Article · Elaris Computing Nexus · April 6, 2026

Cross Modal Explainable Intelligence Architecture for Resource Aware Decision Optimization in Adaptive Computing Systems

Arulmurugan Ramu, Anandakumar Haldorai

Abstract

With the growing diversity of adaptive computing systems, there is a need for intelligent decision-making mechanisms that must optimize resource utilization, computation efficiency and maintain model transparency. However, most of the existing resource-aware optimization approaches do not consider cross-modal information integration and explainability, and thus cannot be applied to dynamic and constrained computing scenarios. In order to address these challenges, this paper presents an architecture for Cross-Modal Explainable Intelligence (CMEIA) for the Resource-Aware Decision Optimization in Adaptive Computing Systems. The proposed architecture involves the adaptive cross-modal fusion framework and the explainable intelligence module, which are used to fuse multimodal inputs and give transparent and interpretable decision pathways, respectively. A dynamic resource management engine continually adjusts the allocation of computational resources to meet contextual awareness, workload characteristics and system constraints, thereby allowing scalable and adaptive decision optimization. Unlike the traditional method which separately optimizes the resource and explains the model, the proposed model addresses all the three aspects of performance, explainability, and adaptability at the same time. Decision accuracy, utilization efficiency of resources, computational latency, energy consumption, throughput, scalability, fidelity of explanations, and level of confidence in decisions under different workloads are some of the critical performance parameters used to measure the effectiveness of CMEIA. An experimental assessment shows that the developed architecture optimizes the resources in a way that is superior, while simultaneously ensuring high levels of explainability and adaptability in heterogeneous computing environments. This framework is being used to greatly boost intelligent decision making, increase computational efficiency and yield transparent and trustworthy optimization strategies for next generation adaptive computing environments.

Explainable Artificial Intelligence (XAI)Cloud Computing and Resource ManagementEnergy Efficiency in Computingcross-modal fusionexplainable AIadaptive computing systems

Cross Modal Explainable Intelligence Architecture for Resource Aware Decision Optimization in Adaptive Computing Systems

Abstract

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