Towards Interactive Video World Modeling: Frontiers, Challenges, Benchmarks, and Future Trends

arXiv 2026 75.1 survey

TLDR

A survey on interactive video world modeling, covering frontiers, challenges, benchmarks, and future trends.

Reasoning

Strengths: comprehensive overview of recent trends, technical challenges, and benchmarks across multiple domains. Weaknesses: lacks original experiments or novel contributions; primarily a literature review.

Read-first score

Read-first score 75.1, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 50.

Methodology quality 18%

100

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

Recency 6%

100

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

Citation impact 18%

92.2

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. citation_normalized_percentile=0.92178559

Reproducibility 18%

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

Topical relevance 29%

71.4

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 velocity 12%

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

Field roles

FoundationFrontierBridgeMethodology anchorReproducibility anchor

Rank sensitivity

Stability: volatile; rank range: 331.

Keyword Scores

interactive world model

world model

video world model

generative world model

world dynamics prediction

world simulator

model-based reinforcement learning world model

Deep Analysis

Innovations

Systematic review of interactive world modeling covering application scenarios, world state evolution, and scene modality
Identification of three crucial technical challenges: action-conditioned controllability, long-horizon interactions and memory, and action-following responsiveness for real-time interactivity
Comprehensive comparison of existing benchmarks and metrics across four application fields: open-world exploration, game engine, autonomous driving, and robotics
Proposal of promising future directions for next-generation interactive world modeling

Methodology

This paper conducts a systematic review of recent research trends, technical developments, evaluation benchmarks, and future directions in interactive world modeling. It categorizes works by application scenarios, world state evolution, and scene modality, then delves into three technical challenges. It also compares existing benchmarks and metrics across four specific application fields.

Key Results

The paper provides a comprehensive survey of interactive world modeling, summarizing current trends and challenges, and comparing benchmarks in open-world exploration, game engines, autonomous driving, and robotics. No new experimental results are presented.

Limitations

The survey may not cover all recent works due to the rapid pace of development in the field
The analysis of challenges and future directions is based on current trends and may evolve as the field progresses

Resources

Paper Code