Retrieval Fusion Strategy

Design a retrieval fusion strategy combining multiple retrieval sources.

## Retrieval Sources
{{retrieval_sources}}

## Fusion Requirements
{{fusion_requirements}}

## Quality Goals
{{quality_goals}}

Implement fusion strategies:

```python
class RetrievalFusion:
    def reciprocal_rank_fusion(self, result_lists: List[List[Result]], k: int = 60) -> List[Result]:
        """RRF: score = sum(1/(k + rank))"""
        pass
    
    def weighted_fusion(self, result_lists: List[List[Result]], weights: List[float]) -> List[Result]:
        """Weighted score combination"""
        pass
    
    def learned_fusion(self, result_lists: List[List[Result]], query: str) -> List[Result]:
        """Neural fusion model"""
        pass
    
    def cascade_fusion(self, query: str, stages: List[Retriever]) -> List[Result]:
        """Progressive refinement"""
        pass
```

Include:
- Score normalization
- Deduplication
- Latency optimization
- A/B testing framework