EPH-International Journal of Applied Science

The rapid evolution of large language models has yielded remarkable generative capabilities; yet, significant chal- lenges remain regarding transparency, modularity, and compu- tational efficiency during complex reasoning tasks. To address these limitations, this paper proposes the Hybrid Reasoning and Generation Architecture, a novel framework that synergizes symbolic reasoning with neural generation to enhance inter- pretability and adaptive performance. This architecture inte- grates a modular multi-expert reasoning layer with a generative transformer backbone, facilitating dynamic switching between reasoning, retrieval, and generation modules based on the imme- diate task context. The system design leverages hybrid pipelines, utilizing knowledge-based reasoning graphs, contextual memory caching, and Mixture-of-Experts routing to optimize computa- tional resource allocation. Experimental evaluations demonstrate that the proposed framework achieves superior accuracy and reduced inference latency compared to conventional monolithic transformer models while providing transparent reasoning traces. Furthermore, the architecture exhibits cross-domain adaptability, proving effective for explainable artificial intelligence applications in sectors such as education, healthcare, and finance. Ultimately, this work offers a scalable paradigm for next-generation language models, bridging the gap between fluent text generation and logical, explainable decision-making.