用控制理论提升多模态大模型的效率与可解释性
MCP: A Control-Theoretic Orchestration Framework for Synergistic Efficiency and Interpretability in Multimodal Large Language Models
- 分层解耦推理、生成、检索模块，结合强化学习动态路由
- 跨模态任务性能提升15%-30%，推理效率提高40%
- 中间结果可解释，人工评分达90%
针对大模型在多轮推理和多模态协作等复杂任务中面临的计算效率低和可解释性不足问题，本文提出基于模型-控制器-任务自适应（MCP）的三层协同框架。通过将大模型功能解耦为推理、生成和检索模块，并结合强化学习驱动的动态路由算法与任务自适应机制，首次实现了控制理论与大模型动态推理的系统融合。实验表明，该框架在GLUE、COCO、ScienceQA等跨模态基准任务上相较基线模型性能提升15%-30%，推理效率提升40%，并通过呈现层生成可解释的中间结果，获得90%的人工可解释性评分，为大模型实际应用瓶颈提供了全新技术路径。
原文摘要 · Abstract (English)
Aiming at the problems of computational inefficiency and insufficient interpretability faced by large models in complex tasks such as multi-round reasoning and multi-modal collaboration, this study proposes a three-layer collaboration framework based on model-controller-task adaptation (MCP). By decoupling large model functions into reasoning, generation and retrieval modules, and combining reinforcement learning-driven dynamic routing algorithms and task adaptation mechanisms, the systematic integration of control theory and large model dynamic reasoning is achieved for the first time. Experiments show that the MCP framework improves the performance of cross-modal benchmarking tasks, such as GLUE, COCO, ScienceQA, etc., by 15-30% compared with the baseline model, improves the reasoning efficiency by 40%, and generates the interpretable intermediate results through the Presenter layer, obtaining 90% of the manual interpretability scores, which provides a brand-new technological path to solve the bottleneck of the practical application of the large model.
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