Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration?

A team of researchers introduced a novel conceptual framework called 'Theory of Space' in a technical paper published on the arXiv preprint server on February 12, 2025, to address the current limitations of multimodal foundation models in autonomous spatial exploration. The researchers aim to determine if artificial intelligence can evolve beyond passive observation to actively acquire environmental data, thereby constructing and revising internal spatial beliefs when faced with partial observability. This development is crucial for the advancement of embodied intelligence, where robots or digital agents must navigate and understand physical spaces without having complete information upfront. The study highlights a significant gap in current artificial intelligence capabilities: while modern large-scale models are highly proficient at identifying objects or describing scenes from static images, they often lack the agency required for self-directed exploration. The 'Theory of Space' framework shifts the focus toward how an agent can strategically move through an environment to fill in gaps in its knowledge. By processing sequential and partial sensory inputs, the AI is expected to maintain a dynamic 'spatial belief'—a mental map that it continuously updates as new areas are discovered or changes are observed. Furthermore, the research explores the exploitation of these spatial beliefs for complex task execution. By moving from passive perception to active reasoning, agents can better predict what lies around a corner or hidden behind obstacles, making them more efficient in real-world applications such as search-and-rescue or domestic robotics. The paper serves as a foundational step toward creating more autonomous and spatially aware systems that do not rely solely on pre-loaded maps but can instead learn and adapt to their surroundings through direct physical or simulated interaction.