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semi implemented and features implemented save checkpoint

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# Ensemble decision engine for multi-tier matching
## Module
BinaryIndex
## Status
IMPLEMENTED
## Description
Ensemble decision engine combines multiple matching tiers (range match, Build-ID, fingerprint) with configurable weight tuning for vulnerability classification.
## Implementation Details
- **Modules**: `src/BinaryIndex/__Libraries/StellaOps.BinaryIndex.Ensemble/`
- **Key Classes**:
- `EnsembleDecisionEngine` (`src/BinaryIndex/__Libraries/StellaOps.BinaryIndex.Ensemble/EnsembleDecisionEngine.cs`) - combines multiple matching signals with configurable weights into a final vulnerability classification decision
- `FunctionAnalysisBuilder` (`src/BinaryIndex/__Libraries/StellaOps.BinaryIndex.Ensemble/FunctionAnalysisBuilder.cs`) - builds function analysis inputs including optional ML embeddings
- `WeightTuningService` (`src/BinaryIndex/__Libraries/StellaOps.BinaryIndex.Ensemble/WeightTuningService.cs`) - tunes ensemble weights based on golden set validation results
- `EnsembleOptions` (`src/BinaryIndex/__Libraries/StellaOps.BinaryIndex.Ensemble/Models.cs`) - configurable weights and thresholds for matching tiers
- `MlEmbeddingMatcherAdapter` (`src/BinaryIndex/__Libraries/StellaOps.BinaryIndex.ML/Training/MlEmbeddingMatcherAdapter.cs`) - adapts ML function embeddings for ensemble use
- **Interfaces**: `IEnsembleDecisionEngine` (`src/BinaryIndex/__Libraries/StellaOps.BinaryIndex.Ensemble/IEnsembleDecisionEngine.cs`)
- **Registration**: `EnsembleServiceCollectionExtensions.AddBinarySimilarityServices()` for full pipeline setup
- **Benchmarks**: `EnsembleAccuracyBenchmarks`, `EnsembleLatencyBenchmarks` (`src/BinaryIndex/__Tests/StellaOps.BinaryIndex.Benchmarks/`)
## E2E Test Plan
- [ ] Submit a binary with known vulnerability and verify ensemble produces correct classification
- [ ] Verify weight tuning: adjust instruction weight to 0.6 and verify it changes classification outcomes
- [ ] Verify multi-tier integration: Build-ID match, fingerprint match, and ML embedding all contribute to score
- [ ] Verify `FunctionAnalysisBuilder` correctly assembles all matching dimensions
- [ ] Verify `WeightTuningService` optimizes weights based on golden set validation accuracy
- [ ] Run accuracy benchmark and verify F1 score meets minimum threshold