Research demonstrating how distilled Large Language Models can significantly improve network Active Queue Management (AQM) with minimal engineering effort.

• Uses few-shot learning and contextual understanding to manage packet traffic
• Addresses limitations of existing Deep Learning-based queuing approaches
• Provides adaptive solutions for dynamic network scenarios
• Reduces manual engineering effort while maintaining performance

Why It Matters: This approach represents a breakthrough in network engineering by applying LLM capabilities to critical infrastructure problems, potentially enabling more reliable, low-latency communication systems with less human intervention.

Distilling Large Language Models for Network Active Queue Management