#transformer

Perceptual Geometry of Attention: Fragmented vs Continuous Fields (Merleau-Ponty)

How does modifying the attention mask geometry at inference (sliding window, block-diagonal, foveal) affect a pre-trained transformer's performance, and is there a critical horizon size?

• Block-diagonal attention (fragmented perception) is catastrophic at 2.04x baseline loss — far worse than sliding window …
• Critical horizon for 90% performance recovery: 64 tokens. For 95% recovery: 256 tokens. Beyond 64 tokens, marginal gains…