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Zhimeng Guo

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4 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Practical and Effective Code Watermarking for Large Language Models

Zhimeng Guo
Minhao Cheng

The rapid advancement of Large Language Models (LLMs) in code generation has raised significant attribution and intellectual property concerns. Code watermarking offers a potential solution but faces unique challenges due to programming languages' strict syntactic constraints and semantic requirements. To address these challenges, we introduce ACW (AST-guided Code Watermarking), a novel adaptive framework that leverages Abstract Syntax Tree (AST) analysis during training to learn watermark embedding strategies. Our framework identifies substitutable code components and strategically biases token selections to embed watermarks. We also propose a novel sampling scheme that distributes tokens between green/red lists according to semantic context, ensuring statistical distinguishability while preserving code functionality. Extensive experiments demonstrate that ACW achieves a significant improvement in watermark detection accuracy compared to existing methods, with negligible impact on code functionality. This adaptive framework offers a promising solution for effective and practical code watermarking in the age of LLMs. Our code is available at: https: //github. com/TimeLovercc/code-watermark.

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NeurIPS Conference 2025 Conference Paper

Simple Distillation for One-Step Diffusion Models

Huaisheng Zhu
Teng Xiao
shijie zhou
Zhimeng Guo
Hangfan Zhang
Siyuan Xu
Vasant Honavar

Diffusion models have established themselves as leading techniques for image generation. However, their reliance on an iterative denoising process results in slow sampling speeds, which limits their applicability to interactive and creative applications. An approach to overcoming this limitation involves distilling multistep diffusion models into efficient one-step generators. However, existing distillation methods typically suffer performance degradation or require complex iterative training procedures which increase their complexity and computational cost. In this paper, we propose Contrastive Energy Distillation (CED), a simple yet effective approach to distill multistep diffusion models into effective one-step generators. Our key innovation is the introduction of an unnormalized joint energy-based model (EBM) that represents the generator and an auxiliary score model. CED optimizes a Noise Contrastive Estimation (NCE) objective to efficiently transfers knowledge from a multistep teacher diffusion model without additional modules or iterative training complexity. We further show that CED implicitly optimizes the KL divergence between the distributions modeled by the multistep diffusion model and the one-step generator. We present results of experiments which demonstrate that CED achieves competitive performance with the representative baselines for distilling multistep diffusion models while maintaining excellent memory efficiency.

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ICML Conference 2024 Conference Paper

Efficient Contrastive Learning for Fast and Accurate Inference on Graphs

Teng Xiao
Huaisheng Zhu
Zhiwei Zhang 0028
Zhimeng Guo
Charu C. Aggarwal
Suhang Wang
Vasant G. Honavar

Graph contrastive learning has made remarkable advances in settings where there is a scarcity of task-specific labels. Despite these advances, the significant computational overhead for representation inference incurred by existing methods that rely on intensive message passing makes them unsuitable for latency-constrained applications. In this paper, we present GraphECL, a simple and efficient contrastive learning method for fast inference on graphs. GraphECL does away with the need for expensive message passing during inference. Specifically, it introduces a novel coupling of the MLP and GNN models, where the former learns to computationally efficiently mimic the computations performed by the latter. We provide a theoretical analysis showing why MLP can capture essential structural information in neighbors well enough to match the performance of GNN in downstream tasks. The extensive experiments on widely used real-world benchmarks that show that GraphECL achieves superior performance and inference efficiency compared to state-of-the-art graph constrastive learning (GCL) methods on homophilous and heterophilous graphs.

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NeurIPS Conference 2022 Conference Paper

Decoupled Self-supervised Learning for Graphs

Teng Xiao
Zhengyu Chen
Zhimeng Guo
Zeyang Zhuang
Suhang Wang

This paper studies the problem of conducting self-supervised learning for node representation learning on graphs. Most existing self-supervised learning methods assume the graph is homophilous, where linked nodes often belong to the same class or have similar features. However, such assumptions of homophily do not always hold in real-world graphs. We address this problem by developing a decoupled self-supervised learning (DSSL) framework for graph neural networks. DSSL imitates a generative process of nodes and links from latent variable modeling of the semantic structure, which decouples different underlying semantics between different neighborhoods into the self-supervised learning process. Our DSSL framework is agnostic to the encoders and does not need prefabricated augmentations, thus is flexible to different graphs. To effectively optimize the framework, we derive the evidence lower bound of the self-supervised objective and develop a scalable training algorithm with variational inference. We provide a theoretical analysis to justify that DSSL enjoys the better downstream performance. Extensive experiments on various types of graph benchmarks demonstrate that our proposed framework can achieve better performance compared with competitive baselines.

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