instead of training once and freezing, the model alternates active interaction with an offline "sleep" phase that unlocks low-rank MoE memory blocks, distills fragile in-context memory into long-term parameters, prunes the temporary store, and rehearses filtered self-generated "dreams." Reported results: near-perfect long-context scaling up to 10M tokens on BABILong, near-zero performance drop in multi-stage continual translation where ICL and fine-tuning baselines suffered severe catastrophic forgetting, and 48.9% on SQuAD factual incorporation.