解决网络故障根因模糊问题，提前预测并精准定位故障源头
MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networking
- 用专家混合模型+门控消歧模块，同时预测多意图故障风险
- 相比基线提升9.4%~45.8%的根因识别准确率，故障预警时间延长3.8%~92.5%
- 可为每条告警提供关键指标解释，支持可操作诊断
在多意图意图驱动型网络中，单一故障可能引发多个意图同时出现性能退化（共漂移），导致根因归属模糊。本文提出MILD框架，将意图保障从被动漂移检测转为固定时域的主动故障预测，并实现意图级根因消歧。MILD采用教师增强的专家混合模型，通过门控消歧模块识别真实根因意图，各意图头输出校准后的风险评分。在包含非线性故障和共漂移的基准测试中，MILD相较基线提升9.4%~45.8%的意图级根因识别准确率，修复准备时间延长3.8%~92.5%。同时，系统可提供每条告警的关键性能指标解释，支持可操作诊断。
原文摘要 · Abstract (English)
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.
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