MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networking
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914331400077312 |
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| author | Hossain, Md. Kamrul Aljoby, Walid |
| author_facet | Hossain, Md. Kamrul Aljoby, Walid |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_14283 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networking Hossain, Md. Kamrul Aljoby, Walid Networking and Internet Architecture Machine Learning 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. |
| title | MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networking |
| topic | Networking and Internet Architecture Machine Learning |
| url | https://arxiv.org/abs/2602.14283 |