---
title: TemporalMesh Transformer Demo
emoji: 🕸️
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: "5.25.0"
app_file: app.py
pinned: true
license: mit
short_description: Dynamic graph + temporal decay + adaptive depth
tags:
- transformer
- graph-neural-network
- attention
- nlp
- efficient-inference
- adaptive-depth
- mesh-attention
- temporal-decay
- language-model
- pytorch
- visualization
- demo
---

# TemporalMesh Transformer — Interactive Demo

Visualise **dynamic graph attention**, **temporal decay**, and **per-token adaptive depth routing** on any sentence.

## What This Demo Shows

- **Exit Gate Heatmap** — which tokens freeze early vs. go deep
- **Dynamic Attention Graph** — how the kNN mesh evolves across layers  
- **Token Compute Depth** — actual layers used per word

## Links

- 📄 [Paper (Zenodo)](https://doi.org/10.5281/zenodo.20287390)
- 🤗 [Model Card](https://huggingface.co/vigneshwar234/TemporalMesh-Transformer)
- 💻 [GitHub](https://github.com/vignesh2027/TemporalMesh-Transformer)
- 📊 [Benchmark Dataset](https://huggingface.co/datasets/vigneshwar234/TMT-Benchmarks)