tasks/task_017_comprehensive_decision_hard_hard011.md

metadata

id: task_017_comprehensive_decision_hard_hard011
name: comprehensive_decision-hard-hard011
category: comprehensive_decision
grading_type: llm_judge
timeout_seconds: 1200
gold_file: qa_gold/comprehensive_decision/hard011.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

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?

Output 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"

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: Single-answer Correctness (Weight: 100%)

Gold answer JSON: 4.9

Scoring rules:

• Judge semantic equivalence between the model final answer and the gold answer.
• Return scores with one key match as 1 or 0.
• Return total as 1.0 if equivalent, otherwise 0.0.