Author name cluster

Hao Cui

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

QuEPT: Quantized Elastic Precision Transformers with One-Shot Calibration for Multi-Bit Switching

Ke Xu
Yixin Wang
Zhongcheng Li
Hao Cui
Jinshui Hu
Xingyi Zhang

Elastic precision quantization enables multi-bit deployment via a single optimization pass, fitting diverse quantization scenarios. Yet, the high storage and optimization costs associated with the Transformer architecture, research on elastic quantization remains limited, particularly for large language models. This paper proposes QuEPT, an efficient post-training scheme that reconstructs block-wise multi-bit errors with one-shot calibration on a small data slice. It can dynamically adapt to various predefined bit-widths by cascading different low-rank adapters, and supports real-time switching between uniform quantization and mixed precision quantization without repeated optimization. To enhance accuracy and robustness, we introduce Multi-Bit Token Merging (MB-ToMe) to dynamically fuse token features across different bit-widths, improving robustness during bit-width switching. Additionally, we propose Multi-Bit Cascaded Low-Rank adapters (MB-CLoRA) to strengthen correlations between bit-width groups, further improve the overall performance of QuEPT. Extensive experiments demonstrate that QuEPT achieves comparable or better performance to existing state-of-the-art post-training quantization methods.

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ICLR Conference 2025 Conference Paper

CURIE: Evaluating LLMs on Multitask Scientific Long-Context Understanding and Reasoning

Hao Cui
Zahra Shamsi
Gowoon Cheon
Xuejian Ma
Shutong Li
Maria Tikhanovskaya
Peter Christian Norgaard
Nayantara Mudur

Scientific problem-solving involves synthesizing information while applying expert knowledge. We introduce CURIE, a scientific long-Context Understanding, Reasoning, and Information Extraction benchmark to measure the potential of Large Language Models (LLMs) in scientific problem-solving and assisting scientists in realistic workflows. This benchmark introduces ten challenging tasks with a total of 580 problems and solution pairs curated by experts in six disciplines - materials science, condensed matter physics, quantum computing, geo-spatial analysis, biodiversity, and proteins - covering both experimental and theoretical work-flows in science. We evaluate a range of closed and open LLMs on tasks in CURIE which requires domain expertise, comprehension of long in-context information,and multi-step reasoning. While Gemini Flash 2.0 and Claude-3 show consistent high comprehension across domains, the popular GPT-4o and command-R+ fail dramatically on protein sequencing tasks. With the best performance at 32% there is much room for improvement for all models. We hope that insights gained from CURIE can guide the future development of LLMs in sciences. Links to the data and evaluation code are in https://github.com/google/curie

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AAAI Conference 2020 Conference Paper

Model Watermarking for Image Processing Networks

Jie Zhang
DongDong Chen
Jing Liao
Han Fang
Weiming Zhang
Wenbo Zhou
Hao Cui
Nenghai Yu

Deep learning has achieved tremendous success in numerous industrial applications. As training a good model often needs massive high-quality data and computation resources, the learned models often have signiﬁcant business values. However, these valuable deep models are exposed to a huge risk of infringements. For example, if the attacker has the full information of one target model including the network structure and weights, the model can be easily ﬁnetuned on new datasets. Even if the attacker can only access the output of the target model, he/she can still train another similar surrogate model by generating a large scale of input-output training pairs. How to protect the intellectual property of deep models is a very important but seriously under-researched problem. There are a few recent attempts at classiﬁcation network protection only. In this paper, we propose the ﬁrst model watermarking framework for protecting image processing models. To achieve this goal, we leverage the spatial invisible watermarking mechanism. Speciﬁcally, given a black-box target model, a uniﬁed and invisible watermark is hidden into its outputs, which can be regarded as a special task-agnostic barrier. In this way, when the attacker trains one surrogate model by using the input-output pairs of the target model, the hidden watermark will be learned and extracted afterward. To enable watermarks from binary bits to high-resolution images, both traditional and deep spatial invisible watermarking mechanism are considered. Experiments demonstrate the robustness of the proposed watermarking mechanism, which can resist surrogate models learned with different network structures and objective functions. Besides deep models, the proposed method is also easy to be extended to protect data and traditional image processing algorithms.

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IJCAI Conference 2016 Conference Paper

Online Symbolic Gradient-Based Optimization for Factored Action MDPs

Hao Cui
Roni Khardon

This paper investigates online stochastic planning for problems with large factored state and action spaces. We introduce a novel algorithm that builds a symbolic representation capturing an approximation of the action-value Q-function in terms of action variables, and then performs gradient based search to select an action for the current state. The algorithm can be seen as a symbolic extension of Monte-Carlo search, induced by independence assumptions on state and action variables, and augmented with gradients to speed up the search. This avoids the space explosion typically faced by symbolic methods, and the dearth of samples faced by Monte-Carlo methods when the action space is large. An experimental evaluation on benchmark problems shows that the algorithm is competitive with state of the art across problem sizes and that it provides significant improvements for large factored action spaces.

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AAAI Conference 2015 Conference Paper

Factored MCTS for Large Scale Stochastic Planning

Hao Cui
Roni Khardon
Alan Fern
Prasad Tadepalli

This paper investigates stochastic planning problems with large factored state and action spaces. We show that even with moderate increase in the size of existing challenge problems, the performance of state of the art algorithms deteriorates rapidly, making them ineffective. To address this problem we propose a family of simple but scalable online planning algorithms that combine sampling, as in Monte Carlo tree search, with “aggregation”, where the aggregation approximates a distribution over random variables by the product of their marginals. The algorithms are correct under some rather strong technical conditions and can serve as an unsound but effective heuristic when the conditions do not hold. An extensive experimental evaluation demonstrates that the new algorithms provide significant improvement over the state of the art when solving large problems in a number of challenge benchmark domains.

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