This paper investigates the reasoning mechanisms of multimodal AI models through the lens of TRIZ (Theory of Inventive Problem Solving) principles. Multimodal AI, which integrates and processes information from multiple data types such as text, images, and audio, has seen significant advancements. However, its reasoning capabilities remain a challenging frontier, particularly in harmonizing diverse modalities to achieve coherent outputs. By applying TRIZ, a systematic methodology widely used in engineering and innovation, we explore how these models address conflicts inherent in multimodal data fusion and reasoning. We identify key TRIZ principles such as Contradiction Resolution, the System of Systems approach, and the Concept of Ideality. We map these to the challenges and mechanisms of current multimodal AI systems. Our analysis highlights how models employ inventive principles to resolve contradictions, such as balancing accuracy across modalities or reconciling disparate representations. We also propose a novel framework inspired by TRIZ for enhancing reasoning in multimodal AI, emphasizing adaptability, scalability, and resource efficiency. This study contributes to a deeper understanding of multimodal reasoning and offers actionable insights for designing more robust and efficient AI systems. By leveraging TRIZ principles, we aim to foster innovative approaches to complex problem-solving in AI, bridging the gap between theoretical understanding and practical application.
Oleksandrovych Moroz Andrii (Fri,) studied this question.