Multimodal Large Models for Context-Aware Generative Applications (Journal of Generative Intelligence E: 3117-6429 P: 3117-6437, 2025)
Context-aware generative applications -- systems that adapt their generated outputs based on rich contextual signals including user history, environmental conditions, task state, and multimodal situational cues -- represent a critical evolution beyond single-turn prompt-response generation. While large language models and multimodal models have demonstrated impressive generation quality in single- turn settings, their adaptation to dynamic, long-horizon contexts remains limited by short context windows, poor long-term memory, and the inability to integrate heterogeneous contextual signals from diverse modalities continuously over application sessions. This paper proposes the Context-Aware Multimodal Generation (CAMG) framework, an architecture and training methodology for large multimodal models that maintain and leverage rich contextual representations across extended application sessions. CAMG integrates four contextual mechanisms: a hierarchical context memory that compresses and retains long-horizon interaction history; a multimodal context encoder that integrates situational signals from text, image, and structured data sources; a context-conditioned generation head that adapts generation style, content, and format based on inferred user preferences and task state; and a context consistency verifier that maintains semantic coherence between generated outputs across session turns. CAMG is evaluated through a contextual generation quality study involving 186 participants using three context-aware application prototypes -- a personalised learning assistant, an adaptive creative writing tool, and a context-aware medical information navigator -- over 30-day deployment periods. CAMG achieves a 41.8% improvement in contextual relevance scores (SD = 5.2%) and a 34.6% improvement in generation consistency (SD = 4.8%) relative to single-turn LLM baselines. User satisfaction ratings improve from 3.4/5.0 to 4.6/5.0 (p < 0.001, Cohen d = 2.84). The study contributes the CAMG architecture, a Context-Aware Generation Quality (CAGQ) evaluation framework, and empirical evidence for the practical value of long-horizon contextual adaptation in generative AI applications.