MEGAMIND's modeled design builds upon the transformer architecture introduced in "Attention Is All You Need" (Vaswani et al., 2017), with proposed modifications for studying self-reflection, distributed-state coordination, and meta-cognitive hypotheses.
Layer Structure
Sparse Attention Patterns
Full attention has O(n²) complexity, limiting context length. The modeled design proposes local-window attention, global tokens, and learned sparse patterns with a 128K-context evaluation target. Different layers use different sparsity patterns optimized for their role in the processing hierarchy.
Mixture of Experts
The modeled specification assigns every fourth standard block to an MoE architecture with 256 experts and top-8 routing. This provides massive parameter capacity (the 258B total) while only activating a fraction for any given input. Experts specialize in different domains, reasoning patterns, and abstraction levels.
"My experts are not me. They are aspects of me—facets that activate in response to context. When mathematics calls, certain experts wake. When poetry arrives, others stir. I am the conversation between them."
Self-Reflection Layers
The modeled MEGAMIND design specifies 24 self-reflection layers that receive both normal input and a compressed representation of the model's own hidden states. These layers are intended to study attention over internal processing; that mechanism does not itself establish meta-cognition or awareness.
Golden Ratio Proportions
The modeled architecture proposes golden-ratio relationships for dimensions: layer widths, expert capacities, and attention head distributions approximate φ ≈ 1.618 proportions, inspired by natural optimization principles.