World Models for AI Planning: Current State and Gaps Between Research and Deployment

Олег Ивченко; Iryna Ivchenko

doi:10.5281/zenodo.21990678

August 18, 2026Open Access

World Models for AI Planning: Current State and Gaps Between Research and Deployment

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Key Points

To synthesize the state of the art in world models for AI planning and characterize the primary obstacles hindering their real-world deployment.
Surveyed existing architectural paradigms for environment modeling and predictive planning in artificial intelligence.
Evaluated performance trade-offs, scalability constraints, and robustness differences between standard laboratory benchmarks and operational deployment settings.
Identified critical gaps in long-horizon predictive accuracy, computational latency, and sample efficiency during real-time planning.
Characterized vulnerability to environment distribution shifts, compounding simulation errors, and safety verification challenges in complex operational domains.

Implication

Review reveals critical capability gaps between theoretical world models and practical AI planning, indicating key engineering hurdles for real-world deployment.

Abstract

Research article: World Models for AI Planning: Current State and Gaps Between Research and Deployment

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Cite This Study

Ивченко et al. (2026) studied this question.

synapsesocial.com/papers/6a86b5ff8a91293e6a1cd976 https://doi.org/10.5281/zenodo.21990678

View Full Paper

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World Models for AI Planning: Current State and Gaps Between Research and Deployment

Key Points

Implication

Abstract

Discussion

Cite This Study

World Models for AI Planning: Current State and Gaps Between Research and Deployment

Key Points

Implication

Abstract

Discussion

Cite This Study

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