A Survey on World Models Grounded in Acoustic Physical Information

arXiv 25.09 2025 53.1 survey, theory

TLDR

A survey on world models that leverage acoustic physical information for perception, reasoning, and simulation across various domains.

Reasoning

The paper provides a comprehensive overview of acoustic world models, covering theory, methods, and applications. Its strength lies in systematically integrating acoustic physics with world models, but as a survey it lacks novel empirical contributions and does not deeply address interactive or video-based world models.

Read-first score

Read-first score 53.1, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 36.

Recency 8%

86.7

Uses a gentle age decay so recent papers surface without erasing older foundations. 2025

Methodology quality 25%

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

Topical relevance 42%

51.4

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

Reproducibility 25%

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

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 318.

Keyword Scores

world model

world dynamics prediction

generative world model

world simulator

model-based reinforcement learning world model

interactive world model

video world model

Deep Analysis

Innovations

Comprehensive overview of world models grounded in acoustic physical information
Theoretical foundation linking fundamental physical laws to encoding of physical information in acoustic signals
Review of core methodological pillars: Physics-Informed Neural Networks (PINNs), generative models, and self-supervised multimodal learning frameworks
Identification of significant applications in robotics, autonomous driving, healthcare, and finance
Proposed roadmap for robust, causal, uncertainty-aware, and responsible acoustic intelligence

Methodology

This survey systematically reviews the theoretical foundations, methodological frameworks (including Physics-Informed Neural Networks, generative models, and self-supervised multimodal learning), and applications of world models grounded in acoustic physical information. It also identifies technical and ethical challenges and proposes a concrete roadmap for future research directions.

Key Results

Acoustic signals encode rich latent information about material properties, internal geometric structures, and complex interaction dynamics, enabling high-fidelity environmental perception, causal physical reasoning, and predictive simulation. The survey outlines significant applications across robotics, autonomous driving, healthcare, and finance, and highlights key technical and ethical challenges along with a future research roadmap.

Limitations

Technical challenges in achieving robust acoustic intelligence
Ethical challenges in responsible deployment of acoustic AI
Uncertainty in acoustic signal interpretation and model predictions
Need for causal reasoning and uncertainty-aware models in acoustic world models

Resources

Paper