Advanced AI Service Provisioning in O-RAN through LLM Engine Integration

• URL: http://arxiv.org/abs/2605.23809v1
• Date: Fri, 22 May 2026 16:12:39 GMT
• Title: Advanced AI Service Provisioning in O-RAN through LLM Engine Integration
• Abstract summary: Open Radio Access Network (O-RAN) allows AI to be embedded directly into the RAN through modular xApps and rApps.<n>We present a proof-of-concept Dual-Brain architecture that combines both strengths: an LLM-based orchestrator operator intents into data-collection policies and deployment code.<n>We describe the architecture and provisioning workflow, share practical insights from a containerized O-RAN 5GSA testbed, and discuss open research directions.
• Score: 7.186017194297505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Open Radio Access Network (O-RAN) architecture allows AI to be embedded directly into the RAN through modular xApps and rApps, yet creating these applications collecting data, training models, writing code, and deploying them safely remains slow and largely manual. Large Language Models (LLMs) offer strong reasoning and code-generation capabilities but are unsuited for the fast, deterministic inference required in real-time RAN control. We present a proof-of-concept Dual-Brain architecture that combines both strengths: an LLM-based orchestrator translates operator intents into data-collection policies and deployment code, while an automated ML engine, NeuralSmith, trains lightweight classifiers on demand via an API. We describe the architecture and provisioning workflow, share practical insights from a containerized O-RAN 5G~SA testbed, and discuss open research directions.

Related papers

• MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning [80.41987492855971]
  We propose a multi-agent collaborative framework built upon a lightweight Multimodal Large Language Model (MLLM)<n>Within this architecture, multi-modal data pertinent to social intelligence is precisely localized.<n>With around 30% of training data from IntentTrain, we achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2026-06-10T12:44:02Z)
• Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management [39.17062930275755]
  This article proposes a multi-scale agentic AI framework for Open Radio Access Networks (O-RAN)<n>It organizes RAN intelligence as a coordinated hierarchy across the Non-Real-Time (Non-RT), Near-Real-Time (Near-RT), and Real-Time (RT) control loops.<n>We show how these agents cooperate through standardized O-RAN interfaces and telemetry.
  arXiv  Detail & Related papers  (2026-02-15T12:34:01Z)
• ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development [72.4729759618632]
  We introduce ABC-Bench, a benchmark to evaluate agentic backend coding within a realistic, executable workflow.<n>We curated 224 practical tasks spanning 8 languages and 19 frameworks from open-source repositories.<n>Our evaluation reveals that even state-of-the-art models struggle to deliver reliable performance on these holistic tasks.
  arXiv  Detail & Related papers  (2026-01-16T08:23:52Z)
• On AI Verification in Open RAN [22.005711879375173]
  We propose a lightweight verification approach based on interpretable models to validate the behavior of Deep Reinforcement Learning (DRL) agents in Open RAN.<n>Specifically, we use Decision Tree (DT)-based verifiers to perform near-real-time consistency checks at runtime.<n>We also outline future challenges to ensure trustworthy AI adoption in Open RAN.
  arXiv  Detail & Related papers  (2025-10-21T08:48:26Z)
• Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6G [20.07205081315289]
  This article presents a novel converged O-RAN and AI-RAN architecture that unifies orchestration and management of both telecommunications and AI workloads on shared infrastructure.<n>We introduce two key architectural innovations: (i) the AI-RAN Orchestrator, which extends the O-RAN Service Management and Orchestration (SMO) to enable integrated resource and allocation across RAN and AI workloads; and (ii) AI-RAN sites that provide distributed edge AI platforms with real-time processing capabilities.
  arXiv  Detail & Related papers  (2025-07-09T14:49:11Z)
• ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing [5.62872273155603]
  We propose textitORAN-GUIDE, a dual-LLM framework that enhances multi-agent (MARL) with task-relevant, semantically enriched state representations.<n>Results show that ORAN-GUIDE improves sample efficiency, policy convergence, and performance generalization over standard MARL and single-LLM baselines.
  arXiv  Detail & Related papers  (2025-05-31T14:21:19Z)
• Large Action Models: From Inception to Implementation [51.81485642442344]
  Large Action Models (LAMs) are designed for action generation and execution within dynamic environments.<n>LAMs hold the potential to transform AI from passive language understanding to active task completion.<n>We present a comprehensive framework for developing LAMs, offering a systematic approach to their creation, from inception to deployment.
  arXiv  Detail & Related papers  (2024-12-13T11:19:56Z)
• ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning [74.58666091522198]
  We present a framework for intuitive robot programming by non-experts. We leverage natural language prompts and contextual information from the Robot Operating System (ROS) Our system integrates large language models (LLMs), enabling non-experts to articulate task requirements to the system through a chat interface.
  arXiv  Detail & Related papers  (2024-06-28T08:28:38Z)
• CodeTF: One-stop Transformer Library for State-of-the-art Code LLM [72.1638273937025]
  We present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. Our library supports a collection of pretrained Code LLM models and popular code benchmarks. We hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering.
  arXiv  Detail & Related papers  (2023-05-31T05:24:48Z)
• Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis [3.8073142980733]
  Open Radio Access Network (O-RAN) has introduced an emerging RAN architecture that enables openness, intelligence, and automated control. The RAN Intelligent Controller (RIC) provides the platform to design and deploy RAN controllers. xApps are the applications which will take this responsibility by leveraging machine learning (ML) algorithms and acting in near-real time.
  arXiv  Detail & Related papers  (2022-09-26T19:12:18Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.