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MELLON - Multimodal Enhanced LLM for Online Navigation

2026-08-10 04:56:22
Ruiyu Li, Haoyang Cai, Zhitong Guo, Tong Hu
arXiv_AI
             
Abstract

Web navigation agents are capable of addressing various types of tasks on different websites. Current baselines on web navigation are either unimodal or lack strong reasoning abilities given multimodal inputs. Focusing on the WebShop benchmark, a real-world website simulation, we explore the alignment of text and images, as well as multimodal reasoning and planning abilities, to enhance the performance of web navigation agents. We propose three innovative multimodal enhancements: Multimodal Enhanced LLM for Online Navigation (MELLON), VQAgent, and Multimodal Ranker. MELLON demonstrates a significant improvement in task completion accuracy, with a 9.26% increase after just one epoch of training. Our findings suggest the necessity of further exploration into multimodal approaches, with a focus on more extensive training and alignment strategies to enhance the effectiveness of web navigation agents.

Abstract (translated)

URL

https://arxiv.org/abs/2608.09121

PDF

https://arxiv.org/pdf/2608.09121.pdf

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