Scaling Multimodal Generative Models: Performance, Alignment, and Cognitive Abstraction Capabilities (Journal of Generative Intelligence E: 3117-6429 P: 3117-6437, 2025)
Recent progress in multimodal generative AI has enabled unified modeling across text, image, audio, video, and sensor-based representations. Scaling these systems introduces improvements in emergent reasoning but also amplifies risks of hallucination, misalignment, bias propagation, and abstraction inconsistency. This paper investigates the scalability frontier of multimodal models with emphasis on three pillars: performance scaling laws, human–AI alignment integrity, and cognitive abstraction layering. A new framework named Cognitive Multimodal Alignment Scaling Architecture (CMASA) is introduced, integrating cross-representation memory binding, hierarchical concept compression, and alignment-aware generation layers. Experiments conducted on vision–language, audio–language, and cross-domain reasoning benchmarks reveal that scaled multimodal models can achieve 42–63% gains in abstraction fidelity, 35% reduction in cross-modal hallucination, and 28% improvement in alignment consistency when reinforced with cognitive layering. This study highlights architectural bottlenecks, alignment failure modes, scalability impacts, and long-term implications for generalizable machine cognition.