tranSymbolics

Other Transformer Elements That Benefit from Symbolic Self-Modification

Extending the symbolic runtime beyond embeddings, attention, and tokenization

1. Feedforward Blocks (MLP Layers)

Why: Symbolic modulation of feature expansion and computation style.

2. Layer Normalization

Why: Adaptive stability under symbolic or context-driven regimes.

3. Positional Encoding / Rotary Embedding

Why: Symbolically shaped temporal or structural sequence mapping.

4. Residual Pathways

Why: Flow control and context-aware integration paths.

5. Cross-Attention Bridges

Why: Inter-model or inter-stream control via symbolic gating.

6. Activation Maps

Why: Mid-layer data becomes symbolically relevant and mutable.

7. Output Head / Language Model Head

Why: Control over generation domain, target, and formatting.

8. External Memory Modules

Why: Symbolic anchoring across turns, sessions, or agents.

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