Future sixth-generation (6G) mobile networks will demand artificial intelligence (AI) agents that are not only autonomous and efficient, but also capable of real-time adaptation in dynamic environments and transparent in their decisionmaking. However, prevailing agentic AI approaches in networking, exhibit significant shortcomings in this regard. Conventional deep reinforcement learning (DRL)-based agents lack explainability and often suffer from brittle adaptation, including catastrophic forgetting of past knowledge under non-stationary conditions. In this paper, we propose an alternative solution for these challenges: Bayesian reasoning via Active Inference (BRAIN) agent. BRAIN harnesses a deep generative model of the network environment and minimizes variational free energy to unify perception and action in a single closed-loop paradigm. We implement BRAIN as O-RAN eXtended application (xApp) on GPU-accelerated testbed and demonstrate its advantages over standard DRL baselines. In our experiments, BRAIN exhibits (i) robust causal reasoning for dynamic radio resource allocation, maintaining slice-specific quality of service (QoS) targets (throughput, latency, reliability) under varying traffic loads, (ii) superior adaptability with up to 28.3% higher robustness to sudden traffic shifts versus benchmarks (achieved without any retraining), and (iii) real-time interpretability of its decisions through human-interpretable belief state diagnostics.

翻译：未来的第六代（6G）移动网络要求人工智能（AI）代理不仅具备自主性与高效性，还需能够在动态环境中实时适应并保持决策过程的透明性。然而，当前网络领域主流的代理式AI方法在此方面存在显著不足。基于传统深度强化学习（DRL）的代理缺乏可解释性，且往往表现出脆弱的适应能力，包括在非平稳条件下对过往知识的灾难性遗忘。本文针对这些挑战提出了一种替代解决方案：基于主动推理的贝叶斯推理（BRAIN）代理。BRAIN利用网络环境的深度生成模型，通过最小化变分自由能将感知与行动统一在单一闭环范式中。我们将BRAIN实现为GPU加速测试平台上的O-RAN扩展应用（xApp），并验证其相对于标准DRL基线的优势。实验表明，BRAIN展现出：（i）针对动态无线资源分配的鲁棒因果推理能力，能够在变化流量负载下维持切片级服务质量（QoS）目标（吞吐量、时延、可靠性）；（ii）卓越的适应能力，面对突发流量变化时鲁棒性较基准方法提升达28.3%（无需任何重新训练）；（iii）通过人类可理解的信念状态诊断实现决策的实时可解释性。