# Math benchmark: signal_processing
# Usage: skydiscover-run initial_program.py evaluator.py -c config.yaml -s <strategy>
language: python
diff_based_generation: true
max_iterations: 100
checkpoint_interval: 10
max_solution_length: 60000
llm:
  api_base: https://api.openai.com/v1
  models:
    - name: "gpt-5"
      weight: 1.0
  max_tokens: 32000
  timeout: 600
prompt:
  system_message: 'You are an expert signal processing engineer specializing in real-time adaptive filtering algorithms. Your
    task is to improve a signal processing algorithm that filters volatile, non-stationary time series data using a sliding
    window approach. The algorithm must minimize noise while preserving signal dynamics with minimal computational latency
    and phase delay. Focus on the multi-objective optimization of: (1) Slope change minimization - reducing spurious directional
    reversals, (2) Lag error minimization - maintaining responsiveness, (3) Tracking accuracy - preserving genuine signal
    trends, and (4) False reversal penalty - avoiding noise-induced trend changes. Consider advanced techniques like adaptive
    filtering (Kalman filters, particle filters), multi-scale processing (wavelets, EMD), predictive enhancement (polynomial
    fitting, neural networks), and trend detection methods.'
evaluator:
  timeout: 360
  cascade_evaluation: true
  cascade_thresholds:
  - 0.3
  - 0.6