mlsanityData to 14 Oct 2026

Agoran: An Agentic Open Marketplace for 6G RAN Automation

arXiv 2508.09159 ↗First posted 5 Aug 2025cs.NI cs.AIcited by 2

On the map: LLM agents and tool use → Agentic AI for wireless networks

Abstract (arXiv)

Next-generation mobile networks must reconcile the often-conflicting goals of multiple service owners. However, today's network slice controllers remain rigid, policy-bound, and unaware of the business context. We introduce Agoran Service and Resource Broker (SRB), an agentic marketplace that brings stakeholders directly into the operational loop. Inspired by the ancient Greek agora, Agoran distributes authority across three autonomous AI branches: a Legislative branch that answers compliance queries using retrieval-augmented Large Language Models (LLMs); an Executive branch that maintains real-time situational awareness through a watcher-updated vector database; and a Judicial branch that evaluates each agent message with a rule-based Trust Score, while arbitrating LLMs detect malicious behavior and apply real-time incentives to restore trust. Stakeholder-side Negotiation Agents and the SRB-side Mediator Agent negotiate feasible, Pareto-optimal offers produced by a multi-objective optimizer, reaching a consensus intent in a single round, which is then deployed to Open and AI RAN controllers. Deployed on a private 5G testbed and evaluated with realistic traces of vehicle mobility, Agoran achieved significant gains: (i) a 37% increase in throughput of eMBB slices, (ii) a 73% reduction in latency of URLLC slices, and concurrently (iii) an end-to-end 8.3% saving in PRB usage compared to a static baseline. An 1B-parameter Llama model, fine-tuned for five minutes on 100 GPT-4 dialogues, recovers approximately 80% of GPT-4.1's decision quality, while operating within 6 GiB of memory and converging in only 1.3 seconds. These results establish Agoran as a concrete, standards-aligned path toward ultra-flexible, stakeholder-centric 6G networks. A live demo is presented https://www.youtube.com/watch?v=h7vEyMu2f5w&abchannel=BubbleRAN.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use. Builds on 7 of its 38 references that are in the map. First posted on arXiv 5 Aug 2025.

Builds on

7 of 38 references.

References · 7

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  3. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  4. 2021AI-Native Network Slicing for 6G Networkscited by 31
  5. 2022dApps: Distributed Applications for Real-time Inference and Control in O-RANcited by 17
  6. 2024Trustworthy Intrusion Detection: Confidence Estimation Using Latent Spacecited by 3
  7. 2025NASP: Network Slice as a Service Platform for 5G Networkscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks
  2. 2026An Agentic AI Control Plane for 6G Network Slice Orchestration, Monitoring, and Trading
  3. 2026From Agentic to Autogenic Network Management for AI-Native 6G and Beyond: A Standards Perspective
  4. 2025Agentic AI for 6G: A New Paradigm for Autonomous RAN Security Compliance
  5. 2025MobiLLM: An Agentic AI Framework for Closed-Loop Threat Mitigation in 6G Open RANs
  6. 2026LLM-Powered Agentic AI for 5G/6G Networks: A Tutorial and Survey on Architectures, Protocols, and Standardization
  7. 2025Agentic AI for Mobile Network RAN Management and Optimization
  8. 2025MX-AI: Agentic Observability and Control Platform for Open and AI-RAN