Predictive Speculative Coupling: Distribution-Preserving Acceleration of Autoregressive Visual Generation

Autoregressive visual generators produce images and videos one token at a time, making sequential target-model evaluations the dominant inference cost. Draft-free speculative Jacobi decoding evaluates a block in parallel, but it initializes each new round with distributions recycled from an earlier speculative context. This one-round lag lowers proposal–target overlap, and the loss compounds along the verified prefix as the window grows. We introduce our PSC method, a training-free framework that separates proposal optimization from sampling correctness. PSC extrapolates aligned proposal drift to form a transported distribution, couples the existing proposal sample to that distribution, and then couples the transported sample to the target through exact maximal correction. The composed decoder preserves the complete autoregressive joint distribution for any non-anticipating transport policy. Across Lumina-mGPT-7B, Janus-Pro-7B, and Cosmos-AR-4B, the exact vectorized path reduces target evaluations and end-to-end latency relative to SJD, with the largest exact endpoint reaching 3.07×NFE and 2.51×latency speedups. The evaluated online selector overwhelmingly chooses the β = 0 reference; the results therefore validate the exact decoding interface and its implementation, but do not establish a benefit from nonzero predictive transport.