Author name cluster

Haoyu Yang

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

An LLM-based Quantitative Framework for Evaluating High-Stealthy Backdoor Risks in OSS Supply Chains

Zihe Yan
Kai Luo
Haoyu Yang
Yang Yu
Zhuosheng Zhang
Guancheng Li

In modern software development workflows, the open-source software supply chain significantly contributes to efficient and convenient engineering practices. With increasing system complexity, it has become a common practice to use open-source software as third-party dependencies. However, due to the lack of maintenance for underlying dependencies and insufficient community auditing, ensuring the security of source code and the legitimacy of repository maintainers has become a challenge, particularly in the context of high-stealth backdoor attacks such as the XZ-Util incident. To address these problems, we propose a fine-grained project evaluation framework for backdoor risk assessment in open-source software. Our evaluation framework models highly stealthy backdoor attacks from the attacker’s perspective and defines targeted metrics for each attack stage. Moreover, to overcome the limitations of static analysis in assessing the reliability of repository maintenance activities, such as irregular committer privilege escalation and insufficient review participation, we employ large language models (LLMs) to perform semantic evaluation of code repositories while avoiding reliance on manually crafted patterns. The effectiveness of our framework is validated on 66 high-priority packages in the Debian ecosystem, and the experimental results reveal that the current open-source software supply chain is exposed to a series of security risks.

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IROS Conference 2025 Conference Paper

EAROL: Environmental Augmented Perception-Aware Planning and Robust Odometry via Downward-Mounted Tilted LiDAR

Xinkai Liang
Yigu Ge
Yangxi Shi
Haoyu Yang
Xu Cao
Hao Fang 0001

To address the challenges of localization drift and perception-planning coupling in unmanned aerial vehicles (UAVs) operating in open-top scenarios (e. g. , collapsed buildings, roofless mazes), this paper proposes EAROL, a novel framework with a downward-mounted tilted LiDAR configuration (20° inclination), integrating a LiDAR-Inertial Odometry (LIO) system and a hierarchical trajectory-yaw optimization algorithm. The hardware innovation enables constraint enhancement via dense ground point cloud acquisition and forward environmental awareness for dynamic obstacle detection. A tightly-coupled LIO system, empowered by an Iterative Error-State Kalman Filter (IESKF) with dynamic motion compensation, achieves high level 6-DoF localization accuracy in feature-sparse environments. The planner, augmented by environment, balancing environmental exploration, target tracking precision, and energy efficiency. Physical experiments demonstrate 81% tracking error reduction, 22% improvement in perceptual coverage, and near-zero vertical drift across indoor maze and 60-meter-scale outdoor scenarios. This work proposes a hardware-algorithm co-design paradigm, offering a robust solution for UAV autonomy in post-disaster search and rescue missions. We will release our software and hardware as an open-source package 3 for the community. Video: https://youtu.be/7av2ueLSiYw.

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AAAI Conference 2025 Conference Paper

Intelligent OPC Engineer Assistant for Semiconductor Manufacturing

Guojin Chen
Haoyu Yang
Bei Yu
Haoxing Ren

Advancements in chip design and manufacturing have enabled the processing of complex tasks such as deep learning and natural language processing, paving the way for the development of artificial general intelligence (AGI). AI, on the other hand, can be leveraged to innovate and streamline semiconductor technology from planning and implementation to manufacturing. In this paper, we present Intelligent OPC Engineer Assistant, an AI/LLM-powered methodology designed to solve the core manufacturing-aware optimization problem known as Optical Proximity Correction (OPC). The methodology involves a reinforcement learning-based OPC recipe search and a customized multi-modal agent system for recipe summarization. Experiments demonstrate that our methodology can efficiently build OPC recipes on various chip designs with specially handled design topologies, a task that typically requires the full-time effort of OPC engineers with years of experience.

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ICML Conference 2024 Conference Paper

ILILT: Implicit Learning of Inverse Lithography Technologies

Haoyu Yang
Haoxing Ren

Lithography, transferring chip design masks to the silicon wafer, is the most important phase in modern semiconductor manufacturing flow. Due to the limitations of lithography systems, Extensive design optimizations are required to tackle the design and silicon mismatch. Inverse lithography technology (ILT) is one of the promising solutions to perform pre-fabrication optimization, termed mask optimization. Because of mask optimization problems’ constrained non-convexity, numerical ILT solvers rely heavily on good initialization to avoid getting stuck on sub-optimal solutions. Machine learning (ML) techniques are hence proposed to generate mask initialization for ILT solvers with one-shot inference, targeting faster and better convergence during ILT. This paper addresses the question of whether ML models can directly generate high-quality optimized masks without engaging ILT solvers in the loop. We propose an implicit learning ILT framework: ILILT, which leverages the implicit layer learning method and lithography-conditioned inputs to ground the model. Trained to understand the ILT optimization procedure, ILILT can outperform the state-of-the-art machine learning solutions, significantly improving efficiency and quality.

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NeurIPS Conference 2023 Conference Paper

LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing

Su Zheng
Haoyu Yang
Binwu Zhu
Bei Yu
Martin Wong

Computational lithography provides algorithmic and mathematical support for resolution enhancement in optical lithography, which is the critical step in semiconductor manufacturing. The time-consuming lithography simulation and mask optimization processes limit the practical application of inverse lithography technology (ILT), a promising solution to the challenges of advanced-node lithography. Although various machine learning methods for ILT have shown promise for reducing the computational burden, this field is in lack of a dataset that can train the models thoroughly and evaluate the performance comprehensively. To boost the development of AI-driven computational lithography, we present the LithoBench dataset, a collection of circuit layout tiles for deep-learning-based lithography simulation and mask optimization. LithoBench consists of more than 120k tiles that are cropped from real circuit designs or synthesized according to the layout topologies of famous ILT testcases. The ground truths are generated by a famous lithography model in academia and an advanced ILT method. Based on the data, we provide a framework to design and evaluate deep neural networks (DNNs) with the data. The framework is used to benchmark state-of-the-art models on lithography simulation and mask optimization. We hope LithoBench can promote the research and development of computational lithography. LithoBench is available at https: //anonymous. 4open. science/r/lithobench-APPL.

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IJCAI Conference 2022 Conference Paper

An Online Learning Approach towards Far-sighted Emergency Relief Planning under Intentional Attacks in Conflict Areas

Haoyu Yang
Kaiming Xiao
Lihua Liu
Hongbin Huang
Weiming Zhang

A large number of emergency humanitarian rescue demands in conflict areas around the world are accompanied by intentional, persistent and unpredictable attacks on rescuers and supplies. Unfortunately, existing work on humanitarian relief planning mostly ignores this challenge in reality resulting a parlous and short-sighted relief distribution plan to a large extent. To address this, we first propose an offline multi-stage optimization problem of emergency relief planning under intentional attacks, in which all parameters in the game between the rescuer and attacker are supposed to be known or predictable. Then, an online version of this problem is introduced to meet the need of online and irrevocable decision making when those parameters are revealed in an online fashion. To achieve a far-sighted emergency relief planning under attacks, we design an online learning approach which is proven to obtain a near-optimal solution of the offline problem when those online reveled parameters are i. i. d. sampled from an unknown distribution. Finally, extensive experiments on a real anti-Ebola relief planning case based on the data of Ebola outbreak and armed attacks in DRC Congo show the scalability and effectiveness of our approach.

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AAAI Conference 2017 Conference Paper

Multitask Dyadic Prediction and Its Application in Prediction of Adverse Drug-Drug Interaction

Bo Jin
Haoyu Yang
Cao Xiao
Ping Zhang
Xiaopeng Wei
Fei Wang

Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality around the world. Identifying potential DDIs during the drug design process is critical in guiding targeted clinical drug safety testing. Although detection of adverse DDIs is conducted during Phase IV clinical trials, there are still a large number of new DDIs founded by accidents after the drugs were put on market. With the arrival of big data era, more and more pharmaceutical research and development data are becoming available, which provides an invaluable resource for digging insights that can potentially be leveraged in early prediction of DDIs. Many computational approaches have been proposed in recent years for DDI prediction. However, most of them focused on binary prediction (with or without DDI), despite the fact that each DDI is associated with a different type. Predicting the actual DDI type will help us better understand the DDI mechanism and identify proper ways to prevent it. In this paper, we formulate the DDI type prediction problem as a multitask dyadic regression problem, where the prediction of each speciﬁc DDI type is treated as a task. Compared with conventional matrix completion approaches which can only impute the missing entries in the DDI matrix, our approach can directly regress those dyadic relationships (DDIs) and thus can be extend to new drugs more easily. We developed an effective proximal gradient method to solve the problem. Evaluation on real world datasets is presented to demonstrate the effectiveness of the proposed approach.

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