Anticipatory Transformer - Phase 0 Validation

Empirical validation of core architectural components for the Anticipatory Transformer as defined in [docs/architecture/23-ANTICIPATORY_TRANSFORMER.md](../../docs/architecture/23-ANTICIPATORY_TRANSFORMER.md). 1. **Frequency Separation**: Dual pathways naturally specialize (fast: syntax 5-50 Hz, slow: semantics 0.5-5 Hz) 2. **Orthogonality**: Cross-covariance penalty prevents mode collapse while maintaining specialization 3. **Trajectory Attention**: Additive bias mechanism is numerically stable and improves context efficiency 4. **Commitment Targets**: Counterfactual stability correlates with generation quality (r > 0.6) 5. **Slice-Based Context**: Priority-queue selection achieves 30% token reduction vs fixed-window baseline - Python 3.10+ - PyTorch 2.1+ with CUDA 12.1 - 1x A100 (40GB) or equivalent GPU - ~100GB disk space for checkpoints and data Each experiment must meet specific success criteria as defined in [23-ANTICIPATORY_TRANSFORMER_PHASE0_CHARTER.md](../../docs/architecture/23-ANTICIPATORY_TRANSFORMER_PHASE0_CHARTER.md): | Experiment | Success Criterion | Measurement | |------------|------------------|-------------| | Frequency Separation | Fast: 5-50 Hz, Slow: 0.5-5 Hz, <10% overlap | FFT on hidden states | | Orthogonality | L_ortho ↓ during training, perplexity stable | Training curves | | Trajectory Attention | No NaN/Inf gradients over 10K steps | Gradient monitoring | | Commitment Correlation | r > 0.6 with quality ratings (n=100) | Pearson correlation | | Context Efficiency | 30% fewer tokens for equivalent perplexity | Token budget sweep |