Thinking Mechanism in Multimodal AI Models based on the TRIZ principles

Recent advancements in multimodal artificial intelligence (AI) have enabled models to process and integrate diverse data types, such as text, images, and audio. However, the underlying thinking mechanisms of these models remain largely heuristic and lack structured problem-solving capabilities. This paper explores the potential of applying TRIZ (Theory of Inventive Problem Solving) principles to enhance the reasoning processes of multimodal AI models. By leveraging TRIZ methodologies— such as contradiction resolution, inventive principles, and the system evolution framework—we propose a structured approach for AI-driven innovation and decision-making. The study investigates how TRIZ can optimize the learning strategies of multimodal models, improve creative problem-solving, and enhance their adaptability to complex, real-world challenges. Experimental validation is conducted on diverse AI tasks, demonstrating that integrating TRIZ-based mechanisms leads to more efficient, systematic, and explainable decision-making in multimodal AI systems. The findings highlight the synergy between TRIZ and AI, offering new pathways for developing intelligent systems capable of higher-order reasoning.