cff-version: 1.2.0
message: "If you use TMT in your research, please cite it as below."
type: software
title: "TemporalMesh Transformer (TMT)"
abstract: >
  A novel autoregressive language model architecture that simultaneously fuses
  dynamic graph topology (Mesh Attention), token-level temporal semantic decay,
  and per-token adaptive depth routing into a single unified model.
  Achieves ~50% compute reduction and lower perplexity vs. parameter-matched baselines.
authors:
  - name: "Vignesh"
    alias: "vignesh2027"
repository-code: "https://github.com/vignesh2027/TemporalMesh-Transformer"
url: "https://huggingface.co/vigneshwar234/TemporalMesh-Transformer"
license: MIT
version: "1.0.0"
date-released: "2026-05-19"
keywords:
  - transformer
  - mesh-attention
  - temporal-decay
  - adaptive-depth
  - graph-neural-network
  - efficient-transformer
  - language-model
  - PyTorch
  - NLP
  - deep-learning
preferred-citation:
  type: generic
  title: >
    TemporalMesh Transformer: Dynamic Graph Attention with Temporal Decay
    and Adaptive Depth Routing
  authors:
    - name: "Vignesh"
  year: 2026
  url: "https://huggingface.co/vigneshwar234/TemporalMesh-Transformer"
  notes: "Preprint. Available at GitHub and Hugging Face."