Master advanced inference scaling techniques that combine multiple AI models for superior performance, achieving breakthrough results like 30% improvement on ARC-AGI-2 benchmarks.
├── Input Processing Layer
│ ├── Problem complexity analysis
│ ├── Input feature extraction
│ ├── Routing decision logic
│ └── Resource allocation planning
├── Model Ensemble Layer
│ ├── Specialized model pool
│ │ ├── Fast lightweight models
│ │ ├── High-accuracy heavy models
│ │ ├── Domain-specific experts
│ │ └── Reasoning specialists
│ ├── Dynamic model selection
│ ├── Parallel processing coordination
│ └── Load balancing and scheduling
├── Collaboration Layer
│ ├── Inter-model communication
│ ├── Shared context management
│ ├── Confidence scoring
│ └── Consensus building
├── Output Integration Layer
│ ├── Prediction aggregation
│ ├── Uncertainty quantification
│ ├── Quality assessment
│ └── Final decision synthesis
└── Feedback and Learning Layer
├── Performance monitoring
├── Model selection optimization
├── Routing rule refinement
└── Continuous improvement
🔄 Sequential Multi-Model Processing Architecture
┌─────────────────────────────────────────────────────────────────┐
│ SEQUENTIAL PROCESSING PIPELINE │
├─────────────────────────────────────────────────────────────────┤
│ Model Hierarchy (Ordered by Complexity/Accuracy) │
│ ├── Tier 1: Fast Lightweight Models │
│ ├── Response time: <50ms │
│ ├── Accuracy threshold: 80-85% │
│ ├── Use cases: Simple classification, filtering │
│ └── Resource usage: Minimal CPU/memory │
│ │
│ ├── Tier 2: Balanced Performance Models │
│ ├── Response time: 50-200ms │
│ ├── Accuracy threshold: 85-92% │
│ ├── Use cases: Standard reasoning, pattern recognition │
│ └── Resource usage: Moderate CPU/memory │
│ │
│ └── Tier 3: High-Accuracy Heavy Models │
│ ├── Response time: 200ms-2s │
│ ├── Accuracy threshold: 92-98% │
│ ├── Use cases: Complex reasoning, high-stakes decisions │
│ └── Resource usage: High CPU/GPU requirements │
│ │
│ Intelligent Escalation Logic │
│ ├── Confidence Threshold Analysis │
│ ├── Low confidence (<70%): Escalate to next tier │
│ ├── Medium confidence (70-90%): Consider complexity │
│ └── High confidence (>90%): Accept result │
│ │
│ ├── Cost-Benefit Calculation │
│ ├── Compute cost vs. accuracy gain assessment │
│ ├── Deadline pressure consideration │
│ └── Business impact evaluation │
└─────────────────────────────────────────────────────────────────┘
Multi-model orchestration is proving invaluable in applications like medical diagnosis (combining imaging, lab results, and symptom analysis), autonomous vehicles (sensor fusion and decision making), and financial trading (risk assessment with multiple data sources).