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Yiwei Liu

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6 papers

2 author rows

EAAI Journal 2026 Journal Article

The role of transformer models in advancing blockchain technology: A systematic survey

Tianxu Liu
Yanbin Wang
Jianguo Sun
Ye Tian
Yanyu Huang
Tao Xue
Peiyue Li
Yiwei Liu

As blockchain technology evolves, the demand for improved efficiency, security, and scalability increases, with Transformer models demonstrating significant potential to address these challenges. However, a systematic review of their blockchain applications is lacking. This paper fills this gap by surveying over 200 relevant studies, offering a comprehensive analysis of Transformer applications across four key areas: anomaly detection, smart contract vulnerability detection, cryptocurrency prediction, and code summarization. We adopt a domain-oriented classification framework that systematically organizes research progress and challenges, enhancing clarity and identifying trends. Furthermore, we offer granular sub-classification within each domain based on algorithmic types, data modalities, or information sources, delivering deeper insights into methodological advancements. Additionally, we conduct a dual-layered comparative analysis, contrasting Transformers with traditional deep learning methods and assessing variations among Transformer approaches within each domain to uncover best practices. We also explore challenges such as data privacy and model complexity, propose future research directions to tailor Transformers to blockchain-specific needs. We will continue to update the latest articles and their released source codes at https: //github. com/LTX001122/Transformers-Blockchain.

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IROS Conference 2025 Conference Paper

MetaFold: Language-Guided Multi-Category Garment Folding Framework via Trajectory Generation and Foundation Model

Haonan Chen
Junxiao Li
Ruihai Wu
Yiwei Liu
Yiwen Hou
Zhixuan Xu
Jingxiang Guo
Chongkai Gao

Garment folding is a common yet challenging task in robotic manipulation. The deformability of garments leads to a vast state space and complex dynamics, which complicates precise and fine-grained manipulation. In this paper, we present MetaFold, a unified framework that disentangles task planning from action prediction and learns each independently to enhance model generalization. It employs language-guided point cloud trajectory generation for task planning and a low-level foundation model for action prediction. This structure facilitates multi-category learning, enabling the model to adapt flexibly to various user instructions and folding tasks. We also construct a large-scale MetaFold dataset comprising folding point cloud trajectories for a total of 1210 garments across multiple categories, each paired with corresponding language annotations. Extensive experiments demonstrate the superiority of our proposed framework. Supplementary materials are available on our website: https://meta-fold.github.io/.

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AAAI Conference 2023 Conference Paper

MSDC: Exploiting Multi-State Power Consumption in Non-intrusive Load Monitoring Based on a Dual-CNN Model

Jialing He
Jiamou Liu
Zijian Zhang
Yang Chen
Yiwei Liu
Bakh Khoussainov
Liehuang Zhu

Non-intrusive load monitoring (NILM) aims to decompose aggregated electrical usage signal into appliance-specific power consumption and it amounts to a classical example of blind source separation tasks. Leveraging recent progress on deep learning techniques, we design a new neural NILM model {\em Multi-State Dual CNN} (MSDC). Different from previous models, MSDC explicitly extracts information about the appliance's multiple states and state transitions, which in turn regulates the prediction of signals for appliances. More specifically, we employ a dual-CNN architecture: one CNN for outputting state distributions and the other for predicting the power of each state. A new technique is invented that utilizes conditional random fields (CRF) to capture state transitions. Experiments on two real-world datasets REDD and UK-DALE demonstrate that our model significantly outperform state-of-the-art models while having good generalization capacity, achieving 6%-10% MAE gain and 33%-51% SAE gain to unseen appliances.

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AAMAS Conference 2022 Conference Paper

Residual Entropy-based Graph Generative Algorithms

Wencong Liu
Jiamou Liu
Zijian Zhang
Yiwei Liu
Liehuang Zhu

Classification and clustering are crucial tasks that recognize the identities and the communities of nodes in a graph. Several methods have been proposed to reduce the accuracy of node classification and clustering through graph neural networks (GNN). Existing defense methods usually modify the model architecture and adopt countermeasure training to enhance the robustness of the node classification and clustering. However, these defense methods are model-oriented and not robust. To alleviate the problem, this paper first proposes a robust node classification metric based on residual entropy. More concretely, we prove that maximizing the residual entropy helps to improve the robustness of the classification accuracy. We them propose two graph generative algorithms to resist against two kinds of GNN-based attacks, the untargeted and the targeted attacks. Finally, experimental analysis show that the proposed algorithms outperform the existing defense works under five classic datasets. 1

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ICML Conference 2021 Conference Paper

From Local to Global Norm Emergence: Dissolving Self-reinforcing Substructures with Incremental Social Instruments

Yiwei Liu
Jiamou Liu
Kaibin Wan
Zhan Qin
Zijian Zhang 0001
Bakhadyr Khoussainov
Liehuang Zhu

Norm emergence is a process where agents in a multi-agent system establish self-enforcing conformity through repeated interactions. When such interactions are confined to a social topology, several self-reinforcing substructures (SRS) may emerge within the population. This prevents a formation of a global norm. We propose incremental social instruments (ISI) to dissolve these SRSs by creating ties between agents. Establishing ties requires some effort and cost. Hence, it is worth to design methods that build a small number of ties yet dissolve the SRSs. By using the notion of information entropy, we propose an indicator called the BA-ratio that measures the current SRSs. We find that by building ties with minimal BA-ratio, our ISI is effective in facilitating the global norm emergence. We explain this through our experiments and theoretical results. Furthermore, we propose the small-degree principle in minimising the BA-ratio that helps us to design efficient ISI algorithms for finding the optimal ties. Experiments on both synthetic and real-world network topologies demonstrate that our adaptive ISI is efficient at dissolving SRS.

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NeurIPS Conference 2019 Conference Paper

REM: From Structural Entropy to Community Structure Deception

Yiwei Liu
Jiamou Liu
Zijian Zhang
Liehuang Zhu
Angsheng Li

This paper focuses on the privacy risks of disclosing the community structure in an online social network. By exploiting the community affiliations of user accounts, an attacker may infer sensitive user attributes. This raises the problem of community structure deception (CSD), which asks for ways to minimally modify the network so that a given community structure maximally hides itself from community detection algorithms. We investigate CSD through an information-theoretic lens. To this end, we propose a community-based structural entropy to express the amount of information revealed by a community structure. This notion allows us to devise residual entropy minimization (REM) as an efficient procedure to solve CSD. Experimental results over 9 real-world networks and 6 community detection algorithms show that REM is very effective in obfuscating the community structure as compared to other benchmark methods.

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