Efficient and fair spectrum allocation is a central challenge in 6G networks, where massive connectivity and heterogeneous services continuously compete for limited radio resources. We investigate the use of Large Language Models (LLMs) as bidding agents in repeated 6G spectrum auctions with budget constraints in vehicular networks. Each user equipment (UE) acts as a rational player optimizing its long-term utility through repeated interactions. Using the Vickrey-Clarke-Groves (VCG) mechanism as a benchmark for incentive-compatible, dominant-strategy truthfulness, we compare LLM-guided bidding against truthful and heuristic strategies. Unlike heuristics, LLMs leverage historical outcomes and prompt-based reasoning to adapt their bidding behavior dynamically. Results show that when the theoretical assumptions guaranteeing truthfulness hold, LLM bidders recover near-equilibrium outcomes consistent with VCG predictions. However, when these assumptions break -- such as under static budget constraints -- LLMs sustain longer participation and achieve higher utilities, revealing their ability to approximate adaptive equilibria beyond static mechanism design. This work provides the first systematic evaluation of LLM bidders in repeated spectrum auctions, offering new insights into how AI-driven agents can interact strategically and reshape market dynamics in future 6G networks.

翻译：高效且公平的频谱分配是6G网络中的核心挑战，其中大规模连接与异构服务持续争夺有限的无线电资源。我们研究了在车载网络中，将大语言模型（LLM）作为具有预算约束的重复性6G频谱拍卖中的投标代理。每个用户设备（UE）作为理性参与者，通过重复交互优化其长期效用。以维克里-克拉克-格罗夫斯（VCG）机制作为激励兼容、占优策略真实性的基准，我们将LLM引导的投标策略与真实性策略及启发式策略进行比较。与启发式策略不同，LLM利用历史结果和基于提示的推理来动态调整其投标行为。结果表明，当保证真实性的理论假设成立时，LLM投标者能恢复与VCG预测一致的近均衡结果。然而，当这些假设失效时（例如在静态预算约束下），LLM能维持更长的参与时间并实现更高的效用，揭示了其逼近静态机制设计之外的自适应均衡的能力。本工作首次系统评估了重复频谱拍卖中的LLM投标者，为人工智能驱动代理如何战略性地交互并重塑未来6G网络市场动力学提供了新见解。