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