add tot and math500 scores

app.py

@@ -18,23 +18,23 @@ with gr.Blocks(title="Open Agent Leaderboard") as demo:
     DATASETS.remove('META')
     print(DATASETS)
 
-    # 确保在定义llm_options之前生成overall_table
+    # Ensure overall_table is generated before defining llm_options
     check_box = BUILD_L1_DF(results, DEFAULT_MATH_BENCH)
     overall_table = generate_table(results, DEFAULT_MATH_BENCH)
 
-    # 保存完整的overall_table为CSV文件
+    # Save the complete overall_table as a CSV file
     csv_path_overall = os.path.join(os.getcwd(), 'src/overall_results.csv')
     overall_table.to_csv(csv_path_overall, index=False)
     print(f"Overall results saved to {csv_path_overall}")
 
-    # 从overall_table中提取所有可能的LLM选项
+    # Extract all possible LLM options from overall_table
     llm_options = list(set(row.LLM for row in overall_table.itertuples() if hasattr(row, 'LLM')))
     
     gr.Markdown(LEADERBORAD_INTRODUCTION.format(EVAL_TIME))
     with gr.Tabs(elem_classes='tab-buttons') as tabs:
         with gr.Tab(label='🏅 Open Agent Overall Math Leaderboard'):
             gr.Markdown(LEADERBOARD_MD['MATH_MAIN'])
-            # 移动check_box和overall_table的定义到这里
+            # Move the definition of check_box and overall_table here
             # check_box = BUILD_L1_DF(results, DEFAULT_MATH_BENCH)
             # overall_table = generate_table(results, DEFAULT_MATH_BENCH)
 
@@ -48,7 +48,7 @@ with gr.Blocks(title="Open Agent Leaderboard") as demo:
                 interactive=True,
             )
 
-            # 新增的CheckboxGroup组件用于选择Algorithm和LLM
+            # New CheckboxGroup component for selecting Algorithm and LLM
             algo_name = gr.CheckboxGroup(
                 choices=ALGORITHMS,
                 value=ALGORITHMS,
@@ -57,7 +57,7 @@ with gr.Blocks(title="Open Agent Leaderboard") as demo:
             )
 
             llm_name = gr.CheckboxGroup(
-                choices=llm_options,  # 使用提取的llm_options
+                choices=llm_options,  # Use the extracted llm_options
                 value=llm_options,
                 label='LLM',
                 interactive=True
@@ -78,7 +78,7 @@ with gr.Blocks(title="Open Agent Leaderboard") as demo:
                 headers = ['Rank'] + check_box['essential'] + fields
                 df = overall_table.copy()
 
-                # 添加过滤逻辑
+                # Add filtering logic
                 df['flag'] = df.apply(lambda row: (
                     row['Algorithm'] in algos and 
                     row['LLM'] in llms
@@ -107,7 +107,7 @@ with gr.Blocks(title="Open Agent Leaderboard") as demo:
 
                 return comp
 
-            # 更新change事件以包含新的过滤条件
+            # Update change events to include new filtering conditions
             checkbox_group.change(
                 fn=filter_df, 
                 inputs=[checkbox_group, algo_name, llm_name], 
@@ -135,7 +135,7 @@ with gr.Blocks(title="Open Agent Leaderboard") as demo:
 
             table, check_box = BUILD_L2_DF(results_detail, DEFAULT_MATH_BENCH)
 
-            # 保存完整的table为CSV文件
+            # Save the complete table as a CSV file
             csv_path_detail = os.path.join(os.getcwd(), 'src/detail_results.csv')
             table.to_csv(csv_path_detail, index=False)
             print(f"Detail results saved to {csv_path_detail}")
@@ -217,7 +217,7 @@ with gr.Blocks(title="Open Agent Leaderboard") as demo:
 
                 return comp
 
-            # 为所有复选框组添加change事件
+            # Add change events for all checkbox groups
             checkbox_group.change(
                 fn=filter_df2, 
                 inputs=[checkbox_group, algo_name, dataset_name, llm_name], 

meta_data.py

@@ -1,9 +1,10 @@
 # CONSTANTS-URL
 OVERALL_MATH_SCORE_FILE = "src/overall_math_score.json"
 DETAIL_MATH_SCORE_FILE = "src/detail_math_score.json"
+
 # CONSTANTS-TEXT
 LEADERBORAD_INTRODUCTION = """# Open Agent Leaderboard
-### Welcome to the Open Agent Leaderboard! We share the evaluation results of open agents: CoT, SC-CoT, PoT, ReAct, etc. The agents are impletemented by the OpenSource Framework: [*OmAgent*](https://github.com/om-ai-lab/OmAgent) 
+### Welcome to the Open Agent Leaderboard! We share the evaluation results of open agents: CoT, SC-CoT, PoT, ReAct, ToT, etc. The agents are implemented by the OpenSource Framework: [*OmAgent*](https://github.com/om-ai-lab/OmAgent) 
 
 This leaderboard was last updated: {}. 
 
@@ -11,8 +12,9 @@ To add your own agent to the leaderboard, please create a PR in [*OmAgent*](http
 """
 
 DEFAULT_MATH_BENCH = [
-    'gsm8k', 'AQuA'
+    'gsm8k', 'AQuA', 'MATH-500',
 ]
+
 # The README file for each benchmark
 LEADERBOARD_MD = {}
 
@@ -42,7 +44,7 @@ LEADERBOARD_MD['MATH_DETAIL'] = f"""
   - Cost: The cost on each math Benchmarks (the lower the better). 
   - Rank: The rank on each math Benchmarks (the lower the better).
 
-- default parameters: temperature=0.0
+- default parameters: temperature=0.0 (except for SC-CoT)
 - LLM prices:
   - gpt-3.5-turbo: 
      - 0.5$/1M tokens (input)
@@ -53,7 +55,7 @@ LEADERBOARD_MD['MATH_DETAIL'] = f"""
   - gpt-4o-2024-08-06:
       - 2.50$ /1M input tokens (input)
     - 10$ /1M output tokens (output)
-  - Qwen2.5-7B-Instruct and Llama-3.3-70B-Instruct:
+  - Qwen2.5-72B-Instruct and Llama-3.3-70B-Instruct:
     - Prices can be found https://cloud.siliconflow.cn/.
   - Other open source LLMs:
       - Deployed locally, please check the [*OmAgent*](https://github.com/om-ai-lab/OmAgent) repository for more information.
@@ -69,7 +71,7 @@ META_FIELDS = [
 ]
 
 DATASETS = [
-    'gsm8k', 'AQuA'
+    'gsm8k', 'AQuA', 'MATH-500'
 ]
 
 LLM = [
@@ -77,7 +79,7 @@ LLM = [
 ]
 
 ALGORITHMS = [
-  'IO',  'CoT', 'SC-CoT', 'PoT', 'ReAct-Pro*'
+  'IO',  'CoT', 'SC-CoT', 'PoT', 'ReAct-Pro*', 'ToT'
 ]
 
 CITATION_BUTTON_TEXT = r"""@misc{open-agent-leaderboard,

preprocess.py

@@ -3,10 +3,10 @@ import json
 from datetime import datetime
 
 def process_csv_to_json():
-    # 读取CSV文件
+    # Read the CSV file
     df = pd.read_csv('src/record.csv')
     
-    # 清理数据：删除空行，重命名列
+    # Clean the data: remove empty rows, rename columns
     df = df.dropna(how='all')
     df = df.rename(columns={
         'dataset': 'Dataset',
@@ -25,36 +25,36 @@ def process_csv_to_json():
         'Average output tokens': 'Average output tokens'
     })
     
-    # 辅助函数：处理包含逗号的数字字符串
+    # Helper function: handle number strings with commas
     def parse_number(value):
-        if pd.isna(value):
+        if pd.isna(value) or value == '-':
             return 0
-        # 先移除逗号，然后转换为浮点数，最后转换为整数
+        # Remove commas, convert to float, then to int
         return int(float(str(value).replace(',', '')))
     
-    # 初始化结果字典
+    # Initialize result dictionary
     result = {
         "time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
         "results": {}
     }
     
-    # 获取所有唯一的LLM
+    # Get all unique LLMs
     llms = df['LLM'].dropna().unique()
     
-    # 遍历每个算法
+    # Iterate through each algorithm
     for algorithm in df['Algorithm'].dropna().unique():
         if not isinstance(algorithm, str):
             continue
             
         result['results'][algorithm] = {}
         
-        # 对每个LLM进行处理
+        # Process each LLM
         for llm in llms:
             llm_data = df[(df['Algorithm'] == algorithm) & (df['LLM'] == llm)]
             if llm_data.empty:
                 continue
                 
-            # 创建LLM对应的字典
+            # Create dictionary for each LLM
             result['results'][algorithm][llm] = {
                 'META': {
                     'Algorithm': str(algorithm),
@@ -63,7 +63,7 @@ def process_csv_to_json():
                 }
             }
             
-            # 对每个数据集进行处理
+            # Process each dataset
             for dataset in df['Dataset'].dropna().unique():
                 if not isinstance(dataset, str):
                     continue
@@ -73,9 +73,9 @@ def process_csv_to_json():
                 if not dataset_data.empty:
                     data_row = dataset_data.iloc[0]
                     result['results'][algorithm][llm][dataset] = {
-                        'Score': round(float(data_row['Score']), 2),  # 保留两位小数
-                        'Pass rate': round(float(data_row['Pass rate']) / 100, 4),  # 转换为小数并保留两位小数
-                        'Cost($)': float(data_row['Cost($)']) if pd.notnull(data_row['Cost($)']) else 0.0,
+                        'Score': round(float(data_row['Score']) if data_row['Score'] != '-' else 0, 2),  # Keep two decimal places
+                        'Pass rate': round(float(data_row['Pass rate']) / 100, 4) if data_row['Pass rate'] != '-' else 0.0,  # Convert to decimal and keep two decimal places
+                        'Cost($)': float(data_row['Cost($)']) if pd.notnull(data_row['Cost($)']) and data_row['Cost($)'] != '-' else 0.0,
                         'Framework': str(data_row['Framework']) if 'Framework' in data_row and pd.notnull(data_row['Framework']) else '',
                         'X-shot': str(data_row['X-shot']) if pd.notnull(data_row['X-shot']) else '',
                         'Samples': parse_number(data_row['Samples']),
@@ -86,12 +86,12 @@ def process_csv_to_json():
                         'Average output tokens': parse_number(data_row['Average output tokens'])
                     }
     
-    # 检查每个字段是否存在
+    # Check if each field exists
     required_fields = ['Score', 'Pass rate', 'Cost($)', 'Framework', 'X-shot', 'Samples', 'All tokens', 'Total input tokens', 'Average input tokens', 'Total output tokens', 'Average output tokens']
     
     for key, value in result['results'].items():
         for llm, datasets in value.items():
-            # 检查 META 信息
+            # Check META information
             meta = datasets.get('META', {})
             if 'LLM' not in meta or 'Eval Date' not in meta:
                 print(f"Missing META fields in algorithm '{key}' for LLM '{llm}'")
@@ -103,15 +103,15 @@ def process_csv_to_json():
                 if missing_fields:
                     print(f"Missing fields {missing_fields} in dataset '{dataset}' for LLM '{llm}' in algorithm '{key}'")
     
-    # 保存为JSON文件
+    # Save as JSON file
     with open('src/detail_math_score.json', 'w', encoding='utf-8') as f:
         json.dump(result, f, indent=4, ensure_ascii=False)
 
 def process_csv_to_overall_json():
-    # 读取CSV文件
+    # Read the CSV file
     df = pd.read_csv('src/record.csv')
     
-    # 清理数据：删除空行，重命名列
+    # Clean the data: remove empty rows, rename columns
     df = df.dropna(how='all')
     df = df.rename(columns={
         'dataset': 'Dataset',
@@ -121,24 +121,24 @@ def process_csv_to_overall_json():
         'Eval Date': 'Eval Date'
     })
     
-    # 初始化结果字典
+    # Initialize result dictionary
     result = {
         "time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
         "results": {}
     }
     
-    # 获取所有唯一的LLM
+    # Get all unique LLMs
     llms = df['LLM'].dropna().unique()
     for llm in llms:
-        # 处理基础算法
+        # Process base algorithms
         for algorithm in df['Algorithm'].dropna().unique():
             if not isinstance(algorithm, str):
                 continue
                 
-            # 为非gpt-3.5-turbo的模型添加后缀
-            # 修改：为llama模型添加更多信息以确保唯一性
+            # Add suffix for non-gpt-3.5-turbo models
+            # Modification: add more information for llama models to ensure uniqueness
             algo_key = algorithm if llm == 'gpt-3.5-turbo' else f"{algorithm}-{llm}"
-            # 检查该算法-LLM组合是否存在
+            # Check if the algorithm-LLM combination exists
             algo_data = df[(df['Algorithm'] == algorithm) & (df['LLM'] == llm)]
             if algo_data.empty:
                 print(f"No data found for algorithm '{algorithm}' and LLM '{llm}'")
@@ -152,29 +152,29 @@ def process_csv_to_overall_json():
                 }
             }
             
-            # 处理每个数据集
-            for dataset in ['gsm8k', 'AQuA']:
+            # Process each dataset
+            for dataset in ['gsm8k', 'AQuA', 'MATH-500']:
                 dataset_data = df[(df['Algorithm'] == algorithm) & 
                                 (df['Dataset'] == dataset) &
                                 (df['LLM'] == llm)]
                 if not dataset_data.empty:
                     result['results'][algo_key][dataset] = {
-                        "Score": float(dataset_data['Score'].iloc[0]) if pd.notnull(dataset_data['Score'].iloc[0]) else 0.0,
-                        "Cost($)": float(dataset_data['Cost($)'].iloc[0]) if pd.notnull(dataset_data['Cost($)'].iloc[0]) else 0.0
+                        "Score": float(dataset_data['Score'].iloc[0]) if pd.notnull(dataset_data['Score'].iloc[0]) and dataset_data['Score'].iloc[0] != '-' else 0.0,
+                        "Cost($)": float(dataset_data['Cost($)'].iloc[0]) if pd.notnull(dataset_data['Cost($)'].iloc[0]) and dataset_data['Cost($)'].iloc[0] != '-' else 0.0
                     }
                 else:
-                    # 如果数据集为空，确保键存在并设置默认值
+                    # If the dataset is empty, ensure the key exists and set default values
                     result['results'][algo_key][dataset] = {
                         "Score": 0.0,
                         "Cost($)": 0.0
                     }
 
 
-    # 保存为JSON文件
+    # Save as JSON file
     with open('src/overall_math_score.json', 'w', encoding='utf-8') as f:
         json.dump(result, f, indent=4, ensure_ascii=False)
 
 if __name__ == "__main__":
-    # 生成两种格式的JSON文件
+    # Generate JSON files in two formats
     process_csv_to_json()
-    process_csv_to_overall_json() 
+    process_csv_to_overall_json()

src/detail_results.csv

@@ -1,101 +1,199 @@
 Rank,Algorithm,Dataset,LLM,Eval Date,Score,Pass rate,X-shot,Cost($),Framework,Samples,All tokens,Total input tokens,Average input tokens,Total output tokens,Average output tokens
-1,SC-CoT,AQuA,gpt-4o,2025/1/22,86.61,0.9882,0.0,8.1485,,254,1373206,744478,2931,628728,2475
-2,CoT,AQuA,Qwen2.5-72B-Instruct,2025/1/22,86.22,0.9921,0.0,0.0808,,254,143289,25143,99,118146,465
-3,SC-CoT,AQuA,Qwen2.5-72B-Instruct,2025/1/22,85.04,0.9921,0.0,1.0348,,254,1835669,1051218,4139,784451,3088
-4,IO,AQuA,Qwen2.5-72B-Instruct,2025/1/22,84.25,0.9961,0.0,0.0742,,254,131604,25397,100,106207,418
-5,CoT,AQuA,Llama-3.3-70B-Instruct,2025/1/22,83.46,0.9843,0.0,0.0927,,254,164389,32555,128,131834,519
-6,IO,AQuA,Llama-3.3-70B-Instruct,2025/1/22,82.68,0.9921,0.0,0.0798,,254,141567,32809,129,108758,428
-7,CoT,AQuA,Doubao-lite-32k,2025/1/7,82.68,0.9724,0.0,0.0066,,254,94577,27978,110,66599,262
-8,CoT,AQuA,gpt-4o,2025/1/22,82.68,0.9803,0.0,1.0417,,254,123017,25123,99,97894,385
-9,SC-CoT,AQuA,Llama-3.3-70B-Instruct,2025/1/22,82.28,0.9921,0.0,1.0756,,254,1907924,1135251,4469,772673,3042
-10,SC-CoT,AQuA,Doubao-lite-32k,2025/1/7,81.1,0.9724,0.0,0.0519,,254,885986,503751,1983,382235,1505
-11,CoT,AQuA,Qwen2.5-7B-Instruct,2025/1/22,80.71,0.9961,0.0,0.0,,254,149736,33017,130,116719,460
-12,SC-CoT,AQuA,Qwen2.5-7B-Instruct,2025/1/22,79.92,1.0,0.0,0.0,,254,1845332,1098280,4324,747052,2941
-13,PoT,AQuA,Llama-3.3-70B-Instruct,2025/1/22,79.53,0.9921,0.0,0.1746,,254,309799,240735,948,69064,272
-14,IO,AQuA,Doubao-lite-32k,2025/1/7,79.13,1.0,0.0,0.0058,,254,87742,33058,130,54684,215
-15,ReAct-Pro*,AQuA,Llama-3.3-70B-Instruct,2025/1/22,79.13,0.9961,0.0,0.768,,254,1362379,1119143,4406,243236,958
-16,IO,AQuA,Qwen2.5-7B-Instruct,2025/1/22,78.74,0.9843,0.0,0.0,,254,137771,33271,131,104500,411
-17,ReAct-Pro*,AQuA,Doubao-lite-32k,2025/1/7,77.56,0.9606,0.0,0.0445,,254,1032841,977890,3850,54951,216
-18,IO,AQuA,gpt-4o,2025/1/22,75.59,0.9724,0.0,1.1453,,254,133752,25631,101,108121,426
-19,PoT,AQuA,gpt-4o,2025/1/22,75.2,1.0,0.0,1.6087,,254,327908,222717,877,105191,414
-20,PoT,AQuA,Qwen2.5-72B-Instruct,2025/1/22,75.2,1.0,0.0,0.1645,,254,291764,249215,981,42549,168
-21,ReAct-Pro*,AQuA,Qwen2.5-7B-Instruct,2025/1/22,74.41,0.9921,0.0,0.0,,254,695844,564165,2221,131679,518
-22,ReAct-Pro*,AQuA,Qwen2.5-72B-Instruct,2025/1/22,73.23,1.0,0.0,0.3177,,254,563603,441765,1739,121838,480
-23,PoT,AQuA,Doubao-lite-32k,2025/1/7,71.65,0.9685,0.0,0.0147,,254,309436,259863,1023,49573,195
-24,PoT,AQuA,Qwen2.5-7B-Instruct,2025/1/22,68.11,1.0,0.0,0.0,,254,313728,264517,1041,49211,194
-25,SC-CoT,AQuA,gpt-3.5-turbo,2025/1/7,66.14,0.9921,0.0,0.7888,,254,847335,482192,1898,365143,1438
-26,ReAct-Pro*,AQuA,gpt-3.5-turbo,2025/1/7,64.57,0.9803,0.0,0.4928,,254,903587,862614,3396,40973,161
-27,CoT,AQuA,gpt-3.5-turbo,2025/1/7,61.02,0.937,0.0,0.0957,,254,80793,25447,100,55346,218
-28,CoT,AQuA,Llama-3.1-8B-Instruct,2025/1/22,60.63,1.0,0.0,0.0,,254,144435,32555,128,111880,440
-29,PoT,AQuA,gpt-3.5-turbo,2025/1/7,59.45,1.0,0.0,0.1748,,254,266654,225162,886,41492,163
-30,SC-CoT,AQuA,Llama-3.1-8B-Instruct,2025/1/22,59.45,0.9724,0.0,0.0,,254,1651333,971003,3823,680330,2678
-31,ReAct-Pro*,AQuA,gpt-4o,2025/1/22,57.48,0.9724,0.0,2.304,,254,692096,615589,2424,76507,301
-32,ReAct-Pro*,AQuA,Llama-3.1-8B-Instruct,2025/1/22,55.51,0.9685,0.0,0.0,,254,4340821,3764723,14822,576098,2268
-33,CoT,AQuA,Internllm2_5-7B,2025/1/22,52.76,0.8937,0.0,0.0,,254,127520,26610,105,100910,397
-34,IO,AQuA,Llama-3.1-8B-Instruct,2025/1/22,51.18,0.9882,0.0,0.0,,254,133106,26459,104,106647,420
-35,IO,AQuA,Internllm2_5-7B,2025/1/22,47.64,0.9094,0.0,0.0,,254,185041,50232,198,134809,531
-36,ReAct-Pro*,AQuA,Internllm2_5-7B,2025/1/22,40.94,0.9685,0.0,0.0,,254,4428801,3592039,14142,836762,3294
-37,CoT,AQuA,Qwen2-1.5B-Instruct,2025/1/22,40.55,0.9882,0.0,0.0,,254,110040,30477,120,79563,313
-38,SC-CoT,AQuA,Internllm2_5-7B,2025/1/22,39.37,0.9803,0.0,0.0,,254,2296222,1420494,5592,875728,3448
-39,IO,AQuA,gpt-3.5-turbo,2025/1/7,38.98,1.0,0.0,0.038,,254,42471,25701,101,16770,66
-40,PoT,AQuA,Llama-3.1-8B-Instruct,2025/1/22,36.61,0.9685,0.0,0.0,,254,290914,240613,947,50301,198
-41,PoT,AQuA,Internllm2_5-7B,2025/1/22,36.61,0.9882,0.0,0.0,,254,301962,233505,919,68457,270
-42,CoT,AQuA,Qwen2-0.5B-Instruct,2025/1/22,33.07,0.9882,0.0,0.0,,254,117339,30477,120,86862,342
-43,PoT,AQuA,Qwen2-1.5B-Instruct,2025/1/22,30.71,0.9646,0.0,0.0,,254,298475,246560,971,51915,204
-44,IO,AQuA,Qwen2-1.5B-Instruct,2025/1/22,29.13,0.9764,0.0,0.0,,254,71047,27937,110,43110,170
-45,IO,AQuA,Qwen2-0.5B-Instruct,2025/1/22,27.17,0.9882,0.0,0.0,,254,110415,27937,110,82478,325
-46,ReAct-Pro*,AQuA,Qwen2-1.5B-Instruct,2025/1/22,25.59,0.9606,0.0,0.0,,254,5072004,4555858,17936,516146,2032
-47,ReAct-Pro*,AQuA,Qwen2-0.5B-Instruct,2025/1/22,24.02,0.9685,0.0,0.0,,254,7170087,6344167,24977,825920,3252
-48,SC-CoT,AQuA,Qwen2-1.5B-Instruct,2025/1/22,23.62,0.9646,0.0,0.0,,254,1775335,1034362,4072,740973,2917
-49,SC-CoT,AQuA,Qwen2-0.5B-Instruct,2025/1/22,22.83,0.9724,0.0,0.0,,254,2215091,1246929,4909,968162,3812
-50,PoT,AQuA,Qwen2-0.5B-Instruct,2025/1/22,17.32,0.9213,0.0,0.0,,254,322281,258867,1019,63414,250
-1,SC-CoT,gsm8k,Llama-3.3-70B-Instruct,2025/1/22,95.07,1.0,8.0,6.2005,,1319,10998794,8413717,6379,2585077,1960
-2,CoT,gsm8k,gpt-4o,2025/1/22,94.09,1.0,8.0,4.5367,,1319,1165166,948668,719,216498,164
-3,CoT,gsm8k,Llama-3.3-70B-Instruct,2025/1/22,93.93,1.0,8.0,0.687,,1319,1218665,990168,751,228497,173
-4,SC-CoT,gsm8k,Qwen2.5-72B-Instruct,2025/1/22,93.86,1.0,8.0,5.9858,,1319,10618008,8136223,6168,2481785,1882
-5,PoT,gsm8k,gpt-4o,2025/1/22,93.1,0.9977,8.0,4.2166,,1319,1247912,1101672,835,146240,111
-6,CoT,gsm8k,Qwen2.5-72B-Instruct,2025/1/22,92.87,1.0,8.0,0.7195,,1319,1276252,1005119,762,271133,206
-7,PoT,gsm8k,Qwen2.5-72B-Instruct,2025/1/22,92.34,0.9939,8.0,0.7054,,1319,1251210,1106682,839,144528,110
-8,IO,gsm8k,Llama-3.3-70B-Instruct,2025/1/22,92.27,1.0,8.0,0.4709,,1319,835275,583916,443,251359,191
-9,SC-CoT,gsm8k,Qwen2.5-7B-Instruct,2025/1/22,91.13,1.0,8.0,0.0,,1319,11140985,8586888,6510,2554097,1936
-10,SC-CoT,gsm8k,gpt-4o,2025/1/22,90.3,0.9992,8.0,31.0542,,1319,5798173,3590336,2722,2207837,1674
-11,CoT,gsm8k,Doubao-lite-32k,2025/1/7,89.31,1.0,8.0,0.0558,,1319,1201820,1042095,790,159725,121
-12,IO,gsm8k,gpt-4o,2025/1/22,88.4,1.0,8.0,3.3463,,1319,741446,542416,411,199030,151
-13,ReAct-Pro*,gsm8k,Llama-3.3-70B-Instruct,2025/1/22,87.64,0.9992,8.0,10.1124,,1319,17937864,17038928,12918,898936,682
-14,ReAct-Pro*,gsm8k,Qwen2.5-72B-Instruct,2025/1/22,87.26,1.0,8.0,10.5479,,1319,18710437,18160983,13769,549454,417
-15,SC-CoT,gsm8k,Doubao-lite-32k,2025/1/7,87.26,0.9992,8.0,0.2083,,1319,3888813,2691714,2041,1197099,908
-16,IO,gsm8k,Qwen2.5-72B-Instruct,2025/1/22,86.58,1.0,8.0,0.4899,,1319,869060,555340,421,313720,238
-17,CoT,gsm8k,Qwen2.5-7B-Instruct,2025/1/22,85.67,1.0,8.0,0.0,,1319,1290805,1046008,793,244797,186
-18,ReAct-Pro*,gsm8k,Doubao-lite-32k,2025/1/7,85.6,0.9962,8.0,0.2512,,1319,5998639,5862016,4444,136623,104
-19,ReAct-Pro*,gsm8k,Qwen2.5-7B-Instruct,2025/1/22,82.87,1.0,8.0,0.0,,1319,14850914,14355752,10884,495162,375
-20,SC-CoT,gsm8k,gpt-3.5-turbo,2025/1/7,79.91,0.9992,8.0,3.3938,,1319,4089612,2740652,2078,1348960,1023
-21,PoT,gsm8k,Doubao-lite-32k,2025/1/7,79.61,0.9257,8.0,0.0576,,1319,1288055,1170038,887,118017,89
-22,CoT,gsm8k,gpt-3.5-turbo,2025/1/7,78.7,1.0,8.0,0.6788,,1319,1088041,953242,723,134799,102
-23,CoT,gsm8k,Internllm2_5-7B,2025/1/22,77.71,0.997,8.0,0.0,,1319,1202163,968163,734,234000,177
-24,PoT,gsm8k,gpt-3.5-turbo,2025/1/7,76.88,0.9924,8.0,0.6902,,1319,1187080,1090418,827,96662,73
-25,CoT,gsm8k,Llama-3.1-8B-Instruct,2025/1/22,75.44,0.9992,8.0,0.0,,1319,1248329,990168,751,258161,196
-26,ReAct-Pro*,gsm8k,gpt-3.5-turbo,2025/1/7,74.91,0.9939,8.0,3.4633,,1319,6646286,6506164,4933,140122,106
-27,SC-CoT,gsm8k,Llama-3.1-8B-Instruct,2025/1/22,73.46,0.9955,8.0,0.0,,1319,11778716,8630514,6543,3148202,2387
-28,PoT,gsm8k,Llama-3.3-70B-Instruct,2025/1/22,73.09,0.7961,8.0,0.9736,,1319,1727044,1126025,854,601019,456
-29,IO,gsm8k,Doubao-lite-32k,2025/1/7,72.02,0.9992,8.0,0.0354,,1319,740483,617377,468,123106,93
-30,ReAct-Pro*,gsm8k,Llama-3.1-8B-Instruct,2025/1/22,67.78,0.9856,8.0,0.0,,1319,22835767,21044978,15955,1790789,1358
-31,ReAct-Pro*,gsm8k,gpt-4o,2025/1/22,63.31,0.9955,8.0,39.0751,,1319,14715887,14411173,10926,304714,231
-32,PoT,gsm8k,Qwen2.5-7B-Instruct,2025/1/22,58.83,0.7051,8.0,0.0,,1319,1362822,1145390,868,217432,165
-33,IO,gsm8k,Qwen2.5-7B-Instruct,2025/1/22,57.24,1.0,8.0,0.0,,1319,887913,596229,452,291684,221
-34,IO,gsm8k,Llama-3.1-8B-Instruct,2025/1/22,57.16,0.9955,8.0,0.0,,1319,1745429,550941,418,1194488,906
-35,CoT,gsm8k,Qwen2-1.5B-Instruct,2025/1/22,55.5,1.0,8.0,0.0,,1319,1218525,1032818,783,185707,141
-36,SC-CoT,gsm8k,Internllm2_5-7B,2025/1/22,48.22,0.9841,8.0,0.0,,1319,14526431,10678792,8096,3847639,2917
-37,PoT,gsm8k,Llama-3.1-8B-Instruct,2025/1/22,38.67,0.5542,8.0,0.0,,1319,1391111,1147538,870,243573,185
-38,PoT,gsm8k,Internllm2_5-7B,2025/1/22,38.21,0.489,8.0,0.0,,1319,1324949,1136843,862,188106,143
-39,IO,gsm8k,gpt-3.5-turbo,2025/1/7,37.83,0.9992,8.0,0.3328,,1319,586553,546990,415,39563,30
-40,CoT,gsm8k,Qwen2-0.5B-Instruct,2025/1/22,35.94,0.9992,8.0,0.0,,1319,1223459,1032818,783,190641,145
-41,ReAct-Pro*,gsm8k,Internllm2_5-7B,2025/1/22,33.51,0.9795,8.0,0.0,,1319,35669989,30120070,22836,5549919,4208
-42,ReAct-Pro*,gsm8k,Qwen2-1.5B-Instruct,2025/1/22,24.87,0.8021,8.0,0.0,,1319,9828001,9133603,6925,694398,526
-43,PoT,gsm8k,Qwen2-1.5B-Instruct,2025/1/22,18.5,0.3101,8.0,0.0,,1319,1327522,1151528,873,175994,133
-44,IO,gsm8k,Qwen2-1.5B-Instruct,2025/1/22,16.68,1.0,8.0,0.0,,1319,736996,568530,431,168466,128
-45,IO,gsm8k,Qwen2-0.5B-Instruct,2025/1/22,14.71,1.0,8.0,0.0,,1319,834897,568116,431,266781,202
-46,SC-CoT,gsm8k,Qwen2-1.5B-Instruct,2025/1/22,11.75,0.9189,8.0,0.0,,1319,12411942,9066115,6873,3345827,2537
-47,IO,gsm8k,Internllm2_5-7B,2025/1/22,11.6,0.9795,8.0,0.0,,1319,1113728,679302,515,434426,329
-48,PoT,gsm8k,Qwen2-0.5B-Instruct,2025/1/22,9.62,0.1691,8.0,0.0,,1319,1389135,1151528,873,237607,180
-49,ReAct-Pro*,gsm8k,Qwen2-0.5B-Instruct,2025/1/22,7.66,0.9522,8.0,0.0,,1319,55392611,52431343,39751,2961268,2245
-50,SC-CoT,gsm8k,Qwen2-0.5B-Instruct,2025/1/22,1.67,0.9469,8.0,0.0,,1319,16465720,11019864,8355,5445856,4129
+1,SC-CoT,AQuA,gpt-4o,2025/1/22,86.61,0.9882,0,8.1485,,254,1373206,744478,2931,628728,2475
+2,CoT,AQuA,Qwen2.5-72B-Instruct,2025/1/22,86.22,0.9921,0,0.0808,,254,143289,25143,99,118146,465
+3,SC-CoT,AQuA,Qwen2.5-72B-Instruct,2025/1/22,85.04,0.9921,0,1.0348,,254,1835669,1051218,4139,784451,3088
+4,IO,AQuA,Qwen2.5-72B-Instruct,2025/1/22,84.25,0.9961,0,0.0742,,254,131604,25397,100,106207,418
+5,CoT,AQuA,Llama-3.3-70B-Instruct,2025/1/22,83.46,0.9843,0,0.0927,,254,164389,32555,128,131834,519
+6,ToT,AQuA,Llama-3.3-70B-Instruct,2025/1/22,83.07,1.0,0,2.9404,,254,5215848,4735188,18642,480660,1892
+7,IO,AQuA,Llama-3.3-70B-Instruct,2025/1/22,82.68,0.9921,0,0.0798,,254,141567,32809,129,108758,428
+8,CoT,AQuA,Doubao-lite-32k,2025/1/7,82.68,0.9724,0,0.0066,,254,94577,27978,110,66599,262
+9,CoT,AQuA,gpt-4o,2025/1/22,82.68,0.9803,0,1.0417,,254,123017,25123,99,97894,385
+10,SC-CoT,AQuA,Llama-3.3-70B-Instruct,2025/1/22,82.28,0.9921,0,1.0756,,254,1907924,1135251,4469,772673,3042
+11,ToT,AQuA,gpt-4o,2025/1/22,81.5,0.9921,0,8.5295,,254,2613607,2347538,9242,266069,1048
+12,SC-CoT,AQuA,Doubao-lite-32k,2025/1/7,81.1,0.9724,0,0.0519,,254,885986,503751,1983,382235,1505
+13,ToT,AQuA,Qwen2.5-72B-Instruct,2025/1/22,81.1,0.9921,0,3.7389,,254,6632255,6371642,25085,260613,1026
+14,CoT,AQuA,Qwen2.5-7B-Instruct,2025/1/22,80.71,0.9961,0,0.0,,254,149736,33017,130,116719,460
+15,SC-CoT,AQuA,Qwen2.5-7B-Instruct,2025/1/22,79.92,1.0,0,0.0,,254,1845332,1098280,4324,747052,2941
+16,PoT,AQuA,Llama-3.3-70B-Instruct,2025/1/22,79.53,0.9921,0,0.1746,,254,309799,240735,948,69064,272
+17,IO,AQuA,Doubao-lite-32k,2025/1/7,79.13,1.0,0,0.0058,,254,87742,33058,130,54684,215
+18,ReAct-Pro*,AQuA,Llama-3.3-70B-Instruct,2025/1/22,79.13,0.9961,0,0.768,,254,1362379,1119143,4406,243236,958
+19,IO,AQuA,Qwen2.5-7B-Instruct,2025/1/22,78.74,0.9843,0,0.0,,254,137771,33271,131,104500,411
+20,ReAct-Pro*,AQuA,Doubao-lite-32k,2025/1/7,77.56,0.9606,0,0.0445,,254,1032841,977890,3850,54951,216
+21,IO,AQuA,gpt-4o,2025/1/22,75.59,0.9724,0,1.1453,,254,133752,25631,101,108121,426
+22,PoT,AQuA,gpt-4o,2025/1/22,75.2,1.0,0,1.6087,,254,327908,222717,877,105191,414
+23,PoT,AQuA,Qwen2.5-72B-Instruct,2025/1/22,75.2,1.0,0,0.1645,,254,291764,249215,981,42549,168
+24,ReAct-Pro*,AQuA,Qwen2.5-7B-Instruct,2025/1/22,74.41,0.9921,0,0.0,,254,695844,564165,2221,131679,518
+25,ReAct-Pro*,AQuA,Qwen2.5-72B-Instruct,2025/1/22,73.23,1.0,0,0.3177,,254,563603,441765,1739,121838,480
+26,PoT,AQuA,Doubao-lite-32k,2025/1/7,71.65,0.9685,0,0.0147,,254,309436,259863,1023,49573,195
+27,CoT,AQuA,deepseek-r1:1.5b,2025/1/23,71.65,0.9685,0,0.0,,254,333072,26413,104,306659,1207
+28,IO,AQuA,deepseek-r1:1.5b,2025/1/22,68.9,0.9488,0,0.0,,254,351767,26667,105,325100,1280
+29,PoT,AQuA,Qwen2.5-7B-Instruct,2025/1/22,68.11,1.0,0,0.0,,254,313728,264517,1041,49211,194
+30,SC-CoT,AQuA,gpt-3.5-turbo,2025/1/7,66.14,0.9921,0,0.7888,,254,847335,482192,1898,365143,1438
+31,ReAct-Pro*,AQuA,gpt-3.5-turbo,2025/1/7,64.57,0.9803,0,0.4928,,254,903587,862614,3396,40973,161
+32,CoT,AQuA,gpt-3.5-turbo,2025/1/7,61.02,0.937,0,0.0957,,254,80793,25447,100,55346,218
+33,CoT,AQuA,Llama-3.1-8B-Instruct,2025/1/22,60.63,1.0,0,0.0,,254,144435,32555,128,111880,440
+34,PoT,AQuA,gpt-3.5-turbo,2025/1/7,59.45,1.0,0,0.1748,,254,266654,225162,886,41492,163
+35,SC-CoT,AQuA,Llama-3.1-8B-Instruct,2025/1/22,59.45,0.9724,0,0.0,,254,1651333,971003,3823,680330,2678
+36,SC-CoT,AQuA,deepseek-r1:1.5b,2025/2/10,59.06,0.9685,0,0.0,,254,5802711,2547772,10031,3254939,12815
+37,ToT,AQuA,Llama-3.1-8B-Instruct,2025/1/22,59.06,1.0,0,0.0,,254,5739684,4896222,19276,843462,3321
+38,ReAct-Pro*,AQuA,gpt-4o,2025/1/22,57.48,0.9724,0,2.304,,254,692096,615589,2424,76507,301
+39,ToT,AQuA,gpt-3.5-turbo,2025/1/7,57.09,0.9961,0,1.1513,,254,2001396,1850767,7286,150629,593
+40,ReAct-Pro*,AQuA,Llama-3.1-8B-Instruct,2025/1/22,55.51,0.9685,0,0.0,,254,4340821,3764723,14822,576098,2268
+41,PoT,AQuA,deepseek-r1:1.5b,2025/2/10,54.72,0.9724,0,0.0,,254,1016647,250690,987,765957,3016
+42,ReAct-Pro*,AQuA,deepseek-r1:1.5b,2025/2/10,54.33,0.9646,0,0.0,,254,14445041,10578715,41648,3866326,15222
+43,ToT,AQuA,Qwen2.5-7B-Instruct,2025/1/22,53.94,1.0,0,0.0,,254,8602682,8224468,32380,378214,1489
+44,CoT,AQuA,Internllm2_5-7B,2025/1/22,52.76,0.8937,0,0.0,,254,127520,26610,105,100910,397
+45,IO,AQuA,Llama-3.1-8B-Instruct,2025/1/22,51.18,0.9882,0,0.0,,254,133106,26459,104,106647,420
+46,IO,AQuA,Internllm2_5-7B,2025/1/22,47.64,0.9094,0,0.0,,254,185041,50232,198,134809,531
+47,ToT,AQuA,Doubao-lite-32k,2025/1/7,45.28,0.7402,0,0.0881,,254,2000550,1850249,7284,150301,592
+48,ReAct-Pro*,AQuA,Internllm2_5-7B,2025/1/22,40.94,0.9685,0,0.0,,254,4428801,3592039,14142,836762,3294
+49,CoT,AQuA,Qwen2-1.5B-Instruct,2025/1/22,40.55,0.9882,0,0.0,,254,110040,30477,120,79563,313
+50,SC-CoT,AQuA,Internllm2_5-7B,2025/1/22,39.37,0.9803,0,0.0,,254,2296222,1420494,5592,875728,3448
+51,IO,AQuA,gpt-3.5-turbo,2025/1/7,38.98,1.0,0,0.038,,254,42471,25701,101,16770,66
+52,PoT,AQuA,Llama-3.1-8B-Instruct,2025/1/22,36.61,0.9685,0,0.0,,254,290914,240613,947,50301,198
+53,PoT,AQuA,Internllm2_5-7B,2025/1/22,36.61,0.9882,0,0.0,,254,301962,233505,919,68457,270
+54,ToT,AQuA,Internllm2_5-7B,2025/1/22,35.83,0.9961,0,0.0,,254,4734560,4263136,16784,471424,1856
+55,CoT,AQuA,Qwen2-0.5B-Instruct,2025/1/22,33.07,0.9882,0,0.0,,254,117339,30477,120,86862,342
+56,ToT,AQuA,Qwen2-1.5B-Instruct,2025/1/22,31.5,0.9882,0,0.0,,254,6250702,6058022,23850,192680,759
+57,PoT,AQuA,Qwen2-1.5B-Instruct,2025/1/22,30.71,0.9646,0,0.0,,254,298475,246560,971,51915,204
+58,ToT,AQuA,Qwen2-0.5B-Instruct,2025/1/22,29.92,1.0,0,0.0,,254,8700281,8100085,31890,600196,2363
+59,IO,AQuA,Qwen2-1.5B-Instruct,2025/1/22,29.13,0.9764,0,0.0,,254,71047,27937,110,43110,170
+60,IO,AQuA,Qwen2-0.5B-Instruct,2025/1/22,27.17,0.9882,0,0.0,,254,110415,27937,110,82478,325
+61,ReAct-Pro*,AQuA,Qwen2-1.5B-Instruct,2025/1/22,25.59,0.9606,0,0.0,,254,5072004,4555858,17936,516146,2032
+62,ToT,AQuA,deepseek-r1:1.5b,2025/2/10,24.8,0.5551,0,0.0,,254,794512,605028,2382,189484,746
+63,ReAct-Pro*,AQuA,Qwen2-0.5B-Instruct,2025/1/22,24.02,0.9685,0,0.0,,254,7170087,6344167,24977,825920,3252
+64,SC-CoT,AQuA,Qwen2-1.5B-Instruct,2025/1/22,23.62,0.9646,0,0.0,,254,1775335,1034362,4072,740973,2917
+65,SC-CoT,AQuA,Qwen2-0.5B-Instruct,2025/1/22,22.83,0.9724,0,0.0,,254,2215091,1246929,4909,968162,3812
+66,PoT,AQuA,Qwen2-0.5B-Instruct,2025/1/22,17.32,0.9213,0,0.0,,254,322281,258867,1019,63414,250
+1,CoT,MATH-500,Qwen2.5-72B-Instruct,2025/1/22,80.2,1.0,4,0.349,,500,619015,338549,677,280466,561
+2,SC-CoT,MATH-500,Llama-3.3-70B-Instruct,2025/1/22,74.2,1.0,4,3.2239,,500,5718739,3959492,7919,1759247,3518
+3,SC-CoT,MATH-500,Qwen2.5-72B-Instruct,2025/1/22,74.0,1.0,4,3.1556,,500,5597513,3823997,7648,1773516,3547
+4,CoT,MATH-500,Llama-3.3-70B-Instruct,2025/1/22,71.2,1.0,4,0.3463,,500,614221,342879,686,271342,543
+5,IO,MATH-500,Qwen2.5-72B-Instruct,2025/1/22,70.2,1.0,4,0.2506,,500,444591,169549,339,275042,550
+6,CoT,MATH-500,Qwen2.5-7B-Instruct,2025/1/22,69.8,1.0,4,0.0,,500,617204,354049,708,263155,526
+7,IO,MATH-500,Llama-3.3-70B-Instruct,2025/1/22,69.4,1.0,4,0.2386,,500,423216,155879,312,267337,535
+8,CoT,MATH-500,gpt-4o,2025/1/22,68.0,1.0,4,3.0569,,500,552688,329332,659,223356,447
+9,SC-CoT,MATH-500,Qwen2.5-7B-Instruct,2025/1/22,67.0,1.0,4,0.0,,500,5451484,3833751,7668,1617733,3235
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+28,SC-CoT,MATH-500,deepseek-r1:1.5b,2025/2/10,38.0,1.0,4,0.0,,500,14742109,7080559,14161,7661550,15323
+29,IO,MATH-500,Doubao-lite-32k,2025/1/7,37.4,1.0,4,0.0187,,500,311730,166870,334,144860,290
+30,SC-CoT,MATH-500,gpt-4o,2025/1/22,34.4,1.0,4,19.6538,,500,3455323,1986584,3973,1468739,2937
+31,PoT,MATH-500,Doubao-lite-32k,2025/1/7,32.6,0.68,4,0.0144,,500,303148,254377,509,48771,98
+32,SC-CoT,MATH-500,Llama-3.1-8B-Instruct,2025/1/22,30.2,1.0,4,0.0,,500,5034937,3546673,7093,1488264,2977
+33,ReAct-Pro*,MATH-500,Llama-3.1-8B-Instruct,2025/1/22,28.8,1.0,4,0.0,,500,8763629,7486706,14973,1276923,2554
+34,PoT,MATH-500,gpt-3.5-turbo,2025/1/7,28.8,0.838,4,0.168,,500,271916,239902,480,32014,64
+35,SC-CoT,MATH-500,gpt-3.5-turbo,2025/1/7,28.8,1.0,4,1.9764,,500,2238812,1381818,2764,856994,1714
+36,CoT,MATH-500,Llama-3.1-8B-Instruct,2025/1/22,25.8,1.0,4,0.0,,500,625568,342879,686,282689,565
+37,PoT,MATH-500,Llama-3.1-8B-Instruct,2025/1/22,25.4,0.684,4,0.0,,500,462271,253879,508,208392,417
+38,ReAct-Pro*,MATH-500,deepseek-r1:1.5b,2025/2/10,24.4,1.0,4,0.0,,500,30177348,20729970,41460,9447378,18895
+39,ReAct-Pro*,MATH-500,gpt-3.5-turbo,2025/1/7,23.8,1.0,4,2.0406,,500,3832714,3708461,7417,124253,249
+40,IO,MATH-500,Internllm2_5-7B,2025/1/22,22.8,1.0,4,0.0,,500,467888,201883,404,266005,532
+41,IO,MATH-500,gpt-3.5-turbo,2025/1/7,17.2,1.0,4,0.2436,,500,265625,154881,310,110744,221
+42,CoT,MATH-500,Qwen2-1.5B-Instruct,2025/1/22,15.2,1.0,4,0.0,,500,536377,349049,698,187328,375
+43,PoT,MATH-500,Internllm2_5-7B,2025/1/22,15.0,0.324,4,0.0,,500,368709,247883,496,120826,242
+44,ReAct-Pro*,MATH-500,Internllm2_5-7B,2025/1/22,14.8,1.0,4,0.0,,500,14186105,11831496,23663,2354609,4709
+45,ToT,MATH-500,Qwen2.5-72B-Instruct,2025/1/22,10.8,1.0,4,9.0421,,500,16039361,15657730,31315,381631,763
+46,SC-CoT,MATH-500,Internllm2_5-7B,2025/1/22,9.8,1.0,4,0.0,,500,5838466,4193296,8387,1645170,3290
+47,ToT,MATH-500,gpt-3.5-turbo,2025/1/7,9.8,1.0,4,5.2914,,500,10001767,9711244,19422,290523,581
+48,ReAct-Pro*,MATH-500,Qwen2-1.5B-Instruct,2025/1/22,8.2,1.0,4,0.0,,500,8987061,8430774,16862,556287,1113
+49,IO,MATH-500,Qwen2-1.5B-Instruct,2025/1/22,7.0,1.0,4,0.0,,500,413878,158777,318,255101,510
+50,CoT,MATH-500,Qwen2-0.5B-Instruct,2025/1/22,6.2,1.0,4,0.0,,500,549188,349049,698,200139,400
+51,SC-CoT,MATH-500,Qwen2-1.5B-Instruct,2025/1/22,3.8,0.99,4,0.0,,500,5569442,3832429,7665,1737013,3474
+52,ToT,MATH-500,gpt-4o,2025/1/22,3.2,1.0,4,40.8094,,500,15242432,14881985,29764,360447,721
+53,IO,MATH-500,Qwen2-0.5B-Instruct,2025/1/22,2.6,1.0,4,0.0,,500,429330,159049,318,270281,541
+54,ToT,MATH-500,Llama-3.1-8B-Instruct,2025/1/22,1.8,0.908,4,0.0,,500,9035000,7729000,15458,1306000,2612
+55,ToT,MATH-500,Llama-3.3-70B-Instruct,2025/1/22,1.4,0.698,4,8.2699,,500,14669500,14099500,28199,570000,1140
+56,ToT,MATH-500,Qwen2.5-7B-Instruct,2025/1/22,1.4,0.916,4,0.0,,500,10167500,9749000,19498,418500,837
+57,ToT,MATH-500,Doubao-lite-32k,2025/1/7,1.2,0.942,4,0.2371,,500,5564500,5338500,10677,226000,452
+58,PoT,MATH-500,deepseek-r1:1.5b,2025/2/10,1.0,0.016,4,0.0,,500,1031067,245549,491,785518,1571
+59,PoT,MATH-500,Qwen2-1.5B-Instruct,2025/1/22,0.8,0.022,4,0.0,,500,786870,248509,497,538361,1077
+60,SC-CoT,MATH-500,Qwen2-0.5B-Instruct,2025/1/22,0.8,1.0,4,0.0,,500,6862056,4448663,8897,2413393,4827
+61,ToT,MATH-500,Qwen2-1.5B-Instruct,2025/1/22,0.8,0.972,4,0.0,,500,4535000,4408000,8816,127000,254
+62,ReAct-Pro*,MATH-500,Qwen2-0.5B-Instruct,2025/1/22,0.6,1.0,4,0.0,,500,19442440,18137392,36275,1305048,2610
+63,ToT,MATH-500,deepseek-r1:1.5b,2025/2/10,0.4,0.716,4,0.0,,500,1941500,1831000,3662,110500,221
+64,ToT,MATH-500,Internllm2_5-7B,2025/1/22,0.2,0.99,4,0.0,,500,8350500,7515000,15030,835500,1671
+65,PoT,MATH-500,Qwen2-0.5B-Instruct,2025/1/22,0.0,0.0,4,0.0,,500,437202,253549,507,183653,367
+66,ToT,MATH-500,Qwen2-0.5B-Instruct,2025/1/22,0.0,0.962,4,0.0,,500,5996500,5590500,11181,406000,812
+1,SC-CoT,gsm8k,Llama-3.3-70B-Instruct,2025/1/22,95.07,1.0,8,6.2005,,1319,10998794,8413717,6379,2585077,1960
+2,CoT,gsm8k,gpt-4o,2025/1/22,94.09,1.0,8,4.5367,,1319,1165166,948668,719,216498,164
+3,CoT,gsm8k,Llama-3.3-70B-Instruct,2025/1/22,93.93,1.0,8,0.687,,1319,1218665,990168,751,228497,173
+4,SC-CoT,gsm8k,Qwen2.5-72B-Instruct,2025/1/22,93.86,1.0,8,5.9858,,1319,10618008,8136223,6168,2481785,1882
+5,PoT,gsm8k,gpt-4o,2025/1/22,93.1,0.9977,8,4.2166,,1319,1247912,1101672,835,146240,111
+6,CoT,gsm8k,Qwen2.5-72B-Instruct,2025/1/22,92.87,1.0,8,0.7195,,1319,1276252,1005119,762,271133,206
+7,PoT,gsm8k,Qwen2.5-72B-Instruct,2025/1/22,92.34,0.9939,8,0.7054,,1319,1251210,1106682,839,144528,110
+8,IO,gsm8k,Llama-3.3-70B-Instruct,2025/1/22,92.27,1.0,8,0.4709,,1319,835275,583916,443,251359,191
+9,ToT,gsm8k,Llama-3.3-70B-Instruct,2025/1/22,91.89,1.0,8,20.8753,,1319,37029687,35096810,26609,1932877,1465
+10,SC-CoT,gsm8k,Qwen2.5-7B-Instruct,2025/1/22,91.13,1.0,8,0.0,,1319,11140985,8586888,6510,2554097,1936
+11,ToT,gsm8k,gpt-4o,2025/1/22,91.13,1.0,8,86.8581,,1319,30769735,29445237,22324,1324498,1004
+12,SC-CoT,gsm8k,gpt-4o,2025/1/22,90.3,0.9992,8,31.0542,,1319,5798173,3590336,2722,2207837,1674
+13,CoT,gsm8k,Doubao-lite-32k,2025/1/7,89.31,1.0,8,0.0558,,1319,1201820,1042095,790,159725,121
+14,ToT,gsm8k,Qwen2.5-72B-Instruct,2025/1/22,88.88,1.0,8,23.5911,,1319,41847148,40435361,30656,1411787,1070
+15,IO,gsm8k,gpt-4o,2025/1/22,88.4,1.0,8,3.3463,,1319,741446,542416,411,199030,151
+16,ReAct-Pro*,gsm8k,Llama-3.3-70B-Instruct,2025/1/22,87.64,0.9992,8,10.1124,,1319,17937864,17038928,12918,898936,682
+17,ReAct-Pro*,gsm8k,Qwen2.5-72B-Instruct,2025/1/22,87.26,1.0,8,10.5479,,1319,18710437,18160983,13769,549454,417
+18,SC-CoT,gsm8k,Doubao-lite-32k,2025/1/7,87.26,0.9992,8,0.2083,,1319,3888813,2691714,2041,1197099,908
+19,IO,gsm8k,Qwen2.5-72B-Instruct,2025/1/22,86.58,1.0,8,0.4899,,1319,869060,555340,421,313720,238
+20,CoT,gsm8k,Qwen2.5-7B-Instruct,2025/1/22,85.67,1.0,8,0.0,,1319,1290805,1046008,793,244797,186
+21,ReAct-Pro*,gsm8k,Doubao-lite-32k,2025/1/7,85.6,0.9962,8,0.2512,,1319,5998639,5862016,4444,136623,104
+22,ReAct-Pro*,gsm8k,Qwen2.5-7B-Instruct,2025/1/22,82.87,1.0,8,0.0,,1319,14850914,14355752,10884,495162,375
+23,SC-CoT,gsm8k,gpt-3.5-turbo,2025/1/7,79.91,0.9992,8,3.3938,,1319,4089612,2740652,2078,1348960,1023
+24,PoT,gsm8k,Doubao-lite-32k,2025/1/7,79.61,0.9257,8,0.0576,,1319,1288055,1170038,887,118017,89
+25,CoT,gsm8k,gpt-3.5-turbo,2025/1/7,78.7,1.0,8,0.6788,,1319,1088041,953242,723,134799,102
+26,CoT,gsm8k,Internllm2_5-7B,2025/1/22,77.71,0.997,8,0.0,,1319,1202163,968163,734,234000,177
+27,PoT,gsm8k,gpt-3.5-turbo,2025/1/7,76.88,0.9924,8,0.6902,,1319,1187080,1090418,827,96662,73
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+29,ReAct-Pro*,gsm8k,gpt-3.5-turbo,2025/1/7,74.91,0.9939,8,3.4633,,1319,6646286,6506164,4933,140122,106
+30,SC-CoT,gsm8k,Llama-3.1-8B-Instruct,2025/1/22,73.46,0.9955,8,0.0,,1319,11778716,8630514,6543,3148202,2387
+31,PoT,gsm8k,Llama-3.3-70B-Instruct,2025/1/22,73.09,0.7961,8,0.9736,,1319,1727044,1126025,854,601019,456
+32,ToT,gsm8k,Qwen2.5-7B-Instruct,2025/1/22,72.21,0.9901,8,0.0,,1319,31657319,20196528,15312,11460791,8689
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+34,CoT,gsm8k,deepseek-r1:1.5b,2025/1/23,70.66,0.9977,8,0.0,,1319,2090625,1011714,767,1078911,818
+35,ToT,gsm8k,gpt-3.5-turbo,2025/1/7,67.93,0.997,8,9.1707,,1319,16727175,15920037,12070,807138,612
+36,ReAct-Pro*,gsm8k,Llama-3.1-8B-Instruct,2025/1/22,67.78,0.9856,8,0.0,,1319,22835767,21044978,15955,1790789,1358
+37,ToT,gsm8k,Llama-3.1-8B-Instruct,2025/1/22,65.05,0.9196,8,0.0,,1319,16432102,15554967,11793,877135,665
+38,IO,gsm8k,deepseek-r1:1.5b,2025/1/22,64.14,0.9962,8,0.0,,1319,1483051,561935,426,921116,698
+39,ReAct-Pro*,gsm8k,gpt-4o,2025/1/22,63.31,0.9955,8,39.0751,,1319,14715887,14411173,10926,304714,231
+40,PoT,gsm8k,Qwen2.5-7B-Instruct,2025/1/22,58.83,0.7051,8,0.0,,1319,1362822,1145390,868,217432,165
+41,IO,gsm8k,Qwen2.5-7B-Instruct,2025/1/22,57.24,1.0,8,0.0,,1319,887913,596229,452,291684,221
+42,IO,gsm8k,Llama-3.1-8B-Instruct,2025/1/22,57.16,0.9955,8,0.0,,1319,1745429,550941,418,1194488,906
+43,CoT,gsm8k,Qwen2-1.5B-Instruct,2025/1/22,55.5,1.0,8,0.0,,1319,1218525,1032818,783,185707,141
+44,SC-CoT,gsm8k,deepseek-r1:1.5b,2025/2/10,55.34,0.997,8,0.0,,1319,25785865,14540096,11024,11245769,8526
+45,SC-CoT,gsm8k,Internllm2_5-7B,2025/1/22,48.22,0.9841,8,0.0,,1319,14526431,10678792,8096,3847639,2917
+46,PoT,gsm8k,Llama-3.1-8B-Instruct,2025/1/22,38.67,0.5542,8,0.0,,1319,1391111,1147538,870,243573,185
+47,PoT,gsm8k,Internllm2_5-7B,2025/1/22,38.21,0.489,8,0.0,,1319,1324949,1136843,862,188106,143
+48,IO,gsm8k,gpt-3.5-turbo,2025/1/7,37.83,0.9992,8,0.3328,,1319,586553,546990,415,39563,30
+49,ToT,gsm8k,Doubao-lite-32k,2025/1/7,37.83,0.8734,8,0.8739,,1319,20274349,19208597,14563,1065752,808
+50,ReAct-Pro*,gsm8k,deepseek-r1:1.5b,2025/2/10,35.94,0.9962,8,0.0,,1319,24219077,19299381,14632,4919696,3730
+51,CoT,gsm8k,Qwen2-0.5B-Instruct,2025/1/22,35.94,0.9992,8,0.0,,1319,1223459,1032818,783,190641,145
+52,ReAct-Pro*,gsm8k,Internllm2_5-7B,2025/1/22,33.51,0.9795,8,0.0,,1319,35669989,30120070,22836,5549919,4208
+53,ReAct-Pro*,gsm8k,Qwen2-1.5B-Instruct,2025/1/22,24.87,0.8021,8,0.0,,1319,9828001,9133603,6925,694398,526
+54,ToT,gsm8k,deepseek-r1:1.5b,2025/2/10,23.12,0.7248,8,0.0,,1319,3421486,2738244,2076,683242,518
+55,ToT,gsm8k,Internllm2_5-7B,2025/1/22,20.85,0.7013,8,0.0,,1319,13178129,11768118,8922,1410011,1069
+56,ToT,gsm8k,Qwen2-1.5B-Instruct,2025/1/22,19.64,0.7726,8,0.0,,1319,12758687,12124248,9192,634439,481
+57,PoT,gsm8k,Qwen2-1.5B-Instruct,2025/1/22,18.5,0.3101,8,0.0,,1319,1327522,1151528,873,175994,133
+58,IO,gsm8k,Qwen2-1.5B-Instruct,2025/1/22,16.68,1.0,8,0.0,,1319,736996,568530,431,168466,128
+59,IO,gsm8k,Qwen2-0.5B-Instruct,2025/1/22,14.71,1.0,8,0.0,,1319,834897,568116,431,266781,202
+60,PoT,gsm8k,deepseek-r1:1.5b,2025/2/10,11.9,0.1744,8,0.0,,1319,1954509,1138872,863,815637,618
+61,SC-CoT,gsm8k,Qwen2-1.5B-Instruct,2025/1/22,11.75,0.9189,8,0.0,,1319,12411942,9066115,6873,3345827,2537
+62,IO,gsm8k,Internllm2_5-7B,2025/1/22,11.6,0.9795,8,0.0,,1319,1113728,679302,515,434426,329
+63,PoT,gsm8k,Qwen2-0.5B-Instruct,2025/1/22,9.63,0.1691,8,0.0,,1319,1389135,1151528,873,237607,180
+64,ReAct-Pro*,gsm8k,Qwen2-0.5B-Instruct,2025/1/22,7.66,0.9522,8,0.0,,1319,55392611,52431343,39751,2961268,2245
+65,SC-CoT,gsm8k,Qwen2-0.5B-Instruct,2025/1/22,1.67,0.9469,8,0.0,,1319,16465720,11019864,8355,5445856,4129
+66,ToT,gsm8k,Qwen2-0.5B-Instruct,2025/1/22,0.0,0.0,8,0.0,,1319,0,0,0,0,0

src/overall_math_score.json

@@ -1,5 +1,5 @@
 {
-    "time": "2025-01-24 15:10:27",
+    "time": "2025-02-11 13:23:00",
     "results": {
         "IO": {
             "META": {
@@ -14,6 +14,10 @@
             "AQuA": {
                 "Score": 38.98,
                 "Cost($)": 0.038
+            },
+            "MATH-500": {
+                "Score": 17.2,
+                "Cost($)": 0.2436
             }
         },
         "ReAct-Pro*": {
@@ -29,6 +33,10 @@
             "AQuA": {
                 "Score": 64.57,
                 "Cost($)": 0.4928
+            },
+            "MATH-500": {
+                "Score": 23.8,
+                "Cost($)": 2.0406
             }
         },
         "PoT": {
@@ -44,6 +52,10 @@
             "AQuA": {
                 "Score": 59.45,
                 "Cost($)": 0.1748
+            },
+            "MATH-500": {
+                "Score": 28.8,
+                "Cost($)": 0.168
             }
         },
         "CoT": {
@@ -59,6 +71,10 @@
             "AQuA": {
                 "Score": 61.02,
                 "Cost($)": 0.0957
+            },
+            "MATH-500": {
+                "Score": 39.8,
+                "Cost($)": 0.3189
             }
         },
         "SC-CoT": {
@@ -74,6 +90,29 @@
             "AQuA": {
                 "Score": 66.14,
                 "Cost($)": 0.7888
+            },
+            "MATH-500": {
+                "Score": 28.8,
+                "Cost($)": 1.9764
+            }
+        },
+        "ToT": {
+            "META": {
+                "Algorithm": "ToT",
+                "LLM": "gpt-3.5-turbo",
+                "Eval Date": "2025/1/7"
+            },
+            "gsm8k": {
+                "Score": 67.93,
+                "Cost($)": 9.1707
+            },
+            "AQuA": {
+                "Score": 57.09,
+                "Cost($)": 1.1513
+            },
+            "MATH-500": {
+                "Score": 9.8,
+                "Cost($)": 5.2914
             }
         },
         "IO-Doubao-lite-32k": {
@@ -89,6 +128,10 @@
             "AQuA": {
                 "Score": 79.13,
                 "Cost($)": 0.0058
+            },
+            "MATH-500": {
+                "Score": 37.4,
+                "Cost($)": 0.0187
             }
         },
         "ReAct-Pro*-Doubao-lite-32k": {
@@ -104,6 +147,10 @@
             "AQuA": {
                 "Score": 77.56,
                 "Cost($)": 0.0445
+            },
+            "MATH-500": {
+                "Score": 47.2,
+                "Cost($)": 0.186
             }
         },
         "PoT-Doubao-lite-32k": {
@@ -119,6 +166,10 @@
             "AQuA": {
                 "Score": 71.65,
                 "Cost($)": 0.0147
+            },
+            "MATH-500": {
+                "Score": 32.6,
+                "Cost($)": 0.0144
             }
         },
         "CoT-Doubao-lite-32k": {
@@ -134,6 +185,10 @@
             "AQuA": {
                 "Score": 82.68,
                 "Cost($)": 0.0066
+            },
+            "MATH-500": {
+                "Score": 59.0,
+                "Cost($)": 0.0255
             }
         },
         "SC-CoT-Doubao-lite-32k": {
@@ -149,6 +204,29 @@
             "AQuA": {
                 "Score": 81.1,
                 "Cost($)": 0.0519
+            },
+            "MATH-500": {
+                "Score": 49.2,
+                "Cost($)": 0.1406
+            }
+        },
+        "ToT-Doubao-lite-32k": {
+            "META": {
+                "Algorithm": "ToT",
+                "LLM": "Doubao-lite-32k",
+                "Eval Date": "2025/1/7"
+            },
+            "gsm8k": {
+                "Score": 37.83,
+                "Cost($)": 0.8739
+            },
+            "AQuA": {
+                "Score": 45.28,
+                "Cost($)": 0.0881
+            },
+            "MATH-500": {
+                "Score": 1.2,
+                "Cost($)": 0.2371
             }
         },
         "IO-gpt-4o": {
@@ -164,6 +242,10 @@
             "AQuA": {
                 "Score": 75.59,
                 "Cost($)": 1.1453
+            },
+            "MATH-500": {
+                "Score": 41.8,
+                "Cost($)": 2.7907
             }
         },
         "ReAct-Pro*-gpt-4o": {
@@ -179,6 +261,10 @@
             "AQuA": {
                 "Score": 57.48,
                 "Cost($)": 2.304
+            },
+            "MATH-500": {
+                "Score": 54.0,
+                "Cost($)": 17.7735
             }
         },
         "PoT-gpt-4o": {
@@ -194,6 +280,10 @@
             "AQuA": {
                 "Score": 75.2,
                 "Cost($)": 1.6087
+            },
+            "MATH-500": {
+                "Score": 46.2,
+                "Cost($)": 1.5994
             }
         },
         "CoT-gpt-4o": {
@@ -209,6 +299,10 @@
             "AQuA": {
                 "Score": 82.68,
                 "Cost($)": 1.0417
+            },
+            "MATH-500": {
+                "Score": 68.0,
+                "Cost($)": 3.0569
             }
         },
         "SC-CoT-gpt-4o": {
@@ -224,6 +318,29 @@
             "AQuA": {
                 "Score": 86.61,
                 "Cost($)": 8.1485
+            },
+            "MATH-500": {
+                "Score": 34.4,
+                "Cost($)": 19.6538
+            }
+        },
+        "ToT-gpt-4o": {
+            "META": {
+                "Algorithm": "ToT",
+                "LLM": "gpt-4o",
+                "Eval Date": "2025/1/22"
+            },
+            "gsm8k": {
+                "Score": 91.13,
+                "Cost($)": 86.8581
+            },
+            "AQuA": {
+                "Score": 81.5,
+                "Cost($)": 8.5295
+            },
+            "MATH-500": {
+                "Score": 3.2,
+                "Cost($)": 40.8094
             }
         },
         "IO-Qwen2.5-72B-Instruct": {
@@ -239,6 +356,10 @@
             "AQuA": {
                 "Score": 84.25,
                 "Cost($)": 0.0742
+            },
+            "MATH-500": {
+                "Score": 70.2,
+                "Cost($)": 0.2506
             }
         },
         "ReAct-Pro*-Qwen2.5-72B-Instruct": {
@@ -254,6 +375,10 @@
             "AQuA": {
                 "Score": 73.23,
                 "Cost($)": 0.3177
+            },
+            "MATH-500": {
+                "Score": 62.8,
+                "Cost($)": 3.4541
             }
         },
         "PoT-Qwen2.5-72B-Instruct": {
@@ -269,6 +394,10 @@
             "AQuA": {
                 "Score": 75.2,
                 "Cost($)": 0.1645
+            },
+            "MATH-500": {
+                "Score": 47.2,
+                "Cost($)": 0.233
             }
         },
         "CoT-Qwen2.5-72B-Instruct": {
@@ -284,6 +413,10 @@
             "AQuA": {
                 "Score": 86.22,
                 "Cost($)": 0.0808
+            },
+            "MATH-500": {
+                "Score": 80.2,
+                "Cost($)": 0.349
             }
         },
         "SC-CoT-Qwen2.5-72B-Instruct": {
@@ -299,6 +432,29 @@
             "AQuA": {
                 "Score": 85.04,
                 "Cost($)": 1.0348
+            },
+            "MATH-500": {
+                "Score": 74.0,
+                "Cost($)": 3.1556
+            }
+        },
+        "ToT-Qwen2.5-72B-Instruct": {
+            "META": {
+                "Algorithm": "ToT",
+                "LLM": "Qwen2.5-72B-Instruct",
+                "Eval Date": "2025/1/22"
+            },
+            "gsm8k": {
+                "Score": 88.88,
+                "Cost($)": 23.5911
+            },
+            "AQuA": {
+                "Score": 81.1,
+                "Cost($)": 3.7389
+            },
+            "MATH-500": {
+                "Score": 10.8,
+                "Cost($)": 9.0421
             }
         },
         "IO-Llama-3.3-70B-Instruct": {
@@ -314,6 +470,10 @@
             "AQuA": {
                 "Score": 82.68,
                 "Cost($)": 0.0798
+            },
+            "MATH-500": {
+                "Score": 69.4,
+                "Cost($)": 0.2386
             }
         },
         "ReAct-Pro*-Llama-3.3-70B-Instruct": {
@@ -329,6 +489,10 @@
             "AQuA": {
                 "Score": 79.13,
                 "Cost($)": 0.768
+            },
+            "MATH-500": {
+                "Score": 64.6,
+                "Cost($)": 3.1806
             }
         },
         "PoT-Llama-3.3-70B-Instruct": {
@@ -344,6 +508,10 @@
             "AQuA": {
                 "Score": 79.53,
                 "Cost($)": 0.1746
+            },
+            "MATH-500": {
+                "Score": 42.6,
+                "Cost($)": 0.2839
             }
         },
         "CoT-Llama-3.3-70B-Instruct": {
@@ -359,6 +527,10 @@
             "AQuA": {
                 "Score": 83.46,
                 "Cost($)": 0.0927
+            },
+            "MATH-500": {
+                "Score": 71.2,
+                "Cost($)": 0.3463
             }
         },
         "SC-CoT-Llama-3.3-70B-Instruct": {
@@ -374,6 +546,29 @@
             "AQuA": {
                 "Score": 82.28,
                 "Cost($)": 1.0756
+            },
+            "MATH-500": {
+                "Score": 74.2,
+                "Cost($)": 3.2239
+            }
+        },
+        "ToT-Llama-3.3-70B-Instruct": {
+            "META": {
+                "Algorithm": "ToT",
+                "LLM": "Llama-3.3-70B-Instruct",
+                "Eval Date": "2025/1/22"
+            },
+            "gsm8k": {
+                "Score": 91.89,
+                "Cost($)": 20.8753
+            },
+            "AQuA": {
+                "Score": 83.07,
+                "Cost($)": 2.9404
+            },
+            "MATH-500": {
+                "Score": 1.4,
+                "Cost($)": 8.2699
             }
         },
         "IO-Qwen2.5-7B-Instruct": {
@@ -389,6 +584,10 @@
             "AQuA": {
                 "Score": 78.74,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 59.4,
+                "Cost($)": 0.0
             }
         },
         "ReAct-Pro*-Qwen2.5-7B-Instruct": {
@@ -404,6 +603,10 @@
             "AQuA": {
                 "Score": 74.41,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 48.8,
+                "Cost($)": 0.0
             }
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         "PoT-Qwen2.5-7B-Instruct": {
@@ -419,6 +622,10 @@
             "AQuA": {
                 "Score": 68.11,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 39.6,
+                "Cost($)": 0.0
             }
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         "CoT-Qwen2.5-7B-Instruct": {
@@ -434,6 +641,10 @@
             "AQuA": {
                 "Score": 80.71,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 69.8,
+                "Cost($)": 0.0
             }
         },
         "SC-CoT-Qwen2.5-7B-Instruct": {
@@ -449,6 +660,29 @@
             "AQuA": {
                 "Score": 79.92,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 67.0,
+                "Cost($)": 0.0
+            }
+        },
+        "ToT-Qwen2.5-7B-Instruct": {
+            "META": {
+                "Algorithm": "ToT",
+                "LLM": "Qwen2.5-7B-Instruct",
+                "Eval Date": "2025/1/22"
+            },
+            "gsm8k": {
+                "Score": 72.21,
+                "Cost($)": 0.0
+            },
+            "AQuA": {
+                "Score": 53.94,
+                "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 1.4,
+                "Cost($)": 0.0
             }
         },
         "IO-Llama-3.1-8B-Instruct": {
@@ -464,6 +698,10 @@
             "AQuA": {
                 "Score": 51.18,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 38.6,
+                "Cost($)": 0.0
             }
         },
         "ReAct-Pro*-Llama-3.1-8B-Instruct": {
@@ -479,6 +717,10 @@
             "AQuA": {
                 "Score": 55.51,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 28.8,
+                "Cost($)": 0.0
             }
         },
         "PoT-Llama-3.1-8B-Instruct": {
@@ -494,6 +736,10 @@
             "AQuA": {
                 "Score": 36.61,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 25.4,
+                "Cost($)": 0.0
             }
         },
         "CoT-Llama-3.1-8B-Instruct": {
@@ -509,6 +755,10 @@
             "AQuA": {
                 "Score": 60.63,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 25.8,
+                "Cost($)": 0.0
             }
         },
         "SC-CoT-Llama-3.1-8B-Instruct": {
@@ -524,6 +774,29 @@
             "AQuA": {
                 "Score": 59.45,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 30.2,
+                "Cost($)": 0.0
+            }
+        },
+        "ToT-Llama-3.1-8B-Instruct": {
+            "META": {
+                "Algorithm": "ToT",
+                "LLM": "Llama-3.1-8B-Instruct",
+                "Eval Date": "2025/1/22"
+            },
+            "gsm8k": {
+                "Score": 65.05,
+                "Cost($)": 0.0
+            },
+            "AQuA": {
+                "Score": 59.06,
+                "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 1.8,
+                "Cost($)": 0.0
             }
         },
         "IO-Internllm2_5-7B": {
@@ -539,6 +812,10 @@
             "AQuA": {
                 "Score": 47.64,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 22.8,
+                "Cost($)": 0.0
             }
         },
         "ReAct-Pro*-Internllm2_5-7B": {
@@ -554,6 +831,10 @@
             "AQuA": {
                 "Score": 40.94,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 14.8,
+                "Cost($)": 0.0
             }
         },
         "PoT-Internllm2_5-7B": {
@@ -569,6 +850,10 @@
             "AQuA": {
                 "Score": 36.61,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 15.0,
+                "Cost($)": 0.0
             }
         },
         "CoT-Internllm2_5-7B": {
@@ -584,6 +869,10 @@
             "AQuA": {
                 "Score": 52.76,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 46.6,
+                "Cost($)": 0.0
             }
         },
         "SC-CoT-Internllm2_5-7B": {
@@ -599,6 +888,29 @@
             "AQuA": {
                 "Score": 39.37,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 9.8,
+                "Cost($)": 0.0
+            }
+        },
+        "ToT-Internllm2_5-7B": {
+            "META": {
+                "Algorithm": "ToT",
+                "LLM": "Internllm2_5-7B",
+                "Eval Date": "2025/1/22"
+            },
+            "gsm8k": {
+                "Score": 20.85,
+                "Cost($)": 0.0
+            },
+            "AQuA": {
+                "Score": 35.83,
+                "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 0.2,
+                "Cost($)": 0.0
             }
         },
         "IO-Qwen2-1.5B-Instruct": {
@@ -614,6 +926,10 @@
             "AQuA": {
                 "Score": 29.13,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 7.0,
+                "Cost($)": 0.0
             }
         },
         "ReAct-Pro*-Qwen2-1.5B-Instruct": {
@@ -629,6 +945,10 @@
             "AQuA": {
                 "Score": 25.59,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 8.2,
+                "Cost($)": 0.0
             }
         },
         "PoT-Qwen2-1.5B-Instruct": {
@@ -644,6 +964,10 @@
             "AQuA": {
                 "Score": 30.71,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 0.8,
+                "Cost($)": 0.0
             }
         },
         "CoT-Qwen2-1.5B-Instruct": {
@@ -659,6 +983,10 @@
             "AQuA": {
                 "Score": 40.55,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 15.2,
+                "Cost($)": 0.0
             }
         },
         "SC-CoT-Qwen2-1.5B-Instruct": {
@@ -674,6 +1002,29 @@
             "AQuA": {
                 "Score": 23.62,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 3.8,
+                "Cost($)": 0.0
+            }
+        },
+        "ToT-Qwen2-1.5B-Instruct": {
+            "META": {
+                "Algorithm": "ToT",
+                "LLM": "Qwen2-1.5B-Instruct",
+                "Eval Date": "2025/1/22"
+            },
+            "gsm8k": {
+                "Score": 19.64,
+                "Cost($)": 0.0
+            },
+            "AQuA": {
+                "Score": 31.5,
+                "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 0.8,
+                "Cost($)": 0.0
             }
         },
         "IO-Qwen2-0.5B-Instruct": {
@@ -689,6 +1040,10 @@
             "AQuA": {
                 "Score": 27.17,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 2.6,
+                "Cost($)": 0.0
             }
         },
         "ReAct-Pro*-Qwen2-0.5B-Instruct": {
@@ -704,6 +1059,10 @@
             "AQuA": {
                 "Score": 24.02,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 0.6,
+                "Cost($)": 0.0
             }
         },
         "PoT-Qwen2-0.5B-Instruct": {
@@ -713,12 +1072,16 @@
                 "Eval Date": "2025/1/22"
             },
             "gsm8k": {
-                "Score": 9.62,
+                "Score": 9.63,
                 "Cost($)": 0.0
             },
             "AQuA": {
                 "Score": 17.32,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 0.0,
+                "Cost($)": 0.0
             }
         },
         "CoT-Qwen2-0.5B-Instruct": {
@@ -734,6 +1097,10 @@
             "AQuA": {
                 "Score": 33.07,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 6.2,
+                "Cost($)": 0.0
             }
         },
         "SC-CoT-Qwen2-0.5B-Instruct": {
@@ -749,6 +1116,143 @@
             "AQuA": {
                 "Score": 22.83,
                 "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 0.8,
+                "Cost($)": 0.0
+            }
+        },
+        "ToT-Qwen2-0.5B-Instruct": {
+            "META": {
+                "Algorithm": "ToT",
+                "LLM": "Qwen2-0.5B-Instruct",
+                "Eval Date": "2025/1/22"
+            },
+            "gsm8k": {
+                "Score": 0.0,
+                "Cost($)": 0.0
+            },
+            "AQuA": {
+                "Score": 29.92,
+                "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 0.0,
+                "Cost($)": 0.0
+            }
+        },
+        "IO-deepseek-r1:1.5b": {
+            "META": {
+                "Algorithm": "IO",
+                "LLM": "deepseek-r1:1.5b",
+                "Eval Date": "2025/1/22"
+            },
+            "gsm8k": {
+                "Score": 64.14,
+                "Cost($)": 0.0
+            },
+            "AQuA": {
+                "Score": 68.9,
+                "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 43.8,
+                "Cost($)": 0.0
+            }
+        },
+        "ReAct-Pro*-deepseek-r1:1.5b": {
+            "META": {
+                "Algorithm": "ReAct-Pro*",
+                "LLM": "deepseek-r1:1.5b",
+                "Eval Date": "2025/2/10"
+            },
+            "gsm8k": {
+                "Score": 35.94,
+                "Cost($)": 0.0
+            },
+            "AQuA": {
+                "Score": 54.33,
+                "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 24.4,
+                "Cost($)": 0.0
+            }
+        },
+        "PoT-deepseek-r1:1.5b": {
+            "META": {
+                "Algorithm": "PoT",
+                "LLM": "deepseek-r1:1.5b",
+                "Eval Date": "2025/2/10"
+            },
+            "gsm8k": {
+                "Score": 11.9,
+                "Cost($)": 0.0
+            },
+            "AQuA": {
+                "Score": 54.72,
+                "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 1.0,
+                "Cost($)": 0.0
+            }
+        },
+        "CoT-deepseek-r1:1.5b": {
+            "META": {
+                "Algorithm": "CoT",
+                "LLM": "deepseek-r1:1.5b",
+                "Eval Date": "2025/1/23"
+            },
+            "gsm8k": {
+                "Score": 70.66,
+                "Cost($)": 0.0
+            },
+            "AQuA": {
+                "Score": 71.65,
+                "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 49.4,
+                "Cost($)": 0.0
+            }
+        },
+        "SC-CoT-deepseek-r1:1.5b": {
+            "META": {
+                "Algorithm": "SC-CoT",
+                "LLM": "deepseek-r1:1.5b",
+                "Eval Date": "2025/2/10"
+            },
+            "gsm8k": {
+                "Score": 55.34,
+                "Cost($)": 0.0
+            },
+            "AQuA": {
+                "Score": 59.06,
+                "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 38.0,
+                "Cost($)": 0.0
+            }
+        },
+        "ToT-deepseek-r1:1.5b": {
+            "META": {
+                "Algorithm": "ToT",
+                "LLM": "deepseek-r1:1.5b",
+                "Eval Date": "2025/2/10"
+            },
+            "gsm8k": {
+                "Score": 23.12,
+                "Cost($)": 0.0
+            },
+            "AQuA": {
+                "Score": 24.8,
+                "Cost($)": 0.0
+            },
+            "MATH-500": {
+                "Score": 0.4,
+                "Cost($)": 0.0
             }
         }
     }

src/overall_results.csv

@@ -1,51 +1,67 @@
-Rank,Algorithm,LLM,Eval Date,Avg Score,gsm8k-Score,gsm8k-Cost($),AQuA-Score,AQuA-Cost($)
-1.0,CoT,Qwen2.5-72B-Instruct,2025/1/22,89.55,92.87,0.7195,86.22,0.0808
-2.0,SC-CoT,Qwen2.5-72B-Instruct,2025/1/22,89.45,93.86,5.9858,85.04,1.0348
-3.0,CoT,Llama-3.3-70B-Instruct,2025/1/22,88.70,93.93,0.687,83.46,0.0927
-4.0,SC-CoT,Llama-3.3-70B-Instruct,2025/1/22,88.68,95.07,6.2005,82.28,1.0756
-5.0,SC-CoT,gpt-4o,2025/1/22,88.46,90.3,31.0542,86.61,8.1485
-6.0,CoT,gpt-4o,2025/1/22,88.39,94.09,4.5367,82.68,1.0417
-7.0,IO,Llama-3.3-70B-Instruct,2025/1/22,87.48,92.27,0.4709,82.68,0.0798
-8.0,CoT,Doubao-lite-32k,2025/1/7,86.00,89.31,0.0558,82.68,0.0066
-9.0,SC-CoT,Qwen2.5-7B-Instruct,2025/1/22,85.53,91.13,0.0,79.92,0.0
-10.0,IO,Qwen2.5-72B-Instruct,2025/1/22,85.42,86.58,0.4899,84.25,0.0742
-11.0,SC-CoT,Doubao-lite-32k,2025/1/7,84.18,87.26,0.2083,81.1,0.0519
-12.0,PoT,gpt-4o,2025/1/22,84.15,93.1,4.2166,75.2,1.6087
-13.0,PoT,Qwen2.5-72B-Instruct,2025/1/22,83.77,92.34,0.7054,75.2,0.1645
-14.0,ReAct-Pro*,Llama-3.3-70B-Instruct,2025/1/22,83.39,87.64,10.1124,79.13,0.768
-15.0,CoT,Qwen2.5-7B-Instruct,2025/1/22,83.19,85.67,0.0,80.71,0.0
-16.0,IO,gpt-4o,2025/1/22,82.00,88.4,3.3463,75.59,1.1453
-17.0,ReAct-Pro*,Doubao-lite-32k,2025/1/7,81.58,85.6,0.2512,77.56,0.0445
-18.0,ReAct-Pro*,Qwen2.5-72B-Instruct,2025/1/22,80.25,87.26,10.5479,73.23,0.3177
-19.0,ReAct-Pro*,Qwen2.5-7B-Instruct,2025/1/22,78.64,82.87,0.0,74.41,0.0
-20.0,PoT,Llama-3.3-70B-Instruct,2025/1/22,76.31,73.09,0.9736,79.53,0.1746
-21.0,PoT,Doubao-lite-32k,2025/1/7,75.63,79.61,0.0576,71.65,0.0147
-22.0,IO,Doubao-lite-32k,2025/1/7,75.58,72.02,0.0354,79.13,0.0058
-23.0,SC-CoT,gpt-3.5-turbo,2025/1/7,73.03,79.91,3.3938,66.14,0.7888
-24.0,CoT,gpt-3.5-turbo,2025/1/7,69.86,78.7,0.6788,61.02,0.0957
-25.0,ReAct-Pro*,gpt-3.5-turbo,2025/1/7,69.74,74.91,3.4633,64.57,0.4928
-26.0,PoT,gpt-3.5-turbo,2025/1/7,68.17,76.88,0.6902,59.45,0.1748
-27.0,CoT,Llama-3.1-8B-Instruct,2025/1/22,68.04,75.44,0.0,60.63,0.0
-28.0,IO,Qwen2.5-7B-Instruct,2025/1/22,67.99,57.24,0.0,78.74,0.0
-29.0,SC-CoT,Llama-3.1-8B-Instruct,2025/1/22,66.46,73.46,0.0,59.45,0.0
-30.0,CoT,Internllm2_5-7B,2025/1/22,65.24,77.71,0.0,52.76,0.0
-31.0,PoT,Qwen2.5-7B-Instruct,2025/1/22,63.47,58.83,0.0,68.11,0.0
-32.0,ReAct-Pro*,Llama-3.1-8B-Instruct,2025/1/22,61.65,67.78,0.0,55.51,0.0
-33.0,ReAct-Pro*,gpt-4o,2025/1/22,60.40,63.31,39.0751,57.48,2.304
-34.0,IO,Llama-3.1-8B-Instruct,2025/1/22,54.17,57.16,0.0,51.18,0.0
-35.0,CoT,Qwen2-1.5B-Instruct,2025/1/22,48.03,55.5,0.0,40.55,0.0
-36.0,SC-CoT,Internllm2_5-7B,2025/1/22,43.80,48.22,0.0,39.37,0.0
-37.0,IO,gpt-3.5-turbo,2025/1/7,38.41,37.83,0.3328,38.98,0.038
-38.0,PoT,Llama-3.1-8B-Instruct,2025/1/22,37.64,38.67,0.0,36.61,0.0
-39.0,PoT,Internllm2_5-7B,2025/1/22,37.41,38.21,0.0,36.61,0.0
-40.0,ReAct-Pro*,Internllm2_5-7B,2025/1/22,37.23,33.51,0.0,40.94,0.0
-41.0,CoT,Qwen2-0.5B-Instruct,2025/1/22,34.51,35.94,0.0,33.07,0.0
-42.0,IO,Internllm2_5-7B,2025/1/22,29.62,11.6,0.0,47.64,0.0
-43.0,ReAct-Pro*,Qwen2-1.5B-Instruct,2025/1/22,25.23,24.87,0.0,25.59,0.0
-44.0,PoT,Qwen2-1.5B-Instruct,2025/1/22,24.61,18.5,0.0,30.71,0.0
-45.0,IO,Qwen2-1.5B-Instruct,2025/1/22,22.91,16.68,0.0,29.13,0.0
-46.0,IO,Qwen2-0.5B-Instruct,2025/1/22,20.94,14.71,0.0,27.17,0.0
-47.0,SC-CoT,Qwen2-1.5B-Instruct,2025/1/22,17.69,11.75,0.0,23.62,0.0
-48.0,ReAct-Pro*,Qwen2-0.5B-Instruct,2025/1/22,15.84,7.66,0.0,24.02,0.0
-49.0,PoT,Qwen2-0.5B-Instruct,2025/1/22,13.47,9.62,0.0,17.32,0.0
-50.0,SC-CoT,Qwen2-0.5B-Instruct,2025/1/22,12.25,1.67,0.0,22.83,0.0
+Rank,Algorithm,LLM,Eval Date,Avg Score,gsm8k-Score,gsm8k-Cost($),AQuA-Score,AQuA-Cost($),MATH-500-Score,MATH-500-Cost($)
+1.0,CoT,Qwen2.5-72B-Instruct,2025/1/22,86.43,92.87,0.7195,86.22,0.0808,80.2,0.349
+2.0,SC-CoT,Qwen2.5-72B-Instruct,2025/1/22,84.30,93.86,5.9858,85.04,1.0348,74.0,3.1556
+3.0,SC-CoT,Llama-3.3-70B-Instruct,2025/1/22,83.85,95.07,6.2005,82.28,1.0756,74.2,3.2239
+4.0,CoT,Llama-3.3-70B-Instruct,2025/1/22,82.86,93.93,0.687,83.46,0.0927,71.2,0.3463
+5.0,CoT,gpt-4o,2025/1/22,81.59,94.09,4.5367,82.68,1.0417,68.0,3.0569
+6.0,IO,Llama-3.3-70B-Instruct,2025/1/22,81.45,92.27,0.4709,82.68,0.0798,69.4,0.2386
+7.0,IO,Qwen2.5-72B-Instruct,2025/1/22,80.34,86.58,0.4899,84.25,0.0742,70.2,0.2506
+8.0,SC-CoT,Qwen2.5-7B-Instruct,2025/1/22,79.35,91.13,0.0,79.92,0.0,67.0,0.0
+9.0,CoT,Qwen2.5-7B-Instruct,2025/1/22,78.73,85.67,0.0,80.71,0.0,69.8,0.0
+10.0,ReAct-Pro*,Llama-3.3-70B-Instruct,2025/1/22,77.12,87.64,10.1124,79.13,0.768,64.6,3.1806
+11.0,CoT,Doubao-lite-32k,2025/1/7,77.00,89.31,0.0558,82.68,0.0066,59.0,0.0255
+12.0,ReAct-Pro*,Qwen2.5-72B-Instruct,2025/1/22,74.43,87.26,10.5479,73.23,0.3177,62.8,3.4541
+13.0,SC-CoT,Doubao-lite-32k,2025/1/7,72.52,87.26,0.2083,81.1,0.0519,49.2,0.1406
+14.0,PoT,Qwen2.5-72B-Instruct,2025/1/22,71.58,92.34,0.7054,75.2,0.1645,47.2,0.233
+15.0,PoT,gpt-4o,2025/1/22,71.50,93.1,4.2166,75.2,1.6087,46.2,1.5994
+16.0,SC-CoT,gpt-4o,2025/1/22,70.44,90.3,31.0542,86.61,8.1485,34.4,19.6538
+17.0,ReAct-Pro*,Doubao-lite-32k,2025/1/7,70.12,85.6,0.2512,77.56,0.0445,47.2,0.186
+18.0,ReAct-Pro*,Qwen2.5-7B-Instruct,2025/1/22,68.69,82.87,0.0,74.41,0.0,48.8,0.0
+19.0,IO,gpt-4o,2025/1/22,68.60,88.4,3.3463,75.59,1.1453,41.8,2.7907
+20.0,IO,Qwen2.5-7B-Instruct,2025/1/22,65.13,57.24,0.0,78.74,0.0,59.4,0.0
+21.0,PoT,Llama-3.3-70B-Instruct,2025/1/22,65.07,73.09,0.9736,79.53,0.1746,42.6,0.2839
+22.0,CoT,deepseek-r1:1.5b,2025/1/23,63.90,70.66,0.0,71.65,0.0,49.4,0.0
+23.0,IO,Doubao-lite-32k,2025/1/7,62.85,72.02,0.0354,79.13,0.0058,37.4,0.0187
+24.0,PoT,Doubao-lite-32k,2025/1/7,61.29,79.61,0.0576,71.65,0.0147,32.6,0.0144
+25.0,ToT,Qwen2.5-72B-Instruct,2025/1/22,60.26,88.88,23.5911,81.1,3.7389,10.8,9.0421
+26.0,CoT,gpt-3.5-turbo,2025/1/7,59.84,78.7,0.6788,61.02,0.0957,39.8,0.3189
+27.0,CoT,Internllm2_5-7B,2025/1/22,59.02,77.71,0.0,52.76,0.0,46.6,0.0
+28.0,IO,deepseek-r1:1.5b,2025/1/22,58.95,64.14,0.0,68.9,0.0,43.8,0.0
+29.0,ToT,Llama-3.3-70B-Instruct,2025/1/22,58.79,91.89,20.8753,83.07,2.9404,1.4,8.2699
+30.0,ToT,gpt-4o,2025/1/22,58.61,91.13,86.8581,81.5,8.5295,3.2,40.8094
+31.0,SC-CoT,gpt-3.5-turbo,2025/1/7,58.28,79.91,3.3938,66.14,0.7888,28.8,1.9764
+32.0,ReAct-Pro*,gpt-4o,2025/1/22,58.26,63.31,39.0751,57.48,2.304,54.0,17.7735
+33.0,PoT,Qwen2.5-7B-Instruct,2025/1/22,55.51,58.83,0.0,68.11,0.0,39.6,0.0
+34.0,PoT,gpt-3.5-turbo,2025/1/7,55.04,76.88,0.6902,59.45,0.1748,28.8,0.168
+35.0,ReAct-Pro*,gpt-3.5-turbo,2025/1/7,54.43,74.91,3.4633,64.57,0.4928,23.8,2.0406
+36.0,SC-CoT,Llama-3.1-8B-Instruct,2025/1/22,54.37,73.46,0.0,59.45,0.0,30.2,0.0
+37.0,CoT,Llama-3.1-8B-Instruct,2025/1/22,53.96,75.44,0.0,60.63,0.0,25.8,0.0
+38.0,SC-CoT,deepseek-r1:1.5b,2025/2/10,50.80,55.34,0.0,59.06,0.0,38.0,0.0
+39.0,ReAct-Pro*,Llama-3.1-8B-Instruct,2025/1/22,50.70,67.78,0.0,55.51,0.0,28.8,0.0
+40.0,IO,Llama-3.1-8B-Instruct,2025/1/22,48.98,57.16,0.0,51.18,0.0,38.6,0.0
+41.0,ToT,gpt-3.5-turbo,2025/1/7,44.94,67.93,9.1707,57.09,1.1513,9.8,5.2914
+42.0,ToT,Qwen2.5-7B-Instruct,2025/1/22,42.52,72.21,0.0,53.94,0.0,1.4,0.0
+43.0,ToT,Llama-3.1-8B-Instruct,2025/1/22,41.97,65.05,0.0,59.06,0.0,1.8,0.0
+44.0,ReAct-Pro*,deepseek-r1:1.5b,2025/2/10,38.22,35.94,0.0,54.33,0.0,24.4,0.0
+45.0,CoT,Qwen2-1.5B-Instruct,2025/1/22,37.08,55.5,0.0,40.55,0.0,15.2,0.0
+46.0,PoT,Llama-3.1-8B-Instruct,2025/1/22,33.56,38.67,0.0,36.61,0.0,25.4,0.0
+47.0,SC-CoT,Internllm2_5-7B,2025/1/22,32.46,48.22,0.0,39.37,0.0,9.8,0.0
+48.0,IO,gpt-3.5-turbo,2025/1/7,31.34,37.83,0.3328,38.98,0.038,17.2,0.2436
+49.0,PoT,Internllm2_5-7B,2025/1/22,29.94,38.21,0.0,36.61,0.0,15.0,0.0
+50.0,ReAct-Pro*,Internllm2_5-7B,2025/1/22,29.75,33.51,0.0,40.94,0.0,14.8,0.0
+51.0,ToT,Doubao-lite-32k,2025/1/7,28.10,37.83,0.8739,45.28,0.0881,1.2,0.2371
+52.0,IO,Internllm2_5-7B,2025/1/22,27.35,11.6,0.0,47.64,0.0,22.8,0.0
+53.0,CoT,Qwen2-0.5B-Instruct,2025/1/22,25.07,35.94,0.0,33.07,0.0,6.2,0.0
+54.0,PoT,deepseek-r1:1.5b,2025/2/10,22.54,11.9,0.0,54.72,0.0,1.0,0.0
+55.0,ReAct-Pro*,Qwen2-1.5B-Instruct,2025/1/22,19.55,24.87,0.0,25.59,0.0,8.2,0.0
+56.0,ToT,Internllm2_5-7B,2025/1/22,18.96,20.85,0.0,35.83,0.0,0.2,0.0
+57.0,IO,Qwen2-1.5B-Instruct,2025/1/22,17.60,16.68,0.0,29.13,0.0,7.0,0.0
+58.0,ToT,Qwen2-1.5B-Instruct,2025/1/22,17.31,19.64,0.0,31.5,0.0,0.8,0.0
+59.0,PoT,Qwen2-1.5B-Instruct,2025/1/22,16.67,18.5,0.0,30.71,0.0,0.8,0.0
+60.0,ToT,deepseek-r1:1.5b,2025/2/10,16.11,23.12,0.0,24.8,0.0,0.4,0.0
+61.0,IO,Qwen2-0.5B-Instruct,2025/1/22,14.83,14.71,0.0,27.17,0.0,2.6,0.0
+62.0,SC-CoT,Qwen2-1.5B-Instruct,2025/1/22,13.06,11.75,0.0,23.62,0.0,3.8,0.0
+63.0,ReAct-Pro*,Qwen2-0.5B-Instruct,2025/1/22,10.76,7.66,0.0,24.02,0.0,0.6,0.0
+64.0,ToT,Qwen2-0.5B-Instruct,2025/1/22,9.97,0.0,0.0,29.92,0.0,0.0,0.0
+65.0,PoT,Qwen2-0.5B-Instruct,2025/1/22,8.98,9.63,0.0,17.32,0.0,0.0,0.0
+66.0,SC-CoT,Qwen2-0.5B-Instruct,2025/1/22,8.43,1.67,0.0,22.83,0.0,0.8,0.0

src/record.csv

@@ -1,148 +1,199 @@
-Algorithm,Dataset,Eval Date,LLM,Score,Pass rate,X-shot,Parameters,Samples,Total input tokens,Average input tokens,Total output tokens,Average output tokens,All tokens,Cost($),Note,,,,,,,,,,,,,,,,,,,
-IO,gsm8k,2025/1/7,gpt-3.5-turbo,37.83,99.92,8,,1319,"546,990",415,"39,563",30,"586,553",0.3328,,,,,,,,,,,,,,,,,,,,
-IO,gsm8k,2025/1/7,Doubao-lite-32k,72.02,99.92,8,,1319,"617,377",468,"123,106",93,"740,483",0.0354,0.2590 （元）,,,,,,,,,,,,,,,,,,,
-IO,gsm8k,2025/1/22,gpt-4o,88.4,100,8,,1319,"542,416",411,"199,030",151,"741,446",3.3463,,,,,,,,,,,,,,,,,,,,
-IO,gsm8k,2025/1/22,Qwen2.5-72B-Instruct,86.58,100,8,,1319,"555,340",421,"313,720",238,"869,060",0.4899,,,,,,,,,,,,,,,,,,,,
-IO,gsm8k,2025/1/22,Llama-3.3-70B-Instruct,92.27,100,8,,1319,"583,916",443,"251,359",191,"835,275",0.4709,,,,,,,,,,,,,,,,,,,,
-IO,gsm8k,2025/1/22,Qwen2.5-7B-Instruct,57.24,100,8,,1319,"596,229",452,"291,684",221,"887,913",0.0000,,,,,,,,,,,,,,,,,,,,
-IO,gsm8k,2025/1/22,Llama-3.1-8B-Instruct,57.16,99.55,8,,1319,"550,941",418,"1,194,488",906,"1,745,429",0.0000,,,,,,,,,,,,,,,,,,,,
-IO,gsm8k,2025/1/22,Internllm2_5-7B,11.6,97.95,8,,1319,"679,302",515,"434,426",329,"1,113,728",0.0000,,,,,,,,,,,,,,,,,,,,
-IO,gsm8k,2025/1/22,Qwen2-1.5B-Instruct,16.68,100,8,,1319,"568,530",431,"168,466",128,"736,996",0.0000,,,,,,,,,,,,,,,,,,,,
-IO,gsm8k,2025/1/22,Qwen2-0.5B-Instruct,14.71,100,8,,1319,"568,116",431,"266,781",202,"834,897",0.0000,,,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,gsm8k,2025/1/7,gpt-3.5-turbo,74.91,99.39,8,max_steps=10,1319,"6,506,164","4,933","140,122",106,"6,646,286",3.4633,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,gsm8k,2025/1/7,Doubao-lite-32k,85.6,99.62,8,max_steps=10,1319,"5,862,016","4,444","136,623",104,"5,998,639",0.2512,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,gsm8k,2025/1/22,gpt-4o,63.31,99.55,8,max_steps=10,1319,"14,411,173","10,926","304,714",231,"14,715,887",39.0751,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,gsm8k,2025/1/22,Qwen2.5-72B-Instruct,87.26,100,8,max_steps=10,1319,"18,160,983","13,769","549,454",417,"18,710,437",10.5479,,,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,gsm8k,2025/1/22,Llama-3.3-70B-Instruct,87.64,99.92,8,max_steps=10,1319,"17,038,928","12,918","898,936",682,"17,937,864",10.1124,,,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,gsm8k,2025/1/22,Qwen2.5-7B-Instruct,82.87,100,8,max_steps=10,1319,"14,355,752","10,884","495,162",375,"14,850,914",0.0000,,,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,gsm8k,2025/1/22,Llama-3.1-8B-Instruct,67.78,98.56,8,max_steps=10,1319,"21,044,978","15,955","1,790,789","1,358","22,835,767",0.0000,,,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,gsm8k,2025/1/22,Internllm2_5-7B,33.51,97.95,8,max_steps=10,1319,"30,120,070","22,836","5,549,919","4,208","35,669,989",0.0000,,,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,gsm8k,2025/1/22,Qwen2-1.5B-Instruct,24.87,80.21,8,max_steps=10,1319,"9,133,603","6,925","694,398",526,"9,828,001",0.0000,,,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,gsm8k,2025/1/22,Qwen2-0.5B-Instruct,7.66,95.22,8,max_steps=10,1319,"52,431,343","39,751","2,961,268","2,245","55,392,611",0.0000,,,,,,,,,,,,,,,,,,,,
-PoT,gsm8k,2025/1/7,gpt-3.5-turbo,76.88,99.24,8,,1319,"1,090,418",827,"96,662",73,"1,187,080",0.6902,,,,,,,,,,,,,,,,,,,,
-PoT,gsm8k,2025/1/7,Doubao-lite-32k,79.61,92.57,8,,1319,"1,170,038",887,"118,017",89,"1,288,055",0.0576,,,,,,,,,,,,,,,,,,,,
-PoT,gsm8k,2025/1/22,gpt-4o,93.1,99.77,8,,1319,"1,101,672",835,"146,240",111,"1,247,912",4.2166,,,,,,,,,,,,,,,,,,,,
-PoT,gsm8k,2025/1/22,Qwen2.5-72B-Instruct,92.34,99.39,8,,1319,"1,106,682",839,"144,528",110,"1,251,210",0.7054,,,,,,,,,,,,,,,,,,,,
-PoT,gsm8k,2025/1/22,Llama-3.3-70B-Instruct,73.09,79.61,8,,1319,"1,126,025",854,"601,019",456,"1,727,044",0.9736,,,,,,,,,,,,,,,,,,,,
-PoT,gsm8k,2025/1/22,Qwen2.5-7B-Instruct,58.83,70.51,8,,1319,"1,145,390",868,"217,432",165,"1,362,822",0.0000,,,,,,,,,,,,,,,,,,,,
-PoT,gsm8k,2025/1/22,Llama-3.1-8B-Instruct,38.67,55.42,8,,1319,"1,147,538",870,"243,573",185,"1,391,111",0.0000,,,,,,,,,,,,,,,,,,,,
-PoT,gsm8k,2025/1/22,Internllm2_5-7B,38.21,48.9,8,,1319,"1,136,843",862,"188,106",143,"1,324,949",0.0000,,,,,,,,,,,,,,,,,,,,
-PoT,gsm8k,2025/1/22,Qwen2-1.5B-Instruct,18.5,31.01,8,,1319,"1,151,528",873,"175,994",133,"1,327,522",0.0000,,,,,,,,,,,,,,,,,,,,
-PoT,gsm8k,2025/1/22,Qwen2-0.5B-Instruct,9.62,16.91,8,,1319,"1,151,528",873,"237,607",180,"1,389,135",0.0000,,,,,,,,,,,,,,,,,,,,
-CoT,gsm8k,2025/1/7,gpt-3.5-turbo,78.7,100,8,,1319,"953,242",723,"134,799",102,"1,088,041",0.6788,,,,,,,,,,,,,,,,,,,,
-CoT,gsm8k,2025/1/7,Doubao-lite-32k,89.31,100,8,,1319,"1,042,095",790,"159,725",121,"1,201,820",0.0558,0.4084635 （元）,,,,,,,,,,,,,,,,,,,
-CoT,gsm8k,2025/1/22,gpt-4o,94.09,100,8,,1319,"948,668",719,"216,498",164,"1,165,166",4.5367,,,,,,,,,,,,,,,,,,,,
-CoT,gsm8k,2025/1/22,Qwen2.5-72B-Instruct,92.87,100,8,,1319,"1,005,119",762,"271,133",206,"1,276,252",0.7195,,,,,,,,,,,,,,,,,,,,
-CoT,gsm8k,2025/1/22,Llama-3.3-70B-Instruct,93.93,100,8,,1319,"990,168",751,"228,497",173,"1,218,665",0.6870,,,,,,,,,,,,,,,,,,,,
-CoT,gsm8k,2025/1/22,Qwen2.5-7B-Instruct,85.67,100,8,,1319,"1,046,008",793,"244,797",186,"1,290,805",0.0000,,,,,,,,,,,,,,,,,,,,
-CoT,gsm8k,2025/1/22,Llama-3.1-8B-Instruct,75.44,99.92,8,,1319,"990,168",751,"258,161",196,"1,248,329",0.0000,,,,,,,,,,,,,,,,,,,,
-CoT,gsm8k,2025/1/22,Internllm2_5-7B,77.71,99.7,8,,1319,"968,163",734,"234,000",177,"1,202,163",0.0000,,,,,,,,,,,,,,,,,,,,
-CoT,gsm8k,2025/1/22,Qwen2-1.5B-Instruct,55.5,100,8,,1319,"1,032,818",783,"185,707",141,"1,218,525",0.0000,,,,,,,,,,,,,,,,,,,,
-CoT,gsm8k,2025/1/22,Qwen2-0.5B-Instruct,35.94,99.92,8,,1319,"1,032,818",783,"190,641",145,"1,223,459",0.0000,,,,,,,,,,,,,,,,,,,,
-SC-CoT,gsm8k,2025/1/7,gpt-3.5-turbo,79.91,99.92,8,"temperature=1, path_num=5",1319,"2,740,652","2,078","1,348,960","1,023","4,089,612",3.3938,,,,,,,,,,,,,,,,,,,,
-SC-CoT,gsm8k,2025/1/7,Doubao-lite-32k,87.26,99.92,8,"temperature=1, path_num=5",1319,"2,691,714","2,041","1,197,099",908,"3,888,813",0.2083,,,,,,,,,,,,,,,,,,,,
-SC-CoT,gsm8k,2025/1/22,gpt-4o,90.3,99.92,8,"temperature=1, path_num=5",1319,"3,590,336","2,722","2,207,837","1,674","5,798,173",31.0542,,,,,,,,,,,,,,,,,,,,
-SC-CoT,gsm8k,2025/1/22,Qwen2.5-72B-Instruct,93.86,100,8,"temperature=1, path_num=5",1319,"8,136,223","6,168","2,481,785","1,882","10,618,008",5.9858,,,,,,,,,,,,,,,,,,,,
-SC-CoT,gsm8k,2025/1/22,Llama-3.3-70B-Instruct,95.07,100,8,"temperature=1, path_num=5",1319,"8,413,717","6,379","2,585,077","1,960","10,998,794",6.2005,,,,,,,,,,,,,,,,,,,,
-SC-CoT,gsm8k,2025/1/22,Qwen2.5-7B-Instruct,91.13,100,8,"temperature=1, path_num=5",1319,"8,586,888","6,510","2,554,097","1,936","11,140,985",0.0000,,,,,,,,,,,,,,,,,,,,
-SC-CoT,gsm8k,2025/1/22,Llama-3.1-8B-Instruct,73.46,99.55,8,"temperature=1, path_num=5",1319,"8,630,514","6,543","3,148,202","2,387","11,778,716",0.0000,,,,,,,,,,,,,,,,,,,,
-SC-CoT,gsm8k,2025/1/22,Internllm2_5-7B,48.22,98.41,8,"temperature=1, path_num=5",1319,"10,678,792","8,096","3,847,639","2,917","14,526,431",0.0000,,,,,,,,,,,,,,,,,,,,
-SC-CoT,gsm8k,2025/1/22,Qwen2-1.5B-Instruct,11.75,91.89,8,"temperature=1, path_num=5",1319,"9,066,115","6,873","3,345,827","2,537","12,411,942",0.0000,,,,,,,,,,,,,,,,,,,,
-SC-CoT,gsm8k,2025/1/22,Qwen2-0.5B-Instruct,1.67,94.69,8,"temperature=1, path_num=5",1319,"11,019,864","8,355","5,445,856","4,129","16,465,720",0.0000,,,,,,,,,,,,,,,,,,,,
-IO,AQuA,2025/1/7,gpt-3.5-turbo,38.98,100,0,,254,"25,701",101,"16,770",66,"42,471",0.0380,,,,,,,,,,,,,,,,,,,,
-IO,AQuA,2025/1/7,Doubao-lite-32k,79.13,100,0,,254,"33,058",130,"54,684",215,"87,742",0.0058,0.0427（元）,,,,,,,,,,,,,,,,,,,
-IO,AQuA,2025/1/22,gpt-4o,75.59,97.24,0,,254,"25,631",101,"108,121",426,"133,752",1.1453,,,,,,,,,,,,,,,,,,,,
-IO,AQuA,2025/1/22,Qwen2.5-72B-Instruct,84.25,99.61,0,,254,"25,397",100,"106,207",418,"131,604",0.0742,,,,,,,,,,,,,,,,,,,,
-IO,AQuA,2025/1/22,Llama-3.3-70B-Instruct,82.68,99.21,0,,254,"32,809",129,"108,758",428,"141,567",0.0798,,,,,,,,,,,,,,,,,,,,
-IO,AQuA,2025/1/22,Qwen2.5-7B-Instruct,78.74,98.43,0,,254,"33,271",131,"104,500",411,"137,771",0.0000,,,,,,,,,,,,,,,,,,,,
-IO,AQuA,2025/1/22,Llama-3.1-8B-Instruct,51.18,98.82,0,,254,"26,459",104,"106,647",420,"133,106",0.0000,,,,,,,,,,,,,,,,,,,,
-IO,AQuA,2025/1/22,Internllm2_5-7B,47.64,90.94,0,,254,"50,232",198,"134,809",531,"185,041",0.0000,,,,,,,,,,,,,,,,,,,,
-IO,AQuA,2025/1/22,Qwen2-1.5B-Instruct,29.13,97.64,0,,254,"27,937",110,"43,110",170,"71,047",0.0000,,,,,,,,,,,,,,,,,,,,
-IO,AQuA,2025/1/22,Qwen2-0.5B-Instruct,27.17,98.82,0,,254,"27,937",110,"82,478",325,"110,415",0.0000,,,,,,,,,,,,,,,,,,,,
-CoT,AQuA,2025/1/7,gpt-3.5-turbo,61.02,93.7,0,,254,"25,447",100,"55,346",218,"80,793",0.0957,,,,,,,,,,,,,,,,,,,,
-CoT,AQuA,2025/1/7,Doubao-lite-32k,82.68,97.24,0,,254,"27,978",110,"66,599",262,"94,577",0.0066,0.0483 （元）,,,,,,,,,,,,,,,,,,,
-CoT,AQuA,2025/1/22,gpt-4o,82.68,98.03,0,,254,"25,123",99,"97,894",385,"123,017",1.0417,,,,,,,,,,,,,,,,,,,,
-CoT,AQuA,2025/1/22,Qwen2.5-72B-Instruct,86.22,99.21,0,,254,"25,143",99,"118,146",465,"143,289",0.0808,,,,,,,,,,,,,,,,,,,,
-CoT,AQuA,2025/1/22,Llama-3.3-70B-Instruct,83.46,98.43,0,,254,"32,555",128,"131,834",519,"164,389",0.0927,,,,,,,,,,,,,,,,,,,,
-CoT,AQuA,2025/1/22,Qwen2.5-7B-Instruct,80.71,99.61,0,,254,"33,017",130,"116,719",460,"149,736",0.0000,,,,,,,,,,,,,,,,,,,,
-CoT,AQuA,2025/1/22,Llama-3.1-8B-Instruct,60.63,100,0,,254,"32,555",128,"111,880",440,"144,435",0.0000,,,,,,,,,,,,,,,,,,,,
-CoT,AQuA,2025/1/22,Internllm2_5-7B,52.76,89.37,0,,254,"26,610",105,"100,910",397,"127,520",0.0000,,,,,,,,,,,,,,,,,,,,
-CoT,AQuA,2025/1/22,Qwen2-1.5B-Instruct,40.55,98.82,0,,254,"30,477",120,"79,563",313,"110,040",0.0000,,,,,,,,,,,,,,,,,,,,
-CoT,AQuA,2025/1/22,Qwen2-0.5B-Instruct,33.07,98.82,0,,254,"30,477",120,"86,862",342,"117,339",0.0000,,,,,,,,,,,,,,,,,,,,
-PoT,AQuA,2025/1/7,gpt-3.5-turbo,59.45,100,0,,254,"225,162",886,"41,492",163,"266,654",0.1748,,,,,,,,,,,,,,,,,,,,
-PoT,AQuA,2025/1/7,Doubao-lite-32k,71.65,96.85,0,,254,"259,863","1,023","49,573",195,"309,436",0.0147,,,,,,,,,,,,,,,,,,,,
-PoT,AQuA,2025/1/22,gpt-4o,75.2,100,0,,254,"222,717",877,"105,191",414,"327,908",1.6087,,,,,,,,,,,,,,,,,,,,
-PoT,AQuA,2025/1/22,Qwen2.5-72B-Instruct,75.2,100,0,,254,"249,215",981,"42,549",168,"291,764",0.1645,,,,,,,,,,,,,,,,,,,,
-PoT,AQuA,2025/1/22,Llama-3.3-70B-Instruct,79.53,99.21,0,,254,"240,735",948,"69,064",272,"309,799",0.1746,,,,,,,,,,,,,,,,,,,,
-PoT,AQuA,2025/1/22,Qwen2.5-7B-Instruct,68.11,100,0,,254,"264,517","1,041","49,211",194,"313,728",0.0000,,,,,,,,,,,,,,,,,,,,
-PoT,AQuA,2025/1/22,Llama-3.1-8B-Instruct,36.61,96.85,0,,254,"240,613",947,"50,301",198,"290,914",0.0000,,,,,,,,,,,,,,,,,,,,
-PoT,AQuA,2025/1/22,Internllm2_5-7B,36.61,98.82,0,,254,"233,505",919,"68,457",270,"301,962",0.0000,,,,,,,,,,,,,,,,,,,,
-PoT,AQuA,2025/1/22,Qwen2-1.5B-Instruct,30.71,96.46,0,,254,"246,560",971,"51,915",204,"298,475",0.0000,,,,,,,,,,,,,,,,,,,,
-PoT,AQuA,2025/1/22,Qwen2-0.5B-Instruct,17.32,92.13,0,,254,"258,867","1,019","63,414",250,"322,281",0.0000,,,,,,,,,,,,,,,,,,,,
-SC-CoT,AQuA,2025/1/7,gpt-3.5-turbo,66.14,99.21,0,"temperature=1, path_num=5",254,"482,192","1,898","365,143","1,438","847,335",0.7888,,,,,,,,,,,,,,,,,,,,
-SC-CoT,AQuA,2025/1/7,Doubao-lite-32k,81.1,97.24,0,"temperature=1, path_num=5",254,"503,751","1,983","382,235","1,505","885,986",0.0519,,,,,,,,,,,,,,,,,,,,
-SC-CoT,AQuA,2025/1/22,gpt-4o,86.61,98.82,0,"temperature=1, path_num=5",254,"744,478","2,931","628,728","2,475","1,373,206",8.1485,,,,,,,,,,,,,,,,,,,,
-SC-CoT,AQuA,2025/1/22,Qwen2.5-72B-Instruct,85.04,99.21,0,"temperature=1, path_num=5",254,"1,051,218","4,139","784,451","3,088","1,835,669",1.0348,,,,,,,,,,,,,,,,,,,,
-SC-CoT,AQuA,2025/1/22,Llama-3.3-70B-Instruct,82.28,99.21,0,"temperature=1, path_num=5",254,"1,135,251","4,469","772,673","3,042","1,907,924",1.0756,,,,,,,,,,,,,,,,,,,,
-SC-CoT,AQuA,2025/1/22,Qwen2.5-7B-Instruct,79.92,100,0,"temperature=1, path_num=5",254,"1,098,280","4,324","747,052","2,941","1,845,332",0.0000,,,,,,,,,,,,,,,,,,,,
-SC-CoT,AQuA,2025/1/22,Llama-3.1-8B-Instruct,59.45,97.24,0,"temperature=1, path_num=5",254,"971,003","3,823","680,330","2,678","1,651,333",0.0000,,,,,,,,,,,,,,,,,,,,
-SC-CoT,AQuA,2025/1/22,Internllm2_5-7B,39.37,98.03,0,"temperature=1, path_num=5",254,"1,420,494","5,592","875,728","3,448","2,296,222",0.0000,,,,,,,,,,,,,,,,,,,,
-SC-CoT,AQuA,2025/1/22,Qwen2-1.5B-Instruct,23.62,96.46,0,"temperature=1, path_num=5",254,"1,034,362","4,072","740,973","2,917","1,775,335",0.0000,,,,,,,,,,,,,,,,,,,,
-SC-CoT,AQuA,2025/1/22,Qwen2-0.5B-Instruct,22.83,97.24,0,"temperature=1, path_num=5",254,"1,246,929","4,909","968,162","3,812","2,215,091",0.0000,,,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,AQuA,2025/1/7,gpt-3.5-turbo,64.57,98.03,0,max_steps=10,254,"862,614","3,396","40,973",161,"903,587",0.4928,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,AQuA,2025/1/7,Doubao-lite-32k,77.56,96.06,0,max_steps=10,254,"977,890","3,850","54,951",216,"1,032,841",0.0445,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,AQuA,2025/1/22,gpt-4o,57.48,97.24,0,max_steps=10,254,"615,589","2,424","76,507",301,"692,096",2.3040,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,AQuA,2025/1/22,Qwen2.5-72B-Instruct,73.23,100,0,max_steps=10,254,"441,765","1,739","121,838",480,"563,603",0.3177,,,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,AQuA,2025/1/22,Llama-3.3-70B-Instruct,79.13,99.61,0,max_steps=10,254,"1,119,143","4,406","243,236",958,"1,362,379",0.7680,,,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,AQuA,2025/1/22,Qwen2.5-7B-Instruct,74.41,99.21,0,max_steps=10,254,"564,165","2,221","131,679",518,"695,844",0.0000,,,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,AQuA,2025/1/22,Llama-3.1-8B-Instruct,55.51,96.85,0,max_steps=10,254,"3,764,723","14,822","576,098","2,268","4,340,821",0.0000,,,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,AQuA,2025/1/22,Internllm2_5-7B,40.94,96.85,0,max_steps=10,254,"3,592,039","14,142","836,762","3,294","4,428,801",0.0000,,,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,AQuA,2025/1/22,Qwen2-1.5B-Instruct,25.59,96.06,0,max_steps=10,254,"4,555,858","17,936","516,146","2,032","5,072,004",0.0000,,,,,,,,,,,,,,,,,,,,
-ReAct-Pro*,AQuA,2025/1/22,Qwen2-0.5B-Instruct,24.02,96.85,0,max_steps=10,254,"6,344,167","24,977","825,920","3,252","7,170,087",0.0000,,,,,,,,,,,,,,,,,,,,
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+Algorithm,Dataset,Eval Date,LLM,Score,Pass rate,X-shot,Parameters,Samples,Total input tokens,Average input tokens,Total output tokens,Average output tokens,All tokens,Cost($),Note,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,gsm8k,2025/1/7,gpt-3.5-turbo,37.83,99.92,8,,"1,319","546,990",415,"39,563",30,"586,553",0.3328,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,gsm8k,2025/1/7,Doubao-lite-32k,72.02,99.92,8,,"1,319","617,377",468,"123,106",93,"740,483",0.0354,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,gsm8k,2025/1/22,gpt-4o,88.40,100.00,8,,"1,319","542,416",411,"199,030",151,"741,446",3.3463,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,gsm8k,2025/1/22,Qwen2.5-72B-Instruct,86.58,100.00,8,,"1,319","555,340",421,"313,720",238,"869,060",0.4899,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,gsm8k,2025/1/22,Llama-3.3-70B-Instruct,92.27,100.00,8,,"1,319","583,916",443,"251,359",191,"835,275",0.4709,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,gsm8k,2025/1/22,Qwen2.5-7B-Instruct,57.24,100.00,8,,"1,319","596,229",452,"291,684",221,"887,913",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,gsm8k,2025/1/22,Llama-3.1-8B-Instruct,57.16,99.55,8,,"1,319","550,941",418,"1,194,488",906,"1,745,429",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,gsm8k,2025/1/22,Internllm2_5-7B,11.60,97.95,8,,"1,319","679,302",515,"434,426",329,"1,113,728",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,gsm8k,2025/1/22,Qwen2-1.5B-Instruct,16.68,100.00,8,,"1,319","568,530",431,"168,466",128,"736,996",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,gsm8k,2025/1/22,Qwen2-0.5B-Instruct,14.71,100.00,8,,"1,319","568,116",431,"266,781",202,"834,897",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,gsm8k,2025/1/22,deepseek-r1:1.5b,64.14,99.62,8,,"1,319","561,935",426,"921,116",698,"1,483,051",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,gsm8k,2025/1/7,gpt-3.5-turbo,74.91,99.39,8,max_steps=10,"1,319","6,506,164","4,933","140,122",106,"6,646,286",3.4633,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,gsm8k,2025/1/7,Doubao-lite-32k,85.60,99.62,8,max_steps=10,"1,319","5,862,016","4,444","136,623",104,"5,998,639",0.2512,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,gsm8k,2025/1/22,gpt-4o,63.31,99.55,8,max_steps=10,"1,319","14,411,173","10,926","304,714",231,"14,715,887",39.0751,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,gsm8k,2025/1/22,Qwen2.5-72B-Instruct,87.26,100.00,8,max_steps=10,"1,319","18,160,983","13,769","549,454",417,"18,710,437",10.5479,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,gsm8k,2025/1/22,Llama-3.3-70B-Instruct,87.64,99.92,8,max_steps=10,"1,319","17,038,928","12,918","898,936",682,"17,937,864",10.1124,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,gsm8k,2025/1/22,Qwen2.5-7B-Instruct,82.87,100.00,8,max_steps=10,"1,319","14,355,752","10,884","495,162",375,"14,850,914",0.0000,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,gsm8k,2025/1/22,Llama-3.1-8B-Instruct,67.78,98.56,8,max_steps=10,"1,319","21,044,978","15,955","1,790,789","1,358","22,835,767",0.0000,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,gsm8k,2025/1/22,Internllm2_5-7B,33.51,97.95,8,max_steps=10,"1,319","30,120,070","22,836","5,549,919","4,208","35,669,989",0.0000,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,gsm8k,2025/1/22,Qwen2-1.5B-Instruct,24.87,80.21,8,max_steps=10,"1,319","9,133,603","6,925","694,398",526,"9,828,001",0.0000,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,gsm8k,2025/1/22,Qwen2-0.5B-Instruct,7.66,95.22,8,max_steps=10,"1,319","52,431,343","39,751","2,961,268","2,245","55,392,611",0.0000,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,gsm8k,2025/2/10,deepseek-r1:1.5b,35.94,99.62,8,max_steps=10,"1,319","19,299,381","14,632","4,919,696","3,730","24,219,077",0.0000,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,gsm8k,2025/1/7,gpt-3.5-turbo,76.88,99.24,8,,"1,319","1,090,418",827,"96,662",73,"1,187,080",0.6902,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,gsm8k,2025/1/7,Doubao-lite-32k,79.61,92.57,8,,"1,319","1,170,038",887,"118,017",89,"1,288,055",0.0576,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,gsm8k,2025/1/22,gpt-4o,93.10,99.77,8,,"1,319","1,101,672",835,"146,240",111,"1,247,912",4.2166,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,gsm8k,2025/1/22,Qwen2.5-72B-Instruct,92.34,99.39,8,,"1,319","1,106,682",839,"144,528",110,"1,251,210",0.7054,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,gsm8k,2025/1/22,Llama-3.3-70B-Instruct,73.09,79.61,8,,"1,319","1,126,025",854,"601,019",456,"1,727,044",0.9736,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,gsm8k,2025/1/22,Qwen2.5-7B-Instruct,58.83,70.51,8,,"1,319","1,145,390",868,"217,432",165,"1,362,822",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,gsm8k,2025/1/22,Llama-3.1-8B-Instruct,38.67,55.42,8,,"1,319","1,147,538",870,"243,573",185,"1,391,111",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,gsm8k,2025/1/22,Internllm2_5-7B,38.21,48.90,8,,"1,319","1,136,843",862,"188,106",143,"1,324,949",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,gsm8k,2025/1/22,Qwen2-1.5B-Instruct,18.50,31.01,8,,"1,319","1,151,528",873,"175,994",133,"1,327,522",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,gsm8k,2025/1/22,Qwen2-0.5B-Instruct,9.63,16.91,8,,"1,319","1,151,528",873,"237,607",180,"1,389,135",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,gsm8k,2025/2/10,deepseek-r1:1.5b,11.90,17.44,8,,"1,319","1,138,872",863,"815,637",618,"1,954,509",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,gsm8k,2025/1/7,gpt-3.5-turbo,78.70,100.00,8,,"1,319","953,242",723,"134,799",102,"1,088,041",0.6788,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,gsm8k,2025/1/7,Doubao-lite-32k,89.31,100.00,8,,"1,319","1,042,095",790,"159,725",121,"1,201,820",0.0558,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,gsm8k,2025/1/22,gpt-4o,94.09,100.00,8,,"1,319","948,668",719,"216,498",164,"1,165,166",4.5367,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,gsm8k,2025/1/22,Qwen2.5-72B-Instruct,92.87,100.00,8,,"1,319","1,005,119",762,"271,133",206,"1,276,252",0.7195,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,gsm8k,2025/1/22,Llama-3.3-70B-Instruct,93.93,100.00,8,,"1,319","990,168",751,"228,497",173,"1,218,665",0.6870,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,gsm8k,2025/1/22,Qwen2.5-7B-Instruct,85.67,100.00,8,,"1,319","1,046,008",793,"244,797",186,"1,290,805",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,gsm8k,2025/1/22,Llama-3.1-8B-Instruct,75.44,99.92,8,,"1,319","990,168",751,"258,161",196,"1,248,329",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,gsm8k,2025/1/22,Internllm2_5-7B,77.71,99.70,8,,"1,319","968,163",734,"234,000",177,"1,202,163",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,gsm8k,2025/1/22,Qwen2-1.5B-Instruct,55.50,100.00,8,,"1,319","1,032,818",783,"185,707",141,"1,218,525",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,gsm8k,2025/1/22,Qwen2-0.5B-Instruct,35.94,99.92,8,,"1,319","1,032,818",783,"190,641",145,"1,223,459",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,gsm8k,2025/1/23,deepseek-r1:1.5b,70.66,99.77,8,,"1,319","1,011,714",767,"1,078,911",818,"2,090,625",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,gsm8k,2025/1/7,gpt-3.5-turbo,79.91,99.92,8,"temperature=1, path_num=5","1,319","2,740,652","2,078","1,348,960","1,023","4,089,612",3.3938,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,gsm8k,2025/1/7,Doubao-lite-32k,87.26,99.92,8,"temperature=1, path_num=5","1,319","2,691,714","2,041","1,197,099",908,"3,888,813",0.2083,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,gsm8k,2025/1/22,gpt-4o,90.30,99.92,8,"temperature=1, path_num=5","1,319","3,590,336","2,722","2,207,837","1,674","5,798,173",31.0542,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,gsm8k,2025/1/22,Qwen2.5-72B-Instruct,93.86,100.00,8,"temperature=1, path_num=5","1,319","8,136,223","6,168","2,481,785","1,882","10,618,008",5.9858,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,gsm8k,2025/1/22,Llama-3.3-70B-Instruct,95.07,100.00,8,"temperature=1, path_num=5","1,319","8,413,717","6,379","2,585,077","1,960","10,998,794",6.2005,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,gsm8k,2025/1/22,Qwen2.5-7B-Instruct,91.13,100.00,8,"temperature=1, path_num=5","1,319","8,586,888","6,510","2,554,097","1,936","11,140,985",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,gsm8k,2025/1/22,Llama-3.1-8B-Instruct,73.46,99.55,8,"temperature=1, path_num=5","1,319","8,630,514","6,543","3,148,202","2,387","11,778,716",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,gsm8k,2025/1/22,Internllm2_5-7B,48.22,98.41,8,"temperature=1, path_num=5","1,319","10,678,792","8,096","3,847,639","2,917","14,526,431",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,gsm8k,2025/1/22,Qwen2-1.5B-Instruct,11.75,91.89,8,"temperature=1, path_num=5","1,319","9,066,115","6,873","3,345,827","2,537","12,411,942",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,gsm8k,2025/1/22,Qwen2-0.5B-Instruct,1.67,94.69,8,"temperature=1, path_num=5","1,319","11,019,864","8,355","5,445,856","4,129","16,465,720",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,gsm8k,2025/2/10,deepseek-r1:1.5b,55.34,99.70,8,"temperature=1, path_num=5","1,319","14,540,096","11,024","11,245,769","8,526","25,785,865",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,gsm8k,2025/1/7,gpt-3.5-turbo,67.93,99.70,8,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true","1,319","15,920,037","12,070","807,138",612,"16,727,175",9.1707,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,gsm8k,2025/1/7,Doubao-lite-32k,37.83,87.34,8,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true","1,319","19,208,597","14,563","1,065,752",808,"20,274,349",0.8739,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,gsm8k,2025/1/22,gpt-4o,91.13,100.00,8,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true","1,319","29,445,237","22,324","1,324,498","1,004","30,769,735",86.8581,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,gsm8k,2025/1/22,Qwen2.5-72B-Instruct,88.88,100.00,8,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true","1,319","40,435,361","30,656","1,411,787","1,070","41,847,148",23.5911,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,gsm8k,2025/1/22,Llama-3.3-70B-Instruct,91.89,100.00,8,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true","1,319","35,096,810","26,609","1,932,877","1,465","37,029,687",20.8753,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,gsm8k,2025/1/22,Qwen2.5-7B-Instruct,72.21,99.01,8,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true","1,319","20,196,528","15,312","11,460,791","8,689","31,657,319",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,gsm8k,2025/1/22,Llama-3.1-8B-Instruct,65.05,91.96,8,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true","1,319","15,554,967","11,793","877,135",665,"16,432,102",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,gsm8k,2025/1/22,Internllm2_5-7B,20.85,70.13,8,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true","1,319","11,768,118","8,922","1,410,011","1,069","13,178,129",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,gsm8k,2025/1/22,Qwen2-1.5B-Instruct,19.64,77.26,8,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true","1,319","12,124,248","9,192","634,439",481,"12,758,687",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,gsm8k,2025/1/22,Qwen2-0.5B-Instruct,-,-,8,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true","1,319",-,-,-,-,-,-,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,gsm8k,2025/2/10,deepseek-r1:1.5b,23.12,72.48,8,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true","1,319","2,738,244","2,076","683,242",518,"3,421,486",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,AQuA,2025/1/7,gpt-3.5-turbo,38.98,100.00,0,,254,"25,701",101,"16,770",66,"42,471",0.0380,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,AQuA,2025/1/7,Doubao-lite-32k,79.13,100.00,0,,254,"33,058",130,"54,684",215,"87,742",0.0058,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,AQuA,2025/1/22,gpt-4o,75.59,97.24,0,,254,"25,631",101,"108,121",426,"133,752",1.1453,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,AQuA,2025/1/22,Qwen2.5-72B-Instruct,84.25,99.61,0,,254,"25,397",100,"106,207",418,"131,604",0.0742,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,AQuA,2025/1/22,Llama-3.3-70B-Instruct,82.68,99.21,0,,254,"32,809",129,"108,758",428,"141,567",0.0798,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,AQuA,2025/1/22,Qwen2.5-7B-Instruct,78.74,98.43,0,,254,"33,271",131,"104,500",411,"137,771",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,AQuA,2025/1/22,Llama-3.1-8B-Instruct,51.18,98.82,0,,254,"26,459",104,"106,647",420,"133,106",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,AQuA,2025/1/22,Internllm2_5-7B,47.64,90.94,0,,254,"50,232",198,"134,809",531,"185,041",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,AQuA,2025/1/22,Qwen2-1.5B-Instruct,29.13,97.64,0,,254,"27,937",110,"43,110",170,"71,047",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,AQuA,2025/1/22,Qwen2-0.5B-Instruct,27.17,98.82,0,,254,"27,937",110,"82,478",325,"110,415",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,AQuA,2025/1/22,deepseek-r1:1.5b,68.90,94.88,0,,254,"26,667",105,"325,100","1,280","351,767",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,AQuA,2025/1/7,gpt-3.5-turbo,61.02,93.70,0,,254,"25,447",100,"55,346",218,"80,793",0.0957,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,AQuA,2025/1/7,Doubao-lite-32k,82.68,97.24,0,,254,"27,978",110,"66,599",262,"94,577",0.0066,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,AQuA,2025/1/22,gpt-4o,82.68,98.03,0,,254,"25,123",99,"97,894",385,"123,017",1.0417,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,AQuA,2025/1/22,Qwen2.5-72B-Instruct,86.22,99.21,0,,254,"25,143",99,"118,146",465,"143,289",0.0808,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,AQuA,2025/1/22,Llama-3.3-70B-Instruct,83.46,98.43,0,,254,"32,555",128,"131,834",519,"164,389",0.0927,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,AQuA,2025/1/22,Qwen2.5-7B-Instruct,80.71,99.61,0,,254,"33,017",130,"116,719",460,"149,736",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,AQuA,2025/1/22,Llama-3.1-8B-Instruct,60.63,100.00,0,,254,"32,555",128,"111,880",440,"144,435",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,AQuA,2025/1/22,Internllm2_5-7B,52.76,89.37,0,,254,"26,610",105,"100,910",397,"127,520",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,AQuA,2025/1/22,Qwen2-1.5B-Instruct,40.55,98.82,0,,254,"30,477",120,"79,563",313,"110,040",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,AQuA,2025/1/22,Qwen2-0.5B-Instruct,33.07,98.82,0,,254,"30,477",120,"86,862",342,"117,339",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,AQuA,2025/1/23,deepseek-r1:1.5b,71.65,96.85,0,,254,"26,413",104,"306,659","1,207","333,072",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,AQuA,2025/1/7,gpt-3.5-turbo,59.45,100.00,0,,254,"225,162",886,"41,492",163,"266,654",0.1748,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,AQuA,2025/1/7,Doubao-lite-32k,71.65,96.85,0,,254,"259,863","1,023","49,573",195,"309,436",0.0147,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,AQuA,2025/1/22,gpt-4o,75.20,100.00,0,,254,"222,717",877,"105,191",414,"327,908",1.6087,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,AQuA,2025/1/22,Qwen2.5-72B-Instruct,75.20,100.00,0,,254,"249,215",981,"42,549",168,"291,764",0.1645,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,AQuA,2025/1/22,Llama-3.3-70B-Instruct,79.53,99.21,0,,254,"240,735",948,"69,064",272,"309,799",0.1746,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,AQuA,2025/1/22,Qwen2.5-7B-Instruct,68.11,100.00,0,,254,"264,517","1,041","49,211",194,"313,728",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,AQuA,2025/1/22,Llama-3.1-8B-Instruct,36.61,96.85,0,,254,"240,613",947,"50,301",198,"290,914",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,AQuA,2025/1/22,Internllm2_5-7B,36.61,98.82,0,,254,"233,505",919,"68,457",270,"301,962",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,AQuA,2025/1/22,Qwen2-1.5B-Instruct,30.71,96.46,0,,254,"246,560",971,"51,915",204,"298,475",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,AQuA,2025/1/22,Qwen2-0.5B-Instruct,17.32,92.13,0,,254,"258,867","1,019","63,414",250,"322,281",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,AQuA,2025/2/10,deepseek-r1:1.5b,54.72,97.24,0,,254,"250,690",987,"765,957","3,016","1,016,647",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,AQuA,2025/1/22,gpt-3.5-turbo,66.14,99.21,0,"temperature=1, path_num=5",254,"482,192","1,898","365,143","1,438","847,335",0.7888,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,AQuA,2025/1/22,Doubao-lite-32k,81.10,97.24,0,"temperature=1, path_num=5",254,"503,751","1,983","382,235","1,505","885,986",0.0519,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,AQuA,2025/1/22,gpt-4o,86.61,98.82,0,"temperature=1, path_num=5",254,"744,478","2,931","628,728","2,475","1,373,206",8.1485,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,AQuA,2025/1/22,Qwen2.5-72B-Instruct,85.04,99.21,0,"temperature=1, path_num=5",254,"1,051,218","4,139","784,451","3,088","1,835,669",1.0348,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,AQuA,2025/1/22,Llama-3.3-70B-Instruct,82.28,99.21,0,"temperature=1, path_num=5",254,"1,135,251","4,469","772,673","3,042","1,907,924",1.0756,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,AQuA,2025/1/22,Qwen2.5-7B-Instruct,79.92,100.00,0,"temperature=1, path_num=5",254,"1,098,280","4,324","747,052","2,941","1,845,332",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,AQuA,2025/1/22,Llama-3.1-8B-Instruct,59.45,97.24,0,"temperature=1, path_num=5",254,"971,003","3,823","680,330","2,678","1,651,333",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,AQuA,2025/1/22,Internllm2_5-7B,39.37,98.03,0,"temperature=1, path_num=5",254,"1,420,494","5,592","875,728","3,448","2,296,222",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,AQuA,2025/1/22,Qwen2-1.5B-Instruct,23.62,96.46,0,"temperature=1, path_num=5",254,"1,034,362","4,072","740,973","2,917","1,775,335",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,AQuA,2025/1/22,Qwen2-0.5B-Instruct,22.83,97.24,0,"temperature=1, path_num=5",254,"1,246,929","4,909","968,162","3,812","2,215,091",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,AQuA,2025/2/10,deepseek-r1:1.5b,59.06,96.85,0,"temperature=1, path_num=5",254,"2,547,772","10,031","3,254,939","12,815","5,802,711",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,AQuA,2025/1/7,gpt-3.5-turbo,64.57,98.03,0,max_steps=10,254,"862,614","3,396","40,973",161,"903,587",0.4928,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,AQuA,2025/1/7,Doubao-lite-32k,77.56,96.06,0,max_steps=10,254,"977,890","3,850","54,951",216,"1,032,841",0.0445,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,AQuA,2025/1/22,gpt-4o,57.48,97.24,0,max_steps=10,254,"615,589","2,424","76,507",301,"692,096",2.3040,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,AQuA,2025/1/22,Qwen2.5-72B-Instruct,73.23,100.00,0,max_steps=10,254,"441,765","1,739","121,838",480,"563,603",0.3177,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,AQuA,2025/1/22,Llama-3.3-70B-Instruct,79.13,99.61,0,max_steps=10,254,"1,119,143","4,406","243,236",958,"1,362,379",0.7680,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,AQuA,2025/1/22,Qwen2.5-7B-Instruct,74.41,99.21,0,max_steps=10,254,"564,165","2,221","131,679",518,"695,844",0.0000,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,AQuA,2025/1/22,Llama-3.1-8B-Instruct,55.51,96.85,0,max_steps=10,254,"3,764,723","14,822","576,098","2,268","4,340,821",0.0000,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,AQuA,2025/1/22,Internllm2_5-7B,40.94,96.85,0,max_steps=10,254,"3,592,039","14,142","836,762","3,294","4,428,801",0.0000,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,AQuA,2025/1/22,Qwen2-1.5B-Instruct,25.59,96.06,0,max_steps=10,254,"4,555,858","17,936","516,146","2,032","5,072,004",0.0000,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
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+ReAct-Pro*,AQuA,2025/2/10,deepseek-r1:1.5b,54.33,96.46,0,max_steps=10,254,"10,578,715","41,648","3,866,326","15,222","14,445,041",0.0000,"think-action 单独返回,prompt v1",,,,,,,,,,,,,,,,,,,,,,,,,,,
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+ToT,AQuA,2025/1/7,Doubao-lite-32k,45.28,74.02,0,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",254,"1,850,249","7,284","150,301",592,"2,000,550",0.0881,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,AQuA,2025/1/22,gpt-4o,81.50,99.21,0,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",254,"2,347,538","9,242","266,069","1,048","2,613,607",8.5295,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,AQuA,2025/1/22,Qwen2.5-72B-Instruct,81.10,99.21,0,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",254,"6,371,642","25,085","260,613","1,026","6,632,255",3.7389,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,AQuA,2025/1/22,Llama-3.3-70B-Instruct,83.07,100.00,0,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",254,"4,735,188","18,642","480,660","1,892","5,215,848",2.9404,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,AQuA,2025/1/22,Qwen2.5-7B-Instruct,53.94,100.00,0,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",254,"8,224,468","32,380","378,214","1,489","8,602,682",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,AQuA,2025/1/22,Llama-3.1-8B-Instruct,59.06,100.00,0,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",254,"4,896,222","19,276","843,462","3,321","5,739,684",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,AQuA,2025/1/22,Internllm2_5-7B,35.83,99.61,0,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",254,"4,263,136","16,784","471,424","1,856","4,734,560",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,AQuA,2025/1/22,Qwen2-1.5B-Instruct,31.50,98.82,0,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",254,"6,058,022","23,850","192,680",759,"6,250,702",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,AQuA,2025/1/22,Qwen2-0.5B-Instruct,29.92,100.00,0,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",254,"8,100,085","31,890","600,196","2,363","8,700,281",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,AQuA,2025/2/10,deepseek-r1:1.5b,24.80,55.51,0,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",254,"605,028","2,382","189,484",746,"794,512",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,MATH-500,2025/1/24,gpt-3.5-turbo,17.20,100.00,4,,500,"154,881",310,"110,744",221,"265,625",0.2436,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,MATH-500,2025/1/24,Doubao-lite-32k,37.40,100.00,4,,500,"166,870",334,"144,860",290,"311,730",0.0187,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,MATH-500,2025/1/22,gpt-4o,41.80,100.00,4,,500,"153,832",308,"240,615",481,"394,447",2.7907,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,MATH-500,2025/1/24,Qwen2.5-72B-Instruct,70.20,100.00,4,,500,"169,549",339,"275,042",550,"444,591",0.2506,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,MATH-500,2025/1/24,Llama-3.3-70B-Instruct,69.40,100.00,4,,500,"155,879",312,"267,337",535,"423,216",0.2386,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,MATH-500,2025/1/24,Qwen2.5-7B-Instruct,59.40,100.00,4,,500,"169,549",339,"241,813",484,"411,362",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,MATH-500,2025/1/24,Llama-3.1-8B-Instruct,38.60,100.00,4,,500,"155,563",311,"348,371",697,"503,934",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,MATH-500,2025/1/24,Internllm2_5-7B,22.80,100.00,4,,500,"201,883",404,"266,005",532,"467,888",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,MATH-500,2025/1/24,Qwen2-1.5B-Instruct,7.00,100.00,4,,500,"158,777",318,"255,101",510,"413,878",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,MATH-500,2025/1/24,Qwen2-0.5B-Instruct,2.60,100.00,4,,500,"159,049",318,"270,281",541,"429,330",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+IO,MATH-500,2025/1/24,deepseek-r1:1.5b,43.80,100.00,4,,500,"157,049",314,"865,499","1,731","1,022,548",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,MATH-500,2025/1/24,gpt-3.5-turbo,39.80,100.00,4,,500,"329,381",659,"102,815",206,"432,196",0.3189,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,MATH-500,2025/1/22,Doubao-lite-32k,59.00,100.00,4,,500,"336,370",673,"143,571",287,"479,941",0.0255,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,MATH-500,2025/1/24,gpt-4o,68.00,100.00,4,,500,"329,332",659,"223,356",447,"552,688",3.0569,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,MATH-500,2025/1/22,Qwen2.5-72B-Instruct,80.20,100.00,4,,500,"338,549",677,"280,466",561,"619,015",0.3490,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,MATH-500,2025/1/24,Llama-3.3-70B-Instruct,71.20,100.00,4,,500,"342,879",686,"271,342",543,"614,221",0.3463,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,MATH-500,2025/1/24,Qwen2.5-7B-Instruct,69.80,100.00,4,,500,"354,049",708,"263,155",526,"617,204",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,MATH-500,2025/1/24,Llama-3.1-8B-Instruct,25.80,100.00,4,,500,"342,879",686,"282,689",565,"625,568",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,MATH-500,2025/1/24,Internllm2_5-7B,46.60,100.00,4,,500,"332,883",666,"213,891",428,"546,774",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,MATH-500,2025/1/24,Qwen2-1.5B-Instruct,15.20,100.00,4,,500,"349,049",698,"187,328",375,"536,377",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,MATH-500,2025/1/24,Qwen2-0.5B-Instruct,6.20,100.00,4,,500,"349,049",698,"200,139",400,"549,188",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+CoT,MATH-500,2025/1/24,deepseek-r1:1.5b,49.40,100.00,4,,500,"341,549",683,"857,580","1,715","1,199,129",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,MATH-500,2025/2/10,gpt-3.5-turbo,28.80,83.80,4,,500,"239,902",480,"32,014",64,"271,916",0.1680,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,MATH-500,2025/2/10,Doubao-lite-32k,32.60,68.00,4,,500,"254,377",509,"48,771",98,"303,148",0.0144,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,MATH-500,2025/2/10,gpt-4o,46.20,86.40,4,,500,"241,357",483,"99,603",199,"340,960",1.5994,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,MATH-500,2025/2/10,Qwen2.5-72B-Instruct,47.20,82.20,4,,500,"242,549",485,"170,823",342,"413,372",0.2330,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,MATH-500,2025/2/10,Llama-3.3-70B-Instruct,42.60,80.20,4,,500,"253,879",508,"249,717",499,"503,596",0.2839,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,MATH-500,2025/2/10,Qwen2.5-7B-Instruct,39.60,74.40,4,,500,"258,549",517,"150,263",301,"408,812",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,MATH-500,2025/2/10,Llama-3.1-8B-Instruct,25.40,68.40,4,,500,"253,879",508,"208,392",417,"462,271",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,MATH-500,2025/2/10,Internllm2_5-7B,15.00,32.40,4,,500,"247,883",496,"120,826",242,"368,709",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,MATH-500,2025/2/10,Qwen2-1.5B-Instruct,0.80,2.20,4,,500,"248,509",497,"538,361","1,077","786,870",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,MATH-500,2025/2/10,Qwen2-0.5B-Instruct,0.00,0.00,4,,500,"253,549",507,"183,653",367,"437,202",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+PoT,MATH-500,2025/2/10,deepseek-r1:1.5b,1.00,1.60,4,,500,"245,549",491,"785,518","1,571","1,031,067",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,MATH-500,2025/2/10,gpt-3.5-turbo,28.80,100.00,4,"temperature=1, path_num=5",500,"1,381,818","2,764","856,994","1,714","2,238,812",1.9764,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,MATH-500,2025/2/10,Doubao-lite-32k,49.20,100.00,4,"temperature=1, path_num=5",500,"1,507,651","3,015","963,159","1,926","2,470,810",0.1406,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,MATH-500,2025/2/10,gpt-4o,34.40,100.00,4,"temperature=1, path_num=5",500,"1,986,584","3,973","1,468,739","2,937","3,455,323",19.6538,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,MATH-500,2025/2/10,Qwen2.5-72B-Instruct,74.00,100.00,4,"temperature=1, path_num=5",500,"3,823,997","7,648","1,773,516","3,547","5,597,513",3.1556,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,MATH-500,2025/2/10,Llama-3.3-70B-Instruct,74.20,100.00,4,"temperature=1, path_num=5",500,"3,959,492","7,919","1,759,247","3,518","5,718,739",3.2239,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,MATH-500,2025/2/10,Qwen2.5-7B-Instruct,67.00,100.00,4,"temperature=1, path_num=5",500,"3,833,751","7,668","1,617,733","3,235","5,451,484",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,MATH-500,2025/2/10,Llama-3.1-8B-Instruct,30.20,100.00,4,"temperature=1, path_num=5",500,"3,546,673","7,093","1,488,264","2,977","5,034,937",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,MATH-500,2025/2/10,Internllm2_5-7B,9.80,100.00,4,"temperature=1, path_num=5",500,"4,193,296","8,387","1,645,170","3,290","5,838,466",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,MATH-500,2025/2/10,Qwen2-1.5B-Instruct,3.80,99.00,4,"temperature=1, path_num=5",500,"3,832,429","7,665","1,737,013","3,474","5,569,442",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,MATH-500,2025/2/10,Qwen2-0.5B-Instruct,0.80,100.00,4,"temperature=1, path_num=5",500,"4,448,663","8,897","2,413,393","4,827","6,862,056",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+SC-CoT,MATH-500,2025/2/10,deepseek-r1:1.5b,38.00,100.00,4,"temperature=1, path_num=5",500,"7,080,559","14,161","7,661,550","15,323","14,742,109",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,MATH-500,2025/2/10,gpt-3.5-turbo,23.80,100.00,4,max_steps=10,500,"3,708,461","7,417","124,253",249,"3,832,714",2.0406,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,MATH-500,2025/2/10,Doubao-lite-32k,47.20,100.00,4,max_steps=10,500,"4,234,620","8,469","154,046",308,"4,388,666",0.1860,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,MATH-500,2025/2/10,gpt-4o,54.00,100.00,4,max_steps=10,500,"5,834,537","11,669","318,718",637,"6,153,255",17.7735,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,MATH-500,2025/2/10,Qwen2.5-72B-Instruct,62.80,100.00,4,max_steps=10,500,"5,747,268","11,495","379,849",760,"6,127,117",3.4541,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,MATH-500,2025/2/10,Llama-3.3-70B-Instruct,64.60,100.00,4,max_steps=10,500,"5,223,611","10,447","418,268",837,"5,641,879",3.1806,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,MATH-500,2025/2/10,Qwen2.5-7B-Instruct,48.80,100.00,4,max_steps=10,500,"4,646,708","9,293","343,532",687,"4,990,240",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,MATH-500,2025/2/10,Llama-3.1-8B-Instruct,28.80,100.00,4,max_steps=10,500,"7,486,706","14,973","1,276,923","2,554","8,763,629",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,MATH-500,2025/2/10,Internllm2_5-7B,14.80,100.00,4,max_steps=10,500,"11,831,496","23,663","2,354,609","4,709","14,186,105",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,MATH-500,2025/2/10,Qwen2-1.5B-Instruct,8.20,100.00,4,max_steps=10,500,"8,430,774","16,862","556,287","1,113","8,987,061",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,MATH-500,2025/2/10,Qwen2-0.5B-Instruct,0.60,100.00,4,max_steps=10,500,"18,137,392","36,275","1,305,048","2,610","19,442,440",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ReAct-Pro*,MATH-500,2025/2/10,deepseek-r1:1.5b,24.40,100.00,4,max_steps=10,500,"20,729,970","41,460","9,447,378","18,895","30,177,348",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,MATH-500,2025/2/10,gpt-3.5-turbo,9.80,100.00,4,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",500,"9,711,244","19,422","290,523",581,"10,001,767",5.2914,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,MATH-500,2025/2/10,Doubao-lite-32k,1.20,94.20,4,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",500,"5,338,500","10,677","226,000",452,"5,564,500",0.2371,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,MATH-500,2025/2/10,gpt-4o,3.20,100.00,4,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",500,"14,881,985","29,764","360,447",721,"15,242,432",40.8094,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,MATH-500,2025/2/10,Qwen2.5-72B-Instruct,10.80,100.00,4,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",500,"15,657,730","31,315","381,631",763,"16,039,361",9.0421,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,MATH-500,2025/2/10,Llama-3.3-70B-Instruct,1.40,69.80,4,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",500,"14,099,500","28,199","570,000","1,140","14,669,500",8.2699,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,MATH-500,2025/2/10,Qwen2.5-7B-Instruct,1.40,91.60,4,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",500,"9,749,000","19,498","418,500",837,"10,167,500",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,MATH-500,2025/2/10,Llama-3.1-8B-Instruct,1.80,90.80,4,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",500,"7,729,000","15,458","1,306,000","2,612","9,035,000",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,MATH-500,2025/2/10,Internllm2_5-7B,0.20,99.00,4,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",500,"7,515,000","15,030","835,500","1,671","8,350,500",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,MATH-500,2025/2/10,Qwen2-1.5B-Instruct,0.80,97.20,4,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",500,"4,408,000","8,816","127,000",254,"4,535,000",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,MATH-500,2025/2/10,Qwen2-0.5B-Instruct,0.00,96.20,4,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",500,"5,590,500","11,181","406,000",812,"5,996,500",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ToT,MATH-500,2025/2/10,deepseek-r1:1.5b,0.40,71.60,4,"search_type=bfs, b=1, max_depth=6, max_steps=6, generation_n=1, evaluation_n=3, evaluation_type=vote, use_llm_completion=true",500,"1,831,000","3,662","110,500",221,"1,941,500",0.0000,,,,,,,,,,,,,,,,,,,,,,,,,,,,