WebRouter: Query-specific Router via Variational Information Bottleneck for Cost-sensitive Web Agent
Tao Li, Jinlong Hu, Yang Wang, Junfeng Liu, Xuejun Liu
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.
BibTeX
@inproceedings{icassp2026_webrouterquerysp,
title = {WebRouter: Query-specific Router via Variational Information Bottleneck for Cost-sensitive Web Agent},
author = {Tao Li and Jinlong Hu and Yang Wang and Junfeng Liu and Xuejun Liu},
booktitle = {ICASSP 2026},
year = {2026}
}