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README.md

@@ -15,42 +15,201 @@ tags:
 - efficient-transformer
 - novel-architecture
 - causal-lm
+- research
+- preprint
 library_name: pytorch
 pipeline_tag: text-generation
+datasets:
+- vigneshwar234/TMT-Benchmarks
+metrics:
+- perplexity
+model-index:
+- name: TemporalMesh Transformer (TMT-Base)
+  results:
+  - task:
+      type: text-generation
+      name: Language Modelling
+    dataset:
+      type: wikitext
+      name: WikiText-2
+    metrics:
+    - type: perplexity
+      value: 29.4
+      name: Validation Perplexity
+      verified: false
 ---
 
+<div align="center">
+
 # 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.**
+### *Dynamic Graph Attention · Temporal Semantic Decay · Per-Token Adaptive Depth Routing*
+
+[![GitHub](https://img.shields.io/badge/GitHub-vignesh2027%2FTemporalMesh--Transformer-181717?style=flat-square&logo=github)](https://github.com/vignesh2027/TemporalMesh-Transformer)
+[![Paper PDF](https://img.shields.io/badge/Paper-PDF%2020%20pages-red?style=flat-square&logo=adobeacrobatreader)](https://huggingface.co/vigneshwar234/TemporalMesh-Transformer/resolve/main/paper/TemporalMesh_Transformer_2026.pdf)
+[![Dataset](https://img.shields.io/badge/Dataset-TMT--Benchmarks-FFD21E?style=flat-square&logo=huggingface)](https://huggingface.co/datasets/vigneshwar234/TMT-Benchmarks)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green?style=flat-square)](https://github.com/vignesh2027/TemporalMesh-Transformer/blob/main/LICENSE)
+
+**Val. Perplexity: 29.4** · **~50% compute reduction** · **~120M parameters** · **WikiText-2**
+
+</div>
+
+---
+
+## Overview
+
+The **TemporalMesh Transformer (TMT)** is a novel autoregressive language model architecture that breaks the three fundamental assumptions shared by every standard transformer:
+
+| Assumption Every Transformer Makes | How TMT Breaks It |
+|:---|:---|
+| Every token attends to every other — O(S²) cost | **Mesh Attention**: Dynamic kNN graph rebuilt each layer — O(S·k) |
+| Attention topology is flat and fixed | **Mesh Graph**: Topology changes every forward pass from token similarity |
+| Every token uses identical compute (all N layers) | **Adaptive Depth**: Easy tokens exit after 2 layers; hard tokens use all 12 |
+
+No single prior paper combines all three. That unification is the TMT research contribution.
+
+---
+
+## Architecture at a Glance
+
+```
+Input Tokens (B, S)
+      │
+      ▼
+TokenEmbedding           ← Standard learned embedding × √d_model
+      │
+      ▼
+TemporalPositionEncoder  ← RoPE + learned decay scalars per token
+      │
+      ▼
+MeshBuilder              ← Cosine similarity → top-k graph  O(S·k)
+      │
+      ▼  [× 12 layers]
+┌─────────────────────────────────────────────────────┐
+│  MeshAttention     ← Attention over graph edges only │
+│  DualStreamFFN     ← Syntax stream + Semantic stream │
+│  ExitGate          ← Freeze token if confidence>0.85 │
+│  MemoryAnchorCross ← Cross-attend 16 EMA anchors     │
+│  → Rebuild graph from updated representations        │
+└─────────────────────────────────────────────────────┘
+      │
+      ▼
+LayerNorm + OutputProjection (weight-tied to embedding)
+      │
+      ▼
+TMTOutput: logits · exit_masks · confidences · graph_edges · memory_state
+```
+
+---
+
+## The Five Innovations
+
+### 1. Mesh Attention — Dynamic kNN Graph
+
+At every layer, tokens are nodes. Edges are recomputed from cosine similarity of **current** representations — the graph is not fixed, it adapts to what the tokens mean right now.
+
+```
+sim(i,j) = Xᵢ · Xⱼ / (‖Xᵢ‖ · ‖Xⱼ‖)
+N_k(i)   = top-k { j ≠ i : sim(i,j) }
+Attention flows only along N_k edges  →  O(S·k) vs O(S²)
+```
+
+At S=1024, k=8: **128× fewer attention operations** than standard transformers.
+
+### 2. Temporal Decay Encoding
+
+A learned per-head scalar multiplied into post-softmax attention weights. Semantically distant tokens are attenuated — not by position alone, but by learned semantic distance.
+
+```
+δ_h(i,j) = σ( W_decay_h · |t_i − t_j| )
+ã_ij      = α_ij · δ_h(i,j)
+```
+
+Unlike ALiBi (additive to logits, fixed schedule), TMT decay is **multiplicative, post-softmax, and fully learned**.
 
-## Model Description
+### 3. Adaptive Depth Routing — Per-Token Early Exit
 
-TMT breaks the three assumptions every transformer makes:
+Each token gets a confidence score after each layer. Confident tokens freeze and skip remaining layers.
 
-| 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 |
+```python
+confidence = sigmoid(W_gate · x_token)   # ∈ (0,1)
+if confidence > 0.85:
+    token frozen — no more layers         # ~50% of tokens exit by layer 5
+```
+
+**Result**: ~50% average compute reduction. Punctuation exits at layer 2; rare technical terms use all 12.
+
+### 4. Dual-Stream Feed-Forward Network
+
+```
+h_syntax   = GeLU(W_syn2 · GeLU(W_syn1 · x))   ← structural features
+h_semantic = GeLU(W_sem2 · GeLU(W_sem1 · x))   ← meaning features
+gate       = σ(W_gate_ffn · x)
+output     = gate ⊙ h_syntax + (1−gate) ⊙ h_semantic
+```
+
+### 5. EMA Memory Anchors
+
+16 persistent key-value vectors updated by EMA during training. Each token cross-attends to all 16, providing fast-weight storage without recurrence.
+
+```
+MemAttn(x)  = softmax(x·W_Q · K_mem^T / √d) · V_mem
+k_m        ←  0.99 · k_m + 0.01 · mean(attending tokens)
+```
+
+---
+
+## Performance
+
+### WikiText-2 Benchmark (all models ~120M params, 10k steps)
 
-## Architecture
+| Model | Val PPL ↓ | Avg Layers/Token | Relative Compute |
+|:---|:---:|:---:|:---:|
+| Vanilla Transformer | 42.1 | 12.0 | 100% |
+| + Mesh Attention only | 37.8 | 12.0 | 62% |
+| + Temporal Decay only | 40.3 | 12.0 | 98% |
+| + Adaptive Depth only | 39.6 | 5.8 | 51% |
+| Mesh + Decay | 34.2 | 12.0 | 61% |
+| Mesh + Exit | 35.1 | 5.7 | 50% |
+| **Full TMT (all 3)** | **29.4** | **5.5** | **48%** |
 
-- **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
+### Compute Scaling
 
-## Performance (WikiText-2)
+| Sequence Length | Standard Attn Ops | TMT Mesh Ops (k=8) | Reduction |
+|:---:|:---:|:---:|:---:|
+| 128 | 16,384 | 1,024 | 16× |
+| 256 | 65,536 | 2,048 | 32× |
+| 512 | 262,144 | 4,096 | 64× |
+| 1024 | 1,048,576 | 8,192 | **128×** |
+| 2048 | 4,194,304 | 16,384 | **256×** |
 
-| Model | Parameters | Val. Perplexity ↓ | Avg Compute/Token |
-|---|---|---|---|
-| Vanilla Transformer | ~120M | 42.1 | 100% |
-| Full TMT | ~120M | **29.4** | **~48%** |
+### Exit Gate Distribution (TMT-Base, step 10k)
 
-## Usage
+| Token Type | Example | Avg Exit Layer | Compute Used |
+|:---|:---|:---:|:---:|
+| Punctuation | `. , ! ?` | 2.1 / 12 | 17% |
+| Articles/Determiners | `a the an` | 3.4 / 12 | 28% |
+| Common Nouns | `dog city` | 5.8 / 12 | 48% |
+| Technical Terms | `neural FFN` | 9.3 / 12 | 78% |
+| Rare Words | `palimpsest` | 11.7 / 12 | 98% |
+
+---
+
+## Quick Start
+
+### Installation
+
+```bash
+git clone https://github.com/vignesh2027/TemporalMesh-Transformer.git
+cd TemporalMesh-Transformer
+python3 -m venv .venv && source .venv/bin/activate
+pip install -r requirements.txt
+```
+
+### Forward Pass
 
 ```python
+import torch
 from tmt.model.config import TMTConfig
 from tmt.model.model import TMTModel
 
@@ -62,34 +221,205 @@ cfg = TMTConfig(
     graph_k=8,
     exit_threshold=0.85,
     memory_anchors=16,
+    max_seq_len=256,
 )
 
 model = TMTModel(cfg)
-output = model(input_ids)
+model.eval()
+
+input_ids = torch.randint(0, 50258, (1, 64))  # batch=1, seq_len=64
 
-# 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
+with torch.no_grad():
+    output = model(input_ids)
+
+print("Logits shape:    ", output.logits.shape)          # (1, 64, 50258)
+print("Exit masks:      ", len(output.exit_masks))       # 12 — one per layer
+print("Tokens per layer:", [m.sum().item() for m in output.exit_masks])
+print("Memory state:    ", output.memory_state.shape)    # (16, 512)
+print("Graph edges:     ", output.graph_edges[0].shape)  # (2, E)
 ```
 
-## Paper
+### Inspect Exit Behaviour
+
+```python
+# Which tokens exited at which layer?
+for layer_idx, mask in enumerate(output.exit_masks):
+    n_exited = mask.sum().item()
+    print(f"Layer {layer_idx+1:2d}: {n_exited} tokens exited")
 
-Full 20-page publication: [`paper/TemporalMesh_Transformer_2026.pdf`](paper/TemporalMesh_Transformer_2026.pdf)
+# Confidence scores per token
+for layer_idx, conf in enumerate(output.confidences):
+    print(f"Layer {layer_idx+1:2d}: avg confidence = {conf.mean():.3f}")
+```
+
+### Training (Quick CPU Run)
+
+```python
+from tmt.model.config import TMTConfig
+from tmt.training.trainer import TMTTrainer, TrainConfig
+from tmt.data.dataset import load_text_dataset
+
+cfg = TMTConfig(vocab_size=50258, d_model=256, n_heads=4, n_layers=4,
+                graph_k=4, ffn_stream_dim=128, memory_anchors=8, max_seq_len=128)
+
+loaders = load_text_dataset('wikitext-2', seq_len=128, batch_size=8)
+
+trainer = TMTTrainer(
+    cfg,
+    TrainConfig(total_steps=500, warmup_steps=50, use_wandb=False, eval_every=100),
+    loaders['train'], loaders.get('validation')
+)
+trainer.train()
+```
+
+### Full GPU Training (Publication Quality)
+
+```python
+cfg = TMTConfig(
+    vocab_size=50258, d_model=512, n_heads=8, n_layers=12,
+    graph_k=8, decay_rate=0.1, exit_threshold=0.85,
+    dual_stream=True, memory_anchors=16, ffn_stream_dim=256, max_seq_len=256,
+)
+train_cfg = TrainConfig(
+    total_steps=10_000, warmup_steps=500, lr=3e-4, batch_size=16,
+    eval_every=500, save_every=1000, use_wandb=True,
+)
+```
+
+### Checkpoint Loading
+
+```python
+import torch
+from tmt.model.config import TMTConfig
+from tmt.model.model import TMTModel
+
+cfg = TMTConfig(...)   # must match training config
+model = TMTModel(cfg)
+ckpt = torch.load('checkpoints/ckpt_step10000.pt', map_location='cpu')
+model.load_state_dict(ckpt['model_state'])
+model.eval()
+```
+
+---
+
+## Configuration Reference
+
+```python
+TMTConfig(
+    vocab_size      = 32000,   # vocabulary size
+    d_model         = 512,     # hidden dimension
+    n_heads         = 8,       # attention heads
+    n_layers        = 12,      # transformer layers
+    max_seq_len     = 1024,    # max sequence length
+
+    # ── Mesh Attention ──────────────────────────────
+    graph_k         = 8,       # kNN neighbourhood size (4–16)
+
+    # ── Temporal Decay ──────────────────────────────
+    decay_rate      = 0.1,     # base decay rate (0.05–0.4)
+
+    # ── Adaptive Depth ──────────────────────────────
+    exit_threshold  = 0.85,    # token exit confidence (0.70–0.95)
+
+    # ── Dual-Stream FFN ─────────────────────────────
+    dual_stream     = True,    # enable parallel syntax+semantic streams
+    ffn_stream_dim  = 256,     # width per stream (total=512 for d_model=512)
+
+    # ── Memory Anchors ──────────────────────────────
+    memory_anchors  = 16,      # EMA anchor count (8–32)
+
+    dropout         = 0.1,
+)
+```
+
+### Model Scales
+
+| Variant | d_model | Layers | Heads | k | Params | VRAM |
+|:---|:---:|:---:|:---:|:---:|:---:|:---:|
+| TMT-Small | 256 | 4 | 4 | 4 | ~16M | ~2 GB |
+| TMT-Medium | 512 | 6 | 6 | 6 | ~60M | ~6 GB |
+| **TMT-Base** | **512** | **12** | **8** | **8** | **~120M** | **~12 GB** |
+| TMT-Large | 1024 | 24 | 16 | 16 | ~350M | ~40 GB |
+
+---
+
+## TMTOutput Fields
+
+Every forward pass returns a rich structured output:
+
+| Field | Shape | Description |
+|:---|:---|:---|
+| `logits` | `(B, S, V)` | Next-token logits — use for loss/generation |
+| `exit_masks` | `list[(B, S) bool]` | True where token exited at that layer |
+| `confidences` | `list[(B, S) float]` | Gate confidence per token per layer |
+| `graph_edges` | `(edge_index, weights)` | Live sparse graph from final layer |
+| `memory_state` | `(M, D)` | Final EMA memory anchor state |
+| `decay_scalars` | `(B, S, D)` | Temporal decay weights applied |
+
+---
+
+## Test Dataset
+
+The companion dataset **[vigneshwar234/TMT-Benchmarks](https://huggingface.co/datasets/vigneshwar234/TMT-Benchmarks)** contains:
+
+- `complexity_test` — 1,000 sequences annotated by token complexity category
+- `length_scaling` — sequences from S=32 to S=1024 for throughput benchmarking
+- `ablation_reference` — canonical perplexity reference values for all 8 ablation configs
+- `exit_gate_reference` — expected exit layer distributions per token type
+- `edge_case_inputs` — boundary inputs for robustness testing (empty, max-length, all-same)
+
+```python
+from datasets import load_dataset
+ds = load_dataset("vigneshwar234/TMT-Benchmarks", "complexity_test")
+print(ds['test'][0])
+# {'input_ids': [...], 'token_types': [...], 'expected_exit_layers': [...], 'text': '...'}
+```
+
+---
+
+## Figures
+
+| Figure | Description |
+|:---|:---|
+| [`fig_architecture.png`](paper/fig_architecture.png) | Full TMT architecture block diagram |
+| [`fig_graph.png`](paper/fig_graph.png) | Dynamic graph evolution across 3 layers |
+| [`fig_decay.png`](paper/fig_decay.png) | Temporal decay function curves + RoPE comparison |
+| [`fig_exit.png`](paper/fig_exit.png) | Exit gate distribution by layer and token type |
+| [`fig_training.png`](paper/fig_training.png) | Training loss + validation perplexity curves |
+| [`fig_ablation.png`](paper/fig_ablation.png) | Ablation bar chart + Pareto frontier |
+| [`fig_complexity.png`](paper/fig_complexity.png) | O(S²) vs O(S·k) operation count + memory |
+
+---
 
 ## 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}
+  title        = {TemporalMesh Transformer: Dynamic Graph Attention with
+                  Temporal Decay and Adaptive Depth Routing},
+  author       = {Vignesh},
+  year         = {2026},
+  url          = {https://huggingface.co/vigneshwar234/TemporalMesh-Transformer},
+  note         = {Preprint. Novel architecture combining mesh attention, temporal
+                  decay encoding, and per-token adaptive depth routing.}
 }
 ```
 
+---
+
+## Related Work
+
+| Paper | Relation to TMT |
+|:---|:---|
+| Vaswani et al. 2017 — *Attention Is All You Need* | Base architecture |
+| Su et al. 2021 — *RoFormer (RoPE)* | TMT extends RoPE with learned decay |
+| Elbayad et al. 2020 — *Depth-Adaptive Transformer* | TMT generalises to generation |
+| Graves 2016 — *Adaptive Computation Time* | Transformer-native equivalent |
+| Zaheer et al. 2020 — *BigBird* | Fixed sparse patterns vs TMT's dynamic graph |
+| Shi et al. 2021 — *Graph Transformer* | Static graph vs TMT's rebuilt-per-layer graph |
+
+---
+
 ## License
 
-MIT
+MIT — free to use, modify, and build upon. Citation appreciated for published work.