# Z2-ACT: End-to-End Verifiable Agentic Intent Control for Open 6G RAN

arXiv: https://arxiv.org/abs/2608.21049 · HTML: https://mlsanity.com/paper/2608.21049/

## Key facts

- No citing papers in the map yet (data through 14 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.

## Placed in

- LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/)

## Builds on (most cited first, 11 of 11)

- [ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms](https://mlsanity.com/paper/2112.09559/) (2021)
- [AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks](https://mlsanity.com/paper/2508.17778/) (2025)
- [Intent-Based Network for RAN Management with Large Language Models](https://mlsanity.com/paper/2507.14230/) (2025)
- [OpenRAN Gym: An Open Toolbox for Data Collection and Experimentation with AI in O-RAN](https://mlsanity.com/paper/2202.10318/) (2022)
- [Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management](https://mlsanity.com/paper/2602.14117/) (2026)
- [Contract-based Agentic Intent Framework for Network Slicing in O-RAN](https://mlsanity.com/paper/2603.01663/) (2026)
- [ZTRAN: Prototyping Zero Trust Security xApps for Open Radio Access Network Deployments](https://mlsanity.com/paper/2403.04113/) (2024)
- [Agentic AI for Intent-driven Optimization in Cell-free O-RAN](https://mlsanity.com/paper/2602.22539/) (2026)
- [Zero-Trust Strategies for O-RAN Cellular Networks: Principles, Challenges and Research Directions](https://mlsanity.com/paper/2511.18568/) (2025)
- [Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence](https://mlsanity.com/paper/2602.24115/) (2026)
- [Agentic Open RAN: A Deterministic and Auditable Framework for Intent-Driven Radio Control](https://mlsanity.com/paper/2604.13384/) (2026)

## 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.