From Generation to Guarantee: Intent-Based Configuration Update with Verification Feedback

Abstract

Intent-Based Networking (IBN) has seen rapid advancement in recent years, largely fueled by the capabilities of large language models (LLMs). While LLMs enable more expressive and automated intent translation solutions, challenges such as hallucinated parameters, missing constraints, and syntax errors still limit their practical deployment. To address these challenges, we propose Artanis, an intent-based configuration framework that combines LLM-powered intent translation with multi-objective verification feedback to iteratively update configurations. Artanis comprises a Translator that produces candidate configuration updates (CUs) from intents, and a scalable multi-objective Verifier to evaluate the CUs. To enhance service guarantee, we introduce Group Relative Policy Optimization (GRPO) with verification feedback, a reinforcement learning method that dynamically adjusts the CUs by leveraging structured feedback from the Verifier. Evaluation on comprehensive network topologies shows that Artanis reduces the policy violation rate by 32.6% compared to existing approaches. Artanis achieves this while maintaining consistency with intents and adapting robustly to diverse network conditions.