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Yuanhui Wang

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AAAI Conference 2026 Conference Paper

MemeBQ:Memory Efficient Binary Quantization of LLMs

Yuanhui Wang
Kunlong Liu
Minnan Pei
Zhangming Li
Peisong Wang
Qinghao Hu

Recent years have witnessed growing scholarly interest in binary post-training quantization (PTQ) techniques for large language models (LLMs). While state-of-the-art (SOTA) binary quantization methods significantly reduce memory footprint and computational demands, they introduce additional memory overhead beyond binary weight tensors to mitigate performance degradation. Moreover, binary LLMs still suffer from substantial accuracy loss. To address these limitations, we propose MemeBQ, a novel binary PTQ framework for LLMs that reduces the memory overhead of auxiliary flag bitmaps in existing binary quantization methods. Specifically, we first design a greedy row clustering method, which leverages the similarity between the row vectors of weights to partition the weight rows into different groups. By sharing the common flag bitmap within each row group, we significantly mitigate the memory overhead associated with flag bitmaps. Besides, to improve the performance of binary LLMs, we propose a novel weight splitting method for each row group of weights, which determines the flag bitmap's values in a fine-grained way. Extensive experiments on OPT, Llama-2, and Llama-3 models demonstrate that MemeBQ reduces 50% extra memory demand while achieving comparable accuracy compared with current SOTA methods. Alternatively, MemeBQ outperforms SOTA binary quantization methods up to 7% with the same extra bits on reasoning benchmarks.

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EAAI Journal 2025 Journal Article

Gray-box dynamic model for wave glider driven by a hybrid of deep learning and physics-based models

Yuanhui Wang
Hui Wang
Yongkuang Zhang
Mingze Xie

Wave gliders, a widely used type of unmanned ocean robot, leverage wave and solar energy to achieve near-unlimited endurance, making them ideal tools for ocean monitoring and meteorological observations. However, existing more accurate wave glider modeling often requires computational fluid dynamics (CFD) calculations, especially considering the complexity of tandem hydrofoils. The computational cost is very high, making them unsuitable for real-time control applications. Therefore, this paper proposes a gray-box dynamic modeling approach that balances model accuracy and computational efficiency by integrating deep learning with traditional physical models. The gray-box model consists of a deep learning-based surrogate model and analytical dynamic equations, with the surrogate model replacing the CFD simulation process for the hydrofoils in the wave glider's dynamic model to improve the overall computational efficiency. After comparing the predictive performance of various deep learning models, this paper ultimately selects the Gated Recurrent Unit-Fully Convolutional Network (GRU-FCN) as the surrogate model. Validation of the proposed gray-box dynamic model under both regular and irregular wave conditions demonstrates excellent agreement with experimental data and CFD results. Furthermore, the gray-box dynamic model significantly improves computational efficiency, offering a reliable dynamic model reference for the real-time control of the wave glider.

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