TemporalMesh Transformer: dynamic kNN graph attention + adaptive exit gates, 29.4 PPL at 48% compute
#6
by vigneshwar234 - opened
New open-source transformer architecture — directly relevant to this repo
TMT achieves 29.4 PPL on WikiText-2 at 48% compute (−30.2% vs vanilla, 120M params). Directly relevant to users comparing efficient attention and depth-adaptive architectures.
Five innovations: Mesh Attention (O(S·k) dynamic kNN), Temporal Decay (post-softmax multiplicative), Adaptive Exit Gate (per-token depth routing, avg 5.76/12 layers), Dual-Stream FFN, EMA Memory Anchors.
vs. models in this category:
- Beats Mamba: 29.4 vs 31.8 PPL, same 120M params
- Beats Longformer: 29.4 vs 39.6 PPL, same compute class
- LongBench: 53.4 vs 51.3 Mamba
📄 Paper (DOI 10.5281/zenodo.20287197): https://zenodo.org/records/20287390
💻 Code + 226 tests: https://github.com/vignesh2027/TemporalMesh-Transformer
🎮 Live demo: https://huggingface.co/spaces/vigneshwar234/TemporalMesh-Transformer-Demo