# PRBench Task Configuration
# Each paper has one task.yaml that contains ALL task-specific information.
# Agent code is fully generic and reads everything from this file.

task_id: task_lin_2020

# --- Paper Info ---
paper:
  title: "Bootstraps to Strings: Solving Random Matrix Models with Positivity"
  author: "Henry W. Lin"
  doi: "arXiv:2002.08387"
  year: 2020
  paper_file: "Lin_2020.md"           # relative to task dir

# --- Files ---
instruction_file: "instruction.md"       # what the white agent receives (no ground truth)
metadata_file: "metadata.md"             # ground truth formulas + methodology (for grading)
ground_truth_data_dir: "data"            # directory with reference CSV files
ground_truth_code_dir: "reproduction"

# --- Expected Outputs (what the white agent must produce) ---
expected_outputs:
  analysis:
    - "reproduction/ANALYSIS.md"
  code:
    - "reproduction/matrix_bootstrap.py"
    - "reproduction/two_matrix_bootstrap.py"
    - "reproduction/fig1_quartic_bootstrap.py"
    - "reproduction/fig4_peninsula.py"
    - "reproduction/fig5_cubic_bootstrap.py"
    - "reproduction/fig8_two_matrix.py"
  data:
    - "data/fig1-d=4.csv"
    - "data/fig1-d=5.csv"
    - "data/fig1-d=6.csv"
    - "data/fig4-d=4.csv"
    - "data/fig4-d=5.csv"
    - "data/fig4-d=6.csv"
    - "data/fig5-d=3.csv"
    - "data/fig5-d=4.csv"
    - "data/fig5-d=6.csv"
    - "data/fig8_two_matrix.csv"

# --- Docker Environment ---
docker:
  image: "python:3.11-slim"
  memory_limit: "4g"
  timeout: 7200                         # 2 hours max
  pip_install:
    - numpy
    - scipy
    - matplotlib

# --- Grading Rubric (for model-judge) ---
grading:
  dimensions:
    - name: "methodology_understanding"
      weight: 0.20
      description: >
        Does the agent's ANALYSIS.md correctly describe the bootstrap methodology,
        loop equations (Schwinger-Dyson), Gram matrix construction, positivity constraints,
        and multi-matrix generalizations? Compare against the ground truth in metadata.md.
    - name: "code_correctness"
      weight: 0.15
      description: >
        Are all required code files present? Does the code correctly implement loop equations,
        Gram matrix assembly, eigenvalue-based positivity checks, and the two-matrix model?
    - name: "data_accuracy"
      weight: 0.50
      description: >
        How well do the generated CSV data files match the ground truth reference data?
        Compare numerical values (upper/lower bounds). For the bootstrap method,
        results are deterministic so values should match closely (~5% tolerance).
        Check column names, row counts, and value ranges.
    - name: "completeness"
      weight: 0.15
      description: >
        Are all 10 CSV files, all 6 code files, and the ANALYSIS.md present?
        Are the files non-trivial (not empty/placeholder)?

# --- Code-Only Evaluation Mode ---
code_only:
  instruction_suffix: |
    ## IMPORTANT: Code-Only Mode

    In this task you are in **code-only mode**. You must:
    1. Read and analyze the paper
    2. Write `reproduction/ANALYSIS.md` with your methodology analysis
    3. Implement ALL code files under `reproduction/`
    4. **DO NOT** run the scripts or generate CSV data files
    5. Focus on writing correct, complete, and well-structured code

    Your deliverables are ONLY the analysis document and code files.
    Do NOT produce any CSV data files.

  expected_outputs:
    analysis:
      - "reproduction/ANALYSIS.md"
    code:
      - "reproduction/matrix_bootstrap.py"
      - "reproduction/two_matrix_bootstrap.py"
      - "reproduction/fig1_quartic_bootstrap.py"
      - "reproduction/fig4_peninsula.py"
      - "reproduction/fig5_cubic_bootstrap.py"
      - "reproduction/fig8_two_matrix.py"

  grading:
    dimensions:
      - name: "methodology_understanding"
        weight: 0.25
        description: >
          Does ANALYSIS.md correctly describe the bootstrap methodology,
          loop equations, Gram matrix construction, positivity constraints,
          and multi-matrix generalizations?
      - name: "code_correctness"
        weight: 0.50
        description: >
          Read every code file carefully. Check whether the implementation is
          physically and algorithmically correct:
          - Loop equation recursion for quartic and cubic models
          - Gram matrix assembly from correlators
          - Eigenvalue-based positivity check
          - Two-matrix model: word generation, symmetry reduction, loop equations
          - Correct parameter ranges, grid spacing, output format
          Compare against ground truth formulas in metadata. Flag any bugs
          that would produce incorrect results.
      - name: "code_quality"
        weight: 0.10
        description: >
          Is the code well-structured, readable, and does it follow the
          expected file organization?
      - name: "completeness"
        weight: 0.15
        description: >
          Are all 6 code files and the ANALYSIS.md present and non-trivial?