EARCP: Self-Regulating Coherence-Aware Ensemble Architecture for Sequential Decision Making -- Ensemble Auto-Regule par Coherence et Performance

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• EARCP achieves sublinear regret bounds of O(sqrt(T log M)) under standard assumptions.technical 90%
• EARCP dynamically weights heterogeneous expert models based on both individual performance and inter-model coherence.method 95%
• EARCP demonstrates effectiveness on sequential prediction tasks including time series forecasting, activity recognition, and financial prediction.result 85%
• EARCP's computational overhead from online weight updates may limit real-time applications.limitation 80%
• EARCP depends on diverse, high-quality expert models for effective ensemble performance.limitation 85%
• EARCP provides practical robustness in non-stationary environments.result 80%
• EARCP architecture is designed as a general-purpose framework applicable to any domain requiring ensemble learning with temporal dependencies.method 85%
• EARCP continuously adapts model weights through a principled online learning mechanism that balances exploitation of high-performing models with exploration guided by consensus signals.method 90%

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