Scaling test-time compute has proven highly effective for language models, yet this opportunity remains largely unexplored for industrial Click-Through Rate (CTR) prediction. CTR models suffer from a fundamental asymmetry: feature combinations well-represented in training yield confident predictions, while sparsely observed ones produce unreliable outputs. Existing training-phase solutions such as adaptive gating learn a fixed selection function subject to the same sparsity, offering no per-instance recourse at deployment.We propose UTTSI (Uncertainty-Triggered Test-Time Selective Inference), a training-free model-agnostic framework that scales inference depth proportionally to per-instance uncertainty. A dual-signal estimator combining model logit confidence with a data-level frequency prior distinguishes epistemic uncertainty from aleatoric ambiguity. Every instance undergoes adaptive feature filtering to remove unreliable embeddings; uncertain instances additionally receive stochastic feature-path explorations whose predictions are aggregated via consistency-weighted ensembling. Confident instances bypass exploration entirely, keeping average overhead at approximately $2.8\times$ base model cost with worst-case latency unchanged.Experiments on four datasets with three backbone architectures demonstrate consistent, statistically significant gains over all training-phase baselines. A seven-day online A/B test further confirms a 5.3% relative CTR gain ($p < 0.01$), establishing selective test-time compute allocation as a practical complement to training-phase advances for CTR prediction.

翻译：测试时计算扩展已在语言模型中展现出显著效果，然而这一策略在工业级点击率预测场景中尚待深入探索。点击率模型面临根本性非对称问题：训练数据中充分覆盖的特征组合能产生高置信度预测，而稀疏观测的特征组合则输出不可靠结果。现有训练阶段方案（如自适应门控）受制于相同的数据稀疏性，仅能学习固定选择函数，无法在部署阶段针对单个实例提供补救措施。为此，我们提出UTTSI（不确定性触发式测试时选择性推理）——一种免训练、模型无关的框架，可根据实例级不确定性等比扩展推理深度。基于模型logit置信度与数据级频率先验的双信号估计器，可有效区分认知不确定性与偶然歧义性。所有实例均需经过自适应特征过滤以剔除不可靠嵌入；不确定性实例额外执行随机特征路径探索，其预测结果通过一致性加权集成进行聚合。高置信度实例则完全跳过探索环节，使得平均计算开销仅为基础模型的约2.8倍，且最坏情况延迟保持不变。在四个数据集、三种骨干架构上的实验表明，该方法相较所有训练阶段基线取得了一致且统计显著的性能提升。为期七天的在线A/B测试进一步验证了5.3%的点击率相对提升（p<0.01），确立了选择性测试时计算分配作为点击率预测训练阶段进展的有效补充方案。