In multi-intent intent-based networks, a single fault can trigger co-drift where multiple intents exhibit symptomatic KPI degradation, creating ambiguity about the true root-cause intent. We present MILD, a proactive framework that reformulates intent assurance from reactive drift detection to fixed-horizon failure prediction with intent-level disambiguation. MILD uses a teacher-augmented Mixture-of-Experts where a gated disambiguation module identifies the root-cause intent while per-intent heads output calibrated risk scores. On a benchmark with non-linear failures and co-drifts, MILD provides 3.8\%--92.5\% longer remediation lead time and improves intent-level root-cause disambiguation accuracy by 9.4\%--45.8\% over baselines. MILD also provides per-alert KPI explanations, enabling actionable diagnosis.

翻译：在多意图的基于意图网络中，单个故障可能引发协同漂移，导致多个意图同时出现关键绩效指标（KPI）劣化症状，从而造成真实根因意图的模糊性。本文提出MILD——一种主动式框架，将意图保障从被动的漂移检测重构为具有意图级消歧能力的固定时间窗故障预测。MILD采用教师增强的混合专家模型，其中门控消歧模块识别根因意图，而各意图专用头输出校准后的风险评分。在包含非线性故障与协同漂移的基准测试中，相较于基线方法，MILD可提供延长3.8%–92.5%的修复提前期，并将意图级根因消歧准确率提升9.4%–45.8%。MILD还提供逐告警的KPI解释，支持可操作的诊断流程。