Multimodal Reasoning Optimization for Resource-Constrained Large Language Models
Keywords:
multimodal reasoning, resource-constrained systems, large language models, edge deployment, model compression, reasoning path selection, AI governanceAbstract
The rapid expansion of large language models has produced substantial gains in multimodal reasoning, yet the computational and memory demands of these systems remain incompatible with many edge, mobile, and embedded deployment environments. This paper presents a system-level examination of multimodal reasoning optimization for resource-constrained large language models. It argues that improving efficiency in such settings requires more than isolated model compression; it demands careful coordination among sensing, representation alignment, reasoning path selection, quantization, parameter-efficient adaptation, and deployment orchestration. The paper analyzes architectural trade-offs in cross-modal encoders, fusion layers, and autoregressive decoders under strict memory, latency, and energy constraints. It further discusses how modular system design, retrieval-style information selection, and trajectory filtering can reduce reasoning overhead without proportional loss of interpretive depth. The discussion extends to deployment infrastructures, edge-to-cloud coordination, governance mechanisms, fairness auditing, and sustainability reporting. The paper emphasizes that resource-constrained multimodal reasoning should be understood as a socio-technical systems problem in which accuracy, robustness, transparency, and environmental impact are jointly negotiated. The analysis draws on recent advances in model compression, reasoning strategies, multimodal pretraining, and policy-oriented AI evaluation. The conclusion outlines open research directions including adaptive reasoning budgets, cross-modal calibration under degraded inputs, lifecycle-aware carbon accounting, and institutional mechanisms for certifying compressed multimodal systems.
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