Abstract This paper proposes a novel design for a multimodal, contextsensitive recommendation and questionanswering (QA) system, MMCARQA that integrates RetrievalAugmented Generation (RAG) with Large Language Models (LLMs). The architecture enables dynamic understanding and reasoning over diverse input modalities—including text, images, audio, video, and documents—thereby enhancing the relevance, personalization, and contextual accuracy of system responses. Drawing inspiration from securityaware architectures in IoMT and federated AI systems, the framework emphasizes modularity, privacy, and scalability, supporting deployment across both edge and cloud environments. A detailed architectural diagram is presented, and key components such as multimodal preprocessing pipelines, adaptive retrieval strategies, memoryaugmented generation modules, graphbased reasoning agents, and LLMdriven response generation are discussed in depth. To rigorously validate system performance, a comprehensive experimental evaluation methodology is proposed. The framework is benchmarked across multiple dimensions, including latency, retrieval-free accuracy, multi-hop graph reasoning, explainability, differential privacy guarantees, and energy efficiency on heterogeneous hardware platforms. Evaluation targets include sub-20 ms on-device classification, ≥ 0.85 precision@5 on QA tasks, F1 ≥ 0.75 on structured reasoning datasets, and strong user trust ratings in interpretability studies. Together, the proposed design and its empirical evaluation demonstrate a scalable, explainable, and privacy-preserving multimodal QA solution capable of adapting to real-world domain-specific scenarios.
Subramanian et al. (Thu,) studied this question.
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