FrameMorrow: Future-guided Frame Selection with Prospective Tokens for Long-Horizon Video Generation

arXiv 2026 23.1 method

TLDR

FrameMorrow selects historical frames based on predicted future information needs, using prospective tokens, improving long-horizon video generation across benchmarks and generative models.

Reasoning

Strengths include a novel future-guided selection mechanism and plug-and-play integration with closed-source generators, supported by extensive benchmarks. Weaknesses include limited abstract-level detail on how prospective tokens are trained and the peripheral relevance of world-model terminology to the core contribution.

Read-first score

Read-first score 23.1, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 19.

Recency 5%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Methodology quality 15%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=benchmark,experiment

Reproducibility 15%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Topical relevance 25%

27.1

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Graph prestige 15%

5.7

PageRank-style prestige over the local citation graph built from available OpenAlex references. local_pagerank=0.0016

Citation impact 15%

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. cited_by_count=0

Citation velocity 10%

Citation velocity estimates citations per publication-year to reduce old-paper bias. velocity=0.00

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 115.

Keyword Scores

world model

interactive world model

video world model

world dynamics prediction

generative world model

world simulator

model-based reinforcement learning world model

Resources

Paper