The rapid advancements in large foundation models and multi-agent systems offer unprecedented capabilities, yet current Human-in-the-Loop (HiTL) paradigms inadequately integrate human expertise, often leading to cognitive overload and decision-making bottlenecks in complex, high-stakes environments. We propose the "Human-Machine Social Hybrid Intelligence" (HMS-HI) framework, a novel architecture designed for deep, collaborative decision-making between groups of human experts and LLM-powered AI agents. HMS-HI is built upon three core pillars: (1) a \textbf{Shared Cognitive Space (SCS)} for unified, multi-modal situational awareness and structured world modeling; (2) a \textbf{Dynamic Role and Task Allocation (DRTA)} module that adaptively assigns tasks to the most suitable agent (human or AI) based on capabilities and workload; and (3) a \textbf{Cross-Species Trust Calibration (CSTC)} protocol that fosters transparency, accountability, and mutual adaptation through explainable declarations and structured feedback. Validated in a high-fidelity urban emergency response simulation, HMS-HI significantly reduced civilian casualties by 72\% and cognitive load by 70\% compared to traditional HiTL approaches, demonstrating superior decision quality, efficiency, and human-AI trust. An ablation study confirms the critical contribution of each module, highlighting that engineered trust and shared context are foundational for scalable, synergistic human-AI collaboration.

翻译：大基础模型与多智能体系统的快速发展带来了前所未有的能力，然而当前的人机回环（HiTL）范式未能充分整合人类专业知识，在复杂、高风险环境中常导致认知过载与决策瓶颈。本文提出“人机社会混合智能”（HMS-HI）框架，这是一种新颖的架构，旨在实现人类专家群体与基于大语言模型的人工智能智能体之间的深度协同决策。HMS-HI建立在三大核心支柱之上：（1）一个用于统一多模态态势感知与结构化世界建模的**共享认知空间（SCS）**；（2）一个**动态角色与任务分配（DRTA）**模块，能根据能力与工作负载自适应地将任务分配给最合适的智能体（人类或AI）；以及（3）一个**跨物种信任校准（CSTC）**协议，通过可解释的声明与结构化反馈，促进透明度、问责制与相互适应。在高保真城市应急响应仿真中的验证表明，与传统HiTL方法相比，HMS-HI将平民伤亡降低了72%，认知负荷减少了70%，展现出更优的决策质量、效率以及人机信任度。消融研究证实了每个模块的关键贡献，强调工程化信任与共享情境是可扩展、协同人机协作的基础。