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arXiv · 2510.11221

WebRouter: Query-specific Router via Variational Information Bottleneck for Cost-sensitive Web Agent

Tao Li·Jinlong Hu·Yang Wang·Junfeng Liu·Xuejun Liu
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Abstract

LLM-brained web agents offer powerful capabilities for web automation but face a critical cost-performance trade-off. The challenge is amplified by web agents' inherently complex prompts that include goals, action histories, and environmental states, leading to degraded LLM ensemble performance. To address this, we introduce WebRouter, a novel query-specific router trained from an information-theoretic perspective. Our core contribution is a cost-aware Variational Information Bottleneck (ca-VIB) objective, which learns a compressed representation of the input prompt while explicitly penalizing the expected operational cost. Experiments on five real-world websites from the WebVoyager benchmark show that WebRouter reduces operational costs by a striking 87.8\% compared to a GPT-4o baseline, while incurring only a 3.8\% accuracy drop.

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Tao Li, Jinlong Hu, Yang Wang, Junfeng Liu, Xuejun Liu. 2025-10-13. WebRouter: Query-specific Router via Variational Information Bottleneck for Cost-sensitive Web Agent. https://arxiv.org/abs/2510.11221

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