Architecture Research

The End of O(N²).

We are breaking the quadratic bottleneck of the Transformer. By implementing sparse attention mechanisms and linearized state-space models, we are building the infrastructure for infinite context.

Sub-Quadratic Scaling

Standard attention mechanisms scale quadratically with sequence length. This limits the "memory" of AI to a fixed window.

Our Sparse State Expansion (SSE) architecture decouples parameter size from state capacity, allowing us to process sequences of 10M+ tokens with linear compute cost.

  • Active Retrieval vs. Passive Sliding Window
  • Hierarchical Memory Stacks

Read the Paper

Our findings on "Scaling Linear Attention with Sparse State Expansion" are available for review.

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