Z2-ACT: End-to-End Verifiable Agentic Intent Control for Open 6G RAN
On the map: LLM agents and tool use → Agentic AI for wireless networks
Abstract (arXiv)
With the progression in open and disaggregated 6G radio access networks, it is expected that the system will be able to host multi-vendors. In order to host multi-vendors, it is essential that AI-assisted control loops remain safe, verifiable, and auditable under concurrent operator intents and untrusted model inputs. The existing studies address the agentic coordination, formal intent constraints, zero-trust prompt verification and cryptographic accountability in isolation, which leaves pre-realization safety, continuous semantic verification and cross-domain audit incomplete when used individually. In this regard, we propose zero-knowledge auditable control and zero-trust verifiable agentic intent architecture (Z2-ACT), which integrates the aforementioned four primitives across the non-real-time and near-real-time RICs. We encode the typed Intent Contracts as operator goals while the large language model inputs are only admitted after a practical adversarial intent check. The skill sequences in the proposed study are released only when a self-management gate is satisfied while every successful commit is recorded as a binding commitment with a zero-knowledge proof. Our experimental evaluation on public ColO-RAN measurements compares the full architecture against targeted ablations and a conventional reinforcement-learning baseline. A live large language model is used in the non-real-time path to translate operator intents into Intent Contracts; we report translation accuracy, the rate of invalid or hallucinated contracts, non-real-time latency, and behavior under adversarial or misleading intents. Near-real-time control remains trace-driven on the public KPM sequences. Results indicate improved actuation filtering and attack resilience at modest latency and signaling cost inside the near-real-time envelope.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use. Builds on 11 of its 21 references that are in the map. First posted on arXiv 21 Aug 2026.
Builds on
11 of 21 references.
Closest ancestors · 4
Same direction, newest first
- 2026Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence27 Feb
- 2026Agentic AI for Intent-driven Optimization in Cell-free O-RAN26 Feb
- 2026Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management15 Feb
- 2025AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks25 Aug
Other references · 7
- 2021ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platformscited by 33
- 2022OpenRAN Gym: An Open Toolbox for Data Collection and Experimentation with AI in O-RANcited by 7
- 2025Intent-Based Network for RAN Management with Large Language Modelscited by 7
- 2024ZTRAN: Prototyping Zero Trust Security xApps for Open Radio Access Network Deploymentscited by 2
- 2026Contract-based Agentic Intent Framework for Network Slicing in O-RANcited by 2
- 2025Zero-Trust Strategies for O-RAN Cellular Networks: Principles, Challenges and Research Directionscited by 1
- 2026Agentic Open RAN: A Deterministic and Auditable Framework for Intent-Driven Radio Controlcited by 1
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