Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models

arXiv 2026 30.9 application

TLDR

Geographic diversity in training data improves cross-domain generalization of JEPA driving world models more than increasing data volume.

Reasoning

The paper presents a clear controlled experiment with quantitative results showing that diverse geographic data reduces surprise scores significantly compared to single-geography data, even when the latter has 3x more data. Strengths include rigorous ablation and real-world datasets; weaknesses are the narrow domain and lack of comparison to other model types.

Read-first score

Read-first score 30.9, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 26.

Recency 6%

100

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

Methodology quality 18%

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

Topical relevance 29%

37.1

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

Reproducibility 18%

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

Citation impact 18%

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

Citation velocity 12%

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

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 112.

Keyword Scores

world model

world dynamics prediction

video world model

generative world model

world simulator

interactive world model

model-based reinforcement learning world model

Resources

Paper