@_akhaliq: World Action Models: A Survey

World Action Models: The Next Frontier in Embodied AI

Hugging Face Daily Papers

This survey paper introduces World Action Models (WAMs), a unified framework for embodied AI that integrates predictive state modeling with action generation. It provides a taxonomy of existing methods, analyzes the data ecosystem, and outlines evaluation protocols for this emerging paradigm.

World Action Models: A Survey

Hugging Face Daily Papers

This survey provides a comprehensive overview of World Action Models (WAMs), predictive-action systems that generate future states for decision-making, and organizes existing works by their required outputs and design choices.

World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications

arXiv cs.LG

A comprehensive survey of world models that provides a multi-axis taxonomy covering architectures, methodologies, reasoning strategies, and applications across AI domains, including key systems like Dreamer, MuZero, and Sora.

World Model for Robot Learning: A Comprehensive Survey

Hugging Face Daily Papers

This comprehensive survey reviews the literature on world models for robot learning, covering their roles in policy learning, planning, and simulation. It highlights key paradigms, benchmarks, and future directions for predictive modeling in embodied agents.

World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis

Hugging Face Daily Papers

This paper introduces World-Language-Action (WLA) models, embodied foundation models that jointly predict textual subtasks, subgoal images, and robot actions from text, images, and robot states, achieving state-of-the-art multi-task and long-horizon learning in simulated and real-world environments.