Zihao Yi, Zhenqing Ling, Delong Zeng, Haohao Luo, Zhe Xu, Wei Liu, Jian Luan, Wanxia Cao, Ying Shen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Automating web navigation which aims to build a web agent that follows user instructions to complete tasks like booking flights by interacting with websites, has received increasing attention due to its practical value.Although existing web agents are mostly equipped with visual perception, planning, and memory abilities, their reasoning process are still deviate from human cognition.In this work, we study the human thought pattern to empower agent with more human-like abilities in web navigation.To tackle this problem, we propose a novel multimodal web agent framework called WebExperT, which is designed to emulate the human planning process of "thinking fast and slow" to effectively decompose complex user instructions.Furthermore, WebExperT leverages experiential learning by reflecting from failure for continuously refining planning and decision-making outcomes.Experimental results on the MIND2WEB benchmark demonstrate the superiority of WebExperT in both supervised and unsupervised settings.

Multiple-choice questions (MCQs) are important in enhancing concept learning and student engagement for educational purposes.Despite the multimodal nature of educational content, current methods focus mainly on text-based inputs and often neglect the integration of visual information.In this work, we study the problem of multimodal educational question generation, which aims at generating subjectspecific educational questions with plausible yet incorrect distractors based on multimodal educational content.To tackle this problem, we introduce a novel framework, named Chainof-Exemplar (CoE), which utilizes multimodal large language models (MLLMs) with Chainof-Thought reasoning to improve the generation of challenging distractors.Furthermore, CoE leverages three-stage contextualized exemplar retrieval to retrieve exemplary questions as guides for generating more subject-specific educational questions.Experimental results on the ScienceQA benchmark demonstrate the superiority of CoE in both question generation and distractor generation over existing methods across various subjects and educational levels.