研究6G网络中微型语言模型的推理能力极限，找到最适合边缘部署的模型规模。
How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks
- 基于6G-Bench基准测试，评估135M到7B参数模型在五类任务中的表现。
- 1.5B至3B参数模型在准确性与资源效率间取得最佳平衡，超越更大模型。
- 发现模型越大越不稳定，且单位资源效益不随参数增长而单调提升。
6G愿景正推动网络向以人工智能为核心的系统演进，高阶语义推理层将运行于标准化控制与数据平面之上。尽管前沿大语言模型（如Qwen2.5-7B、Olmo-3-7B）具备强大推理能力，但其计算开销限制了在低延迟、边缘原生架构中的部署。本文对紧凑型语言模型在AI-native 6G网络中的缩放行为与部署效率进行了系统性实证研究。基于6G-Bench——一个涵盖五个能力域共30个决策任务、与标准对齐的基准，评估了从135M（SmolLM2-135M）到7B（Qwen2.5-7B）参数的模型，包括1B级的Llama-3.2-1B、Granite-1B和3B级的Qwen2.5-3B。确定性准确率（pass@1）从135M时的0.224升至7B时的0.707，但收益非均匀分布。1至1.5B区间出现显著稳定性跃迁：准确率由1B的0.373升至1.5B的0.531，不稳定性差距Δ₅由0.356降至0.138。超过3B后改进放缓（+0.064，从3B到7B）。通过单次查询推理分析及“边缘得分”（结合准确率、延迟与内存占用）评估，发现语义可靠性每单位边缘资源并不随参数量单调上升。相反，约1.5至3B的中等规模模型在确定性稳定性和计算效率间达到最优平衡，为AI-native 6G架构提供可部署指导。所有代码与结果公开于https://github.com/maferrag/6G-Bench。
原文摘要 · Abstract (English)
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. Using 6G-Bench, a standardization-aligned benchmark comprising 30 decision-making tasks across five capability domains, we evaluate models ranging from 135M (SmolLM2-135M) to 7B parameters (Qwen2.5-7B), including mid-scale architectures such as Llama-3.2-1B, Granite-1B, and Qwen2.5-3B. Deterministic accuracy (pass@1) increases from 0.224 at 135M to 0.707 at 7B, but scaling gains are highly non-uniform. A pronounced stability transition occurs in the 1 to 1.5B range, where accuracy rises from 0.373 (Llama-3.2-1B) to 0.531 (Qwen2.5-1.5B) and the instability gap Delta_5 contracts from 0.356 to 0.138. Beyond 3B parameters, improvements diminish (+0.064 from 3B to 7B). Through single-query inference profiling and an Edge Score metric that normalizes accuracy by latency and memory footprint, we show that semantic reliability per unit edge resource does not scale monotonically with parameter count. Instead, mid-scale models (approximately 1.5 to 3B) achieve the most favorable balance between deterministic stability and computational efficiency, providing deployment-relevant guidance for AI-native 6G architectures. All scripts and results are publicly available at https://github.com/maferrag/6G-Bench
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