandingPageGenerator-H_A2A,A2A_mix,DeepSeek-R1,60,48688.96666666667,47.55399236616546,8513.85,54.77863518040499,3034.4666666666667,49.85622388144263,5479.383333333333,81.93384854881126,57202.816666666666,45.788066015339176 -LandingPageGenerator-H_A2A,A2A_mix,DeepSeek-V3-1,60,28286.116666666665,81.94525762708331,6210.016666666666,66.5555570764984,0.0,0,6210.016666666666,66.5555570764984,34496.13333333333,78.38070167716067 -LandingPageGenerator-H_A2A,A2A_mix,GPT-4o-mini,60,41538.816666666666,31.413371581076042,2049.7833333333333,16.877012831944924,0.0,0,2049.7833333333333,16.877012831944924,43588.6,29.996163300610394 -LandingPageGenerator-H_A2A,A2A_mix,GPT-5,60,22920.1,45.06496190433171,2375.883333333333,63.84147721029255,0.0,0,2375.883333333333,63.84147721029255,25295.983333333334,45.08953105444501 -LandingPageGenerator-H_A2A,A2A_mix,Gemini-2.5-flash,60,30017.5,168.11622631918712,14149.0,189.8863122174641,963.4666666666667,89.10326517065144,13185.533333333333,203.32727394447616,45250.36666666667,169.4043709982898 -LandingPageGenerator-H_A2A,A2A_mix,Gemini-2.5-flash-nothinking,60,6106.95,92.32369527757355,8049.7,42.904601030844745,0.0,0,8049.7,42.904601030844745,14949.25,44.851433646838174 -LandingPageGenerator-H_A2A,A2A_mix,Qwen3-235b,60,14496.4,40.90078389521843,3131.3166666666666,67.27240088941639,0.0,0,3131.3166666666666,67.27240088941639,17627.716666666667,41.923420614714566 MarkdownValidator,MCP,DeepSeek-R1,60,1859.0333333333333,11.571333397286649,4026.2166666666667,39.10133252821658,3818.2166666666667,40.21647818711991,208.0,54.91773132075755,5885.25,28.059563621577578 MarkdownValidator,MCP,DeepSeek-V3-1,60,1825.9,6.902634218986391,237.26666666666668,56.86954915985149,0.0,0,237.26666666666668,56.86954915985149,2063.1666666666665,12.533909107840454 MarkdownValidator,MCP,GPT-4o-mini,60,1767.45,9.40812776011145,174.06666666666666,69.8921832500779,0.0,0,174.06666666666666,69.8921832500779,1941.5166666666667,13.554127972211544 @@ -83,72 +41,108 @@ MarkdownValidator,Unknown,GPT-5,60,1652.3666666666666,7.284279451268969,200.15,6 MarkdownValidator,Unknown,Gemini-2.5-flash,60,47256.13333333333,165.68595903118614,87968.26666666666,104.03087612737892,252.05,126.83580621187565,87716.21666666666,104.34138425638979,135224.4,113.85079932048086 MarkdownValidator,Unknown,Gemini-2.5-flash-nothinking,60,9918.483333333334,308.9575842122058,21411.533333333333,272.7783318313396,0.0,0,21411.533333333333,272.7783318313396,31330.016666666666,282.3671386793007 MarkdownValidator,Unknown,Qwen3-235b,60,1657.4833333333333,12.81147185692845,196.48333333333332,64.92124356411036,0.0,0,196.48333333333332,64.92124356411036,1853.9666666666667,17.160459611445265 -RecruitmentAssistant,A2A,DeepSeek-R1,60,54159.86666666667,58.01577939402214,18808.066666666666,34.559542017598005,7081.616666666667,40.52764072521723,11726.45,42.67116726064058,72967.93333333333,50.61862376258402 -RecruitmentAssistant,A2A,DeepSeek-V3-1,60,56166.6,20.861041192156762,9495.816666666668,17.524797989756834,0.0,0,9495.816666666668,17.524797989756834,65662.41666666667,19.460292982459848 -RecruitmentAssistant,A2A,GPT-4o-mini,60,67874.1,49.12197898268533,7077.266666666666,40.87006885580345,0.0,0,7077.266666666666,40.87006885580345,74951.36666666667,46.927054000753806 -RecruitmentAssistant,A2A,GPT-5,60,26210.316666666666,15.067191855819512,5780.816666666667,18.451625440710693,0.0,0,5780.816666666667,18.451625440710693,31991.133333333335,14.283748196148121 -RecruitmentAssistant,A2A,Gemini-2.5-flash,60,32618.283333333333,76.71938090848761,36100.75,32.36259572997574,2410.45,72.88839581196503,33690.3,34.066751622626384,68719.03333333334,52.18901521517516 -RecruitmentAssistant,A2A,Gemini-2.5-flash-nothinking,60,25770.45,42.95883405520041,29889.066666666666,28.676152565513274,0.0,0,29889.066666666666,28.676152565513274,55659.51666666667,28.7709748420511 -RecruitmentAssistant,A2A,Qwen3-235b,60,84677.5,59.36544234512861,13039.583333333334,38.32756073796254,0.0,0,13039.583333333334,38.32756073796254,97717.08333333333,56.0387321285989 -RecruitmentAssistant,MCP,DeepSeek-R1,60,63108.7,56.173958098770626,21577.216666666667,38.00812200594329,7706.683333333333,50.57274950805309,13870.533333333333,45.73411570237905,84685.91666666667,50.188875998823676 -RecruitmentAssistant,MCP,DeepSeek-V3-1,60,56493.61666666667,22.147902986985216,9253.916666666666,11.975698204790772,0.0,0,9253.916666666666,11.975698204790772,65747.53333333334,20.18885869339952 -RecruitmentAssistant,MCP,GPT-4o-mini,60,92014.11666666667,116.6865590618565,7919.0,53.551398177460804,0.0,0,7919.0,53.551398177460804,99933.11666666667,110.8395897893208 -RecruitmentAssistant,MCP,GPT-5,60,26912.7,20.70611719410981,5497.816666666667,19.86727093675454,0.0,0,5497.816666666667,19.86727093675454,32410.516666666666,18.965146379243382 -RecruitmentAssistant,MCP,Gemini-2.5-flash,60,28782.45,61.930962573640905,36081.96666666667,32.421097608977554,2547.483333333333,66.49131942988667,33534.48333333333,33.93529360996478,64864.416666666664,42.81412902482686 -RecruitmentAssistant,MCP,Gemini-2.5-flash-nothinking,60,24652.85,59.81227163711509,29749.2,33.341911231866526,0.0,0,29749.2,33.341911231866526,54402.05,41.03561787413621 -RecruitmentAssistant,MCP,Qwen3-235b,60,74869.26666666666,58.88334640846181,12228.233333333334,33.24982201750224,0.0,0,12228.233333333334,33.24982201750224,87097.5,54.85846789488785 -RecruitmentAssistant-H_A2A,A2A,DeepSeek-R1,60,56324.61666666667,46.638180123718165,22703.65,31.44684474256745,9447.433333333332,40.13794554657694,13256.216666666667,33.99696438860375,79028.26666666666,40.80710213184929 -RecruitmentAssistant-H_A2A,A2A,DeepSeek-V3-1,60,56964.333333333336,20.770844254104656,8083.616666666667,19.87680283281874,0.0,0,8083.616666666667,19.87680283281874,65047.95,18.845369668774392 -RecruitmentAssistant-H_A2A,A2A,GPT-4o-mini,60,92803.01666666666,84.22001253041903,9133.1,30.095360049800217,0.0,0,9133.1,30.095360049800217,101936.11666666667,78.18110570733062 -RecruitmentAssistant-H_A2A,A2A,GPT-5,60,27869.6,27.176931004439837,4802.4,29.013553508051526,0.0,0,4802.4,29.013553508051526,32672.0,26.476282856988835 -RecruitmentAssistant-H_A2A,A2A,Gemini-2.5-flash,60,31888.5,43.32828727558922,22074.366666666665,26.449027051858288,3744.866666666667,40.45973286628417,18329.5,28.967182282630056,57127.76666666667,32.64808698789394 -RecruitmentAssistant-H_A2A,A2A,Gemini-2.5-flash-nothinking,60,22611.966666666667,25.18997690886123,12184.583333333334,29.406794156705875,0.0,0,12184.583333333334,29.406794156705875,36887.13333333333,22.325546915164466 -RecruitmentAssistant-H_A2A,A2A,Qwen3-235b,60,63020.833333333336,45.29707737302475,10238.383333333333,26.51454847392326,0.0,0,10238.383333333333,26.51454847392326,73259.21666666666,41.795162465682274 -SQLAssistant,A2A,DeepSeek-R1,60,64578.96666666667,54.246440179150454,31890.0,36.015997428413996,18168.033333333333,38.52993722786796,13721.966666666667,45.517558807508884,96468.96666666666,43.197532743026535 -SQLAssistant,A2A,DeepSeek-V3-1,60,34717.4,44.01504081924513,2087.6833333333334,37.60888307749363,0.0,0,2087.6833333333334,37.60888307749363,36805.083333333336,43.41323351694425 -SQLAssistant,A2A,GPT-4o-mini,60,17691.033333333333,24.161100815749656,944.0,21.451436323891514,0.0,0,944.0,21.451436323891514,18635.033333333333,23.888357764460753 -SQLAssistant,A2A,GPT-5,60,10345.983333333334,38.745804084075004,842.95,30.118895712243866,0.0,0,842.95,30.118895712243866,11188.933333333332,37.987824876398136 -SQLAssistant,A2A,Gemini-2.5-flash,60,14474.5,28.73309319644318,18056.7,62.834975862965855,4829.25,30.18952243889795,13227.45,82.55922536007088,32531.2,44.1788256438843 -SQLAssistant,A2A,Gemini-2.5-flash-nothinking,60,14839.8,124.0129974722556,10942.55,100.98245357884501,0.0,0,10942.55,100.98245357884501,25782.35,110.91997815902663 -SQLAssistant,A2A,Qwen3-235b,60,16313.1,35.241447996400886,1068.05,32.522853130855204,0.0,0,1068.05,32.522853130855204,17381.15,34.97535556987088 -SQLAssistant,MCP,DeepSeek-R1,60,53330.96666666667,67.92613342636497,27191.133333333335,48.491041039641956,16479.483333333334,57.54187422823842,10711.65,50.586316382261344,80522.1,55.11701546256273 -SQLAssistant,MCP,DeepSeek-V3-1,60,23246.1,47.78021334245314,2390.45,37.7026690628785,0.0,0,2390.45,37.7026690628785,25636.55,46.595796234729605 -SQLAssistant,MCP,GPT-4o-mini,60,22803.95,42.035781842688934,1437.3333333333333,32.64344496247,0.0,0,1437.3333333333333,32.64344496247,24241.283333333333,41.22901949343439 -SQLAssistant,MCP,GPT-5,60,12699.95,46.12433417703989,1181.2333333333333,34.24593736617182,0.0,0,1181.2333333333333,34.24593736617182,13881.183333333332,44.73450348644751 -SQLAssistant,MCP,Gemini-2.5-flash,60,8884.866666666667,75.63249897174933,10224.75,81.73120920567932,1143.1,101.91772167683193,9081.65,82.80493643132279,19109.616666666665,76.23284755983595 -SQLAssistant,MCP,Gemini-2.5-flash-nothinking,60,7178.266666666666,59.37816042696649,7835.4,119.02230235582887,0.0,0,7835.4,119.02230235582887,15013.666666666666,83.30612058279223 -SQLAssistant,MCP,Qwen3-235b,60,15765.816666666668,46.24317860568809,1465.9333333333334,36.36116966721287,0.0,0,1465.9333333333334,36.36116966721287,17231.75,45.13167901985752 -SQLAssistant-H_A2A,A2A,DeepSeek-R1,55,41798.2,91.67067745488829,23203.890909090907,69.72542272249298,12612.418181818182,73.82460011165396,10591.472727272727,74.12901870373625,65002.09090909091,80.24409370637208 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Dict[str, str] = { - "success_no_retry": "Direct Success", - "success_with_retry": "Retry Success", + "success_no_retry": "Pass (no retries)", + "success_with_retry": "Pass (with retries)", "failed": "Failure", } @@ -272,57 +272,13 @@ def load_projects( def classify_status(status_raw: str, with_retry_raw: str) -> str: s = (status_raw or "").strip().lower() w = (with_retry_raw or "").strip().lower() - if s in {"success", "pass", "passed", "ok"} and w == "false": + if s == "success" and w == "false": return "success_no_retry" - if s in {"success", "pass", "passed", "ok"} and w == "true": + if s == "success" and w == "true": return "success_with_retry" return "failed" -def select_details_csv(part1_dir: Path) -> Path: - candidates = [ - part1_dir / "task_token_statistics-DETAILS.csv", - part1_dir / "performance_reports" / "task_token_statistics-DETAILS.csv", - Path( - "/Users/wzr/TOSEM-2025/RESULTS/RQ3/performance_reports/task_token_statistics-DETAILS.csv" - ), - ] - - existing = [p for p in candidates if p.exists()] - if not existing: - return candidates[0] - - def score_csv(path: Path, sample_rows: int = 500) -> Tuple[float, float, float]: - """Return (success_ratio, -unknown_ratio, mtime) for tie-breaking.""" - total = 0 - success = 0 - unknown = 0 - try: - with path.open("r", encoding="utf-8", newline="") as f: - reader = csv.DictReader(f) - for row in reader: - if total >= sample_rows: - break - total += 1 - s = (row.get("status") or "").strip().lower() - if s == "success": - success += 1 - if s == "unknown": - unknown += 1 - except Exception: - total = 0 - success = 0 - unknown = 0 - - if total <= 0: - return (0.0, 0.0, float(path.stat().st_mtime)) - success_ratio = success / float(total) - unknown_ratio = unknown / float(total) - return (success_ratio, -unknown_ratio, float(path.stat().st_mtime)) - - return max(existing, key=score_csv) - - def load_total_tokens( details_csv: Path, project_map: Dict[Tuple[str, str], str] ) -> Dict[str, Dict[str, Dict[str, List[float]]]]: @@ -821,57 +777,47 @@ def generate_project_model_distribution_md( return task[: -len(suffix)] return task - def render_distribution_block( - title: str, pdata: Dict[str, Dict[str, List[float]]] - ) -> None: - lines.append("") - lines.append(f"## {title}") - if not pdata: - lines.append("") - lines.append("> No token data found.") - return + # Collect overall and series data + overall_raw: Dict[str, List[float]] = defaultdict(list) + series_raw: Dict[str, Dict[str, List[float]]] = defaultdict( + lambda: defaultdict(list) + ) - # overall per-model summary + for task, arch, name in projects: + pdata = project_data.get(name, {}) + base_task = _base_task_name(task) + for model in MODEL_ORDER: + for status in STATUS_ORDER: + vals = pdata.get(status, {}).get(model, []) + overall_raw[model].extend(vals) + series_raw[base_task][model].extend(vals) + + def append_dist_section( + lines: List[str], + title: str, + raw_data: Dict[str, List[float]], + level_label: str = "###", + ) -> Dict[str, float]: lines.append("") - lines.append("### Overall per-model total tokens (all statuses)") + lines.append(f"{level_label} {title}") lines.append("| Model | n | Mean |") lines.append("| --- | --- | --- |") - model_means: Dict[str, float] = {} + means: Dict[str, float] = {} for model in MODEL_ORDER: - aggregated: List[float] = [] - for status in STATUS_ORDER: - aggregated.extend(pdata.get(status, {}).get(model, [])) - stats = summarize_values(aggregated) + vals = raw_data.get(model, []) + stats = summarize_values(vals) if stats: - model_means[model] = stats["mean"] - mean = _fmt_mean(stats) + means[model] = stats["mean"] + mean_str = _fmt_mean(stats) lines.append( - f"| {MODEL_LABELS.get(model, model)} | {stats.get('count', 0)} | {mean} |" + f"| {MODEL_LABELS.get(model, model)} | {stats.get('count', 0)} | {mean_str} |" ) - # status breakdown - for status in STATUS_ORDER: - by_model = pdata.get(status, {}) - if not by_model: - continue - lines.append("") - lines.append(f"### {STATUS_TITLES.get(status, status)} distribution") - lines.append("| Model | n | Mean |") - lines.append("| --- | --- | --- |") - for model in MODEL_ORDER: - vals = by_model.get(model, []) - stats = summarize_values(vals) - mean = _fmt_mean(stats) - lines.append( - f"| {MODEL_LABELS.get(model, model)} | {stats.get('count', 0)} | {mean} |" - ) - - # baseline vs max comparison lines.append("") - lines.append("### Baseline vs maximum (overall mean tokens)") - if model_means: - min_model = min(model_means.items(), key=lambda kv: kv[1]) - max_model = max(model_means.items(), key=lambda kv: kv[1]) + lines.append(f"{level_label} Baseline vs maximum ({title})") + if means: + min_model = min(means.items(), key=lambda kv: kv[1]) + max_model = max(means.items(), key=lambda kv: kv[1]) diff = max_model[1] - min_model[1] ratio = ( "n/a" if min_model[1] == 0 else f"{(diff / min_model[1]) * 100:.1f}%" @@ -887,78 +833,43 @@ def generate_project_model_distribution_md( lines.append(f"- Delta: {_fmt_number(diff)} ({ratio})") else: lines.append("- No data to compare.") + return means - # per-status baseline vs max - for status in STATUS_ORDER: - lines.append("") - lines.append( - f"### Baseline vs maximum — {STATUS_TITLES.get(status, status)}" - ) - by_model = pdata.get(status, {}) - status_means: Dict[str, float] = {} - for model in MODEL_ORDER: - vals = by_model.get(model, []) - mean_val = _safe_mean(vals) - if mean_val is not None: - status_means[model] = mean_val - if status_means: - min_model_s = min(status_means.items(), key=lambda kv: kv[1]) - max_model_s = max(status_means.items(), key=lambda kv: kv[1]) - diff_s = max_model_s[1] - min_model_s[1] - ratio_s = ( - "n/a" - if min_model_s[1] == 0 - else f"{(diff_s / min_model_s[1]) * 100:.1f}%" - ) - lines.append( - f"- Baseline (lowest mean): {MODEL_LABELS.get(min_model_s[0], min_model_s[0])} " - f"= {_fmt_number(min_model_s[1])} tokens" - ) - lines.append( - f"- Maximum (highest mean): {MODEL_LABELS.get(max_model_s[0], max_model_s[0])} " - f"= {_fmt_number(max_model_s[1])} tokens" - ) - lines.append(f"- Delta: {_fmt_number(diff_s)} ({ratio_s})") - else: - lines.append("- No data to compare.") - - # 1. Total aggregation across all projects - total_data: Dict[str, Dict[str, List[float]]] = defaultdict( - lambda: defaultdict(list) - ) - for _, _, name in projects: - pdata = project_data.get(name, {}) - for status in STATUS_ORDER: - by_model = pdata.get(status, {}) - for model, vals in by_model.items(): - total_data[status][model].extend(vals) - render_distribution_block("TOTAL AGGREGATION (All Projects)", total_data) - - # 2. Series aggregation - series_data: Dict[str, Dict[str, Dict[str, List[float]]]] = defaultdict( - lambda: defaultdict(lambda: defaultdict(list)) - ) - for task, arch, name in projects: - base_task = _base_task_name(task) - pdata = project_data.get(name, {}) - for status in STATUS_ORDER: - by_model = pdata.get(status, {}) - for model, vals in by_model.items(): - series_data[base_task][status][model].extend(vals) + # 1. Global Overall Summary + lines.append("") + lines.append("## Global Summary (All Projects Combined)") + append_dist_section(lines, "Overall per-model total tokens", overall_raw) + # 2. Series Aggregates lines.append("") - lines.append("---") - lines.append("# Series Aggregates") - for task in sorted(series_data.keys()): - render_distribution_block(f"Series: {task}", series_data[task]) + lines.append("## Series Aggregates (Aggregated by Base Task)") + for base_task in sorted(series_raw.keys()): + lines.append("") + lines.append(f"### Series: {base_task}") + append_dist_section( + lines, f"Aggregated tokens for {base_task}", series_raw[base_task], "####" + ) - # 3. Individual projects + # 3. Individual Projects lines.append("") - lines.append("---") - lines.append("# Individual Project Details") + lines.append("## Individual Project Details") for task, arch, name in projects: pdata = project_data.get(name, {}) - render_distribution_block(name, pdata) + lines.append("") + lines.append(f"### {name}") + if not pdata: + lines.append("") + lines.append("> No token data found.") + continue + + project_raw: Dict[str, List[float]] = {} + for model in MODEL_ORDER: + combined: List[float] = [] + for status in STATUS_ORDER: + combined.extend(pdata.get(status, {}).get(model, [])) + project_raw[model] = combined + + append_dist_section(lines, "Per-model total tokens", project_raw, "####") out_path = out_dir / "project_model_distribution.md" out_dir.mkdir(parents=True, exist_ok=True) @@ -1044,7 +955,7 @@ def plot_violin_for_project( fontsize=10, ) - ax.set_title(STATUS_TITLES.get(status, status), fontsize=18) + ax.set_title(STATUS_TITLES.get(status, status), fontsize=24, fontweight="bold") ax.set_xticks(range(1, len(MODEL_ORDER) + 1)) ax.set_xticklabels([]) ax.set_xlim(0.5, len(MODEL_ORDER) + 0.5) @@ -1053,10 +964,13 @@ def plot_violin_for_project( ax.set_yticks(yticks) ax.yaxis.set_major_formatter(FuncFormatter(_token_formatter)) ax.grid(axis="y", linestyle="-", linewidth=0.5, alpha=0.3) - ax.tick_params(axis="both", labelsize=16) + ax.tick_params(axis="y", labelsize=22) + ax.tick_params(axis="x", labelsize=16) + for lbl in ax.get_yticklabels(): + lbl.set_fontweight("bold") if idx_status == 0: - ax.set_ylabel("Total tokens", fontsize=18) + ax.set_ylabel("") fig.subplots_adjust(left=0.07, right=0.98, bottom=0.10, top=0.98, wspace=0.03) @@ -1069,15 +983,12 @@ def plot_violin_for_project( def main() -> None: part1_dir = Path(__file__).resolve().parent - details_csv = select_details_csv(part1_dir) - out_dir = part1_dir / "Violin" - + details_csv = part1_dir / "task_token_statistics-DETAILS.csv" if not details_csv.exists(): - raise FileNotFoundError( - f"task_token_statistics-DETAILS.csv not found: {details_csv}" + details_csv = ( + part1_dir / "performance_reports" / "task_token_statistics-DETAILS.csv" ) - - print(f"using token details: {details_csv}") + out_dir = part1_dir / "Violin" projects, project_map = load_projects(details_csv)