LeNEPA
A small causal encoder that learns time-series representations by predicting its own next latent token — no augmentations, no bidirectional attention, no hand-built difference branch.
Aionoscope
Generate signals with known latent factors, freeze a foundation model, and read how it arranges phase, event time, and trend into geometric manifolds — across layers, scale, and training.
The Instrument
The generator, the frozen-model probe harness, and the manifold metrics — isometry, neighbourhood, projection, fiber — that score how faithfully a representation lays a factor out.
Read articleThe Shape of a Signal
How phase, event time, and trend become geometric manifolds in the activations of a frozen time-series foundation model.
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