Year: 2026
Type: conference-paper
Abstract: Reasoning capabilities have significantly improved the performance of vision-language models (VLMs) in domains such as mathematical problem-solving, coding, and visual question-answering.However, their impact on real-world applications remains unclear.This paper presents a large-scale empirical study on the effectiveness of reasoning-enabled VLMs in mobile GUI agents.We evaluate six pairs of VLMs, including both commercial and opensource lightweight models, by comparing their base and reasoning-enhanced versions across static and interactive benchmarks.Our findings show that, under the evaluated benchmarks and configurations, reasoning-enabled VLMs generally provide only marginal improvements over their non-reasoning counterparts and can even degrade performance in certain agent configurations.Notably, reasoning and nonreasoning VLMs fail on different sets of tasks, suggesting that reasoning does have an impact, but its benefits and drawbacks counterbalance each other.We attribute these inconsistencies to the limitations of benchmarks and VLMs.Based on the findings, we provide insights for further enhancing mobile GUI agents in terms of benchmarks, VLMs, and their adaptability in dynamically invoking reasoning VLMs.
Authors: Li Zhang, Longxi Gao, Mengwei Xu
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166876441