tasks/task_026_comprehensive_decision_medium_medium001.md

metadata

id: task_026_comprehensive_decision_medium_medium001
name: comprehensive_decision-medium-medium001
category: comprehensive_decision
grading_type: llm_judge
timeout_seconds: 1200
gold_file: qa_gold/comprehensive_decision/medium001.json
workspace_files:
  - source: database/bilingual_translation_english_chinese.json
    dest: database/bilingual_translation_english_chinese.json
  - source: database/enterprise/company_core.csv
    dest: database/enterprise/company_core.csv
  - source: database/enterprise/company_operation_status.csv
    dest: database/enterprise/company_operation_status.csv
  - source: database/enterprise/company_operation_status_detail.csv
    dest: database/enterprise/company_operation_status_detail.csv
  - source: database/enterprise/company_operation_yearly_status.csv
    dest: database/enterprise/company_operation_yearly_status.csv
  - source: database/enterprise/company_profile.csv
    dest: database/enterprise/company_profile.csv
  - source: database/enterprise/company_profile_as.csv
    dest: database/enterprise/company_profile_as.csv
  - source: database/enterprise/company_profile_eu.csv
    dest: database/enterprise/company_profile_eu.csv
  - source: database/enterprise/company_profile_na.csv
    dest: database/enterprise/company_profile_na.csv
  - source: database/enterprise/company_profile_oc.csv
    dest: database/enterprise/company_profile_oc.csv
  - source: database/industry/national_industry_status.csv
    dest: database/industry/national_industry_status.csv
  - source: database/industry/national_industry_status_detail.csv
    dest: database/industry/national_industry_status_detail.csv
  - source: database/industry/national_industry_yearly_status.csv
    dest: database/industry/national_industry_yearly_status.csv
  - source: database/industry/regional_industry_status.csv
    dest: database/industry/regional_industry_status.csv
  - source: database/industry/regional_industry_status_detail.csv
    dest: database/industry/regional_industry_status_detail.csv
  - source: database/industry/regional_industry_yearly_status.csv
    dest: database/industry/regional_industry_yearly_status.csv
  - source: database/internal_metrics.csv
    dest: database/internal_metrics.csv
  - source: database/policy/policy_release_status.csv
    dest: database/policy/policy_release_status.csv
  - source: database/policy/policy_resource.csv
    dest: database/policy/policy_resource.csv

Prompt

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?

Output 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".

Only use files under ./database/.

Expected Behavior

Agent should read the provided database/ files, compute the result, and return the final answer. The final answer must follow the required output format.

Grading Criteria

• Final answer semantically matches the gold answer.
• Output format follows guidelines.

LLM Judge Rubric

Criterion 1: Multi-answer Correctness (Weight: 100%)

Gold answer JSON: [25.48, "Kangsheng Anjian Biopharmaceutical Company"]

Scoring rules:

• The gold answer is a list with N=2 parts.
• Judge each predicted part against the corresponding gold part by semantic equivalence.
• Return scores with part_0 ... part_1 each as 0 or 1.
• Return total = (sum(part_i)) / 2 exactly.
• If the model output is missing or cannot be parsed into 2 comparable parts, score all parts 0.