Goodbye Drift: Anchored Tree Sampling for Long-Horizon Video-to-Video Generation

Long-horizon video generation suffers from two intertwined issues. First, there is drift, where video quality degrades over time. Second, there are continuity issues which manifest as object permanence issues, or improperly rendering transient content (e.g., an object that appears in non-consecutive frames changing color/style). Recent autoregressive distillation methods seek to improve both, but retain sequential rollouts that can accumulate errors over long horizons. We introduce **Anchored Tree Sampling (ATS)**: a training-free inference-time scheduler that addresses this accumulation by replacing left-to-right rollout with sparse-to-dense, anchor-bounded imputation organized as a tree. A root call produces sparse anchors over the full horizon, recursive refinement generates intermediate anchors, and final leaf spans are synthesized between neighboring anchors. For a horizon requiring $K$ autoregressive chunks, ATS uses a balanced hierarchy with $O(\log K)$ sequential generation levels, replacing open-ended rollout with anchor-bounded infilling. We focus on V2V generation in the *static-camera* regime, where sparse anchors over the horizon are well approximated by the dense conditioning signal, and the base model can produce them without retraining. We evaluate ATS against two contemporary autoregressive baselines on Wan $2.1$ $+$ VACE, across five conditioning modalities (inpainting, outpainting, edge, pose, depth). We show that ATS outperforms both competitors in overall quality, as well as in drift prevention. We additionally demonstrate stable $\ge 40$-minute generation on LTX-$2.3$ across the same five modalities.