Control Enters Through the Memory: Self-Conditioning as a Control Interface in Continuous Language Flows

Continuous language flows keep a real-valued state until the final decode, which makes the state a natural target for inference-time guidance. On the released sampler of Embedded Language Flows (ELF), however, a state push moves the state's decode toward the target while the emitted text lags. We show that the final decode reads exactly the model's clean prediction, its belief, which a state push does not directly optimize. The evidence points to stale self-conditioning: the denoiser's previous-prediction memory lags the pushed state, and the belief it conditions lags with it. Holding the self-conditioning scale fixed and editing only the memory's content restores control on three checkpoints, more than any of four control edits of the memory. The self-conditioning input is therefore a control interface, and memory-port gradient writing writes the reward gradient into the memory at every step. On three public ELF checkpoints, inside a pre-registered quality gate, the write alone beats state guidance at scale $.25$ on held-out keyword coverage at equal cost. Added to state guidance, it beats every state-guidance scale inside the gate at under twice the cost and also beats state guidance on two further reward families. Controllability in continuous language flows depends not only on what guidance optimizes but on where it enters.