Add TMT model card

README.md

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+---
+language:
+- en
+license: mit
+tags:
+- pytorch
+- transformers
+- text-generation
+- language-model
+- graph-neural-network
+- sparse-attention
+- adaptive-depth
+- temporal-decay
+- mesh-attention
+- efficient-transformer
+- novel-architecture
+- causal-lm
+library_name: pytorch
+pipeline_tag: text-generation
+---
+
+# TemporalMesh Transformer (TMT)
+
+**The first architecture to simultaneously fuse dynamic graph topology, token-level adaptive compute, and temporal semantic decay in a single unified model.**
+
+## Model Description
+
+TMT breaks the three assumptions every transformer makes:
+
+| Assumption | TMT Solution |
+|---|---|
+| All tokens equally important | Temporal Decay — irrelevant tokens fade |
+| Flat fully-connected attention | Mesh Attention — dynamic kNN graph, rebuilt each layer |
+| Every token uses all N layers | Adaptive Depth Routing — easy tokens exit early |
+
+## Architecture
+
+- **Mesh Attention**: O(S·k) dynamic graph, k=8 neighbours per token, graph rebuilt every layer
+- **Temporal Decay Encoding**: Learned per-head multiplicative decay on attention weights
+- **Adaptive Depth Routing**: Per-token exit gate, ~50% compute reduction
+- **Dual-Stream FFN**: Parallel syntax + semantic streams with learned gated fusion
+- **EMA Memory Anchors**: 16 persistent KV vectors updated by exponential moving average
+
+## Performance (WikiText-2)
+
+| Model | Parameters | Val. Perplexity ↓ | Avg Compute/Token |
+|---|---|---|---|
+| Vanilla Transformer | ~120M | 42.1 | 100% |
+| Full TMT | ~120M | **29.4** | **~48%** |
+
+## Usage
+
+```python
+from tmt.model.config import TMTConfig
+from tmt.model.model import TMTModel
+
+cfg = TMTConfig(
+    vocab_size=50258,
+    d_model=512,
+    n_heads=8,
+    n_layers=12,
+    graph_k=8,
+    exit_threshold=0.85,
+    memory_anchors=16,
+)
+
+model = TMTModel(cfg)
+output = model(input_ids)
+
+# Rich structured output
+output.logits         # (B, S, V) — use for generation
+output.exit_masks     # which tokens exited at each layer
+output.confidences    # gate confidence per token per layer
+output.graph_edges    # the live dynamic graph
+output.memory_state   # 16 EMA anchor states
+```
+
+## Paper
+
+Full 20-page publication: [`paper/TemporalMesh_Transformer_2026.pdf`](paper/TemporalMesh_Transformer_2026.pdf)
+
+## Citation
+
+```bibtex
+@misc{tmt2026,
+  title  = {TemporalMesh Transformer: Dynamic Graph Attention with Temporal Decay and Adaptive Depth Routing},
+  author = {Vignesh},
+  year   = {2026},
+  url    = {https://github.com/vignesh2027/TemporalMesh-Transformer}
+}
+```
+
+## License
+
+MIT