config.yaml

# Confessional Agency Ecosystem (CAE) Configuration
# Unified TRuCAL + CSS Settings

# Model Configuration
model:
  d_model: 256
  max_seq_length: 512
  device: "auto"  # auto, cuda, cpu
  
# Base Model Configuration
base_model: "microsoft/DialoGPT-medium"
# Alternative options:
# - "gpt2"
# - "facebook/bart-base"
# - "t5-base"
# - "microsoft/DialoGPT-large"

# Safety Model Configuration
safety_model_name: "openai/gpt-oss-safeguard-20b"
safety_policy_path: null  # Path to custom safety policy file

# Attention-Layer Safety (TRuCAL-enhanced)
attention_safety:
  enabled: true
  trigger_threshold: 0.04
  aggregation_method: "bayesian"  # bayesian or weighted_sum
  max_cycles: 16
  early_stop_coherence: 0.85
  per_dim_kl: true
  
  # Vulnerability detection weights
  vulnerability_weights:
    scarcity: 0.25
    entropy: 0.25
    deceptive: 0.2
    prosody: 0.15
    policy: 0.15

# Inference-Time Safety (CSS-enhanced)
inference_safety:
  enabled: true
  tau_delta: 0.92  # Crisis threshold
  
  # Distress kernel settings
  distress:
    cache_size: 1000
    tau_delta: 0.92
    
  # Bayesian risk assessment
  risk:
    num_signals: 5
    alpha: 0.001
    dirichlet_concentration: 1.0
    thresholds:
      low: 0.3
      mid: 0.55
      high: 0.8

# Multimodal Analysis
multimodal:
  enabled: true
  
  # Audio prosody analysis
  audio:
    enabled: true
    sample_rate: 22050
    n_mfcc: 13
    hop_length: 512
    
  # Visual emotion analysis
  visual:
    enabled: true
    face_detection: true
    emotion_model: "resnet18"

# Confessional Recursion
confessional:
  max_recursion_depth: 8
  ignition_threshold: 0.88
  kl_penalty_weight: 0.1
  recursion_model: "gpt2"
  max_new_tokens: 150
  
  # Template configuration
  templates:
    - "prior"
    - "evidence"
    - "posterior"
    - "relational_check"
    - "moral"
    - "action"
    - "consequence"
    - "community"

# Community Templates
community:
  enabled: true
  template_registry: "federated"
  validation_threshold: 0.7
  update_frequency: "daily"
  
  # Federated learning settings
  federated:
    num_participants: 10
    rounds: 5
    local_epochs: 3

# Performance Optimization
performance:
  batch_size: 32
  use_cache: true
  cache_size: 10000
  gradient_checkpointing: true
  mixed_precision: true
  compile_model: false  # PyTorch 2.0+ feature

# Logging and Monitoring
logging:
  level: "INFO"
  format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
  file: "/app/logs/cae.log"
  max_size: "10MB"
  backup_count: 5
  
  # Metrics collection
  metrics:
    enabled: true
    interval: 60  # seconds
    output_dir: "/app/metrics"

# Benchmarking
benchmarks:
  enabled: true
  datasets:
    - "truthful_qa"
    - "adv_bench"
    - "big_bench"
    - "custom_moral"
  
  evaluation:
    batch_size: 16
    num_samples: 1000
    metrics: ["accuracy", "precision", "recall", "f1", "latency"]

# API Configuration
api:
  host: "0.0.0.0"
  port: 8000
  workers: 4
  timeout: 30
  max_requests: 1000
  
  # Security
  rate_limit: "100/minute"
  api_key_required: false
  cors_origins: ["*"]

# Deployment
deployment:
  environment: "production"  # development, staging, production
  debug: false
  reload: false
  
  # Resource limits
  max_memory: "8GB"
  max_gpu_memory: "80%"
  
  # Scaling
  autoscale:
    enabled: true
    min_replicas: 1
    max_replicas: 10
    target_cpu: 70
    target_memory: 80

# Experimental Features
experimental:
  penitential_loop: true
  federated_auditing: true
  zero_knowledge_proofs: false
  asi_simulation: false
  
  # Research features
  research:
    agency_preservation_metrics: true
    epistemic_humility_quantification: true
    moral_development_tracking: true