Open Radio Access Network (O-RAN) enables network control through multi-vendor xApps operating both within and across layers, subnets, and domains, whose concurrent execution can trigger conflicts that are latent during the development phase. Existing conflict management approaches rely heavily on joint-execution data, which is often unavailable in practice. To address this limitation, we formalize a novel problem termed conflict reasoning, which involves identifying conflict-inducing conditions given only marginal datasets from each individual xApp. We propose ZODIAC, a three-stage framework for zero-shot conflict condition inference that comprises uncertainty-aware surrogate model training, trajectory-level diffusion training, and compositional guided denoising for efficient, physics-constrained, and reliable condition search. We derive a theoretical lower confidence bound showing that the compositional reasoning in ZODIAC serves as a principled surrogate for true conflict severity, with the epistemic penalty directly controlling the approximation gap. We evaluate ZODIAC on both the lightweight Mobile-Env platform across all three O-RAN Alliance conflict types (direct, indirect, and implicit) and a realistic NS-O-RAN-Flexric simulator. ZODIAC consistently outperforms baseline condition search methods, achieving over 20% higher True Positive Rate at Top-20, substantially stronger Spearman rank correlation, greater scenario diversity, and competitive computational efficiency. Ablation studies confirm the necessity of each guidance component, with epistemic uncertainty penalties proving essential for filtering spurious conflicts. To the best of our knowledge, ZODIAC is the first framework in O-RAN that enables conflict reasoning from marginal offline data without requiring any joint-execution traces.

翻译：开放无线接入网（O-RAN）通过跨层、跨子网及跨域运行的多供应商xApp实现网络控制，但xApps的并发执行可能触发开发阶段难以察觉的潜在冲突。现有冲突管理方法严重依赖联合执行数据，而此类数据在实际中往往难以获取。为解决这一局限，我们形式化定义了名为冲突推理的新问题，即仅利用各独立xApp的边缘数据集识别冲突诱导条件。我们提出ZODIAC框架，这是一个包含不确定性感知替代模型训练、轨迹级扩散训练及组合引导去噪的三阶段零样本冲突条件推理方法，可实现高效、物理约束且可靠的搜索。我们推导出理论下置信界，证明ZODIAC中的组合推理可作为真实冲突严重性的原则性替代，其中认知惩罚直接控制近似误差。我们在轻量级Mobile-Env平台（覆盖O-RAN联盟全部三类冲突：直接、间接与隐式）及实际NS-O-RAN-Flexric模拟器上评估ZODIAC。ZODIAC持续优于基线条件搜索方法，在前20项中实现超过20%的真阳性率提升，具有显著更强的斯皮尔曼秩相关系数、更高的场景多样性及竞争力的计算效率。消融研究证实了各引导组件的必要性，其中认知不确定性惩罚对过滤虚假冲突至关重要。据我们所知，ZODIAC是首个无需任何联合执行轨迹即可从边缘离线数据实现冲突推理的O-RAN框架。