# Intent2Tc: Automated Intent-to-Traffic Control Translation with Language Models

arXiv: https://arxiv.org/abs/2609.31397 · HTML: https://mlsanity.com/paper/2609.31397/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 25 Sep 2026.

## Builds on (most cited first, 8 of 8)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Few-shot Natural Language Generation for Task-Oriented Dialog](https://mlsanity.com/paper/2002.12328/) (2020)
- [LLM-based policy generation for intent-based management of applications](https://mlsanity.com/paper/2402.10067/) (2024)
- [Intent-Based Network for RAN Management with Large Language Models](https://mlsanity.com/paper/2507.14230/) (2025)
- [NetIntent: Leveraging Large Language Models for End-to-End Intent-Based SDN Automation](https://mlsanity.com/paper/2507.14398/) (2025)
- [INTA: Intent-Based Translation for Network Configuration with LLM Agents](https://mlsanity.com/paper/2501.08760/) (2025)
- [Towards End-to-End Network Intent Management with Large Language Models](https://mlsanity.com/paper/2504.13589/) (2025)
- [Intent2QoS: Language Model-Driven Automation of Traffic Shaping Configurations](https://mlsanity.com/paper/2601.18974/) (2026)

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

- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

Automated and highly usable Quality-of-Service (QoS) enforcement requires translating high-level service intents into deployable traffic-management policies. Although intent-based networking (IBN) has simplified policy specification, bridging the gap between business-level intents and executable network configurations remains complex, error-prone, and difficult to automate. This paper presents Intent2Tc, a closed-loop language-model-driven framework that translates business-level traffic-shaping intents into declarative sub-intents and subsequently into validated, executable Linux traffic control (tc) configurations. The framework integrates an Active Queue Management (AQM)-based digital twin (DT) semantic model, automated metadata extraction, critique-driven refinement, and Retrieval-Augmented Generation (RAG)-based knowledge reuse to improve semantic consistency and configuration reliability. We evaluate multiple open-source large language models (LLMs) and small language models (SLMs), together with Claude Sonnet-4.6, on 100 Request for Comments (RFC) 9315-compliant traffic-shaping intents. Across both translation stages, Intent2Tc achieves high semantic fidelity, configuration accuracy, and deployment readiness, with Claude Sonnet-4.6 reaching 0.98 semantic similarity, 1.0 semantic unit coverage, and 0.045 normalized edit distance. Furthermore, RAG reduces token consumption and inference latency while enabling compact models such as Phi-4-mini to approach the performance of substantially larger models. Linux tc serves as the target configuration platform, demonstrating the practical applicability of the proposed framework.