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Yuting Huang

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5 papers

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JBHI Journal 2026 Journal Article

Substructure-guided Deep Graph Learning in Molecular Toxicity Prediction

Yuting Huang
Debby D. Wang

Computational toxicity prediction has become a key component in modern drug discovery. Although machine learning or deep learning techniques have reformed this field in recent years, more in-depth studies on addressing data imbalance, missing labels, and lack of model interpretability are still needed. In this work, we develop a substructure-based deep graph learning architecture, by introducing various functional groups into the construction of molecular graphs and handling them through deep learning models. Our model, with several strategies adopted to deal with the missing labels and class imbalance in the datasets, performed well in toxicity prediction tasks. A functional group-based feature importance analysis provided further insights into different toxicity predictions and improved the interpretability of our model. It provides a solid foundation for the development of reliable toxicity prediction tools and supports rational decision-making in the drug development process.

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NeurIPS Conference 2025 Conference Paper

Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approach

Yuting Huang
Ziquan Fang
Zhihao Zeng
Lu Chen
Yunjun Gao

Spatio-temporal prediction plays a crucial role in intelligent transportation, weather forecasting, and urban planning. While integrating multi-modal data has shown potential for enhancing prediction accuracy, key challenges persist: (i) inadequate fusion of multi-modal information, (ii) confounding factors that obscure causal relations, and (iii) high computational complexity of prediction models. To address these challenges, we propose E$^2$-CSTP, an Effective and Efficient Causal multi-modal Spatio-Temporal Prediction framework. E$^2$-CSTP leverages cross-modal attention and gating mechanisms to effectively integrate multi-modal data. Building on this, we design a dual-branch causal inference approach: the primary branch focuses on spatio-temporal prediction, while the auxiliary branch mitigates bias by modeling additional modalities and applying causal interventions to uncover true causal dependencies. To improve model efficiency, we integrate GCN with the Mamba architecture for accelerated spatio-temporal encoding. Extensive experiments on 4 real-world datasets show that E$^2$-CSTP significantly outperforms 9 state-of-the-art methods, achieving up to 9. 66% improvements in accuracy as well as 17. 37%-56. 11% reductions in computational overhead.

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IJCAI Conference 2025 Conference Paper

Improving Efficiency of Answer Set Planning with Rough Solutions from Large Language Models for Robotic Task Planning

Xinrui Lin
Yangfan Wu
Huanyu Yang
Yuting Huang
Yu Zhang
Jianmin Ji
Yanyong Zhang

Answer Set Programming (ASP) planning can be used to refine the rough solutions generated by Large Language Models (LLMs) to handle specific restrictions of actions, i. e. , reconstruct the rough solutions to be executable, for robotic task planning. However, it is still challenging to efficiently solve ASP programs that have multiple variables with large domains, which prevents the above application of ASP planning from real-world task planning problems. In this paper, we consider how to reduce the domains of variables without losing possible solutions for ASP planning, while given these rough solutions from LLMs. Based on the above reduction, we introduce CLMASP, an approach that couples LLMs with ASP for robotic task planning. We evaluate CLMASP on the VirtualHome platform for common indoor tasks, demonstrating a significant improvement in the executable rate from under 10% to nearly 90% and reducing average ASP planning time from over 2 hours to under 5 seconds. Code is available at https: //github. com/CLMASP/CLMASP.

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AILAW Journal 2025 Journal Article

Specialized or general AI? a comparative evaluation of LLMs’ performance in legal tasks

Xue Guo
Yuting Huang
Bin Wei
Kun Kuang
Yiquan Wu
Leilei Gan
Xianshan Huang
Xianglin Dong

Abstract The rise of large language models (LLMs) such as ChatGPT and GPT-4 developed by OpenAI have generated significant interest in the legal domain due to their sophisticated language processing capabilities. In particular, regions like China are vigorously developing legal-specific LLMs for legal purposes. Fine-tuned with fewer parameters and based on judicial documents and Chinese case data sets, these specialized LLMs are widely expected to meet practical needs in the judicial field more effectively. However, the ability of these law-specific LLMs to perform legal tasks and their potential to outperform general LLMs has not yet been established. To fill in this research gap, we systematically evaluate a range of general and legal-specific LLMs on various legal tasks. The results show that GPT-4 maintains superior performance on most legal tasks, although legal-specific LLMs show superior performance in specific cases. This study provides insight into the factors leading to these results, hoping to enrich the discourse on the use of LLMs in the legal field.

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EAAI Journal 2024 Journal Article

An integrated assessment framework for the evaluation of niche suitability of digital innovation ecosystem with interval-valued Fermatean fuzzy information

Yuan Rong
Ran Qiu
LinYu Wang
Liying Yu
Yuting Huang

The construction of a digital innovation ecosystem (DIE) is of great strategic significance in facilitating digital technology innovation, expanding the scale of the digital economy and achieving high-quality development. Evaluating the niche suitability of DIE (NSDIE) in different regions can not only effectively improve the development level of regional NSDIE, but also accelerate the process of regional digital development in multiple aspects. This article aims to establish a NSDIE assessment framework that considers multiple aspects criteria from an uncertain perspective. In this regard, an interval-valued Fermatean fuzzy (IVFF) integrated assessment framework through incorporating logarithmic percentage change-driven objective weighting (LOPCOW), decision-making trial and evaluation laboratory (DEMATEL) and measurement of alternatives and ranking based on compromise solution (MARCOS) method is constructed. First, the interactive operations on IVFF numbers (IVFFNs) are proffered, and then some novel interactive operators are propounded to fuse assessment information of experts. Second, an innovative model is presented by merging DEMATEL method and LOPCOW approach to identify the weight of criteria. Thirdly, an improved Heronian mean-based MARCOS method is developed by considering the correlation among the assessment criteria for attaining the prioritization of candidate regions. Fourth, a niche suitability of digital innovation ecosystem indicator system is constructed based upon four ecological dimensions to measure the development level of digital innovation ecosystem. Lastly, the developed assessment framework is utilized to evaluate the NSDIE in six major regions of China, which not only verifies the rationality of the proposed evaluation framework and proves the applicability of the constructed indicator system. The results show that the East China region has the highest level of DIE development in China. The susceptiveness and contrastive analysis are executed to expound the stability and practicability of the developed framework.

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