**1. Dynamic Key-Value Cache:**

1. Dynamic Key-Value Cache:

Maintain a persistent KV cache for autobiographical content that updates via:
- Linear gating: $new_cache = γ \cdot old_cache + (1 - γ) \cdot current_bio$
- Content-aware update rules learned through backpropagation

2. Positional Encoding Strategies

Hierarchical Positional Embeddings:

Implement dual positional encoding streams:
- Autobiographical Position: Use learned positional embeddings with windowed attention
- Main Context Position: Apply RoPE (Rotary Position Embedding) for better length extrapolation

Component	Encoding Type	Dimensions	Interaction Mechanism
Autobiography	Learned + Windowed	64	Content-aware gating
Main Context	RoPE	128	Standard self-attention

Position-Aware Layer Norm:

Modify layer normalization to separately process positional signals:

class BioAwareLayerNorm(nn.Module):
    def __init__(self, hidden_size):
        super().__init__()
        self.main_norm = nn.LayerNorm(hidden_size)
        self.bio_norm = nn.LayerNorm(hidden_size//2)
        
    def forward(self, x):
        bio, main = x.split([x.size(-1)//2, x.size(-1)//2], dim=-1)
        return torch.cat([self.bio_norm(bio), self.main_norm(main)], dim=-1)