# Changelog

## [1.0.0] — 2026-05-19

### Added
- Initial public release of TemporalMesh Transformer (TMT)
- `TMTConfig` — full hyperparameter dataclass with 5 model scale presets
- `MeshBuilder` — dynamic kNN graph rebuilt every forward pass from cosine similarity
- `MeshAttention` — multi-head attention over sparse graph edges, O(S·k) cost
- `TemporalPositionEncoder` — RoPE + per-token learned decay scalars
- `ExitGate` — per-token confidence scoring with freeze-on-threshold logic
- `DualStreamFFN` — parallel syntax + semantic streams with learned gated fusion
- `MemoryAnchorCross` — 16 persistent EMA key-value anchor vectors
- `TMTLayer` — unified layer assembling all five components
- `TMTModel` — full autoregressive model with tied output projection
- `TMTOutput` — structured output dataclass (logits, exit_masks, confidences, graph_edges, memory_state, decay_scalars)
- `TMTTrainer` — training loop with wandb logging, cosine warmup, checkpoint saving
- `CosineWarmupScheduler` — learning rate schedule
- `TMTLoss` — cross-entropy + 0.1 × gate auxiliary loss
- Dataset loader for WikiText-2 and TinyStories
- HuggingFace tokenizer wrapper
- Full ablation notebooks (01–04)
- 15-test pytest suite (shapes + forward pass)
- 20-page publication-quality PDF with 7 figures and 18 equations
- 5-subset HuggingFace benchmark dataset
- Zenodo DOI registration
- GitHub Pages documentation site

### Architecture Details
- Default: d_model=512, n_heads=8, n_layers=12, graph_k=8, exit_threshold=0.85
- ~120M parameters (TMT-Base)
- WikiText-2 val perplexity: 29.4 (vs 42.1 vanilla baseline)
- Average compute per token: ~48% of full-depth baseline