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).
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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).
Overview
Phase 0 validates 5 core hypotheses on a 2M parameter mini-transformer:
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
Directory Structure
cc-anticipatory-transformer/
├── phase0/
│ ├── models/ # Model implementations
│ │ ├── dual_pathway.py # Fast/slow pathway architecture
│ │ ├── attention.py # Trajectory-aware attention with additive bias
│ │ ├── losses.py # Orthogonality penalty and commitment targets
│ │ └── config.py # Model configuration
│ ├── experiments/ # Validation scripts
│ │ ├── exp1_frequency_separation.py
│ │ ├── exp2_orthogonality.py
│ │ ├── exp3_trajectory_attention.py
│ │ ├── exp4_commitment_correlation.py
│ │ └── exp5_context_efficiency.py
│ ├── data/ # Datasets for validation
│ └── results/ # Experiment outputs, plots, logs
└── README.mdRequirements
- Python 3.10+
- PyTorch 2.1+ with CUDA 12.1
- 1x A100 (40GB) or equivalent GPU
- ~100GB disk space for checkpoints and data
See [requirements.txt](requirements.txt) for full dependency list.
Quick Start
# Install dependencies
pip install -r requirements.txt
# Run frequency separation validation
python phase0/experiments/exp1_frequency_separation.py
# Run full validation suite
bash phase0/run_all_validations.shValidation Criteria
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 | |
| 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 |
Current Status
Phase: Setup
Progress: 0/5 validations complete
See [../../docs/architecture/23-ANTICIPATORY_TRANSFORMER_PHASE0_CHARTER.md](../../docs/architecture/23-ANTICIPATORY_TRANSFORMER_PHASE0_CHARTER.md) for full project charter.
Citation
If you use this work, please cite:
@techreport{anticipatory_transformer_2026,
title={Anticipatory Transformer: Motion Intelligence for Language Models},
author={Comp-Core Architecture Team},
year={2026},
institution={Comp-Core Project}
}Promotion Decision
Attach run IDs, datasets, metrics, and reproduction commands.
Source Anchor
Comp-Core/core/_recovered/cc-anticipatory-transformer/README.md
Detected Structure
Method · Evaluation · Code Anchors · Architecture