PerceptionExperienceGPT: A General-Purpose Multimodal Artificial Intelligence Framework for Embodied, Spatial, Visual, Contextual, and Experiential Understanding

Contemporary generative artificial intelligence systems have achieved substantial progress in language understanding, visual recognition, multimodal interaction, and complex reasoning. Nevertheless, a fundamental challenge remains: understanding the world requires more than processing symbolic information or identifying statistical relationships between input tokens. Human cognition is deeply associated with bodily experience, spatial representations, visual perception, situational awareness, emotional information, narrative structures, and the mental simulation of possible events. These cognitive processes allow individuals to interpret information in relation to their surroundings, previous experiences, physical constraints, and anticipated consequences. Inspired by these characteristics, this paper proposes PerceptionExperienceGPT, a conceptual framework for a general-purpose multimodal artificial intelligence system designed to develop richer perceptual, experiential, and context-sensitive representations of the world. Unlike conventional question–response architectures that primarily transform user inputs into linguistic outputs, the proposed framework integrates eight interconnected cognitive capabilities: Embodied Thinking, Spatial Thinking, Visual Thinking, Contextual Thinking, Experiential Thinking, Emotional Thinking, Narrative Thinking, and Mental Simulation. These capabilities are organized within a unified architecture comprising multimodal perception, perceptual representation, experiential memory, contextual interpretation, cognitive simulation, cross-modal integration, and response generation. A central proposition is that artificial intelligence should interpret information not only according to its semantic content but also according to its spatial configuration, temporal development, environmental circumstances, experiential associations, and potential consequences. The framework is designed to process multiple modalities, including text, images, video, audio, speech, and other sensor-derived information, allowing different perceptual channels to contribute to a shared internal representation. Furthermore, it introduces an experiential modeling mechanism through which previously encountered situations can inform the interpretation of new situations without assuming that computational systems possess human subjective experience. The proposed architecture is intended for general-purpose applications in education, robotics, scientific research, healthcare support, intelligent assistants, engineering, and everyday human–AI interaction. This paper establishes a conceptual and technically oriented foundation for perception- and experience-oriented artificial intelligence, proposes mathematical formulations and research hypotheses, and identifies implementation and evaluation directions. No experimental performance or demonstrated superiority is claimed at this conceptual stage.