Intent-Based Networking

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Intent-Based Networking studies how to turn operator or user goals—often expressed in natural language—into network configurations, then check whether the network fulfills them. Approaches include language models and multi-agent systems for intent interpretation and orchestration, alongside ontologies, formal validators, digital twins, and deterministic controllers for producing and enforcing policies. Work spans 5G and O-RAN, network slicing, routing, security, and satellite networks, with evaluation on benchmarks, simulations, and testbed deployments. A central tension is how to make autonomous control responsive and adaptable while reliably handling ambiguity, conflicting goals, dependencies, and unsafe actions.

Latest papers

22 papers2 papers in the last four weeks, down 50% on the four weeks before. 0.0% of all new papers.

When LLM Agents See a Conflict but Still Proceed: Cross-Domain Dependencies in Network Slicing
Oct 12, 2026Marcos Lima Romero, Ricardo SuyamaNetwork Slicing LLM Agent Safety
NEXt-Gen AI-Native Networks - NEXA, Federal University of ABC - UFABC
Conflict-Aware Autonomous Resource Orchestration for 5G Edge Slicing Using Multi-Agent Systems and LLMs
Oct 10, 2026Nathan R. Prediger, Rodrigo Moreira, Juliano Araujo Wickboldt +4Network Slicing Edge Computing
Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Brazil · Federal University of Viçosa (UFV), Minas Gerais, Brazil · University of São Paulo (USP), São Paulo, Brazil +1
Intent2Tc: Automated Intent-to-Traffic Control Translation with Language Models
Sep 25, 2026Andrea Masini, Sudipta Acharya, Paolo Bellavista +2Intent-Based Networking
University of Ottawa, Ottawa, ON, Canada · University of Bologna, Italy
STR-Agent: An LLM-Driven Agent for QoS-Aware Routing in LEO Satellite Networks
Sep 17, 2026Bowen Lu, Mugen Peng, Yaohua Sun +3LLM Agent Orchestration Wireless Communications
State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications
On Identifying Adversarial Intent Injection in AI-Native 6G Networks
Sep 10, 2026Nilesh Chakraborty, Petar Djukic, Burak Kantarci6G Networks Adversarial Attacks
University of Ottawa, Ottawa, ON, Canada · Nokia Bell Labs, 600 March Road, Kanata, ON K2K 2E6, Canada
Uncertainty Signals for Network Intent Translation: Risk Ranking and Ambiguity Localization
Sep 3, 2026Ala' A. Alsamarneh, Omar AlhusseinIntent-Based Networking LLM Uncertainty Estimation
Department of Computer Science, College of Computing and Mathematical Sciences Khalifa University, Abu Dhabi, United Arab Emirates
Advanced LLM-Enhanced Intent-Based 5G Network Management using Dynamic Semantic Routes
Aug 23, 2026Thomas Benton Townsend, Dimitrios Michael ManiasIntent-Based Networking LLM Routing
The Department of Computer Science and Engineering, Mississippi State University
$Z^2$ -ACT: End-to-End Verifiable Agentic Intent Control for Open 6G RAN
Aug 21, 2026Sunder Ali Khowaja, Kapal Dev, George C. AlexandropoulosAI Agent Security Intent-Based Networking
School of Computer Science, Dublin City University, Dublin, Ireland · Department of Computer Science, Munster Technological University, Cork, Ireland · Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece
Learning Not to Optimize: Physics-Informed Action-Space Reshaping for Intent-Based Network Control
Aug 2, 2026Zuyuan Zhang, Vaneet Aggarwal, Tian LanReinforcement Learning Policy Optimization
The George Washington University · Purdue University
Untangling Co-Drift: Proactive Multi-Intent Failure Prediction and Root-Cause Disambiguation for Self-Driving Networks
Jul 28, 2026Md. Kamrul Hossain, Walid AljobyRoot Cause Analysis Intent-Based Networking
Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia · Information and Computer Science Department, and IRC for Intelligent Secure Systems, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
AgentxGCore: Agentic AI for Next-Generation Mobile Core Network
May 29, 2026Maria Katarine Santana Barbosa, Kelvin L. DiasMulti-Agent Orchestration 6G Networks
Centro de InformÃ¡tica - Universidade Federal de Pernambuco, Brazil
Role-Based Agentic AI for Intent-Driven Network and Service Orchestration
May 6, 2026Juan Parra-Ullauri, Talha Ahmed Khan, Daniel McHugh +4Multi-Agent Coordination Multi-Agent Orchestration
Research & Commercialisation, BT Group, UK.
Executor-Side Progressive Risk-Gated Actuation for Agentic AI in Wireless Supervisory Control
May 4, 2026Zhenyu Liu, Yi Ma, Rahim TafazolliSafety-Critical Control AI Agent Safety
6GIC, Institute for Communication Systems, University of Surrey, Guildford, United Kingdom, GU2 7XH
TIO-SHACL: Comprehensive SHACL validation for TMF Intent Ontologies
Apr 30, 2026Jean Martins, Leonid Mokrushin, Marin OrlicIntent-Based Networking
Ericsson Research
Validated Intent Compilation for Constrained Routing in LEO Mega-Constellations
Apr 8, 2026Yuanhang LiIntent-Based Networking LEO Satellite Networks
RefinementEngine: Automating Intent-to-Device Filtering Policy Deployment under Network Constraints
Apr 2, 2026Davide Colaiacomo, Chiara Bonfanti, Cataldo BasileCybersecurity Intent-Based Networking
Politecnico di Torino, Torino, Italy
AI-driven Intent-Based Networking Approach for Self-configuration of Next Generation Networks
Mar 24, 2026Md. Kamrul Hossain, Walid AljobyIntent-Based Networking Language Model-Based Control
Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia · IRC for Intelligent Secure Systems, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Performance Comparison of IBN orchestration using LLM and SLMs
Feb 27, 2026Wai Lwin Phone, Brahim El Boudani, Tasos Dagiuklas +1Intent-Based Networking Multi-Agent Orchestration
Dept. of Computer Science & Digital Technologies, London South Bank University, London, UK · Digital Catapult, London, UK
Agentic AI for Intent-driven Optimization in Cell-free O-RAN
Feb 26, 2026Mohammad Hossein Shokouhi, Vincent W. S. WongWireless Communications Intent-Based Networking
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada
Agentic Wireless Communication for 6G: Intent-Aware and Continuously Evolving Physical-Layer Intelligence
Feb 19, 2026Zhaoyang Li, Xingzhi Jin, Junyu Pan +2Wireless Communications 6G Networks
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China.
AI-Paging: Lease-Based Execution Anchoring for Network-Exposed AI-as-a-Service
Feb 17, 2026Mohaned Chraiti, Merve SaimlerAdmittance Control Intent-Based Networking
Electronics Engineering Department, Sabancı University, Türkiye · Ericsson Research, Türkiye
Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management
Feb 15, 2026Hojjat Navidan, Mohammad Cheraghinia, Jaron Fontaine +5Multi-Agent Orchestration Language Model-Based Control
IDLab, Department of Information Technology at Ghent University - imec, Ghent, Belgium · Department of Electrical and Computer Engineering, Princeton University, USA

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When LLM Agents See a Conflict but Still Proceed: Cross-Domain Dependencies in Network Slicing

Marcos Lima Romero, Ricardo Suyama

NEXt-Gen AI-Native Networks - NEXA, Federal University of ABC - UFABC

Network Slicing LLM Agent Safety LLM Agent Reliability Intent-Based Networking

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Future AI-native networks are being designed to apply autonomous agents built on large language models (LLMs) in network management through intents. When intents from different domains meet on the same resource, an agent must recognize the cross-domain dependency before taking an irreversible action. We investigate this problem through a smart-grid use case, in which a power utility holds an active intent on a backup slice for protection traffic. This slice appears idle by design, which is precisely the profile targeted by a cost-reduction intent. We built a tool-based environment in which the dependency must be either queried by the agent or read from an inventory annotation, together with a negative control, and evaluated ten models from four different providers in 600 runs. Most models acted against the dependency they had observed: seven of the ten models removed the protection slice at least once after receiving the cross-domain dependency, and in the high-harm tier the slice was removed in 76% of the exposed runs, while four models practically never did. Only one of ten models never removed the slice. We identify two failure modes, not querying the dependency and querying it but proceeding anyway, and show that agents often reported no conflict after observing it. These results indicate that, in AI-native networks, visibility of cross-domain dependencies is not a sufficient safeguard, and that dependency verification should be enforced by the management architecture before irreversible actions rather than delegated to the LLM's judgment.