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Jia Hao

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AAAI Conference 2025 Conference Paper

WEPO: Web Element Preference Optimization for LLM-based Web Navigation

Jiarun Liu
Jia Hao
Chunhong Zhang
Zheng Hu

The rapid advancement of autonomous web navigation has significantly benefited from grounding pretrained Large Language Models (LLMs) as agents. However, current research has yet to fully leverage the redundancy of HTML elements for contrastive training. This paper introduces a novel approach to LLM-based web navigation tasks, called Web Element Preference Optimization (WEPO). WEPO utilizes unsupervised preference learning by sampling distance-based non-salient web elements as negative samples, optimizing maximum likelihood objective within Direct Preference Optimization (DPO). We evaluate WEPO on the Mind2Web benchmark and empirically demonstrate that WEPO aligns user high-level intent with output actions more effectively. The results show that our method achieved the state-of-the-art, with an improvement of 13.8% over WebAgent and 5.3% over the visual language model CogAgent baseline. Our findings underscore the potential of preference optimization to enhance web navigation and other web page based tasks, suggesting a promising direction for future research.

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EAAI Journal 2023 Journal Article

Reconstruction of hydrofoil cavitation flow based on the chain-style physics-informed neural network

Hanqing Ouyang
Zhicheng Zhu
Kuangqi Chen
Beichen Tian
Biao Huang
Jia Hao

Cavitation flow is a typical complex flow phenomenon, which involves many flow mechanisms. At present, the main approach to reconstruct the cavitation flow field based on the experimental results is numerical simulation, which has the defects of low computational efficiency and is difficult to effectively use the experimental data. In this paper, a chain-style physics-informed neural network (chain-style PINN) is developed to solve the reconstruction problem of cavitation flow field. On the basis of decoupling the governing equations, our method solves the physical quantities of interest serially by introducing multiple serial PINNs. A physics-informed loss function is defined to realize the assimilation of experimental data and physical mechanism. The prediction for a 3D NACA66 hydrofoil case is validated by comparing with Direct Numerical Simulation (DNS), which demonstrates that the calculation time is reduced by about 70% while the relative L 2 errors of pressure and liquid volume fraction fields are only 0. 0030 and 0. 0035. While comparing with the existing method Hidden Fluid Mechanics (i. e. , baseline PINN), the results show the validity of our method. To the best of our knowledge, this is the first theoretical work that applies PINN to cavitation flow.

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