LS-AR: Future-Predictive Latent Steering in Autoregressive LLMs

Standard autoregressive (AR) models process high-level task instructions, state history, and transient tokens within a single shared sequence of tokens. Consequently, they lack the architectural mechanisms needed to isolate macro-objectives from context noise. To overcome this single-channel limitation, we introduce Latent-Steered Autoregressive (LS-AR), a dual-channel architecture that decouples continuous goal steering from discrete token decoding via FiLM conditioning. We evaluate a Static Goal Encoder ($P_0$) for persistent macro-objective retention across long rollouts and a Dynamic State Tracker ($P_t$) for recurrent latent updates during generation. On long-horizon retrieval past context limits ($H{=}1024, W{=}500$), LS-AR (Static) achieves 100% target recall where parameter-matched baselines collapse (0%), while increasing throughput by $\sim$ 35% and cutting peak VRAM by 52.8%. In Blocksworld planning under forced perturbations ($k{=}1$), LS-AR (Dynamic) sustains an 89.0% completion rate vs. 71.0% for the baseline, though zero-shot entity scaling ($N \to N+1$) exposes single-vector capacity limits (0%). Finally, dual-channel authority analysis shows that text goal dropout establishes latent-dominant control, offering structural defence against text prompt injection while introducing a latent vector attack surface.