Add Parquet preview exports and point HF YAML configs to themGenerate schema-stable Parquet copies for Hub Dataset Viewer and update README data_files to use the preview parquet files.

README.md

@@ -7,19 +7,19 @@ task_categories:
 configs:
   - config_name: comprehensive_decision
     default: true
-    data_files: "assets/qa_gold/comprehensive_decision/*.json"
+    data_files: "assets/qa_gold_hf_preview/comprehensive_decision.parquet"
   - config_name: enterprise_industry_analysis
-    data_files: "assets/qa_gold/enterprise_industry_analysis/*.json"
+    data_files: "assets/qa_gold_hf_preview/enterprise_industry_analysis.parquet"
   - config_name: enterprise_industry_policy_analysis
-    data_files: "assets/qa_gold/enterprise_industry_policy_analysis/*.json"
+    data_files: "assets/qa_gold_hf_preview/enterprise_industry_policy_analysis.parquet"
   - config_name: hypothesis_verification
-    data_files: "assets/qa_gold/hypothesis_verification/*.json"
+    data_files: "assets/qa_gold_hf_preview/hypothesis_verification.parquet"
   - config_name: industry_planning
-    data_files: "assets/qa_gold/industry_planning/*.json"
+    data_files: "assets/qa_gold_hf_preview/industry_planning.parquet"
   - config_name: international_comparison
-    data_files: "assets/qa_gold/international_comparison/*.json"
+    data_files: "assets/qa_gold_hf_preview/international_comparison.parquet"
   - config_name: risk_assessment
-    data_files: "assets/qa_gold/risk_assessment/*.json"
+    data_files: "assets/qa_gold_hf_preview/risk_assessment.parquet"
 ---
 
 <div align="center">

assets/qa_gold/comprehensive_decision/easy001.json

@@ -14,9 +14,5 @@
     "Look up the record for enterprise name \"Yaoshi Shenkang Medical Equipment Company\" in company_profile.csv, extract the province field, and obtain the province where the enterprise is located as \"Jiangsu Province\"."
   ],
   "steps_num": 3,
-  "milestone": {
-    "Enterprise with highest R&D investment ratio": "Yaoshi Shenkang Medical Equipment Company",
-    "R&D investment ratio (%)": 934642.8,
-    "Province of location": "Jiangsu Province"
-  }
-}
+  "milestone": "{\"Enterprise with highest R&D investment ratio\": \"Yaoshi Shenkang Medical Equipment Company\", \"R&D investment ratio (%)\": 934642.8, \"Province of location\": \"Jiangsu Province\"}"
+}

assets/qa_gold/comprehensive_decision/easy002.json

@@ -2,13 +2,7 @@
   "id": "easy002",
   "question": "In 2022, nationwide, how is the chemical raw materials and chemical products manufacturing industry ranked by asset scale? Please list the top five provinces.",
   "guidelines": "Answer format: [Province A, Province B, ...]. Output only province names, do not add any other explanatory text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": [
-    "Shandong Province",
-    "Zhejiang Province",
-    "Jiangsu Province",
-    "Shanghai",
-    "Guangdong Province"
-  ],
+  "answer": "[\"Shandong Province\", \"Zhejiang Province\", \"Jiangsu Province\", \"Shanghai\", \"Guangdong Province\"]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "easy",
@@ -19,18 +13,5 @@
     "Sort all provincial data by total assets in descending order to determine the asset scale ranking of each province. Extract the top five provinces and their total assets: Shandong Province (544109686731.85), Zhejiang Province (401644798867.80), Jiangsu Province (250743006622.33), Shanghai (230472528900.23), Guangdong Province (176926240169.03)."
   ],
   "steps_num": 2,
-  "milestone": {
-    "Total assets in Shandong Province (yuan)": 544109686731.85,
-    "Total assets in Zhejiang Province (yuan)": 401644798867.8,
-    "Total assets in Jiangsu Province (yuan)": 250743006622.33,
-    "Total assets in Shanghai (yuan)": 230472528900.23,
-    "Total assets in Guangdong Province (yuan)": 176926240169.03,
-    "Top five provinces": [
-      "Shandong Province",
-      "Zhejiang Province",
-      "Jiangsu Province",
-      "Shanghai",
-      "Guangdong Province"
-    ]
-  }
-}
+  "milestone": "{\"Total assets in Shandong Province (yuan)\": 544109686731.85, \"Total assets in Zhejiang Province (yuan)\": 401644798867.8, \"Total assets in Jiangsu Province (yuan)\": 250743006622.33, \"Total assets in Shanghai (yuan)\": 230472528900.23, \"Total assets in Guangdong Province (yuan)\": 176926240169.03, \"Top five provinces\": [\"Shandong Province\", \"Zhejiang Province\", \"Jiangsu Province\", \"Shanghai\", \"Guangdong Province\"]}"
+}

assets/qa_gold/comprehensive_decision/easy003.json

@@ -2,7 +2,7 @@
   "id": "easy003",
   "question": "In 2022, in the Qilu region (Shandong Province), how many policies support Zhongbai Jinmao Chain Company in its industry?",
   "guidelines": "The answer must be an exact number. Output only the number, do not add units, commas, or any textual explanation. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 2,
+  "answer": "2",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "easy",
@@ -14,10 +14,5 @@
     "Count the number of policies meeting the criteria, which is 2."
   ],
   "steps_num": 3,
-  "milestone": {
-    "Industry of Zhongbai Jinmao Chain Company": "Wholesale and Retail Trade",
-    "Shandong Province Local Policy - Shandong Provincial People's Government General Office Policy Count": 1,
-    "Shandong Province Local Policy - Shandong Provincial Development and Reform Commission Policy Count": 1,
-    "Number of policies in Shandong Province related to Wholesale and Retail Trade": 2
-  }
-}
+  "milestone": "{\"Industry of Zhongbai Jinmao Chain Company\": \"Wholesale and Retail Trade\", \"Shandong Province Local Policy - Shandong Provincial People's Government General Office Policy Count\": 1, \"Shandong Province Local Policy - Shandong Provincial Development and Reform Commission Policy Count\": 1, \"Number of policies in Shandong Province related to Wholesale and Retail Trade\": 2}"
+}

assets/qa_gold/comprehensive_decision/easy004.json

@@ -2,13 +2,7 @@
   "id": "easy004",
   "question": "In 2022, nationwide, what is the ranking of provinces by asset size in the Information Transmission, Software and Information Technology Services industry? Please list the top five provinces.",
   "guidelines": "Answer format: [Province A, Province B, ...]. Output only the province ranking, do not add any other explanatory text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": [
-    "Beijing",
-    "Zhejiang Province",
-    "Guangdong Province",
-    "Shanghai",
-    "Jiangsu Province"
-  ],
+  "answer": "[\"Beijing\", \"Zhejiang Province\", \"Guangdong Province\", \"Shanghai\", \"Jiangsu Province\"]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "easy",
@@ -19,18 +13,5 @@
     "Sort all provincial data in descending order by total assets to determine the asset size ranking of each province. Extract the top five provinces and their total assets: Beijing (10297490896006.5), Zhejiang Province (4115693929492.25), Guangdong Province (2262247330030.01), Shanghai (1282711003966.55), Jiangsu Province (177568006242.47)."
   ],
   "steps_num": 2,
-  "milestone": {
-    "Total assets of Beijing (CNY)": 10297490896006.5,
-    "Total assets of Zhejiang Province (CNY)": 4115693929492.25,
-    "Total assets of Guangdong Province (CNY)": 2262247330030.01,
-    "Total assets of Shanghai (CNY)": 1282711003966.55,
-    "Total assets of Jiangsu Province (CNY)": 177568006242.47,
-    "Top five provinces by ranking": [
-      "Beijing",
-      "Zhejiang Province",
-      "Guangdong Province",
-      "Shanghai",
-      "Jiangsu Province"
-    ]
-  }
-}
+  "milestone": "{\"Total assets of Beijing (CNY)\": 10297490896006.5, \"Total assets of Zhejiang Province (CNY)\": 4115693929492.25, \"Total assets of Guangdong Province (CNY)\": 2262247330030.01, \"Total assets of Shanghai (CNY)\": 1282711003966.55, \"Total assets of Jiangsu Province (CNY)\": 177568006242.47, \"Top five provinces by ranking\": [\"Beijing\", \"Zhejiang Province\", \"Guangdong Province\", \"Shanghai\", \"Jiangsu Province\"]}"
+}

assets/qa_gold/comprehensive_decision/easy005.json

@@ -2,13 +2,7 @@
   "id": "easy005",
   "question": "In 2022, nationwide, what is the ranking of provinces by profitability in the Real Estate industry? Please list the top five provinces.",
   "guidelines": "Answer format: [Province A, Province B, ...]. Output only the province ranking, do not add any other explanatory text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": [
-    "Hong Kong SAR",
-    "Guangdong Province",
-    "Zhejiang Province",
-    "Beijing",
-    "Jilin Province"
-  ],
+  "answer": "[\"Hong Kong SAR\", \"Guangdong Province\", \"Zhejiang Province\", \"Beijing\", \"Jilin Province\"]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "easy",
@@ -20,18 +14,5 @@
     "Extract the top five provinces and their total net profit amounts: Hong Kong SAR (86497465420.52 CNY), Guangdong Province (70559018502.22 CNY), Zhejiang Province (12297928184.06 CNY), Beijing (8975618268.55 CNY), Jilin Province (675521026.00 CNY)."
   ],
   "steps_num": 3,
-  "milestone": {
-    "Total net profit amount of Hong Kong SAR (CNY)": 86497465420.52,
-    "Total net profit amount of Guangdong Province (CNY)": 70559018502.22,
-    "Total net profit amount of Zhejiang Province (CNY)": 12297928184.06,
-    "Total net profit amount of Beijing (CNY)": 8975618268.55,
-    "Total net profit amount of Jilin Province (CNY)": 675521026.0,
-    "Top five provinces by ranking": [
-      "Hong Kong SAR",
-      "Guangdong Province",
-      "Zhejiang Province",
-      "Beijing",
-      "Jilin Province"
-    ]
-  }
-}
+  "milestone": "{\"Total net profit amount of Hong Kong SAR (CNY)\": 86497465420.52, \"Total net profit amount of Guangdong Province (CNY)\": 70559018502.22, \"Total net profit amount of Zhejiang Province (CNY)\": 12297928184.06, \"Total net profit amount of Beijing (CNY)\": 8975618268.55, \"Total net profit amount of Jilin Province (CNY)\": 675521026.0, \"Top five provinces by ranking\": [\"Hong Kong SAR\", \"Guangdong Province\", \"Zhejiang Province\", \"Beijing\", \"Jilin Province\"]}"
+}

assets/qa_gold/comprehensive_decision/easy006.json

@@ -2,7 +2,7 @@
   "id": "easy006",
   "question": "In 2022, what is Sichuan Province's national ranking by average R&D investment in the Information Transmission, Software and Information Technology Services industry?",
   "guidelines": "The answer must be an exact number representing the ranking. Output only the number, do not add units, commas, or any textual explanation. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 10,
+  "answer": "10",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "easy",
@@ -14,8 +14,5 @@
     "Locate Sichuan Province's position in the sorted list. Sichuan Province's mean R&D investment amount is 147274301.44 CNY, and its ranking is 10th."
   ],
   "steps_num": 3,
-  "milestone": {
-    "Mean R&D investment amount of Sichuan Province (CNY)": 147274301.44,
-    "Sichuan Province ranking": 10
-  }
-}
+  "milestone": "{\"Mean R&D investment amount of Sichuan Province (CNY)\": 147274301.44, \"Sichuan Province ranking\": 10}"
+}

assets/qa_gold/comprehensive_decision/hard001.json

@@ -2,7 +2,7 @@
   "id": "hard001",
   "question": "In 2022, a strategic consulting firm was commissioned by a provincial government to quantitatively rank the comprehensive attractiveness of pharmaceutical manufacturing across provinces, in order to identify priority target regions for attracting leading enterprises. The company designed a four-dimensional weighted scoring system: four original indicators—enterprise agglomeration level (weight 30%), R&D expenditure as a share of revenue (weight 30%), regional policy coverage intensity (weight 20%), and R&D human resource penetration rate (weight 20%)—were normalized (min-max) and then weighted to produce a composite score. Among these, agglomeration level is measured by the proportion of enterprises in each province to the national total in pharmaceutical manufacturing; policy intensity is measured by the ratio of relevant policy items in each province to the total number of relevant policies nationwide; and human resource penetration rate is the total number of R&D personnel in each province divided by total employees. What is the specific composite score value of the province with the highest weighted composite score after normalization across provinces?",
   "guidelines": "The answer should be a numerical value with 2 decimal places. Output only the number, without units or text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 0.92,
+  "answer": "0.92",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -18,14 +18,5 @@
     "Sort by composite score in descending order. Shanghai has the highest composite score, with industry agglomeration 0.1203, R&D intensity 0.2548, policy support 0.1375, talent density 0.1620, and composite score = 0.9160."
   ],
   "steps_num": 7,
-  "milestone": {
-    "National total pharmaceutical manufacturing enterprises": 449.0,
-    "National total pharmaceutical manufacturing-related policies": 80,
-    "Number of valid provinces": 16,
-    "Shanghai industry agglomeration": 0.1203,
-    "Shanghai R&D intensity": 0.2548,
-    "Shanghai policy support": 0.1375,
-    "Shanghai talent density": 0.162,
-    "Shanghai composite score": 0.916
-  }
-}
+  "milestone": "{\"National total pharmaceutical manufacturing enterprises\": 449.0, \"National total pharmaceutical manufacturing-related policies\": 80, \"Number of valid provinces\": 16, \"Shanghai industry agglomeration\": 0.1203, \"Shanghai R&D intensity\": 0.2548, \"Shanghai policy support\": 0.1375, \"Shanghai talent density\": 0.162, \"Shanghai composite score\": 0.916}"
+}

assets/qa_gold/comprehensive_decision/hard002.json

@@ -2,7 +2,7 @@
   "id": "hard002",
   "question": "In 2022, conduct a quantitative assessment of the investment value of the semiconductor industry across provinces. The evaluation framework requires incorporating three dimensions: first, industry scale (40% weight), measured by the inter-provincial rank percentile of total operating revenue in each province; second, profitability quality (30% weight), reflected by the inter-provincial rank percentile of operating profit margin (total operating profit divided by total operating revenue) in each province; third, technology output intensity (30% weight), measured by the inter-provincial rank percentile of the ratio of total patent applications to R&D expenditure (converted to 100 million yuan) in each province. The rank percentile for each indicator is calculated by sorting values from low to high, using the formula (rank - 1) / (total number of provinces - 1). Note that only provinces with complete data for all three indicators are included in the calculation. Under this weighted scoring system, what is the final score of the province ranked first in comprehensive investment value?",
   "guidelines": "The answer should be a numerical value with 2 decimal places. Output only the number, without units or text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 0.67,
+  "answer": "0.67",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -18,11 +18,5 @@
     "Sort by composite score in descending order. Zhejiang Province has the highest composite score, with industry scale rank percentile 0.6667, profitability rank percentile 0.5000, innovation output rank percentile 0.8333, and composite score = 0.6667."
   ],
   "steps_num": 7,
-  "milestone": {
-    "Number of valid provinces": 13,
-    "Zhejiang Province industry scale rank percentile": 0.6667,
-    "Zhejiang Province profitability rank percentile": 0.5,
-    "Zhejiang Province innovation output rank percentile": 0.8333,
-    "Zhejiang Province composite score": 0.6667
-  }
-}
+  "milestone": "{\"Number of valid provinces\": 13, \"Zhejiang Province industry scale rank percentile\": 0.6667, \"Zhejiang Province profitability rank percentile\": 0.5, \"Zhejiang Province innovation output rank percentile\": 0.8333, \"Zhejiang Province composite score\": 0.6667}"
+}

assets/qa_gold/comprehensive_decision/hard003.json

@@ -2,7 +2,7 @@
   "id": "hard003",
   "question": "In 2022, an automotive manufacturing enterprise commissioned a third-party institution to score and rate the industrial supporting capacity of each province before selecting a site for a new plant. The scoring rules are as follows: first, rank provinces in descending order by the number of government policies related to automotive manufacturing, and take the top five provinces by policy count as the candidate pool; then, within the candidate provinces, calculate the industrial supporting composite index, which is a weighted combination of three components—upstream and downstream supply chain density (weight 0.4), local labor reserve (weight 0.3), and government subsidy intensity per enterprise (weight 0.3). Supply chain density is defined as the ratio of total automotive manufacturing enterprises in the province to the national total in the industry; labor reserve is defined as the ratio of total industry employees in the province to the national total in the industry; subsidy intensity is defined as total government rewards and subsidies for automotive manufacturing in the province divided by the number of enterprises in the province (subsidy intensity must be normalized across all provinces before being used in the formula). Among the top five provinces by policy ranking, what is the composite index value of the province with the highest industrial supporting composite index?",
   "guidelines": "Answer format: numerical value (4 decimal places). Output only the number, without units or text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 0.3187,
+  "answer": "0.3187",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -18,19 +18,5 @@
     "Filter among top 5 policy provinces ['Guangdong Province', 'Shanghai', 'Hunan Province', 'Sichuan Province', 'Chongqing'] (Chongqing excluded due to missing data for composite index calculation). Guangdong Province has the highest composite index: upstream-downstream enterprise density 0.1174, talent reserve 0.4499, normalized subsidy intensity 0.4558, composite index = 0.3187."
   ],
   "steps_num": 7,
-  "milestone": {
-    "National total automotive manufacturing enterprises": 230.0,
-    "National total automotive manufacturing employees": 3254510.0,
-    "Top 5 provinces by policy": [
-      "Guangdong Province",
-      "Shanghai",
-      "Hunan Province",
-      "Sichuan Province",
-      "Chongqing"
-    ],
-    "Guangdong Province upstream-downstream enterprise density": 0.1174,
-    "Guangdong Province talent reserve": 0.4499,
-    "Guangdong Province normalized subsidy intensity": 0.4558,
-    "Guangdong Province composite index": 0.3187
-  }
-}
+  "milestone": "{\"National total automotive manufacturing enterprises\": 230.0, \"National total automotive manufacturing employees\": 3254510.0, \"Top 5 provinces by policy\": [\"Guangdong Province\", \"Shanghai\", \"Hunan Province\", \"Sichuan Province\", \"Chongqing\"], \"Guangdong Province upstream-downstream enterprise density\": 0.1174, \"Guangdong Province talent reserve\": 0.4499, \"Guangdong Province normalized subsidy intensity\": 0.4558, \"Guangdong Province composite index\": 0.3187}"
+}

assets/qa_gold/comprehensive_decision/hard004.json

@@ -2,7 +2,7 @@
   "id": "hard004",
   "question": "In 2022, a provincial development and reform commission, when reviewing the effectiveness of fiscal subsidies for the chemical raw materials and chemical products manufacturing industry, needed to identify enterprises with misallocated subsidy resources. Specifically, analysts must first define the scope: only examine enterprises located in provinces that have policy entries for \"Chemical Raw Materials and Chemical Products Manufacturing\" in the policy release status data; then use the industry-wide median of government subsidies and the median operating profit margin (profit margin = operating profit ÷ operating revenue × 100%) as dual thresholds to identify \"capital misallocation\" enterprises—those that simultaneously have \"subsidy amount above the industry median\" but \"profit margin below the industry median\". Among the valid enterprises in the policy-covered provinces, what is the proportion of capital misallocation enterprises as a percentage of total valid enterprises in those provinces (express the result as a percentage with 2 decimal places, without the % symbol)?",
   "guidelines": "The answer should be a percentage value with 2 decimal places. Output only the number, without the % symbol or text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 23.18,
+  "answer": "23.18",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -18,14 +18,5 @@
     "Among the 233 valid enterprises in policy-covered provinces, filter \"high subsidy, low output\" enterprises with government subsidy > 10,019,029.08 and operating profit margin < 10.00%; 54 enterprises, proportion = 54/233 × 100% = 23.18%."
   ],
   "steps_num": 7,
-  "milestone": {
-    "Number of policy-covered provinces": 23,
-    "Total chemical enterprises": 364,
-    "Number of valid samples": 362,
-    "Median government subsidy (yuan)": 10019029.08,
-    "Median operating profit margin (%)": 10.0,
-    "Valid enterprises in policy-covered provinces": 233,
-    "High subsidy low output enterprises": 54,
-    "Proportion (%)": 23.18
-  }
-}
+  "milestone": "{\"Number of policy-covered provinces\": 23, \"Total chemical enterprises\": 364, \"Number of valid samples\": 362, \"Median government subsidy (yuan)\": 10019029.08, \"Median operating profit margin (%)\": 10.0, \"Valid enterprises in policy-covered provinces\": 233, \"High subsidy low output enterprises\": 54, \"Proportion (%)\": 23.18}"
+}

assets/qa_gold/comprehensive_decision/hard005.json

@@ -2,7 +2,7 @@
   "id": "hard005",
   "question": "In 2022, for the information transmission, software and information technology services industry, an industry research institute sought to obtain a policy-adjusted comprehensive innovation efficiency indicator by superimposing the incentive effect of local policy support on top of raw innovation efficiency. The calculation logic is as follows: first, exclude from enterprise microdata any samples with missing R&D expenditure or annual domestic invention patent grants; for the remaining valid enterprises, aggregate by province and calculate the ratio of total invention patent grants to total R&D expenditure (converted to 100 million yuan) for each province as the province's raw innovation efficiency benchmark; then use the proportion of policy items in that province out of all information technology policies as the policy support coefficient, and multiply the raw efficiency benchmark by (1 plus the policy support coefficient) to obtain the final policy-adjusted innovation efficiency. Among all provinces with data, what is the specific value of the province with the highest adjusted efficiency?",
   "guidelines": "The answer should be a numerical value with 2 decimal places. Output only the number, without units or text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 63.74,
+  "answer": "63.74",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -18,13 +18,5 @@
     "Calculate policy-adjusted innovation efficiency = raw innovation efficiency × (1 + policy support coefficient), sort by adjusted efficiency descending. Hong Kong Special Administrative Region has the highest: raw efficiency 63.7360, policy support coefficient 0.0000, adjusted efficiency = 63.7360."
   ],
   "steps_num": 7,
-  "milestone": {
-    "Total information technology-related policies": 206,
-    "Total information technology services enterprises": 644,
-    "Number of valid enterprises": 432,
-    "Number of valid provinces": 28,
-    "Hong Kong SAR raw innovation efficiency": 63.736,
-    "Hong Kong SAR policy support coefficient": 0.0,
-    "Hong Kong SAR adjusted innovation efficiency": 63.74
-  }
-}
+  "milestone": "{\"Total information technology-related policies\": 206, \"Total information technology services enterprises\": 644, \"Number of valid enterprises\": 432, \"Number of valid provinces\": 28, \"Hong Kong SAR raw innovation efficiency\": 63.736, \"Hong Kong SAR policy support coefficient\": 0.0, \"Hong Kong SAR adjusted innovation efficiency\": 63.74}"
+}

assets/qa_gold/comprehensive_decision/hard006.json

@@ -2,10 +2,7 @@
   "id": "hard006",
   "question": "In 2022, to measure the impact of different ownership backgrounds on the operating performance of specialized equipment manufacturing enterprises, an analysis team compared each enterprise's return on equity (ROE) level with the industry-wide return level in its province to calculate \"excess ROE\" as a relative performance indicator. Specifically: enterprise ROE is calculated as net profit divided by net assets (total assets minus total liabilities) multiplied by 100%; provincial industry benchmark ROE is extracted from provincial industry summary tables, calculated as total industry net profit divided by total industry net assets (total assets minus total liabilities) multiplied by 100%; each enterprise's excess ROE is the difference between its own ROE and its province's benchmark ROE. After grouping by ownership type, which ownership category has the highest mean excess ROE among enterprises? What is that mean value in percentage points?",
   "guidelines": "The answer should be a percentage value with 2 decimal places. Output only the number, without the % symbol or text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": [
-    "Collective Enterprise",
-    5.17
-  ],
+  "answer": "[\"Collective Enterprise\", 5.17]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -21,12 +18,5 @@
     "Group by ownership to calculate mean excess ROE for each ownership type; 6 ownership types. Collective enterprises (2 enterprises) have the highest mean excess ROE = 5.17%."
   ],
   "steps_num": 7,
-  "milestone": {
-    "Total specialized equipment manufacturing enterprises": 447,
-    "Number of valid enterprises": 444,
-    "Enterprises matched with provincial data": 389,
-    "Number of ownership types": 6,
-    "Collective enterprise count": 2,
-    "Collective enterprise mean excess ROE (%)": 5.17
-  }
-}
+  "milestone": "{\"Total specialized equipment manufacturing enterprises\": 447, \"Number of valid enterprises\": 444, \"Enterprises matched with provincial data\": 389, \"Number of ownership types\": 6, \"Collective enterprise count\": 2, \"Collective enterprise mean excess ROE (%)\": 5.17}"
+}

assets/qa_gold/comprehensive_decision/hard007.json

@@ -2,7 +2,7 @@
   "id": "hard007",
   "question": "In 2022, when a research institution was reviewing the implementation effectiveness of provincial industrial policies in the food and beverage industry, it found that although some provinces had issued many support policies, the profitability of enterprises within their jurisdictions was not ideal. To identify such \"policy-heavy, low-return\" provinces, the institution planned to analyze separately those provinces with a relatively large number of policies (including national-level policies, totaling 3 or more): sum the operating profit amounts of all food and beverage industry enterprises in these provinces and divide by the sum of operating revenue amounts to obtain the comprehensive operating profit margin for each province, then identify the province with the lowest profit margin. What is the profit margin value (as a percentage, rounded to two decimal places) for the province with the lowest comprehensive operating profit margin?",
   "guidelines": "The answer should be a percentage value with 2 decimal places. Output only the number, without the % symbol or text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 0.59,
+  "answer": "0.59",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -18,22 +18,5 @@
     "Among the 9 provinces with >=3 policies, sort by operating profit margin in ascending order; the province with the lowest operating profit margin is Hainan, with total operating profit of 78,834,917.61 yuan, total operating revenue of 13,274,274,000.99 yuan, and operating profit margin = 0.59%."
   ],
   "steps_num": 7,
-  "milestone": {
-    "Total food and beverage industry policies": 16,
-    "Provinces with >=3 policies": [
-      "Yunnan",
-      "Sichuan",
-      "Ningxia",
-      "Hebei",
-      "Henan",
-      "Hainan",
-      "Hunan",
-      "Gansu",
-      "Guizhou"
-    ],
-    "Number of valid enterprises": 247,
-    "Hainan total operating profit": 78834917.61,
-    "Hainan total operating revenue": 13274274000.99,
-    "Hainan operating profit margin (%)": 0.59
-  }
-}
+  "milestone": "{\"Total food and beverage industry policies\": 16, \"Provinces with >=3 policies\": [\"Yunnan\", \"Sichuan\", \"Ningxia\", \"Hebei\", \"Henan\", \"Hainan\", \"Hunan\", \"Gansu\", \"Guizhou\"], \"Number of valid enterprises\": 247, \"Hainan total operating profit\": 78834917.61, \"Hainan total operating revenue\": 13274274000.99, \"Hainan operating profit margin (%)\": 0.59}"
+}

assets/qa_gold/comprehensive_decision/hard008.json

@@ -2,7 +2,7 @@
   "id": "hard008",
   "question": "In 2022, a private equity institution sought to identify high-quality provinces in the electricity, heat, gas and water production and supply industry that combine growth potential, market undervaluation, and innovation resilience. The screening logic has three layers: The first layer requires that the median year-over-year change in operating revenue of enterprises within the province be positive (>0%), to exclude regions where revenue is already shrinking; The second layer, based on the first layer results, further requires that the province's market valuation level be relatively low, i.e., the P/S ratio of all enterprises in the province must be lower than the median P/S ratio across all provinces nationwide (national median is calculated from the provincial P/S ratio series); The third layer adds an innovation requirement, i.e., the mean R&D investment ratio of enterprises in the province must be higher than the mean of all enterprises in the industry with R&D investment ratio records. How many provinces satisfy all three conditions simultaneously?",
   "guidelines": "The answer should be an integer. Output only the number, without units or text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 4,
+  "answer": "4",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -18,12 +18,5 @@
     "Among high-growth, low-valuation provinces, further filter provinces where average enterprise R&D investment ratio > 1.1470%; 4 provinces ultimately satisfy all three conditions: ['Guangdong', 'Shanghai', 'Henan', 'Hebei']."
   ],
   "steps_num": 7,
-  "milestone": {
-    "Number of valid provinces": 16,
-    "National median P/S ratio": 1.024358,
-    "National average enterprise R&D investment ratio (%)": 1.147,
-    "Number of high-growth provinces": 15,
-    "Number of high-growth, low-valuation provinces": 7,
-    "Number of provinces meeting all conditions": 4
-  }
-}
+  "milestone": "{\"Number of valid provinces\": 16, \"National median P/S ratio\": 1.024358, \"National average enterprise R&D investment ratio (%)\": 1.147, \"Number of high-growth provinces\": 15, \"Number of high-growth, low-valuation provinces\": 7, \"Number of provinces meeting all conditions\": 4}"
+}

assets/qa_gold/comprehensive_decision/hard009.json

@@ -2,7 +2,7 @@
   "id": "hard009",
   "question": "In 2022, an investment manager at a merger and acquisition fund was seeking \"high R&D, low valuation\" M&A targets in the textile, footwear and apparel industry, but the scope was limited to provinces covered by textile, footwear and apparel industry-related policies. The prerequisite for screening valid enterprises is: net profit amount strictly greater than zero, and both R&D investment ratio and company market cap fields have data records. On this basis, first use all valid enterprises in the industry as the benchmark population to calculate the median R&D investment ratio and the median P/E ratio respectively; then from the subset of valid enterprises located in policy-covered provinces, filter enterprises whose R&D investment ratio is higher than the industry median and whose P/E ratio is lower than the industry median. How many enterprises satisfy the above dual screening conditions? (P/E ratio = company market cap (100 million yuan) ÷ net profit amount (100 million yuan))",
   "guidelines": "The answer should be an integer. Output only the number, without units or text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 9,
+  "answer": "9",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -19,13 +19,5 @@
     "Among the 29 valid enterprises in policy-covered provinces, filter enterprises with R&D investment ratio > 2.8 and P/E < 23.13; 9 enterprises in total."
   ],
   "steps_num": 8,
-  "milestone": {
-    "Number of policy-covered provinces": 11,
-    "Total textile, footwear and apparel enterprises": 177,
-    "Number of valid enterprises": 81,
-    "Median R&D investment ratio": 2.8,
-    "Median P/E": 23.13,
-    "Valid enterprises in policy-covered provinces": 29,
-    "Number of enterprises meeting conditions": 9
-  }
-}
+  "milestone": "{\"Number of policy-covered provinces\": 11, \"Total textile, footwear and apparel enterprises\": 177, \"Number of valid enterprises\": 81, \"Median R&D investment ratio\": 2.8, \"Median P/E\": 23.13, \"Valid enterprises in policy-covered provinces\": 29, \"Number of enterprises meeting conditions\": 9}"
+}

assets/qa_gold/comprehensive_decision/hard010.json

@@ -2,7 +2,7 @@
   "id": "hard010",
   "question": "In 2022, to quantify the comprehensive competitive strength of the construction industry across regions, an industry association constructed a provincial competitiveness index system. The index is composed of four weighted sub-dimensions: market size share of national total (weight 30%), asset operation efficiency i.e. operating profit to total assets ratio (weight 30%), technology accumulation level i.e. cumulative invention patent grants to number of enterprises in jurisdiction ratio (weight 20%), and talent structure i.e. R&D personnel as share of total employees (weight 20%). The four raw indicators are each min-max normalized across all valid provinces, then weighted and summed to obtain the final index. Only provinces with data records for all four indicators are included in the calculation. Finally, please calculate the index difference between the first-ranked province and the last-ranked province in the competitiveness index ranking (rounded to two decimal places).",
   "guidelines": "The answer should be a numeric value with 2 decimal places. Output only the number, without units or text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 0.71,
+  "answer": "0.71",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -18,13 +18,5 @@
     "Sort by competitiveness index in descending order; the difference between first-ranked Beijing (0.7287) and last-ranked Liaoning (0.0142) = 0.7145."
   ],
   "steps_num": 7,
-  "milestone": {
-    "Number of valid provinces": 16,
-    "National total construction enterprises": 148,
-    "First-ranked province": "Beijing",
-    "First-ranked score": 0.7287,
-    "Last-ranked province": "Liaoning",
-    "Last-ranked score": 0.0142,
-    "Difference": 0.7145
-  }
-}
+  "milestone": "{\"Number of valid provinces\": 16, \"National total construction enterprises\": 148, \"First-ranked province\": \"Beijing\", \"First-ranked score\": 0.7287, \"Last-ranked province\": \"Liaoning\", \"Last-ranked score\": 0.0142, \"Difference\": 0.7145}"
+}

assets/qa_gold/comprehensive_decision/hard011.json

@@ -2,7 +2,7 @@
   "id": "hard011",
   "question": "In 2022, a think tank was commissioned to study the impact of policy intervention on R&D behavior in the communication transmission equipment industry. The research design divides all enterprises with R&D investment ratio data records into two groups: one group from provinces that have appeared in policy release information with \"Communication Transmission Equipment\" related policy entries (\"National\" level entries do not count as provinces and are not included in either group); the other group from provinces that have never appeared in the above policy entries. After grouping, calculate the arithmetic mean of R&D investment ratio for each group respectively, then compute the difference between them (policy-covered provinces mean minus non-policy-covered provinces mean). This difference reflects the association between policy coverage and R&D intensity of communication transmission equipment enterprises within the jurisdiction. What is this difference in percentage points?",
   "guidelines": "The answer should be a numeric value with 2 decimal places. A positive number indicates policy-covered provinces are higher; a negative number indicates non-policy-covered provinces are higher. Output only the number, without units or text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 4.9,
+  "answer": "4.9",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -18,33 +18,5 @@
     "Calculate difference = policy-covered average R&D ratio - non-policy-covered average R&D ratio = 14.35 - 9.45 = 4.90 percentage points."
   ],
   "steps_num": 7,
-  "milestone": {
-    "Number of communication transmission equipment policies": 70,
-    "Policy-covered provinces": [
-      "Anhui",
-      "Shandong",
-      "Guangdong",
-      "Sichuan",
-      "Hubei",
-      "Fujian",
-      "Jiangxi",
-      "Chongqing",
-      "Hunan",
-      "Yunnan",
-      "Guizhou",
-      "Henan",
-      "Shaanxi",
-      "Hainan",
-      "Beijing",
-      "Shanghai",
-      "Xinjiang"
-    ],
-    "Number of communication transmission equipment enterprises": 120,
-    "Number of valid enterprises": 117,
-    "Enterprises in policy-covered provinces": 86,
-    "Enterprises in non-policy-covered provinces": 31,
-    "Policy-covered average R&D ratio (%)": 14.35,
-    "Non-policy-covered average R&D ratio (%)": 9.45,
-    "Difference (percentage points)": 4.9
-  }
-}
+  "milestone": "{\"Number of communication transmission equipment policies\": 70, \"Policy-covered provinces\": [\"Anhui\", \"Shandong\", \"Guangdong\", \"Sichuan\", \"Hubei\", \"Fujian\", \"Jiangxi\", \"Chongqing\", \"Hunan\", \"Yunnan\", \"Guizhou\", \"Henan\", \"Shaanxi\", \"Hainan\", \"Beijing\", \"Shanghai\", \"Xinjiang\"], \"Number of communication transmission equipment enterprises\": 120, \"Number of valid enterprises\": 117, \"Enterprises in policy-covered provinces\": 86, \"Enterprises in non-policy-covered provinces\": 31, \"Policy-covered average R&D ratio (%)\": 14.35, \"Non-policy-covered average R&D ratio (%)\": 9.45, \"Difference (percentage points)\": 4.9}"
+}

assets/qa_gold/comprehensive_decision/hard012.json

@@ -2,7 +2,7 @@
   "id": "hard012",
   "question": "In 2022, an antitrust research team analyzed the provincial market structure of the metal smelting and rolling processing industry. To ensure statistical reliability, only provinces with operating revenue amount records and at least 5 enterprises in the industry within the jurisdiction were included. Among qualifying provinces, the Herfindahl-Hirschman Index (HHI) was used to measure market concentration in each province: calculate each enterprise's operating revenue as a share of total operating revenue of all valid enterprises in the province, sum the squares of these shares and multiply by 100% to obtain the province's HHI value. Higher HHI indicates more concentrated markets and greater monopoly risk. After identifying the province with the highest HHI, extract the province's total operating profit amount and total operating revenue amount from provincial industry summary data, and calculate the corresponding operating profit margin. What is the operating profit margin of the province with the highest HHI?",
   "guidelines": "The answer should be a percentage value with 2 decimal places. Output only the number, without the % symbol or text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 4.14,
+  "answer": "4.14",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -18,12 +18,5 @@
     "The province with the highest HHI is Shanghai, HHI = 88.47. Total operating profit of valid enterprises in this province is 16,186,839,594.21 yuan, total operating revenue is 391,233,407,230.44 yuan; operating profit margin = 4.14%."
   ],
   "steps_num": 7,
-  "milestone": {
-    "Number of metal smelting and rolling processing enterprises": 145,
-    "Enterprises with non-null operating revenue": 111,
-    "Number of valid provinces (enterprises >= 5)": 13,
-    "Province with highest HHI": "Shanghai",
-    "HHI value": 88.47,
-    "Shanghai operating profit margin (%)": 4.14
-  }
-}
+  "milestone": "{\"Number of metal smelting and rolling processing enterprises\": 145, \"Enterprises with non-null operating revenue\": 111, \"Number of valid provinces (enterprises >= 5)\": 13, \"Province with highest HHI\": \"Shanghai\", \"HHI value\": 88.47, \"Shanghai operating profit margin (%)\": 4.14}"
+}

assets/qa_gold/comprehensive_decision/hard013.json

@@ -2,10 +2,7 @@
   "id": "hard013",
   "question": "In 2022, a provincial industry and information department sought to evaluate the government subsidy utilization efficiency of enterprises of different sizes in the rubber and plastic products industry. After dividing enterprises into three groups by total assets—large (top 1/3 rounded up), medium (middle 1/3 rounded up), and small (bottom 1/3)—which enterprise size group has the highest subsidy utilization efficiency? What is its efficiency value?",
   "guidelines": "The answer should be a numeric value with 2 decimal places. Output only the number, without units or text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": [
-    "Large",
-    166.33
-  ],
+  "answer": "[\"Large\", 166.33]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -21,15 +18,5 @@
     "The group with the highest subsidy utilization efficiency is the large enterprise group, efficiency value = 166.33."
   ],
   "steps_num": 7,
-  "milestone": {
-    "National total rubber and plastic enterprises": 107,
-    "Number of valid enterprises": 107,
-    "Small group enterprise count": 35,
-    "Medium group enterprise count": 36,
-    "Large group enterprise count": 36,
-    "Small group efficiency": 95.58,
-    "Medium group efficiency": 161.13,
-    "Large group efficiency": 166.33,
-    "Highest efficiency group": "Large"
-  }
-}
+  "milestone": "{\"National total rubber and plastic enterprises\": 107, \"Number of valid enterprises\": 107, \"Small group enterprise count\": 35, \"Medium group enterprise count\": 36, \"Large group enterprise count\": 36, \"Small group efficiency\": 95.58, \"Medium group efficiency\": 161.13, \"Large group efficiency\": 166.33, \"Highest efficiency group\": \"Large\"}"
+}

assets/qa_gold/comprehensive_decision/hard014.json

@@ -2,7 +2,7 @@
   "id": "hard014",
   "question": "In 2022, a technology innovation fund evaluated the \"R&D-patent conversion\" full-chain efficiency of the consumer electronics and electrical industry across provinces, seeking to identify the province with optimal conversion efficiency (only provinces with valid enterprise count >= 3 are included). What is the R&D-patent conversion efficiency value of that province? (R&D-patent conversion efficiency = sum of annual Chinese invention patent grants / sum of annual Chinese invention patent applications × R&D output density; R&D output density = sum of annual Chinese invention patent applications / sum of R&D investment amount (100 million yuan))",
   "guidelines": "The answer should be a numeric value with 2 decimal places. Output only the number, without units or text. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 47.29,
+  "answer": "47.29",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -18,13 +18,5 @@
     "Sort by conversion efficiency in descending order; the province with the highest conversion efficiency is Shandong, with 24,694 patent applications, 15,143 patent grants, R&D output density 77.1105, conversion efficiency = 47.29."
   ],
   "steps_num": 7,
-  "milestone": {
-    "National total consumer electronics and electrical enterprises": 358,
-    "Number of valid enterprises": 266,
-    "Number of valid provinces": 13,
-    "Shandong patent applications": 24694.0,
-    "Shandong patent grants": 15143.0,
-    "Shandong R&D output density": 77.1105,
-    "Shandong conversion efficiency": 47.29
-  }
-}
+  "milestone": "{\"National total consumer electronics and electrical enterprises\": 358, \"Number of valid enterprises\": 266, \"Number of valid provinces\": 13, \"Shandong patent applications\": 24694.0, \"Shandong patent grants\": 15143.0, \"Shandong R&D output density\": 77.1105, \"Shandong conversion efficiency\": 47.29}"
+}

assets/qa_gold/comprehensive_decision/hard015.json

@@ -2,7 +2,7 @@
   "id": "hard015",
   "question": "In 2022, a provincial government evaluated the comprehensive financial health of real estate enterprises to decide which provinces (where the province has an effective enterprise count >= 3) should face strengthened risk supervision for real estate firms. What is the health score of the province with the lowest financial health? (Financial health = Profitability score × 0.4 + Solvency score × 0.3 + Growth capability score × 0.3; Profitability is measured by the average net profit margin of enterprises in that province, where net profit margin = net profit amount / operating revenue amount; Solvency is measured as 1 − the arithmetic mean of enterprises' asset-liability ratio in that province / 100; Growth capability is measured as the median of enterprises' year-over-year change in operating revenue in that province / 100; each indicator is min-max normalized across all valid provinces before being substituted into the formula.)",
   "guidelines": "The answer should be a numerical value with 2 decimal places. Output only the number without units or text explanation. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 0.07,
+  "answer": "0.07",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -19,13 +19,5 @@
     "Sort the 17 valid provinces by financial health ascending; the lowest is Beijing: raw profitability ≈ −1.0774, raw solvency ≈ −21.9888, raw growth capability ≈ −0.1341, health score ≈ 0.0713, rounded to two decimals as 0.07."
   ],
   "steps_num": 8,
-  "milestone": {
-    "real_estate_enterprises_profile_merged_2022": 295,
-    "enterprise_records_province_aggregation_no_al_ratio_exclusion": 295,
-    "valid_provinces_count_enterprises_ge_3": 17,
-    "Beijing_raw_profitability": -1.0774,
-    "Beijing_raw_solvency": -21.9888,
-    "Beijing_raw_growth_capability": -0.1341,
-    "Beijing_financial_health": 0.0713
-  }
-}
+  "milestone": "{\"real_estate_enterprises_profile_merged_2022\": 295, \"enterprise_records_province_aggregation_no_al_ratio_exclusion\": 295, \"valid_provinces_count_enterprises_ge_3\": 17, \"Beijing_raw_profitability\": -1.0774, \"Beijing_raw_solvency\": -21.9888, \"Beijing_raw_growth_capability\": -0.1341, \"Beijing_financial_health\": 0.0713}"
+}

assets/qa_gold/comprehensive_decision/hard016.json

@@ -2,12 +2,7 @@
   "id": "hard016",
   "question": "In 2022, an institutional investor plans to build an equity portfolio among Haishan Chang Industrial Equipment Company, Zhongbai Jinmao Chain Company, and Sansan Dateng Heavy Industry Company. The total portfolio weight must equal 1, the portfolio-weighted asset-liability ratio must equal exactly 45%, and the portfolio-weighted year-on-year operating revenue change must equal exactly 0%. Based on these three companies' 2022 operating data, find their portfolio weights and compute the portfolio-weighted ROE. Note: each company's asset-liability ratio is computed as total liabilities ÷ total assets × 100%.",
   "guidelines": "Answer format: weight of Haishan Chang Industrial Equipment Company, weight of Zhongbai Jinmao Chain Company, weight of Sansan Dateng Heavy Industry Company, weighted ROE. The first three weights to four decimal places; weighted ROE to three decimal places. Output numbers and commas only, with no explanatory text. If relevant data cannot be found, answer \"No relevant data found\".",
-  "answer": [
-    0.1318,
-    0.4954,
-    0.3728,
-    10.376
-  ],
+  "answer": "[0.1318, 0.4954, 0.3728, 10.376]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -22,14 +17,5 @@
     "Compute portfolio-weighted ROE=15.2002%×0.13180488+11.5040%×0.49541694+7.1713%×0.37277818; the result is 10.376%."
   ],
   "steps_num": 6,
-  "milestone": {
-    "Firm count": 3,
-    "Haishan Chang Industrial Equipment Company asset-liability ratio": 86.388,
-    "Zhongbai Jinmao Chain Company asset-liability ratio": 57.6074,
-    "Sansan Dateng Heavy Industry Company asset-liability ratio": 13.6112,
-    "Haishan Chang Industrial Equipment Company weight": 0.1318,
-    "Zhongbai Jinmao Chain Company weight": 0.4954,
-    "Sansan Dateng Heavy Industry Company weight": 0.3728,
-    "Portfolio-weighted ROE": 10.376
-  }
-}
+  "milestone": "{\"Firm count\": 3, \"Haishan Chang Industrial Equipment Company asset-liability ratio\": 86.388, \"Zhongbai Jinmao Chain Company asset-liability ratio\": 57.6074, \"Sansan Dateng Heavy Industry Company asset-liability ratio\": 13.6112, \"Haishan Chang Industrial Equipment Company weight\": 0.1318, \"Zhongbai Jinmao Chain Company weight\": 0.4954, \"Sansan Dateng Heavy Industry Company weight\": 0.3728, \"Portfolio-weighted ROE\": 10.376}"
+}

assets/qa_gold/comprehensive_decision/hard017.json

@@ -2,7 +2,7 @@
   "id": "hard017",
   "question": "In 2022, in the chemical raw materials and chemical products manufacturing industry covered by the Implementation Plan for \"Three Products\" in Raw Materials Industry, Hualu Runyuan Technology Co., Ltd. plans to restore profitability through product upgrade and price increases. After implementing the \"Three Products\" reforms, the company can obtain two types of certain profit improvements: one from process and quality improvement, equal to 1.5% of that year's operating revenue; the other from special support and subsidies. Assuming sales volume is unchanged, price increases have no effect on costs, and the goal is to bring net profit exactly to zero, find the minimum price increase rate required based on the company's 2022 operating data and policy information.",
   "guidelines": "Answer format: minimum price increase rate. Four decimal places. Output the number only, no percent sign or text. If relevant data cannot be found, answer \"No relevant data found\".",
-  "answer": 14.2792,
+  "answer": "14.2792",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -17,12 +17,5 @@
     "Under the assumption that sales volume is unchanged and all price-increase revenue flows to profit, minimum price increase rate = 94,858,485.41 / 664,310,105.79 = 14.2792%."
   ],
   "steps_num": 6,
-  "milestone": {
-    "Operating revenue (yuan)": 664310105.79,
-    "Net profit (yuan)": -109823137,
-    "Process improvement profit gain (yuan)": 9964651.59,
-    "Subsidy profit gain (yuan)": 5000000,
-    "Remaining profit gap (yuan)": 94858485.41,
-    "Minimum price increase rate (%)": 14.2792
-  }
-}
+  "milestone": "{\"Operating revenue (yuan)\": 664310105.79, \"Net profit (yuan)\": -109823137, \"Process improvement profit gain (yuan)\": 9964651.59, \"Subsidy profit gain (yuan)\": 5000000, \"Remaining profit gap (yuan)\": 94858485.41, \"Minimum price increase rate (%)\": 14.2792}"
+}

assets/qa_gold/comprehensive_decision/hard018.json

@@ -18,13 +18,5 @@
     "Adjusted R&D ratio = 53,456,098.35 / 1,082,827,667.36 × 100% = 4.9367%. Compared with disclosed 5.37%, decline = 5.37% − 4.9367% = 0.4333%, or 43.33 basis points."
   ],
   "steps_num": 7,
-  "milestone": {
-    "Total employees": 1276,
-    "R&D personnel count": 290,
-    "Non-R&D employee count": 986,
-    "Total new subsidies (yuan)": 4901000,
-    "Adjusted R&D investment (yuan)": 53456098.35,
-    "Adjusted R&D ratio (%)": 4.9367,
-    "Decline (basis points)": 43.33
-  }
-}
+  "milestone": "{\"Total employees\": 1276, \"R&D personnel count\": 290, \"Non-R&D employee count\": 986, \"Total new subsidies (yuan)\": 4901000, \"Adjusted R&D investment (yuan)\": 53456098.35, \"Adjusted R&D ratio (%)\": 4.9367, \"Decline (basis points)\": 43.33}"
+}

assets/qa_gold/comprehensive_decision/hard019.json

@@ -2,11 +2,7 @@
   "id": "hard019",
   "question": "2022年，以山安泽医疗科技公司具有“微生态活菌业务”和“高温合金业务”双主业特征。某基金经理希望用以山安泽医疗科技公司与三三达腾重工公司构建一个两股票组合，来替代连机创机机床公司的增长暴露，并进一步检验该替代组合在剔除补贴后的盈利质量与研发强度溢价。若组合要求加权营业收入同比增减幅恰好等于连机创机机床公司2022年的对应指标，请基于本地数据计算：以山安泽医疗科技公司的组合权重、剔除政府奖励资金和补贴后的组合加权净利率，以及该组合研发投入占比相对连机创机机床公司高出的基点数。",
   "guidelines": "答案格式为：以山安泽医疗科技公司权重,剔除补贴后的组合加权净利率,研发投入占比高出的基点数。前两项按百分比口径保留2位小数，最后一项保留2位小数。仅输出数字和逗号，不要添加单位或文字说明。如无法找到相关数据，请回答“未查询到相关数据”。",
-  "answer": [
-    38.58,
-    14.82,
-    708.28
-  ],
+  "answer": "[38.58, 14.82, 708.28]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "hard",
@@ -21,13 +17,5 @@
     "按组合权重计算组合研发投入占比=38.5757%×18.11%+61.4243%×8.90%=12.4528%。连机创机机床公司研发投入占比字段值为5.37%，因此组合研发投入占比高出12.4528%-5.37%=7.0828个百分点，即708.28个基点。"
   ],
   "steps_num": 6,
-  "milestone": {
-    "以山安泽医疗科技公司权重(%)": 38.58,
-    "三三达腾重工公司权重(%)": 61.42,
-    "以山安泽医疗科技公司剔除补贴后净利率(%)": 9.89,
-    "三三达腾重工公司剔除补贴后净利率(%)": 17.91,
-    "剔除补贴后的组合加权净利率(%)": 14.82,
-    "组合研发投入占比(%)": 12.45,
-    "相对连机创机机床公司高出的基点数": 708.28
-  }
-}
+  "milestone": "{\"以山安泽医疗科技公司权重(%)\": 38.58, \"三三达腾重工公司权重(%)\": 61.42, \"以山安泽医疗科技公司剔除补贴后净利率(%)\": 9.89, \"三三达腾重工公司剔除补贴后净利率(%)\": 17.91, \"剔除补贴后的组合加权净利率(%)\": 14.82, \"组合研发投入占比(%)\": 12.45, \"相对连机创机机床公司高出的基点数\": 708.28}"
+}

assets/qa_gold/comprehensive_decision/medium001.json

@@ -2,10 +2,7 @@
   "id": "medium001",
   "question": "For 2022 pharmaceutical manufacturing industry data by province, if R&D funding intensity is measured as each province's total R&D expenditure as a percentage of its total operating revenue, among all provinces with complete data records, what is the specific value of this ratio for the province with the highest level? Which company has the highest R&D funding intensity in that province?",
   "guidelines": "The first answer is a numeric value (2 decimal places), unit is %; the second answer is the full company name, which must exactly match the \"Company Name\" field in company_profile.csv. If either question cannot be answered, respond with \"No relevant data found\".",
-  "answer": [
-    25.48,
-    "Kangsheng Anjian Biopharmaceutical Company"
-  ],
+  "answer": "[25.48, \"Kangsheng Anjian Biopharmaceutical Company\"]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "medium",
@@ -19,14 +16,5 @@
     "For each company, compute R&D funding intensity = R&D expenditure ÷ operating revenue × 100% using the same formula. The highest is \"Kangsheng Anjian Biopharmaceutical Company\" (bmCode=505404): R&D expenditure 809,733,452.00 yuan, operating revenue 12,792,315.00 yuan, intensity = 809,733,452.00 ÷ 12,792,315.00 × 100% = 6329.84%."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Province with highest R&D funding intensity": "Shanghai",
-    "Shanghai total R&D expenditure (yuan)": 40798081760.73,
-    "Shanghai total operating revenue (yuan)": 160133198188.25,
-    "Provincial R&D funding intensity (%)": 25.48,
-    "Company with highest R&D funding intensity in that province": "Kangsheng Anjian Biopharmaceutical Company",
-    "Company R&D expenditure (yuan)": 809733452.0,
-    "Company operating revenue (yuan)": 12792315.0,
-    "Company R&D funding intensity (%)": 6329.84
-  }
-}
+  "milestone": "{\"Province with highest R&D funding intensity\": \"Shanghai\", \"Shanghai total R&D expenditure (yuan)\": 40798081760.73, \"Shanghai total operating revenue (yuan)\": 160133198188.25, \"Provincial R&D funding intensity (%)\": 25.48, \"Company with highest R&D funding intensity in that province\": \"Kangsheng Anjian Biopharmaceutical Company\", \"Company R&D expenditure (yuan)\": 809733452.0, \"Company operating revenue (yuan)\": 12792315.0, \"Company R&D funding intensity (%)\": 6329.84}"
+}

assets/qa_gold/comprehensive_decision/medium002.json

@@ -2,10 +2,7 @@
   "id": "medium002",
   "question": "In 2022, a semiconductor company plans to expand production and wishes to locate in the province with the highest enterprise concentration to gain industrial synergy effects. What is the proportion of that province's semiconductor industry enterprise count to the national total? What proportion does that province's total operating profit in the semiconductor industry account for of the national semiconductor industry's total operating profit?",
   "guidelines": "Two answers required, both numeric values (2 decimal places), unit is %. If relevant data cannot be found, respond with \"No relevant data found\".",
-  "answer": [
-    31.4,
-    6.22
-  ],
+  "answer": "[31.4, 6.22]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "medium",
@@ -19,13 +16,5 @@
     "Compute that province's semiconductor industry operating profit share = Guangdong total operating profit / national total operating profit × 100% = 25,562,691,329.46 / 411,298,557,285.26 × 100% = 6.22%."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Province with highest enterprise concentration": "Guangdong Province",
-    "Guangdong semiconductor industry enterprise count": 54,
-    "National semiconductor industry enterprise count": 172,
-    "Enterprise concentration (%)": 31.4,
-    "Guangdong semiconductor industry total operating profit (yuan)": 25562691329.46,
-    "National semiconductor industry total operating profit (yuan)": 411298557285.26,
-    "Guangdong operating profit share (%)": 6.22
-  }
-}
+  "milestone": "{\"Province with highest enterprise concentration\": \"Guangdong Province\", \"Guangdong semiconductor industry enterprise count\": 54, \"National semiconductor industry enterprise count\": 172, \"Enterprise concentration (%)\": 31.4, \"Guangdong semiconductor industry total operating profit (yuan)\": 25562691329.46, \"National semiconductor industry total operating profit (yuan)\": 411298557285.26, \"Guangdong operating profit share (%)\": 6.22}"
+}

assets/qa_gold/comprehensive_decision/medium003.json

@@ -2,10 +2,7 @@
   "id": "medium003",
   "question": "In 2022, measuring per capita output efficiency of automobile manufacturing by province using revenue per capita (total operating revenue ÷ total employee count), among all provinces nationwide, what is the specific value in yuan per person for the province with the highest indicator? Compared to the national average, by what percentage (1 decimal place) is that province's per capita revenue higher?",
   "guidelines": "Two answers required: first is a numeric value (2 decimal places), unit yuan/person; second is a percentage (1 decimal place), unit %. If relevant data cannot be found, respond with \"No relevant data found\".",
-  "answer": [
-    3898878.23,
-    175.0
-  ],
+  "answer": "[3898878.23, 175.0]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "medium",
@@ -19,13 +16,5 @@
     "Compute Beijing's excess over national average = (3,898,878.23 - 1,417,863.20) / 1,417,863.20 × 100% = 174.98%, rounded to 1 decimal place per requirement: 175.0%."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Beijing total operating revenue (yuan)": 341596317171.5,
-    "Beijing total employee count": 87614,
-    "Beijing per capita revenue (yuan/person)": 3898878.23,
-    "National total operating revenue (yuan)": 4614449954119.47,
-    "National total employee count": 3254510,
-    "National average per capita revenue (yuan/person)": 1417863.2,
-    "Beijing excess over national average (%)": 175.0
-  }
-}
+  "milestone": "{\"Beijing total operating revenue (yuan)\": 341596317171.5, \"Beijing total employee count\": 87614, \"Beijing per capita revenue (yuan/person)\": 3898878.23, \"National total operating revenue (yuan)\": 4614449954119.47, \"National total employee count\": 3254510, \"National average per capita revenue (yuan/person)\": 1417863.2, \"Beijing excess over national average (%)\": 175.0}"
+}

assets/qa_gold/comprehensive_decision/medium004.json

@@ -2,10 +2,7 @@
   "id": "medium004",
   "question": "In 2022, rank provinces by profitability in chemical raw materials and chemical products manufacturing. Provincial operating profit margin is computed as total operating profit divided by total operating revenue. Using this as the ranking criterion, what is Guangdong Province's rank? Apply the same ranking to all relevant enterprises within Guangdong Province—which enterprise ranks first?",
   "guidelines": "Two answers required: first is Guangdong Province's rank in the provincial ranking (integer, e.g. \"6\" means 6th place); second is the full name of the top-ranked enterprise in Guangdong, which must match the \"Company Name\" field in company_profile.csv. If relevant data cannot be found, respond with \"No relevant data found\".",
-  "answer": [
-    6,
-    "Hengyi Changhua Technology Co., Ltd."
-  ],
+  "answer": "[6, \"Hengyi Changhua Technology Co., Ltd.\"]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "medium",
@@ -19,14 +16,5 @@
     "For Guangdong enterprises, compute operating profit margin = operating profit / operating revenue × 100% (requiring operating revenue ≠ 0), sort by operating profit margin descending. First place: \"Hengyi Changhua Technology Co., Ltd.\" (bmCode=533611), operating profit margin = 2,190,338,633.59 / 3,466,111,075.75 × 100% = 63.19%."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Guangdong total operating profit (yuan)": 11690448651.68,
-    "Guangdong total operating revenue (yuan)": 101800752670.91,
-    "Guangdong operating profit margin (%)": 11.4837,
-    "Guangdong provincial rank": 6,
-    "Top-ranked enterprise in Guangdong": "Hengyi Changhua Technology Co., Ltd.",
-    "Top enterprise operating profit (yuan)": 2190338633.59,
-    "Top enterprise operating revenue (yuan)": 3466111075.75,
-    "Top enterprise operating profit margin (%)": 63.19
-  }
-}
+  "milestone": "{\"Guangdong total operating profit (yuan)\": 11690448651.68, \"Guangdong total operating revenue (yuan)\": 101800752670.91, \"Guangdong operating profit margin (%)\": 11.4837, \"Guangdong provincial rank\": 6, \"Top-ranked enterprise in Guangdong\": \"Hengyi Changhua Technology Co., Ltd.\", \"Top enterprise operating profit (yuan)\": 2190338633.59, \"Top enterprise operating revenue (yuan)\": 3466111075.75, \"Top enterprise operating profit margin (%)\": 63.19}"
+}

assets/qa_gold/comprehensive_decision/medium005.json

@@ -2,7 +2,7 @@
   "id": "medium005",
   "question": "In 2022, from all private enterprises in the food and beverage industry, aggregate government rewards and subsidy amounts by province of registration to find the province with the highest provincial subsidy total. How many hundred million yuan in government subsidies did private enterprises in that province receive in total?",
   "guidelines": "The answer should be a numerical value (2 decimal places), unit is hundred million yuan. If relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 12.6,
+  "answer": "12.6",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "medium",
@@ -16,8 +16,5 @@
     "Sort provinces by total government subsidies in descending order. The province with the highest total subsidies is Inner Mongolia, with total subsidies of 1,259,874,619.23 yuan; convert from yuan to hundred million yuan (divide by 100,000,000), yielding 12.60 hundred million yuan."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Inner Mongolia food and beverage private enterprise total subsidies (yuan)": 1259874619.23,
-    "Inner Mongolia food and beverage private enterprise total subsidies (hundred million yuan)": 12.6
-  }
-}
+  "milestone": "{\"Inner Mongolia food and beverage private enterprise total subsidies (yuan)\": 1259874619.23, \"Inner Mongolia food and beverage private enterprise total subsidies (hundred million yuan)\": 12.6}"
+}

assets/qa_gold/comprehensive_decision/medium006.json

@@ -15,8 +15,5 @@
     "Zhejiang's value is not greater than the national value, therefore the answer is \"No\"."
   ],
   "steps_num": 4,
-  "milestone": {
-    "Zhejiang average R&D personnel proportion (%)": 18.25,
-    "National average R&D personnel proportion (%)": 20.1
-  }
-}
+  "milestone": "{\"Zhejiang average R&D personnel proportion (%)\": 18.25, \"National average R&D personnel proportion (%)\": 20.1}"
+}

assets/qa_gold/comprehensive_decision/medium007.json

@@ -2,10 +2,7 @@
   "id": "medium007",
   "question": "In 2022, analyze government subsidy leverage in the information transmission, software and information technology services industry. Define government subsidy leverage effect as the ratio of each province's total operating profit to total government subsidies. What is the specific ratio for the province with the highest government subsidy leverage effect? Which enterprise in that province has the highest leverage effect?",
   "guidelines": "Two answers required: first is a numeric value (2 decimal places, unitless ratio); second is the full company name. If relevant data cannot be found, respond with \"No relevant data found\".",
-  "answer": [
-    67.19,
-    "Dongche Kexin Systems Company"
-  ],
+  "answer": "[67.19, \"Dongche Kexin Systems Company\"]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "medium",
@@ -19,14 +16,5 @@
     "Sort enterprises by government subsidy leverage effect descending; top enterprise: Dongche Kexin Systems Company (bmCode=591984); operating profit 20,080,241.24 yuan, government rewards and subsidies 1,391,005.09 yuan, enterprise-level leverage ≈ 14.44; Zhongke Ruanchuang Software Company is excluded from enterprise-level ranking due to missing subsidy data."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Provincial aggregation note": "Aggregate by province from the enterprise table; do not use Jiangxi totals missing from the regional table",
-    "Jiangxi total operating profit (yuan)": 93463073.13,
-    "Jiangxi total government rewards and subsidies (yuan)": 1391005.09,
-    "Jiangxi government subsidy leverage effect": 67.19,
-    "Enterprise with highest government subsidy leverage effect": "Dongche Kexin Systems Company",
-    "Enterprise operating profit (yuan)": 20080241.24,
-    "Enterprise government rewards and subsidies (yuan)": 1391005.09,
-    "Enterprise government subsidy leverage effect": 14.44
-  }
-}
+  "milestone": "{\"Provincial aggregation note\": \"Aggregate by province from the enterprise table; do not use Jiangxi totals missing from the regional table\", \"Jiangxi total operating profit (yuan)\": 93463073.13, \"Jiangxi total government rewards and subsidies (yuan)\": 1391005.09, \"Jiangxi government subsidy leverage effect\": 67.19, \"Enterprise with highest government subsidy leverage effect\": \"Dongche Kexin Systems Company\", \"Enterprise operating profit (yuan)\": 20080241.24, \"Enterprise government rewards and subsidies (yuan)\": 1391005.09, \"Enterprise government subsidy leverage effect\": 14.44}"
+}

assets/qa_gold/comprehensive_decision/medium008.json

@@ -2,7 +2,7 @@
   "id": "medium008",
   "question": "In 2022, to study the capital turnover of central state-owned enterprises in the electricity, heat, gas and water production and supply industry, calculate the asset turnover ratio for each enterprise by dividing its annual operating revenue by its total assets. Find the arithmetic mean of the asset turnover ratios for these enterprises.",
   "guidelines": "The answer should be a numerical value (rounded to 4 decimal places). If the relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 0.3266,
+  "answer": "0.3266",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "medium",
@@ -16,9 +16,5 @@
     "Calculate the average asset turnover ratio for all qualifying central state-owned enterprises = sum of asset turnover ratios (14.369275) / number of enterprises (44) = 0.3266."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Sum of Asset Turnover Ratios": 14.369275,
-    "Number of Valid Enterprises": 44,
-    "Average Asset Turnover Ratio": 0.3266
-  }
-}
+  "milestone": "{\"Sum of Asset Turnover Ratios\": 14.369275, \"Number of Valid Enterprises\": 44, \"Average Asset Turnover Ratio\": 0.3266}"
+}

assets/qa_gold/comprehensive_decision/medium009.json

@@ -16,9 +16,5 @@
     "Sort by average market capitalization in descending order. Shanghai Stock Exchange has the highest average market capitalization, with 49 mining enterprises, total market capitalization of 4,771.8 billion CNY, and average market capitalization of 973.84 billion CNY."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Shanghai Stock Exchange Total Market Capitalization of Mining Enterprises (billion CNY)": 47718.0,
-    "Number of Mining Enterprises on Shanghai Stock Exchange": 49,
-    "Shanghai Stock Exchange Average Market Capitalization of Mining Enterprises (billion CNY)": 973.84
-  }
-}
+  "milestone": "{\"Shanghai Stock Exchange Total Market Capitalization of Mining Enterprises (billion CNY)\": 47718.0, \"Number of Mining Enterprises on Shanghai Stock Exchange\": 49, \"Shanghai Stock Exchange Average Market Capitalization of Mining Enterprises (billion CNY)\": 973.84}"
+}

assets/qa_gold/comprehensive_decision/medium010.json

@@ -2,10 +2,7 @@
   "id": "medium010",
   "question": "In 2022, in the provincial data for the construction industry, each province has an indicator reflecting the average asset-liability ratio (financial leverage level) of enterprises in that province's industry (considering only enterprises with valid total assets and total liabilities). Among the provinces covered by valid data, which province has the lowest value for this mean indicator, and what is that value?",
   "guidelines": "The answer should be a numerical value (rounded to 2 decimal places), in units of %. If the relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": [
-    "Shanxi Province",
-    27.17
-  ],
+  "answer": "[\"Shanxi Province\", 27.17]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "medium",
@@ -19,10 +16,5 @@
     "Sort all provinces by mean asset-liability ratio in ascending order. Shanxi Province has the lowest mean asset-liability ratio of 27.17%."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Total Number of Construction Enterprises": 148,
-    "Number of Valid Enterprises (total liabilities and total assets not empty)": 148,
-    "Number of Valid Provinces": 22,
-    "Shanxi Province Mean Asset-Liability Ratio (%)": 27.17
-  }
-}
+  "milestone": "{\"Total Number of Construction Enterprises\": 148, \"Number of Valid Enterprises (total liabilities and total assets not empty)\": 148, \"Number of Valid Provinces\": 22, \"Shanxi Province Mean Asset-Liability Ratio (%)\": 27.17}"
+}

assets/qa_gold/comprehensive_decision/medium011.json

@@ -2,7 +2,7 @@
   "id": "medium011",
   "question": "In 2022, among all enterprises in the rubber and plastic products industry with R&D investment records, what is the R&D concentration CR5?",
   "guidelines": "The answer should be a numerical value (rounded to 2 decimal places), in units of %. If the relevant data cannot be found, please answer \"No relevant data found\"",
-  "answer": 34.66,
+  "answer": "34.66",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "medium",
@@ -16,9 +16,5 @@
     "Calculate R&D concentration CR5 = (4,221,126,553.77 / 12,179,847,530.98) × 100% = 34.66%."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Sum of R&D Investment Amount of Top 5 Enterprises (CNY)": 4221126553.77,
-    "Total R&D Investment Amount of the Industry (CNY)": 12179847530.98,
-    "R&D Concentration CR5 (%)": 34.66
-  }
-}
+  "milestone": "{\"Sum of R&D Investment Amount of Top 5 Enterprises (CNY)\": 4221126553.77, \"Total R&D Investment Amount of the Industry (CNY)\": 12179847530.98, \"R&D Concentration CR5 (%)\": 34.66}"
+}

assets/qa_gold/comprehensive_decision/medium012.json

@@ -2,10 +2,7 @@
   "id": "medium012",
   "question": "In 2022, among all enterprises in Guangdong Province belonging to the wholesale and retail trade industry, using each enterprise's net profit margin as the comparison standard, what is the indicator value for the enterprise with the highest net profit margin? What is that enterprise's rank among all enterprises in this industry nationwide?",
   "guidelines": "Two answers required: first is a numeric value (2 decimal places, unit %); second is the rank number (integer, e.g. \"7\" means 7th place). If relevant data cannot be found, respond with \"No relevant data found\".",
-  "answer": [
-    31.25,
-    7
-  ],
+  "answer": "[31.25, 7]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "medium",
@@ -19,10 +16,5 @@
     "From company_profile.csv, filter all enterprises nationwide with industry=\"Wholesale and Retail Trade\"; from company_operation_status.csv, take year=2022 with operating revenue > 0 and non-null net profit. Sort nationwide valid enterprises by net profit margin descending; Yonghui Changda Wholesale Company ranks 7th."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Yonghui Changda Wholesale Company net profit (yuan)": 292221119.71,
-    "Yonghui Changda Wholesale Company operating revenue (yuan)": 935248730.59,
-    "Yonghui Changda Wholesale Company net profit margin (%)": 31.25,
-    "Yonghui Changda Wholesale Company nationwide rank": 7
-  }
-}
+  "milestone": "{\"Yonghui Changda Wholesale Company net profit (yuan)\": 292221119.71, \"Yonghui Changda Wholesale Company operating revenue (yuan)\": 935248730.59, \"Yonghui Changda Wholesale Company net profit margin (%)\": 31.25, \"Yonghui Changda Wholesale Company nationwide rank\": 7}"
+}

assets/qa_gold/comprehensive_decision/medium013.json

@@ -2,10 +2,7 @@
   "id": "medium013",
   "question": "In 2022, a scientific research and technical services enterprise wishes to identify the province with the fastest net profit growth in the industry to guide market expansion. What is the indicator value for the province with the highest median year-on-year net profit growth rate in the national scientific research and technical services industry? What is the nationwide rank of the enterprise with the highest such indicator in that province among all enterprises in this industry?",
   "guidelines": "Two answers required: first is a numeric value (2 decimal places, unit %), i.e. the indicator value for the province with the highest \"median year-on-year net profit growth rate\" in this industry nationwide; second is a rank number (integer, e.g. \"23\" means 23rd place), i.e. the nationwide rank of the enterprise with the highest \"year-on-year net profit growth rate\" in that province among all enterprises in this industry. If relevant data cannot be found, respond with \"No relevant data found\".",
-  "answer": [
-    13.81,
-    23
-  ],
+  "answer": "[13.81, 23]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "medium",
@@ -19,11 +16,5 @@
     "From company_profile.csv, filter nationwide enterprises with industry=\"Scientific Research and Technical Services\"; from company_operation_status.csv, filter year=2022 with valid year-on-year net profit growth rate. Sort by year-on-year net profit growth rate descending; Zhongqi Shengyuan Technology Research Institute ranks 23rd nationwide."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Valid province count for scientific research and technical services (non-null median YoY net profit growth)": 16,
-    "Anhui Province median year-on-year net profit growth rate (%)": 13.81,
-    "Enterprise with highest indicator in Anhui Province": "Zhongqi Shengyuan Technology Research Institute",
-    "Enterprise year-on-year net profit growth rate (%)": 32.58,
-    "Enterprise nationwide rank": 23
-  }
-}
+  "milestone": "{\"Valid province count for scientific research and technical services (non-null median YoY net profit growth)\": 16, \"Anhui Province median year-on-year net profit growth rate (%)\": 13.81, \"Enterprise with highest indicator in Anhui Province\": \"Zhongqi Shengyuan Technology Research Institute\", \"Enterprise year-on-year net profit growth rate (%)\": 32.58, \"Enterprise nationwide rank\": 23}"
+}

assets/qa_gold/comprehensive_decision/medium014.json

@@ -2,10 +2,7 @@
   "id": "medium014",
   "question": "In 2022, for the metal smelting and rolling processing industry, among provinces with valid records for both total government subsidies and total industry employee count, per capita subsidy is computed as each province's total government rewards and subsidies divided by that province's industry employee count. What is the per capita subsidy in yuan for the province with the highest per capita subsidy? What is the nationwide rank of the enterprise with the highest such indicator in that province among all enterprises in this industry?",
   "guidelines": "Two answers required: first is a numeric value (2 decimal places, unit yuan/person), i.e. the highest provincial per capita subsidy; second is a rank number (integer), indicating the nationwide rank of the enterprise with the highest indicator in that province among all enterprises in this industry. If relevant data cannot be found, respond with \"No relevant data found\".",
-  "answer": [
-    17569.95,
-    12
-  ],
+  "answer": "[17569.95, 12]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "medium",
@@ -19,12 +16,5 @@
     "Nationwide, for all enterprises in this industry (year=2022, government rewards and subsidies > 0, total employee count > 0), compute per capita subsidy using the same formula and sort descending; Xin Ge Jinze Materials Company ranks 12th nationwide."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Shanghai total government rewards and subsidies (yuan)": 893080778.37,
-    "Shanghai total employee count": 50830.0,
-    "Shanghai per capita subsidy (yuan/person)": 17569.95,
-    "Enterprise with highest indicator in Shanghai": "Xin Ge Jinze Materials Company",
-    "Enterprise per capita subsidy (yuan/person)": 31941.25,
-    "Enterprise nationwide rank": 12
-  }
-}
+  "milestone": "{\"Shanghai total government rewards and subsidies (yuan)\": 893080778.37, \"Shanghai total employee count\": 50830.0, \"Shanghai per capita subsidy (yuan/person)\": 17569.95, \"Enterprise with highest indicator in Shanghai\": \"Xin Ge Jinze Materials Company\", \"Enterprise per capita subsidy (yuan/person)\": 31941.25, \"Enterprise nationwide rank\": 12}"
+}

assets/qa_gold/comprehensive_decision/medium015.json

@@ -2,36 +2,7 @@
   "id": "medium015",
   "question": "List the 2022 indicators for which Shandong Province's financial industry enterprise averages are below the national financial industry medians.",
   "guidelines": "The answer must list all qualifying indicator names, separated by semicolons. If relevant data cannot be found, respond with \"No relevant data found\".",
-  "answer": [
-    "Year-on-year R&D personnel growth rate",
-    "Year-on-year operating profit growth rate",
-    "Year-on-year net profit growth rate",
-    "Year-on-year employee growth rate",
-    "Capitalized R&D expenditure",
-    "Year-on-year capitalized R&D expenditure growth rate",
-    "Annual PCT patent applications",
-    "Annual PCT invention patent applications",
-    "Provincial/ministerial science and technology progress award",
-    "Participation in drafting national standards",
-    "Participation in drafting industry standards",
-    "Annual Chinese patent applications",
-    "Annual Chinese invention patent applications",
-    "Annual Chinese patent grants",
-    "Cumulative Chinese invention patent applications",
-    "Annual Chinese invention patent grants",
-    "Cumulative PCT patent applications",
-    "Cumulative PCT invention patent applications",
-    "Cumulative Chinese patent applications",
-    "Cumulative Chinese invention patent grants",
-    "Cumulative patent citations",
-    "R&D personnel ratio",
-    "R&D personnel count",
-    "Year-on-year R&D expenditure growth rate",
-    "Cumulative Chinese invention patent lapses",
-    "Company market value",
-    "Asset-liability ratio",
-    "Total employee count"
-  ],
+  "answer": "[\"Year-on-year R&D personnel growth rate\", \"Year-on-year operating profit growth rate\", \"Year-on-year net profit growth rate\", \"Year-on-year employee growth rate\", \"Capitalized R&D expenditure\", \"Year-on-year capitalized R&D expenditure growth rate\", \"Annual PCT patent applications\", \"Annual PCT invention patent applications\", \"Provincial/ministerial science and technology progress award\", \"Participation in drafting national standards\", \"Participation in drafting industry standards\", \"Annual Chinese patent applications\", \"Annual Chinese invention patent applications\", \"Annual Chinese patent grants\", \"Cumulative Chinese invention patent applications\", \"Annual Chinese invention patent grants\", \"Cumulative PCT patent applications\", \"Cumulative PCT invention patent applications\", \"Cumulative Chinese patent applications\", \"Cumulative Chinese invention patent grants\", \"Cumulative patent citations\", \"R&D personnel ratio\", \"R&D personnel count\", \"Year-on-year R&D expenditure growth rate\", \"Cumulative Chinese invention patent lapses\", \"Company market value\", \"Asset-liability ratio\", \"Total employee count\"]",
   "metadata": {
     "db": "bm_rag_qa",
     "level": "medium",
@@ -45,21 +16,5 @@
     "Compare each Shandong financial industry indicator average with the corresponding national financial industry median; exclude indicators such as enterprise count and totals that are not suitable for comparison. Filter indicators where Shandong average is below national median; 28 indicators found, mainly including year-on-year R&D personnel growth rate, year-on-year operating profit growth rate, year-on-year net profit growth rate, annual Chinese patent applications, cumulative Chinese patent applications, R&D personnel count, R&D personnel ratio, etc. Final result: Shandong Province financial industry enterprises have 28 indicators with averages below the national financial industry medians."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Shandong Province financial industry enterprise count": 12,
-    "National financial industry enterprise count": 297,
-    "Indicator count below national median": 28,
-    "Main indicator list": [
-      "Year-on-year R&D personnel growth rate",
-      "Year-on-year operating profit growth rate",
-      "Year-on-year net profit growth rate",
-      "Year-on-year employee growth rate",
-      "Capitalized R&D expenditure",
-      "Year-on-year capitalized R&D expenditure growth rate",
-      "Annual PCT patent applications",
-      "Annual PCT invention patent applications",
-      "Provincial/ministerial science and technology progress award",
-      "Participation in drafting national standards"
-    ]
-  }
-}
+  "milestone": "{\"Shandong Province financial industry enterprise count\": 12, \"National financial industry enterprise count\": 297, \"Indicator count below national median\": 28, \"Main indicator list\": [\"Year-on-year R&D personnel growth rate\", \"Year-on-year operating profit growth rate\", \"Year-on-year net profit growth rate\", \"Year-on-year employee growth rate\", \"Capitalized R&D expenditure\", \"Year-on-year capitalized R&D expenditure growth rate\", \"Annual PCT patent applications\", \"Annual PCT invention patent applications\", \"Provincial/ministerial science and technology progress award\", \"Participation in drafting national standards\"]}"
+}

assets/qa_gold/comprehensive_decision/medium016.json

@@ -16,7 +16,5 @@
     "Checked all fields in company_operation_status.csv, company_profile.csv, company_core.csv, etc.; no tax payment related fields found. Since the data files do not contain tax payment information, the tax payments of the two enterprises cannot be compared."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Tax payment field exists": false
-  }
-}
+  "milestone": "{\"Tax payment field exists\": false}"
+}

assets/qa_gold/comprehensive_decision/medium017.json

@@ -16,13 +16,5 @@
     "Compare the enterprise with highest year-on-year R&D expenditure growth rate (Yonghui Zesheng Chain Company) and the enterprise with highest R&D expenditure (Bubusheng Jin Commerce Company); they are not the same enterprise, so the answer is \"No\"."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Enterprise with highest year-on-year R&D expenditure growth rate": "Yonghui Zesheng Chain Company",
-    "Highest growth rate enterprise YoY R&D expenditure growth rate (%)": 2221.3,
-    "Highest growth rate enterprise R&D expenditure (yuan)": 516766.56,
-    "Enterprise with highest R&D expenditure": "Bubusheng Jin Commerce Company",
-    "Highest R&D enterprise R&D expenditure (yuan)": 2800235364.0,
-    "Highest R&D enterprise YoY R&D expenditure growth rate (%)": 11.8,
-    "Is same enterprise": false
-  }
-}
+  "milestone": "{\"Enterprise with highest year-on-year R&D expenditure growth rate\": \"Yonghui Zesheng Chain Company\", \"Highest growth rate enterprise YoY R&D expenditure growth rate (%)\": 2221.3, \"Highest growth rate enterprise R&D expenditure (yuan)\": 516766.56, \"Enterprise with highest R&D expenditure\": \"Bubusheng Jin Commerce Company\", \"Highest R&D enterprise R&D expenditure (yuan)\": 2800235364.0, \"Highest R&D enterprise YoY R&D expenditure growth rate (%)\": 11.8, \"Is same enterprise\": false}"
+}

assets/qa_gold/comprehensive_decision/medium018.json

@@ -16,13 +16,5 @@
     "Compare whether the province with the highest R&D expenditure growth rate (Hong Kong SAR) and the province with the lowest R&D expenditure (Jilin Province) are the same province.Conclusion: The province with the highest R&D expenditure growth rate is not the province with the lowest R&D expenditure; the answer is No."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Province with highest R&D expenditure growth rate": "Hong Kong Special Administrative Region",
-    "Highest R&D expenditure growth rate (%)": 212.35,
-    "Highest growth province total R&D expenditure (yuan)": 14248827635.96,
-    "Province with lowest R&D expenditure": "Jilin Province",
-    "Lowest R&D expenditure (yuan)": 8574294268.49,
-    "Lowest R&D province R&D expenditure growth rate (%)": 16.95,
-    "Is same province": false
-  }
-}
+  "milestone": "{\"Province with highest R&D expenditure growth rate\": \"Hong Kong Special Administrative Region\", \"Highest R&D expenditure growth rate (%)\": 212.35, \"Highest growth province total R&D expenditure (yuan)\": 14248827635.96, \"Province with lowest R&D expenditure\": \"Jilin Province\", \"Lowest R&D expenditure (yuan)\": 8574294268.49, \"Lowest R&D province R&D expenditure growth rate (%)\": 16.95, \"Is same province\": false}"
+}

assets/qa_gold/comprehensive_decision/medium019.json

@@ -16,9 +16,5 @@
     "Filter enterprise records with non-null R&D expenditure; sort all enterprises by R&D expenditure descending to determine R&D expenditure ranking. Enterprise ranked 15th: Lianhua Tongze Commerce Company, R&D expenditure 265,616,054.7 yuan, province Beijing. Compare Zhongbai Jinmao Chain Company's R&D expenditure (11,270,987.0 yuan) with the 15th-ranked nationwide wholesale and retail enterprise's R&D expenditure (265,616,054.7 yuan). Since 11,270,987.0 < 265,616,054.7, the answer is No."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Zhongbai Jinmao Chain Company R&D expenditure (yuan)": 11270987.0,
-    "15th-ranked nationwide wholesale and retail enterprise R&D expenditure (yuan)": 265616054.7,
-    "Comparison result": "No"
-  }
-}
+  "milestone": "{\"Zhongbai Jinmao Chain Company R&D expenditure (yuan)\": 11270987.0, \"15th-ranked nationwide wholesale and retail enterprise R&D expenditure (yuan)\": 265616054.7, \"Comparison result\": \"No\"}"
+}

assets/qa_gold/comprehensive_decision/medium020.json

@@ -16,13 +16,5 @@
     "Filter enterprises with non-null company market capitalization, sort by company market capitalization descending, determine nationwide market capitalization ranking for this industry; identify top 3: No.1 \"Hengyi Changhua Fine Chemical Company\" (Shandong Province) 2,754.0, No.2 \"Rongsheng Jinsheng Chemical Company\" (Qinghai Province) 1,040.0, No.3 \"Hengyi Yuanjin Fine Chemical Company\" (Ningxia Hui Autonomous Region) 927.0. Determine whether the Shandong Province revenue-leading enterprise \"Hengyi Changhua Fine Chemical Company\" ranks among the nationwide top 3 by market capitalization: this enterprise is No.1 nationwide by market capitalization, thus it is among the top 3, conclusion: Yes."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Shandong Province operating revenue leading enterprise name": "Hengyi Changhua Fine Chemical Company",
-    "Shandong Province operating revenue leading enterprise operating revenue (yuan)": 165565462711.66,
-    "Shandong Province operating revenue leading enterprise company market capitalization": 2754.0,
-    "Chemical raw materials and chemical products manufacturing nationwide market cap No.1 enterprise": "Hengyi Changhua Fine Chemical Company",
-    "Chemical raw materials and chemical products manufacturing nationwide market cap No.3 enterprise": "Hengyi Yuanjin Fine Chemical Company",
-    "Chemical raw materials and chemical products manufacturing nationwide market cap No.3 enterprise market capitalization": 927.0,
-    "Comparison result": "Yes"
-  }
-}
+  "milestone": "{\"Shandong Province operating revenue leading enterprise name\": \"Hengyi Changhua Fine Chemical Company\", \"Shandong Province operating revenue leading enterprise operating revenue (yuan)\": 165565462711.66, \"Shandong Province operating revenue leading enterprise company market capitalization\": 2754.0, \"Chemical raw materials and chemical products manufacturing nationwide market cap No.1 enterprise\": \"Hengyi Changhua Fine Chemical Company\", \"Chemical raw materials and chemical products manufacturing nationwide market cap No.3 enterprise\": \"Hengyi Yuanjin Fine Chemical Company\", \"Chemical raw materials and chemical products manufacturing nationwide market cap No.3 enterprise market capitalization\": 927.0, \"Comparison result\": \"Yes\"}"
+}

assets/qa_gold/comprehensive_decision/medium021.json

@@ -16,10 +16,5 @@
     "Filter enterprises with non-null company market capitalization, sort by company market capitalization descending, determine nationwide market capitalization ranking for this industry; identify top 3: No.1 \"Hengyi Changhua Fine Chemical Company\" (Shandong Province) 2,754.0, No.2 \"Rongsheng Jinsheng Chemical Company\" (Qinghai Province) 1,040.0, No.3 \"Hengyi Yuanjin Fine Chemical Company\" (Ningxia Hui Autonomous Region) 927.0. Determine whether the Shandong Province revenue-leading enterprise \"Hengyi Changhua Fine Chemical Company\" ranks among the nationwide top 3 by market capitalization: this enterprise is No.1 nationwide by market capitalization, thus it is among the top 3, conclusion: Yes."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Shandong Province operating revenue leading enterprise operating revenue (yuan)": 165565462711.66,
-    "Shandong Province operating revenue leading enterprise company market capitalization": 2754.0,
-    "Chemical raw materials and chemical products manufacturing nationwide market cap No.3 enterprise market capitalization": 927.0,
-    "Comparison result": "Yes"
-  }
-}
+  "milestone": "{\"Shandong Province operating revenue leading enterprise operating revenue (yuan)\": 165565462711.66, \"Shandong Province operating revenue leading enterprise company market capitalization\": 2754.0, \"Chemical raw materials and chemical products manufacturing nationwide market cap No.3 enterprise market capitalization\": 927.0, \"Comparison result\": \"Yes\"}"
+}

assets/qa_gold/comprehensive_decision/medium022.json

@@ -16,9 +16,5 @@
     "Identify the region with the most policies as \"Guangdong Province\" with 59 policies. Compare the region with the highest average R&D investment growth rate (Hong Kong Special Administrative Region) with the region with the most policies (Guangdong Province), determine they are not the same, conclusion: No."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Region with highest average R&D investment growth rate": "Hong Kong Special Administrative Region",
-    "Region with most policies": "Guangdong Province",
-    "Comparison result": "No"
-  }
-}
+  "milestone": "{\"Region with highest average R&D investment growth rate\": \"Hong Kong Special Administrative Region\", \"Region with most policies\": \"Guangdong Province\", \"Comparison result\": \"No\"}"
+}

assets/qa_gold/comprehensive_decision/medium023.json

@@ -15,10 +15,5 @@
     "Compare the average year-on-year net profit growth rate (-79.47492958%) with the R&D investment growth rate (10.1399523809524%), determine that -79.47492958 is less than 10.1399523809524, conclusion: No."
   ],
   "steps_num": 4,
-  "milestone": {
-    "Industry where Haishan Changgong Equipment Company operates": "General equipment manufacturing",
-    "General equipment manufacturing net profit year-on-year growth rate mean (%)": -79.47492958,
-    "General equipment manufacturing R&D investment year-on-year growth rate mean (%)": 10.1399523809524,
-    "Comparison result": "No"
-  }
-}
+  "milestone": "{\"Industry where Haishan Changgong Equipment Company operates\": \"General equipment manufacturing\", \"General equipment manufacturing net profit year-on-year growth rate mean (%)\": -79.47492958, \"General equipment manufacturing R&D investment year-on-year growth rate mean (%)\": 10.1399523809524, \"Comparison result\": \"No\"}"
+}

assets/qa_gold/comprehensive_decision/medium024.json

@@ -16,9 +16,5 @@
     "Identify the region with the largest total enterprise market capitalization (yuan) as \"Beijing\" with total enterprise market capitalization of 3,959,736,000.0 yuan. Compare the region with the highest total operating revenue amount (yuan) (Beijing) with the region with the largest total enterprise market capitalization (yuan) (Beijing), determine they are the same, conclusion: Yes."
   ],
   "steps_num": 5,
-  "milestone": {
-    "Region with highest total operating revenue amount (yuan)": "Beijing",
-    "Region with largest total enterprise market capitalization (yuan)": "Beijing",
-    "Comparison result": "Yes"
-  }
-}
+  "milestone": "{\"Region with highest total operating revenue amount (yuan)\": \"Beijing\", \"Region with largest total enterprise market capitalization (yuan)\": \"Beijing\", \"Comparison result\": \"Yes\"}"
+}