How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks
Emerging 6G visions, reflected in ongoing standardization efforts within 3GPP, IETF, ETSI, ITU-T, and the O-RAN Alliance, increasingly characterize networks as AI-native systems in which high-level semantic reasoning layers operate above standardized control and data-plane functions. Although frontier-scale large language models (LLMs) such as Qwen2.5-7B and Olmo-3-7B demonstrate strong reasoning capability, their computational footprint limits deployment in latency-sensitive, edge-native infrastructures. This paper presents a systematic empirical study of the scaling behavior and deployment efficiency of compact language models for network-level semantic reasoning in AI-native 6G systems...
| Authors | Mohamed Amine Ferrag, Abderrahmane Lakas, Merouane Debbah |
|---|---|
| Primary category | cs.NI |
| Published | 2 March 2026 |
| Last updated at source | 23 June 2026 |
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