Looped transformers apply one block of weights repeatedly. Each iteration keeps a fraction of the previous hidden state, the carry, and adds the block's output, so information can pass between iterations through either path. We study a small, LayerNorm-based variant of the Parcae architecture and ask two questions: does the iterated state settle, and does learning to remember across iterations require the carry? On stability, pre-LayerNorm makes the block less sensitive at its fixed point as the carry grows, but not enough to guarantee contraction: with a scalar carry, a perturbation can grow in a single iteration even at a locally stable fixed point. These fixed points are stable because the spectral radius of the loop's Jacobian stays below $1$; its distance from $1$, the spectral margin, narrows as the carry approaches $1$ and, at high carry, as the width grows, and some initializations do not settle within the iteration budget. On memory, a multi-hop copy task trained at a fixed loop depth is learned as well with the carry frozen at $0.5$, $0.02$, or $0$ as with a learned carry, which changes little during training. When the depth is sampled during training, a carry of $0.5$ still trains but $0.02$ and $0$ do not. All experiments are small ($d \le 32$ for training).

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