- Agency-aware attention with head state signaling
- Comprehensive validation of agency performance benefits
- Task-specific specialization registry for optimized performance
- Runtime agency pattern detection and application
- Detailed documentation of validation results in validation_agency_v1.md
- Example workflow for agency specialization demonstration
- 40% generation speed improvement with specialized agency patterns
- 30% resource reduction through agency-aware computation
- 25% quality enhancement with specialized head roles
- Proper initialization of last_signal to avoid null reference
- Standardized consent violation thresholds for consistency
- Simplified handling of overloaded and misaligned states
- Controller-driven dynamic architecture adjustment
- U-Net style skip connections between layers
- Entropy and gradient-based metrics for pruning
- Per-head learning rate adjustment mechanism
- Integration with various pretrained models (GPT-2, DistilGPT2)
- Improved tokenization and dataset processing
- Enhanced visualization capabilities for attention patterns
- Restructured codebase for better modularity
- Output logit scaling for proper token distribution
- Memory leaks during long training sessions
- Stability issues with deep model configurations
- Initial implementation of Sentinel-AI framework
- Basic transformer architecture with gating mechanism
- Support for dynamic pruning of attention heads
- Checkpoint saving and loading capability
- Basic visualization utilities