The longer a video generation gets, the harder it is to know which past frames to remember. This paper picks them based on future usefulness instead of present similarity. Title: FrameMorrow: Future-guided Frame Selection with Prospective Tokens for Long-Horizon Video Generation URL: 📝 Overview FrameMorrow selects historical frames based on relevance to future information needs rather than similarity to the present, using "prospective tokens" that predict what will matter next without actually generating future video. ❗ Problem it solves Existing methods keep frames similar to recent content, but that's myopic — visually similar frames often don't help future generation, and long videos lose coherence as a result. 🛠️ Method A frozen visual encoder (DINOv2) feeds a small set of learnable projections and attention layers that autoregressively predict prospective tokens, trained via "future-grounded ranking distillation" against actual continuation frames. Because the generator itself stays frozen and FrameMorrow just hands it explicit selected frames through a per-backbone adapter, it plugs into open-source models as well as closed-source APIs like Seedance 2.0 and Kling O3. 📊 Results Tested across 5 benchmarks and 11 models, it lifts interactive-generation consistency from 84.95 to 89.30 and improves action alignment on the WorldMem model from 0.866 to 0.889. In a user study, raters preferred FrameMorrow's consistency 62.7% of the time versus 16.7% for native models. #VideoGeneration# #AIResearch#