From Agentic to Autogenic Network Management for AI-Native 6G and Beyond: A Standards Perspective
2026-07-09T07:23:51Z•8eeb148d42b8666a55f137c78a4c07dda91bf76190ce1de7a481ed5fde9f77aa
6GAI-in-RANAdversarial MLAgentic AIAutogenic network managementBBR congestion controlCooperative perceptionDRL/PPOGNSS spoofingGhost vehiclesIAB/Sub6 backhaulLEO satellite networksLarge AI Models (LAM)Model poisoningModel-based controlNear-RT RICO-RANPrivacy/leakagePrompt injectionSmall Language Models (SLM)Supply chain riskThroughput/availability attacksV2XWi‑Fi MLOxApp
What happened
This collection of recent papers covers agentic/autogenic AI for 6G network management, AI embedded in RAN control (Near-RT RIC xApps / O-RAN), ML/DRL agents for Wi‑Fi MLO, IAB forwarding, and backhaul, congestion control (BBR) behavior and ML-guided adaptation for LEO satellite Internet, cooperative perception for CAVs using V2X fusion, THz near-field beamforming under UE location uncertainty, and multi-domain intent-driven network coordination. Security-relevant implications recur across items: introducing LAM/SLM-based agents and self-programming/autogenic capabilities expands attack/supply
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_ni
- Record identifier
- 8eeb148d42b8666a55f137c78a4c07dda91bf76190ce1de7a481ed5fde9f77aa
- Enrichment time
- 2026-07-09T07:23:51Z
- AI-assisted enrichment
- Yes
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