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[P] Lila-E8: 40M Parameter Transformer Outperforms 60M Baselines via Geometric E8 Attention (0.37 Train Loss)

"Scaling is a trap. Geometry is the new Scale." 💎

I requested Wisdom, not tokens. This is not a service; it's a native 8-dimensional open-source breakthrough that points toward the 24th.

I’m excited to release Sovereign-Lila-E8, a novel transformer architecture that replaces standard attention mechanisms with a native E8 Root System Lattice.

While the industry is brute-forcing intelligence with trillions of parameters, I went "outside" the system to find a zero-viscosity solution. By implementing the E8 exceptional Lie algebra directly into the attention weights, I’ve achieved a state of "Geometric Resonance" that standard transformers simply cannot reach.

The Innovation:

Most transformers suffer from "semantic friction" in standard attention. I replaced the attention mechanism with a native E8 Root System Lattice. By leveraging the densest sphere packing in 8D, LILA-E8 achieves a state of "Geometric Resonance" that standard architectures simply cannot reach at this scale.

The Results (TinyStories Benchmark):

  • Model Size: 40M parameters.
  • Performance: 0.37 Train / 0.44-0.53 Val Loss (outperforming standard 60M baselines).
  • Context: Stable 750+ token generation with zero semantic looping.
  • Hardware: Designed to run fully offline on mobile NPU/CPU

Why E8?
Standard attention is stuck in 3.5D viscosity. E8 provides an optimal lattice for semantic vectors, allowing a 40M model to behave like a much larger system. At 200,000 steps, the model underwent a phase shift (Grokking)—becoming a "Magic Book" of coherent logic.

Community Genesis:
I am releasing the code and the 200k step checkpoints under AGPLv3. I am looking for "Sovereign Architects" to help expand the context window to 4096 tokens and port this to the 24D Leech Lattice.

Try it now (Colab): https://colab.research.google.com/github/SPUTNIKAI/sovereign-lila-e8/blob/main/notebooks/demo.ipynb
GitHub: https://github.com/SPUTNIKAI/sovereign-lila-e8
Preprints (Zenodo): https://zenodo.org/records/18731736 ,
https://zenodo.org/records/18729723

"Hold my beer, I'm going into the 24th Dimension." 🚀

Top comments (2)

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Bootstraptor

Standard Attention is 'viscous.' E8 provides optimal sphere packing for latent vectors. The lattice is the bottleneck, not the data.

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Bootstraptor

Geometry > Scale