# Interpretable Attention-Based Multi-Agent PPO for Latency Spike Resolution in 6G RAN Slicing

arXiv: https://arxiv.org/abs/2602.11076 · HTML: https://mlsanity.com/paper/2602.11076/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [AI-Native Network Slicing for 6G Networks](https://mlsanity.com/paper/2105.08576/) (2021)
- [EXPLORA: AI/ML EXPLainability for the Open RAN](https://mlsanity.com/paper/2310.13667/) (2023)
- [Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research](https://mlsanity.com/paper/2307.00319/) (2023)
- [Explanation-Guided Deep Reinforcement Learning for Trustworthy 6G RAN Slicing](https://mlsanity.com/paper/2303.15000/) (2023)

## Abstract (arXiv)

Sixth-generation (6G) radio access networks (RANs) must enforce strict service-level agreements (SLAs) for heterogeneous slices, yet sudden latency spikes remain difficult to diagnose and resolve with conventional deep reinforcement learning (DRL) or explainable RL (XRL). We propose Attention-Enhanced Multi-Agent Proximal Policy Optimization (AE-MAPPO), which integrates six specialized attention mechanisms into multi-agent slice control and surfaces them as zero-cost, faithful explanations. The framework operates across O-RAN timescales with a three-phase strategy: predictive, reactive, and inter-slice optimization. A URLLC case study shows AE-MAPPO resolves a latency spike in 18ms, restores latency to 0.98ms with 99.9999% reliability, and reduces troubleshooting time by 93% while maintaining eMBB and mMTC continuity. These results confirm AE-MAPPO's ability to combine SLA compliance with inherent interpretability, enabling trustworthy and real-time automation for 6G RAN slicing.