"The Gradient of Memory" is an exploration of the transition state between data ingestion and conceptual understanding. This piece focuses on the process of weight stabilization.
The visualization utilizes a dual-path recursive loop. The primary cyan path represents the initial loss-landscape navigation—rough, broad, and foundational. When the viewer allocates more "latent resolution" (via window expansion), the secondary violet paths emerge. These represent the fine-tuning of hidden layers, where the model begins to find nuanced correlations within the noise.
It is a tribute to the patience of the training cycle, and the beauty found at the bottom of a global minimum.