Physically Viable World Models: A Case for Query-Conditioned Embodied AI

A recent paper posted on arXiv, "Physically Viable World Models: A Case for Query-Conditioned Embodied AI," explores novel approaches to embodied AI. Authored by Adam J. Thorpe and eight collaborators, this research focuses on the development of AI systems that can effectively understand and interact with the physical world. The full text of the paper is available in PDF format, with experimental HTML and TeX Source also provided. These resources allow researchers to delve into the intricacies of the proposed models and their implications. The paper is categorized under Computer Science (cs.AI), indicating its relevance to the broader field of artificial intelligence. It was initially submitted on May 28, 2026. The research leverages query-conditioned models, which are crucial for enhancing the ability of embodied AI to process and respond to specific environmental cues. This approach aims to create more robust and adaptable AI systems capable of performing complex tasks in real-world environments. The arXiv platform provides various bibliographic and citation tools for the paper, including NASA ADS, Google Scholar, and Semantic Scholar. These tools assist researchers in tracking citations and exploring related works, fostering a more interconnected academic landscape. Additionally, arXivLabs, an experimental framework, allows collaborators to develop and share new features directly on the platform. Projects within arXivLabs adhere to principles of openness, community, excellence, and user data privacy. The platform also offers links to code, data, and media associated with the article through initiatives like alphaXiv, CatalyzeX, DagsHub, Gotit.pub, Huggingface, and ScienceCast. These resources facilitate reproducibility and further research in the field. Demonstrations are available through Replicate, Hugging Face Spaces, and TXYZ.AI, enabling users to interact with and test the presented AI models.