ExplorationGPT: A General-Purpose Multimodal Artificial Intelligence Framework for Discovery-Oriented Thinking Through Active Exploration, Hypothesis Formation, Experimental Validation, and Iterative Search

Contemporary generative artificial intelligence systems have demonstrated increasingly sophisticated capabilities in information retrieval, language understanding, reasoning, and problem solving. Nevertheless, a fundamental distinction remains between answering an existing question and actively discovering possibilities that have not yet been identified. Conventional question–response systems generally operate within a problem space defined by the user's initial request, whereas meaningful discovery frequently requires exploring beyond established assumptions, identifying unknown information, generating alternative hypotheses, predicting possible outcomes, and testing previously unexplored possibilities. Inspired by exploratory cognition, scientific inquiry, experimental learning, probabilistic reasoning, and computational search, this paper proposes ExplorationGPT, a conceptual framework for a general-purpose artificial intelligence system designed to perform exploration-oriented thinking and discovery across diverse domains. Unlike conventional systems that primarily optimize immediate response generation, ExplorationGPT treats an initial question, observation, objective, or environmental signal as an opportunity to initiate an evolving discovery process. The proposed framework integrates nine interconnected cognitive mechanisms: exploratory thinking, hypothetical thinking, experimental thinking, curiosity-driven thinking, predictive thinking, counterfactual thinking, probabilistic thinking, intuitive thinking, search-based thinking, and iterative thinking. Together, these mechanisms enable the system to identify unexplored possibilities, formulate competing hypotheses, anticipate potential developments, examine alternative conditions, evaluate uncertainty, design experiments, search solution spaces, and progressively refine its understanding through feedback. A central component is an adaptive exploration controller that determines which possibilities deserve further investigation, how computational resources should be allocated, and when an investigation should be continued, redirected, validated, or terminated. Importantly, ExplorationGPT is designed as a multimodal architecture rather than a text-only conversational system. It may process and generate information through text, images, audio, speech, video, structured data, and other machine-readable representations, allowing exploration to extend from abstract conceptual reasoning to the examination of visual phenomena, auditory patterns, dynamic environments, and experimentally observable outcomes. The framework is intended for general-purpose applications, including scientific research, education, engineering, business, technology development, creative problem solving, and everyday decision-making. This paper establishes a conceptual and technically oriented foundation for exploration-oriented artificial intelligence, proposes an integrated computational architecture, and identifies research hypotheses and evaluation strategies for future empirical investigation. No experimental performance or demonstrated superiority is claimed at this conceptual stage.