# LLM-Sketch: Enhancing Network Sketches with LLM

arXiv: https://arxiv.org/abs/2502.07495 · HTML: https://mlsanity.com/paper/2502.07495/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2025.

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

- [Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting](https://mlsanity.com/paper/2507.01997/) (2025)
- [OmniPlan: An Adaptive Framework for Timely and Near-Optimal Network Planning Optimization](https://mlsanity.com/paper/2606.18105/) (2026)
- [On the (Generative) Linear Sketching Problem](https://mlsanity.com/paper/2603.14474/) (2026)

## Abstract (arXiv)

Network stream mining is fundamental to many network operations. Sketches, as compact data structures that offer low memory overhead with bounded accuracy, have emerged as a promising solution for network stream mining. Recent studies attempt to optimize sketches using machine learning; however, these approaches face the challenges of lacking adaptivity to dynamic networks and incurring high training costs. In this paper, we propose LLM-Sketch, based on the insight that fields beyond the flow IDs in packet headers can also help infer flow sizes. By using a two-tier data structure and separately recording large and small flows, LLM-Sketch improves accuracy while minimizing memory usage. Furthermore, it leverages fine-tuned large language models (LLMs) to reliably estimate flow sizes. We evaluate LLM-Sketch on three representative tasks, and the results demonstrate that LLM-Sketch outperforms state-of-the-art methods by achieving a 7.5× accuracy improvement.