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Graph-Symbolic Policy Enforcement and Control (G-SPEC): A Neuro-Symbolic Framework for Safe Agentic AI in 5G Autonomous Networks

Divya Vijay, Vignesh Ethiraj

TL;DR

G-SPEC addresses the governance gap in agentic AI for telecom networks by integrating a deterministic Network Knowledge Graph, a telecom-optimized large language model (TSLAM-4B), and SHACL-driven policy enforcement. The framework validates action plans on subgraphs before execution, resulting in zero safety violations, negligible hallucinations, and strong remediation performance (94.1%) in a 450-node 5G core simulation. Ablation shows NKG grounding drives the majority of safety gains (68%), with sublinear scalability in validation latency ($O(k^{1.2})$) and modest memory requirements, making SMO-layer deployment feasible. These results demonstrate that a neuro-symbolic, formally verifiable approach can deliver auditable, safe autonomy for 5G/6G network management and orchestration, with practical paths toward real-world deployment and cross-vendor standardization.

Abstract

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.

Graph-Symbolic Policy Enforcement and Control (G-SPEC): A Neuro-Symbolic Framework for Safe Agentic AI in 5G Autonomous Networks

TL;DR

G-SPEC addresses the governance gap in agentic AI for telecom networks by integrating a deterministic Network Knowledge Graph, a telecom-optimized large language model (TSLAM-4B), and SHACL-driven policy enforcement. The framework validates action plans on subgraphs before execution, resulting in zero safety violations, negligible hallucinations, and strong remediation performance (94.1%) in a 450-node 5G core simulation. Ablation shows NKG grounding drives the majority of safety gains (68%), with sublinear scalability in validation latency () and modest memory requirements, making SMO-layer deployment feasible. These results demonstrate that a neuro-symbolic, formally verifiable approach can deliver auditable, safe autonomy for 5G/6G network management and orchestration, with practical paths toward real-world deployment and cross-vendor standardization.

Abstract

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 where is subgraph size. With a processing overhead of 142ms, G-SPEC is viable for SMO-layer operations.
Paper Structure (52 sections, 4 equations, 3 figures, 3 tables, 1 algorithm)

This paper contains 52 sections, 4 equations, 3 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Contributions
  3. Related Work
  4. Deterministic Automation
  5. Deep Reinforcement Learning (DRL)
  6. Generative and Agentic AI
  7. Neuro-Symbolic Approaches
  8. Formal Verification in Networks
  9. Problem Formulation
  10. The Stochasticity Problem
  11. Graph-Based State Representation
  12. Methodology: The G-SPEC Framework
  13. Architecture Overview
  14. Layer 1: Network Knowledge Graph (NKG)
  15. Layer 2: TSLAM-4B (Telecom Specific Large Action Model)
  16. ...and 37 more sections

Figures (3)

  • Figure 1: The G-SPEC Governance Triad Architecture. Safety is enforced by encapsulating the probabilistic TSLAM (Layer 2) between the NKG's deterministic truth (Layer 1) and policy validation (Layer 3).
  • Figure 2: Ghost Node Detection. The TSLAM attempts to utilize decommissioned node gNB_B. SHACL queries the NKG, detects absence of active node, and blocks action before execution. This prevents silent failures where traffic would be discarded.
  • Figure 3: Latency Comparison Across O-RAN Control Loops (Log Scale). The 142 ms verification overhead is prohibitive for Real-Time layers but negligible for the target Service Management and Orchestration (SMO) layer.