WildCity: A Real-World City-Scale Testbed for Rendering, Simulation, and Spatial Intelligence

arXiv 2026 22.3 method

TLDR

WildCity is a real-world city-scale multimodal dataset and simulator for rendering, simulation, and spatial intelligence research.

Reasoning

Strengths: large-scale real-world data, addresses city-scale spatial intelligence gap. Weaknesses: limited to 18 trajectories, no explicit world model or dynamics prediction; focuses on rendering and simulation rather than world model learning.

Read-first score

Read-first score 22.3, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 3.

Recency 6%

100

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

Reproducibility 18%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=dataset,github

Methodology quality 18%

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

Topical relevance 29%

4.3

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

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: 30.

Keyword Scores

world simulator

world model

generative world model

interactive world model

video world model

world dynamics prediction

model-based reinforcement learning world model

Resources

Paper