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Xiaohan Zhao

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7 papers

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NeurIPS Conference 2025 Conference Paper

A Frustratingly Simple Yet Highly Effective Attack Baseline: Over 90% Success Rate Against the Strong Black-box Models of GPT-4.5/4o/o1

Zhaoyi Li
Xiaohan Zhao
Dong-Dong Wu
Jiacheng Cui
Zhiqiang Shen

Despite promising performance on open-source large vision-language models (LVLMs), transfer-based targeted attacks often fail against closed-source commercial LVLMs. Analyzing failed adversarial perturbations reveals that the learned perturbations typically originate from a uniform distribution and lack clear semantic details, resulting in unintended responses. This critical absence of semantic information leads commercial black-box LVLMs to either ignore the perturbation entirely or misinterpret its embedded semantics, thereby causing the attack to fail. To overcome these issues, we propose to refine semantic clarity by encoding explicit semantic details within local regions, thus ensuring the capture of finer-grained features and inter-model transferability, and by concentrating modifications on semantically rich areas rather than applying them uniformly. To achieve this, we propose *a simple yet highly effective baseline*: at each optimization step, the adversarial image is cropped randomly by a controlled aspect ratio and scale, resized, and then aligned with the target image in the embedding space. While the naive source-target matching method has been utilized before in the literature, we are the first to provide a tight analysis, which establishes a close connection between perturbation optimization and semantics. Experimental results confirm our hypothesis. Our adversarial examples crafted with local-aggregated perturbations focused on crucial regions exhibit surprisingly good transferability to commercial LVLMs, including GPT-4. 5, GPT-4o, Gemini-2. 0-flash, Claude-3. 5/3. 7-sonnet, and even reasoning models like o1, Claude-3. 7-thinking and Gemini-2. 0-flash-thinking. Our approach achieves success rates exceeding 90\% on GPT-4. 5, 4o, and o1, significantly outperforming all prior state-of-the-art attack methods with lower $\ell_1/\ell_2$ perturbations. Our optimized adversarial examples under different configurations are available at https: //huggingface. co/datasets/MBZUAI-LLM/M-Attack_AdvSamples and our training code at https: //github. com/VILA-Lab/M-Attack.

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NeurIPS Conference 2025 Conference Paper

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation

Jiacheng Cui
Xinyue Bi
Yaxin Luo
Xiaohan Zhao
Jiacheng Liu
Zhiqiang Shen

Residual connection has been extensively studied and widely applied at the model architecture level. However, its potential in the more challenging data-centric approaches remains unexplored. In this work, we introduce the concept of Data Residual Matching for the first time, leveraging data-level skip connections to facilitate data generation and mitigate data information vanishing. This approach maintains a balance between newly acquired knowledge through pixel space optimization and existing core local information identification within raw data modalities, specifically for the dataset distillation task. Furthermore, by incorporating training-time refinements, our method significantly improves computational efficiency, achieving superior performance while reducing training time and peak GPU memory usage by 50\%. Consequently, the proposed method F ast and A ccurate D ata R esidual M atching for Dataset Distillation ( FADRM ) establishes a new state-of-the-art, demonstrating substantial improvements over existing methods across multiple dataset benchmarks in both efficiency and effectiveness. For instance, with ResNet-18 as the student model and a 0. 8\% compression ratio on ImageNet-1K, the method achieves 48. 4\% test accuracy in single-model dataset distillation and 50. 9\% in multi-model dataset distillation, surpassing RDED by +6. 4\% and outperforming state-of-the-art multi-model approaches, EDC and CV-DD, by +2. 3\% and +4. 9\%.

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NeurIPS Conference 2025 Conference Paper

Open CaptchaWorld: A Comprehensive Web-based Platform for Testing and Benchmarking Multimodal LLM Agents

Yaxin Luo
Zhaoyi Li
Jiacheng Liu
Jiacheng Cui
Xiaohan Zhao
Zhiqiang Shen

CAPTCHAs have been a critical bottleneck for deploying web agents in real-world applications, often blocking them from completing end-to-end automation tasks. While modern multimodal LLM agents have demonstrated impressive performance in static perception tasks, their ability to handle interactive, multi-step reasoning challenges like CAPTCHAs is largely untested. To address this gap, we introduce Open CaptchaWorld, the first web-based benchmark and platform specifically designed to evaluate the visual reasoning and interaction capabilities of MLLM-powered agents through diverse and dynamic CAPTCHA puzzles. Our benchmark spans 20 modern CAPTCHA types, totaling 225 CAPTCHAs, annotated with a new metric we propose: CAPTCHA Reasoning Depth, which quantifies the number of cognitive and motor steps required to solve each puzzle. Experimental results show that humans consistently achieve near-perfect scores, state-of-the-art MLLM agents struggle significantly, with success rates at most 40. 0\% by Browser-Use Openai-o3, far below human-level performance, 93. 3\%. This highlights Open CaptchaWorld as a vital benchmark for diagnosing the limits of current multimodal agents and guiding the development of more robust multimodal reasoning systems.

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NeurIPS Conference 2023 Conference Paper

Accelerated On-Device Forward Neural Network Training with Module-Wise Descending Asynchronism

Xiaohan Zhao
Hualin Zhang
Zhouyuan Huo
Bin Gu

On-device learning faces memory constraints when optimizing or fine-tuning on edge devices with limited resources. Current techniques for training deep models on edge devices rely heavily on backpropagation. However, its high memory usage calls for a reassessment of its dominance. In this paper, we propose forward gradient descent (FGD) as a potential solution to overcome the memory capacity limitation in on-device learning. However, FGD's dependencies across layers hinder parallel computation and can lead to inefficient resource utilization. To mitigate this limitation, we propose AsyncFGD, an asynchronous framework that decouples dependencies, utilizes module-wise stale parameters, and maximizes parallel computation. We demonstrate its convergence to critical points through rigorous theoretical analysis. Empirical evaluations conducted on NVIDIA's AGX Orin, a popular embedded device, show that AsyncFGD reduces memory consumption and enhances hardware efficiency, offering a novel approach to on-device learning.

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NeurIPS Conference 2023 Conference Paper

Direct Training of SNN using Local Zeroth Order Method

Bhaskar Mukhoty
Velibor Bojkovic
William de Vazelhes
Xiaohan Zhao
Giulia De Masi
Huan Xiong
Bin Gu

Spiking neural networks are becoming increasingly popular for their low energy requirement in real-world tasks with accuracy comparable to traditional ANNs. SNN training algorithms face the loss of gradient information and non-differentiability due to the Heaviside function in minimizing the model loss over model parameters. To circumvent this problem, the surrogate method employs a differentiable approximation of the Heaviside function in the backward pass, while the forward pass continues to use the Heaviside as the spiking function. We propose to use the zeroth-order technique at the local or neuron level in training SNNs, motivated by its regularizing and potential energy-efficient effects and establish a theoretical connection between it and the existing surrogate methods. We perform experimental validation of the technique on standard static datasets (CIFAR-10, CIFAR-100, ImageNet-100) and neuromorphic datasets (DVS-CIFAR-10, DVS-Gesture, N-Caltech-101, NCARS) and obtain results that offer improvement over the state-of-the-art results. The proposed method also lends itself to efficient implementations of the back-propagation method, which could provide 3-4 times overall speedup in training time. The code is available at \url{https: //github. com/BhaskarMukhoty/LocalZO}.

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NeurIPS Conference 2022 Conference Paper

GAGA: Deciphering Age-path of Generalized Self-paced Regularizer

Xingyu Qu
Diyang Li
Xiaohan Zhao
Bin Gu

Nowadays self-paced learning (SPL) is an important machine learning paradigm that mimics the cognitive process of humans and animals. The SPL regime involves a self-paced regularizer and a gradually increasing age parameter, which plays a key role in SPL but where to optimally terminate this process is still non-trivial to determine. A natural idea is to compute the solution path w. r. t. age parameter (i. e. , age-path). However, current age-path algorithms are either limited to the simplest regularizer, or lack solid theoretical understanding as well as computational efficiency. To address this challenge, we propose a novel Generalized Age-path Algorithm (GAGA) for SPL with various self-paced regularizers based on ordinary differential equations (ODEs) and sets control, which can learn the entire solution spectrum w. r. t. a range of age parameters. To the best of our knowledge, GAGA is the first exact path-following algorithm tackling the age-path for general self-paced regularizer. Finally the algorithmic steps of classic SVM and Lasso are described in detail. We demonstrate the performance of GAGA on real-world datasets, and find considerable speedup between our algorithm and competing baselines.

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AAAI Conference 2018 Conference Paper

Substructure Assembling Network for Graph Classification

Xiaohan Zhao
Bo Zong
Ziyu Guan
Kai Zhang
Wei Zhao

Graphs are natural data structures adopted to represent realworld data of complex relationships. In recent years, a surge of interest has been received to build predictive models over graphs, with prominent examples in chemistry, computational biology, and social networks. The overwhelming complexity of graph space often makes it challenging to extract interpretable and discriminative structural features for classiﬁcation tasks. In this work, we propose a novel neural network structure called Substructure Assembling Network (SAN) to extract graph features and improve the generalization performance of graph classiﬁcation. The key innovation of our work is a uniﬁed substructure assembling unit, which is a variant of Recurrent Neural Network (RNN) designed to hierarchically assemble useful pieces of graph components so as to fabricate discriminative substructures. SAN adopts a sequential, probabilistic decision process, and therefore it can tune substructure features in a ﬁner granularity. Meanwhile, the parameterized soft decisions can be continuously improved with supervised learning through back-propagation, leading to optimizable search trajectories. Overall, SAN embraces both the ﬂexibility of combinatorial pattern search and the strong optimizability of deep learning, and delivers promising results as well as interpretable structural features in graph classiﬁcation against state-of-the-art techniques.

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