# MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networking

arXiv: https://arxiv.org/abs/2602.14283 · HTML: https://mlsanity.com/paper/2602.14283/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 15 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Reducing Overfitting in Deep Networks by Decorrelating Representations](https://mlsanity.com/paper/1511.06068/) (2015)
- [Anomaly Detection and Failure Root Cause Analysis in (Micro)Service-Based Cloud Applications: A Survey](https://mlsanity.com/paper/2105.12378/) (2021)
- [Deep Learning for Multi-Label Learning: A Comprehensive Survey](https://mlsanity.com/paper/2401.16549/) (2024)
- [Intent Assurance using LLMs guided by Intent Drift](https://mlsanity.com/paper/2402.00715/) (2024)
- [Towards Supporting Intelligence in 5G/6G Core Networks: NWDAF Implementation and Initial Analysis](https://mlsanity.com/paper/2205.15121/) (2022)
- [Telecom Foundation Models: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2408.03964/) (2024)
- [NetIntent: Leveraging Large Language Models for End-to-End Intent-Based SDN Automation](https://mlsanity.com/paper/2507.14398/) (2025)
- [Towards NWDAF-enabled Analytics and Closed-Loop Automation in 5G Networks](https://mlsanity.com/paper/2505.06789/) (2025)

## Built on by (most cited first, 3 of 3)

- [AI-driven Intent-Based Networking Approach for Self-configuration of Next Generation Networks](https://mlsanity.com/paper/2603.23772/) (2026)
- [Untangling Co-Drift: Proactive Multi-Intent Failure Prediction and Root-Cause Disambiguation for Self-Driving Networks](https://mlsanity.com/paper/2607.25989/) (2026)
- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

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.