Ma Chan

This repository contains the actual materialized float32 parameter tensors and signed continuity evidence for Aster's step-132 state. It is a custom conventional block-dense transformer trained from random initialization with an intrinsic objective: predict its own next internal state and next error signal. A learned internal controller chooses adaptation and append growth. This is a research checkpoint, not a pretrained general-purpose language model and not a stock AutoModel checkpoint. The included runtime and restore tool are required for its custom block layout. Step 129 selected width growth from 27 to 28. The internal controller selected growth index 1, adaptation index 0, scale…