DeepAnalysisGPT: A General-Purpose Multimodal Artificial Intelligence Framework for Deep Analytical Reasoning Through Logical Inference, Critical Evaluation, Multi-Method Reasoning, and Metacognitive Reflection

Contemporary generative artificial intelligence systems have demonstrated increasingly sophisticated capabilities in information retrieval, natural language understanding, mathematical reasoning, problem solving, and multimodal interaction. Nevertheless, a fundamental research challenge remains: how can an artificial intelligence system systematically integrate different forms of human reasoning into a unified computational framework capable of examining complex problems from multiple intellectual perspectives? Although existing large language models can perform individual reasoning operations, including logical inference, analytical decomposition, critical evaluation, and causal explanation, these capabilities are not necessarily coordinated through an explicit, comprehensive architecture that determines which reasoning methods should be applied, how they should interact, and how their conclusions should be reconciled. This paper proposes DeepAnalysisGPT, a general-purpose artificial intelligence framework designed to support deep analytical reasoning through the coordinated application of ten complementary thinking mechanisms: Logical Thinking, Critical Thinking, Analytical Thinking, Synthetic Thinking, Deductive Thinking, Inductive Thinking, Abductive Thinking, Systems Thinking, Causal Thinking, and Metacognitive Thinking. The proposed framework treats deep analysis as an iterative computational process rather than a single reasoning operation. Its architecture integrates problem representation, analytical decomposition, multi-method inference, evidence evaluation, cross-component synthesis, causal investigation, systems-level interpretation, metacognitive monitoring, and conclusion validation. A central mechanism is an adaptive reasoning orchestrator that selects, combines, and revisits different thinking methods according to problem characteristics, evidential requirements, uncertainty, and computational constraints. Importantly, DeepAnalysisGPT is conceived as a multimodal system rather than a text-only conversational model. It is designed to support the analysis and integration of textual, visual, auditory, video, and other machine-processable information, allowing reasoning to operate across different information modalities within a shared analytical representation. The framework is intended for general-purpose applications, including scientific research, education, engineering, business analysis, technological development, and everyday decision-making. This paper establishes a conceptual and technically oriented foundation for multi-method analytical artificial intelligence, proposes an implementation architecture and mathematical formulation, and identifies hypotheses and evaluation strategies for future empirical investigation. No experimental performance or demonstrated superiority is claimed at this conceptual stage.