GoKawiilTechnical essay models transformer layers as memory stores with measurable recall capacity
A technical blog post argues that linear layers, MLPs, and self-attention can each be understood as a type of memory store that holds key-value pairs and retrieves them later. The author introduces an 'interference-to-signal ratio' to measure how well each layer type recalls stored information as the number of memories grows, estimating capacities of roughly 64 memories for linear layers, 2,700 for MLPs, and over a million for self-attention with 64-dimensional keys.
GoKawiil's interpretation of the reporting above, not reported fact.
The framing suggests that transformer architecture improvements, from linear layers to MLPs to attention, can be explained as successive gains in memory capacity and recall quality rather than arbitrary design choices. This could offer researchers and students an intuitive mental model for why self-attention scales so well in large language models, though it remains a conceptual explanation rather than a new empirical result.
- The post frames linear layers, MLPs, and self-attention as different types of memory stores
- An 'interference-to-signal ratio' is proposed to quantify recall quality as stored memories increase
- Estimated capacities range from ~64 for linear layers to over a million for self-attention with 64-dimensional keys
Build a Large Language Model (From Scratch) book — This book walks you through implementing transformers, attention, and memory mechanisms step-by-step in code, which pairs perfectly with the conceptual deep dive in this article. It's a great way to turn the intuition about memory stores and self-attention into hands-on understanding by building a working model yourself.
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Published there as: “You could have invented transformers from memory”
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