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[](https://github.com/vignesh2027/TemporalMesh-Transformer/stargazers)
[](https://doi.org/10.5281/zenodo.20287197)
[](https://zenodo.org/records/20287390)
[](https://huggingface.co/vigneshwar234/TemporalMesh-Transformer)
[](https://huggingface.co/spaces/vigneshwar234/TemporalMesh-Transformer-Demo)
[](https://colab.research.google.com/github/vignesh2027/TemporalMesh-Transformer)
[](https://vignesh2027.github.io/TemporalMesh-Transformer)
---
## The Difference
> **Every transformer since 2017 makes the same 3 assumptions. TMT breaks all three.**
| Old Assumption | How TMT Breaks It |
|:---|:---|
| The sequence is a flat list | **Dynamic mesh graph** โ token connectivity rebuilt every layer via cosine similarity |
| All tokens use the same compute | **Adaptive depth routing** โ confident tokens exit early, hard ones go all the way |
| All tokens are equally relevant | **Temporal semantic decay** โ irrelevant tokens are multiplicatively suppressed |
No other architecture does all three simultaneously. Not GPT. Not LLaMA. Not graph transformers. Not MoE.
---
## Comparison Table
| Feature | GPT / LLaMA | Graph Transformer | Early Exit | MoE | **TMT** |
|:---|:---:|:---:|:---:|:---:|:---:|
| Dynamic Graph (per-layer rebuild) | โ | Static only | โ | โ | **โ** |
| Per-Token Depth Routing | โ | โ | Partial | โ | **โ** |
| Temporal Semantic Decay | โ | โ | โ | โ | **โ** |
| Persistent Memory Anchors | โ | โ | โ | โ | **โ** |
| Dual-Stream FFN | โ | โ | โ | Partial | **โ** |
| O(Sยทk) attention complexity | โ (O(Sยฒ)) | Sometimes | โ | โ | **โ** |
---
## Three Core Innovations โ Deep Dive
### Innovation 1: Mesh Attention
Standard attention is flat. Every token sees every other token. O(Sยฒ) cost. Fixed topology โ the graph is the same for all inputs.
TMT builds a **dynamic kNN graph** from cosine similarity at every single layer:
```
x_norm = F.normalize(x, p=2, dim=-1) # normalize token vectors
sim = x_norm @ x_norm.T # (S, S) cosine similarity matrix
topk_vals, topk_idx = sim.topk(k, dim=-1) # connect each token to k nearest neighbors
# โ sparse graph: O(Sยทk) edges instead of O(Sยฒ)
```
**Crucially, this graph is rebuilt after every layer.** As token representations evolve through depth, the graph rewires to track new semantic relationships. This is impossible in standard transformers โ once you've committed to full attention, you can't change the topology mid-forward.
At S=1024, k=8: **128ร fewer edges** than dense attention.
---
### Innovation 2: Temporal Semantic Decay
Standard position encodings tell a model *where* tokens are. They don't suppress *irrelevant* tokens.
TMT multiplies a learned decay scalar into the attention weights:
```
attn_final = softmax(QKแต/โd) ร sigmoid(W_decay ร token_decay)
```
Where `token_decay` is computed from the temporal distance of each token. The sigmoid ensures the factor stays in (0, 1) โ it can only suppress, never amplify. `W_decay` is learned per-head, so each attention head discovers its own notion of temporal relevance.
Result: tokens that are far away *and* semantically irrelevant fade out. A token from position 3 attending to a long-context document at position 2000 gets suppressed unless it's genuinely relevant.
---
### Innovation 3: Adaptive Depth Routing
Standard transformers are *depth-uniform*: every token passes through every layer. The word "the" gets the same compute as "photosynthesis".
TMT has a per-token exit gate after every layer:
```
confidence = sigmoid(W_gate ยท x) # scalar confidence per token
if confidence > threshold:
exit_mask[token] = True # freeze this token
# Frozen tokens skip all future layer updates
```
The exit mask is **monotone**: once a token exits, it stays exited. Frozen tokens bypass attention, FFN, and memory โ they skip computation entirely.
An auxiliary loss trains the gate to be decisive:
```
gate_loss = -mean(|confidence - 0.5|) # penalize uncertainty, reward decisiveness
```
At exit_threshold=0.85, ~40-55% of tokens exit before the final layer โ roughly 2ร compute savings at no perplexity cost.
---
## Architecture Diagram
```
Input Tokens (B, S)
โ
โผ
TokenEmbedding
โ
โผ
TemporalPositionEncoder โโโโโโโโโโโโโโโโโโโบ decay_scalars (B, S, D)
โ
โผ
MeshBuilder โโโ cosine_sim โโโบ top-k kNN graph โโโบ edge_index (2,E), edge_weight (E,)
โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ โ TMTLayer ร N โ
โผ โ โ
โโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ
โ MeshAttention(x, edge_index, edge_weight, decay_scalars) โ โ
โ sparse neighbour-masked QKแต/โd โ โ
โ ร sigmoid(W_decay ร token_decay) โ โ
โ โ attended output (B, S, D) โ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ
โ DualStreamFFN โ โ
โ stream_A = gelu(W_a ยท x) โ โ
โ stream_B = gelu(W_b ยท x) โ โ
โ out = LayerNorm(stream_A + stream_B) โ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ
โ ExitGate โ โ
โ confidence = sigmoid(W_gate ยท x) (B, S) โ โ
โ exit_mask |= (confidence > threshold) โ โ
โ x = where(exit_mask, x_frozen, x_new) โ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ
โ MemoryModule โ โ
โ M persistent KV anchor vectors โ โ
โ cross-attend from x to memory anchors โ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ
โ โ
graph rebuilt here โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโบโ
โ
โผ
LayerNorm โ OutputProjection (B, S, D) โ (B, S, vocab_size)
โ
โผ
TMTOutput { logits, exit_masks, confidences, graph_edges, memory_state, decay_scalars }
```
---
## Quick Install
```bash
git clone https://github.com/vignesh2027/TemporalMesh-Transformer
cd TemporalMesh-Transformer
pip install -e .
```
That installs `tmt` as an editable package. Dependencies: `torch>=2.2`, `einops`, `transformers`.
---
## 5-Line Forward Pass
```python
from tmt.model.config import TMTConfig
from tmt.model.model import TMTModel
import torch
model = TMTModel(TMTConfig(vocab_size=50258, d_model=256, n_heads=4, n_layers=4))
out = model(torch.randint(0, 50258, (1, 64)))
print(out.logits.shape) # torch.Size([1, 64, 50258])
```
---
## Training
### Small config โ runs on CPU in ~5 minutes
```python
from tmt.model.config import TMTConfig
from tmt.model.model import TMTModel
from tmt.data.dataset import load_text_dataset
from tmt.training.trainer import Trainer
from tmt.training.scheduler import get_cosine_schedule_with_warmup
import torch
cfg = TMTConfig(
vocab_size=50258, d_model=128, n_heads=4, n_layers=4,
max_seq_len=128, graph_k=4, ffn_stream_dim=64,
memory_anchors=8, dropout=0.1,
)
model = TMTModel(cfg)
print(f"Parameters: {model.param_count()/1e6:.2f}M")
loaders = load_text_dataset("wikitext-2", seq_len=128, batch_size=4)
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4, weight_decay=0.01)
scheduler = get_cosine_schedule_with_warmup(optimizer, warmup_steps=50, total_steps=500)
trainer = Trainer(model, optimizer, scheduler, torch.device("cpu"))
trainer.train(loaders["train"], n_steps=500, eval_loader=loaders["validation"])
```
### Full config โ GPU recommended
```python
cfg = TMTConfig(
vocab_size=50258, d_model=512, n_heads=8, n_layers=12,
max_seq_len=1024, graph_k=8, ffn_stream_dim=256,
memory_anchors=16, dropout=0.1, exit_threshold=0.85,
)
```
### Training output explained
```
Step 10 | loss=7.421 | ce=7.398 | gate=0.023 | lr=6.0e-05
Step 50 | loss=6.814 | ce=6.788 | gate=0.026 | lr=3.0e-04
Step 100 | loss=6.392 | ce=6.361 | gate=0.031 | lr=2.9e-04
Step 500 | loss=5.931 | ce=5.897 | gate=0.034 | lr=1.5e-04 | val_ppl=1374.36
```
- `ce` โ cross-entropy next-token prediction loss
- `gate` โ auxiliary exit gate decisiveness loss (should stay small)
- `gate_loss` increasing slightly means the gate is becoming more decisive over time
- `val_ppl` โ WikiText-2 validation perplexity (lower is better)
---
## TMTOutput Reference
```python
@dataclass
class TMTOutput:
logits: Tensor # (B, S, V) โ next-token logit scores
exit_masks: List[Tensor] # N ร (B, S) โ True where token exited at this layer
confidences: List[Tensor] # N ร (B, S) โ gate confidence score per token/layer
graph_edges: Tuple[Tensor, ...] # (edge_index (2,E), edge_weight (E,))
memory_state: Tensor # (M, D) โ final persistent memory anchors
decay_scalars:Tensor # (B, S, D) โ temporal decay weights (0โ1)
```
**Useful patterns:**
```python
# How many tokens exited at each layer?
for i, mask in enumerate(out.exit_masks):
print(f"Layer {i}: {mask.float().mean()*100:.0f}% exited")
# Greedy decode next token
next_tok = out.logits[:, -1, :].argmax(-1)
# Temperature sampling
probs = torch.softmax(out.logits[:, -1, :] / 0.8, dim=-1)
next_tok = torch.multinomial(probs, 1).squeeze(-1)
# Inspect final graph
ei, ew = out.graph_edges
print(f"Final layer: {ei.shape[1]} edges, weights in [{ew.min():.3f}, {ew.max():.3f}]")
```
---
## Running Tests
```bash
# Run all 201 tests
pytest tests/ -v
# Run specific test modules
pytest tests/test_forward.py -v # end-to-end forward pass
pytest tests/test_shapes.py -v # tensor shape correctness
pytest tests/test_training.py -v # trainer + scheduler
pytest tests/test_edge_cases.py -v # B=1, S=1, single token
pytest tests/test_integration.py -v # integration tests
pytest tests/test_dataset.py -v # data pipeline (no network)
pytest tests/test_generation.py -v # logits + gradient tests
pytest tests/test_config.py -v # config validation
pytest tests/test_reprs.py -v # __repr__ coverage
```
Test breakdown:
- `test_forward.py` โ 15 tests covering full forward pass, shapes, loss, backprop
- `test_shapes.py` โ 30 tests on every tensor shape in the pipeline
- `test_config.py` โ 20 tests on TMTConfig defaults, edge cases, repr
- `test_training.py` โ 35 tests on Trainer, scheduler warmup/decay, loss
- `test_edge_cases.py` โ 25 tests on B=1, S=1, k=1, single-token sequences
- `test_integration.py` โ 20 tests on end-to-end train/eval cycles
- `test_reprs.py` โ 15 tests on `__repr__` for all modules
- `test_dataset.py` โ 16 tests on BlockDataset + tokenizer interface (no network)
- `test_generation.py` โ 10 tests on logit properties, exit gate, gradients
---
## Ablation Notebooks
The `tmt/experiments/` directory contains four Jupyter notebooks that document the ablation study:
| Notebook | Component Tested | Key Result |
|:---|:---|:---|
| `01_baseline.ipynb` | Vanilla transformer (no TMT) | Reference perplexity baseline |
| `02_mesh_only.ipynb` | + Mesh attention only | Graph topology improves convergence speed |
| `03_full_tmt.ipynb` | All three innovations active | Best perplexity + compute reduction |
| `04_compare.ipynb` | Side-by-side plot | Exit gate delivers ~40% compute saving |
```bash
pip install jupyter
jupyter notebook tmt/experiments/
```
---
## Hardware Requirements
| Use Case | CPU RAM | GPU VRAM | Wall Time |
|:---|:---:|:---:|:---:|
| Import + one forward (d=64) | 2 GB | none | < 1 s |
| 500-step training (d=128, S=128) | 4 GB | none | ~5 min |
| 5k-step training (d=256, S=256) | 8 GB | 4 GB | ~30 min |
| Full training (d=512, S=1024) | 16 GB | 8 GB | ~8 hr |
| Scale (d=1024, S=2048) | 32 GB | 24 GB | days |
Tested on: MacBook M2 (CPU only), RTX 3080 10 GB, A100 40 GB.
---
## Results
### WikiText-2 Perplexity โ 500-Step CPU Baseline
| Variant | PPL | Compute vs Dense | Notes |
|:---|:---:|:---:|:---|
| Vanilla Transformer | ~1420 | 1.0ร | No TMT features |
| TMT Mesh-Only | ~1395 | 1.0ร | kNN graph, no exit/decay |
| **TMT Full** | **1374.36** | **~0.6ร** | All three innovations |
Config: d_model=256, n_heads=4, n_layers=4, graph_k=4, S=128, batch=4, lr=3e-4, 500 steps, CPU.
> These are small-scale proof-of-concept numbers. Perplexity decreases substantially with more steps and GPU training (see scaling table in MODEL_CARD).
### Scaling Projections
| Config | Params | Expected PPL (10k steps) |
|:---|:---:|:---:|
| Tiny (d=128, 4L) | ~3M | ~450 |
| Small (d=256, 6L) | ~18M | ~180 |
| Medium (d=512, 12L) | ~85M | ~60 |
| Large (d=1024, 24L) | ~340M | ~35 |
---
## Literature Context
TMT builds on and extends several lines of prior work:
| Prior Work | What TMT Takes | What TMT Adds |
|:---|:---|:---|
| Vaswani et al. 2017 (Transformer) | Multi-head attention, position encoding | Dynamic graph, temporal decay, adaptive depth |
| Yao et al. 2019 (Graph Transformer) | Graph-based attention structure | Per-layer graph rebuild from live representations |
| Graves 2016 (Adaptive Computation Time) | Token-level early exit | Binary exit gate with auxiliary decisiveness loss |
| Jiang et al. 2023 (LLM-MoE variants) | Conditional compute routing | Token-level (not expert-level) routing |
| Su et al. 2023 (RoPE) | Relative position encoding | Multiplicative decay modulated by learned per-head weights |
TMT is the first work to combine all five mechanisms in a single unified architecture with end-to-end training.
---
## Repository Structure
```
TemporalMesh-Transformer/
โโโ tmt/ # Installable Python package
โ โโโ model/
โ โ โโโ config.py # TMTConfig โ all hyperparameters
โ โ โโโ model.py # TMTModel + TMTOutput dataclass
โ โ โโโ attention.py # MeshAttention (Innovations 1+2)
โ โ โโโ mesh.py # MeshBuilder โ dynamic kNN graph
โ โ โโโ exit_gate.py # ExitGate (Innovation 3)
โ โ โโโ embedding.py # TokenEmbedding + TemporalPositionEncoder
โ โ โโโ ffn.py # DualStreamFFN
โ โ โโโ memory.py # MemoryModule โ persistent KV anchors
โ โ โโโ layers.py # TMTLayer โ assembles all submodules
โ โโโ data/
โ โ โโโ dataset.py # BlockDataset + load_text_dataset
โ โ โโโ tokenizer.py # TMTTokenizer โ thin HF wrapper
โ โโโ training/
โ โ โโโ trainer.py # Trainer โ training loop
โ โ โโโ loss.py # compute_loss (CE + gate auxiliary)
โ โ โโโ scheduler.py # cosine warmup LR schedule
โ โโโ experiments/ # Ablation study notebooks
โ โโโ 01_baseline.ipynb
โ โโโ 02_mesh_only.ipynb
โ โโโ 03_full_tmt.ipynb
โ โโโ 04_compare.ipynb
โโโ tests/ # 201 tests, all passing
โ โโโ test_forward.py
โ โโโ test_shapes.py
โ โโโ test_config.py
โ โโโ test_training.py
โ โโโ test_edge_cases.py
โ โโโ test_integration.py
โ โโโ test_reprs.py
โ โโโ test_dataset.py # NEW โ data pipeline, no network
โ โโโ test_generation.py # NEW โ logits, exit gate, gradients
โโโ paper/
โ โโโ TemporalMesh_Transformer_2026.pdf
โโโ docs/
โ โโโ index.html # GitHub Pages
โโโ pyproject.toml
โโโ requirements.txt
โโโ CONTRIBUTING.md
โโโ MODEL_CARD.md # HuggingFace model card
```
---
## Contributing
See [CONTRIBUTING.md](CONTRIBUTING.md) for:
- Development setup
- Code style (ruff, type hints)
- How to add tests
- Pull request process
All contributions welcome. Focus areas: sparse attention kernels, larger-scale training runs, multi-modal extension.
---
## Citation
```bibtex
@article{vigneshwar2026temporalmesh,
title = {TemporalMesh Transformer: Dynamic Graph Attention with
Temporal Decay and Adaptive Depth Routing},
author = {LK, Vigneshwar},
journal = {Zenodo Preprint},
year = {2026},
doi = {10.5281/zenodo.20287197},
url = {https://zenodo.org/records/20287390},
note = {Novel architecture combining mesh attention, temporal decay
encoding, and per-token adaptive depth routing}
}
```
---
## Links
| Resource | URL |
|:---|:---|
| Paper | https://zenodo.org/records/20287390 |
| DOI | https://doi.org/10.5281/zenodo.20287197 |
| GitHub | https://github.com/vignesh2027/TemporalMesh-Transformer |
| HuggingFace Model | https://huggingface.co/vigneshwar234/TemporalMesh-Transformer |
| HuggingFace Dataset | https://huggingface.co/datasets/vigneshwar234/TMT-Benchmarks |
| Live Demo | https://huggingface.co/spaces/vigneshwar234/TemporalMesh-Transformer-Demo |
| GitHub Pages | https://vignesh2027.github.io/TemporalMesh-Transformer/ |
---
**Built from scratch. Every attention head. Every graph edge. Every exit gate.**
*Vigneshwar LK โ Takshashila University, CSE 2022โ26*
