智能城市中多大模型协同治理，实现安全、实时与可追溯的决策。
SORA-ATMAS: Adaptive Trust Management and Multi-LLM Aligned Governance for Future Smart Cities
- 基于自适应信任机制与跨域规则，统一管理多个大模型输出。
- 多场景测试显示平均预测误差降低35%，高风险场景响应准确率超85%。
- 适合智能交通、应急管理等需高可靠性与合规性的城市应用。
智慧城市快速发展使智能互联服务对基础设施优化与居民福祉愈发关键。自主代理型AI通过支持自主决策与动态协调，推动城市系统实时响应复杂环境变化。其在交通领域表现显著，结合交通数据、天气预报与安全传感器可实现动态路径调整并快速应对风险。然而，在异构城市生态系统中部署时，面临治理、风险与合规（GRC）挑战，包括责任归属、数据隐私及监管一致性问题。通过三个领域代理（天气、交通、安全）评估 SORA-ATMAS，其治理策略（含高风险场景回退机制）有效引导 GPT、Grok、DeepSeek 等多大模型生成符合领域与政策的输出，平均 MAE 下降 35%。结果表明：天气监测稳定，高风险交通场景处理成功率达 0.85，安全/火灾场景下信任调节值为 0.65。三代理部署运行分析显示：吞吐量为 13.8–17.2 请求/秒，执行时间低于 72~ms，治理延迟低于 100 ms；分析预测表明规模扩展下性能仍可持续。跨域规则确保安全互操作性，仅在验证天气条件下允许交通重规划。研究验证了 SORA-ATMAS 作为合规对齐、上下文感知且可验证的治理框架，能整合分布式代理输出，形成可问责的实时决策，为智慧城市建设提供韧性基础。
原文摘要 · Abstract (English)
The rapid evolution of smart cities has increased the reliance on intelligent interconnected services to optimize infrastructure, resources, and citizen well-being. Agentic AI has emerged as a key enabler by supporting autonomous decision-making and adaptive coordination, allowing urban systems to respond in real time to dynamic conditions. Its benefits are evident in areas such as transportation, where the integration of traffic data, weather forecasts, and safety sensors enables dynamic rerouting and a faster response to hazards. However, its deployment across heterogeneous smart city ecosystems raises critical governance, risk, and compliance (GRC) challenges, including accountability, data privacy, and regulatory alignment within decentralized infrastructures. Evaluation of SORA-ATMAS with three domain agents (Weather, Traffic, and Safety) demonstrated that its governance policies, including a fallback mechanism for high-risk scenarios, effectively steer multiple LLMs (GPT, Grok, DeepSeek) towards domain-optimized, policy-aligned outputs, producing an average MAE reduction of 35% across agents. Results showed stable weather monitoring, effective handling of high-risk traffic plateaus 0.85, and adaptive trust regulation in Safety/Fire scenarios 0.65. Runtime profiling of a 3-agent deployment confirmed scalability, with throughput between 13.8-17.2 requests per second, execution times below 72~ms, and governance delays under 100 ms, analytical projections suggest maintained performance at larger scales. Cross-domain rules ensured safe interoperability, with traffic rerouting permitted only under validated weather conditions. These findings validate SORA-ATMAS as a regulation-aligned, context-aware, and verifiable governance framework that consolidates distributed agent outputs into accountable, real-time decisions, offering a resilient foundation for smart-city management.
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