# WiFi Pathologies Detection using LLMs

arXiv: https://arxiv.org/abs/2506.06943 · HTML: https://mlsanity.com/paper/2506.06943/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [Evidence-Grounded Ensemble Diagnosis of 802.11 Packet Captures: A Multi-Stage Pipeline with Deterministic Reliability Scoring](https://mlsanity.com/paper/2606.06871/) (2026)

## Abstract (arXiv)

In this paper, we fine-tune encoder-only and decoder-only large language models (LLMs) to detect pathologies in IEEE 802.11 networks, commonly known as WiFi. Our approach involves manually crafting prompts followed by fine-tuning. Evaluations show that the sequential model achieves high detection accuracy using labeled data, while the causal model performs equally well for unlabeled data.