用符号规则约束大模型，让5G网络自治更安全可靠
Graph-Symbolic Policy Enforcement and Control (G-SPEC): A Neuro-Symbolic Framework for Safe Agentic AI in 5G Autonomous Networks
- 结合知识图谱与规则校验，让大模型决策可验证
- 零安全违规，修复成功率94.1%，远超基线82.4%
- 适合需要高安全性的5G/6G网络运维场景
随着网络向5G独立组网和6G演进，传统静态自动化与深度强化学习已难以应对编排挑战。尽管大语言模型（LLM）代理为意图驱动网络提供了可能，但存在拓扑幻觉和策略违规等随机风险。为此，我们提出图-符号策略执行与控制框架（G-SPEC），通过确定性验证约束概率规划。该框架采用治理三元组——电信适配的代理（TSLAM-4B）、网络知识图谱（NKG）和SHACL约束。在模拟的450节点5G核心网中评估，实现零安全违规，修复成功率达94.1%，显著优于82.4%的基线。消融分析显示，NKG验证贡献了68%的安全提升，SHACL策略占24%。在10K至100K节点的拓扑上进行可扩展性测试，验证延迟符合$O(k^{1.2})$，其中$k$为子图规模。处理开销仅142ms，适用于SMO层操作。
原文摘要 · Abstract (English)
As networks evolve toward 5G Standalone and 6G, operators face orchestration challenges that exceed the limits of static automation and Deep Reinforcement Learning. Although Large Language Model (LLM) agents offer a path toward intent-based networking, they introduce stochastic risks, including topology hallucinations and policy non-compliance. To mitigate this, we propose Graph-Symbolic Policy Enforcement and Control (G-SPEC), a neuro-symbolic framework that constrains probabilistic planning with deterministic verification. The architecture relies on a Governance Triad - a telecom-adapted agent (TSLAM-4B), a Network Knowledge Graph (NKG), and SHACL constraints. We evaluated G-SPEC on a simulated 450-node 5G Core, achieving zero safety violations and a 94.1% remediation success rate, significantly outperforming the 82.4% baseline. Ablation analysis indicates that NKG validation drives the majority of safety gains (68%), followed by SHACL policies (24%). Scalability tests on topologies ranging from 10K to 100K nodes demonstrate that validation latency scales as $O(k^{1.2})$ where $k$ is subgraph size. With a processing overhead of 142ms, G-SPEC is viable for SMO-layer operations.
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