JAUNT: Joint Alignment of User Intent and Network State for QoE-centric LLM Tool Routing

TL;DR

JAUNT is a framework that improves LLM tool routing by jointly aligning user intent and network conditions, leading to better user experience in diverse network environments.

Contribution

The paper introduces JAUNT, a novel dual-view alignment strategy that considers both user intent and network state for QoE-centric LLM tool routing.

Findings

JAUNT significantly outperforms baselines in QoE metrics.

The benchmark enables systematic evaluation of routing strategies.

Aligning intent and network state improves scalability of LLM services.

Abstract

Large Language Models (LLMs) increasingly rely on emerging protocols such as the Model Context Protocol (MCP) to invoke external tools and services. However, current tool routing mechanisms remain fragile because they only consider functional matching between users' queries and tools. In practice, user intent expressed through queries can be vague or underspecified, and the actual Quality of Experience (QoE) also depends on external factors such as link latency and server availability that are not captured by semantics alone. To address this challenge, we propose JAUNT, a framework for Joint Alignment of User intent and Network state in QoE-centric Tool routing. JAUNT introduces a dual-view alignment strategy that interprets user intent while employing LLM agents to construct network profiles, mapping numerical performance indicators into the semantic space to guide routing. We further design a benchmark that integrates diverse user request patterns with heterogeneous network states, enabling systematic evaluation of QoE outcomes. Experimental results show that JAUNT significantly improves QoE compared with several baselines, demonstrating the importance of aligning both intent and network state for scalable LLM service orchestration.