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Yuqing Ma

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15 papers

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AAAI Conference 2026 Conference Paper

Activation Manipulation Attack: Penetrating and Harmful Jailbreak Attack Against Large Vision-Language Models

Haojie Hao
Jiakai Wang
Aishan Liu
Yuqing Ma
Haotong Qin
Yuanfang Guo
Xianglong Liu

Recently, Large Vision-Language Models (LVLMs) have been demonstrated to be vulnerable to jailbreak attacks, highlighting the urgent need for further research to comprehensively identify and mitigate these threats. Unfortunately, existing jailbreak studies primarily focus on coarse-grained input manipulation to elicit specific responses, overlooking the exploitation of internal representations, i.e., intermediate activations, which constrains their ability to penetrate alignment safeguards and generate harmful responses. To tackle this issue, we propose the Activation Manipulation (ActMan) Attack framework, which performs fine-grained activation manipulations inspired by the perception and cognition stages of human decision-making, enhancing both the penetration capability and harmfulness of attacks. To improve penetration capability, we introduce a Deceptive Visual Camouflage module inspired by the masking effect in human perception. This module uses a benign activation-guided attention redirection strategy to conceal abnormal activation patterns, thereby suppressing LVLM's defense detection during early-stage decoding. To enhance harmfulness, we design a Malicious Semantic Induction module drawing from the framing effect in human cognition, which reconstructs jailbreak instructions using malicious activation guidance to change LVLM’s risk assessment during late-stage decoding, thereby amplifying the harmfulness of model responses. Extensive experiments on six mainstream LVLMs demonstrate that our method remarkably outperforms state-of-the-art baselines, achieving an average relative ASR improvement of 12.06%.

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AAAI Conference 2026 Conference Paper

CMedBench: A Comprehensive Benchmark for Efficient Medical Large Language Models

Shengbo Gao
Jinyang Guo
Lixian Su
Yifu Ding
Shiqiao Gu
Aishan Liu
Yuqing Ma
Zhiwang Zhang

Large Language Models (LLMs) hold significant potential for enhancing healthcare applications, yet their deployment is hindered by high computational and memory demands. Model compression techniques offer solutions to reduce these demands, but their impact on medical LLMs remains underexplored. In this paper, we introduce CMedBench, the first comprehensive benchmark for evaluating compressed LLMs in medical contexts. CMedBench assesses five core dimensions: Medical Knowledge Ability, Medical Application Ability, Trustworthiness Maintenance, Compression Cross Combination, and Computational Efficiency. Through extensive empirical studies, we analyze the trade-offs between model efficiency and clinical performance across diverse models, datasets, and compression strategies. Our findings highlight critical limitations in current evaluation practices and provide a robust framework for aligning compression strategies with medical requirements. CMedBench serves as a vital resource for researchers and practitioners, guiding the development of efficient, trustworthy, and clinically effective LLMs for healthcare applications.

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AAAI Conference 2026 Conference Paper

Query-Routed Activation Editing with Truth-hierarchical Preference Optimization

Kewei Liao
Tianbo Wang
Yuqing Ma
Zhange Zhang
Zhicheng Geng
Xiaowei Zhao
Jiakai Wang
Xianglong Liu

Hallucination has emerged as a pivotal challenge of Large Language Models (LLMs) that generate plausible yet non‑factual content, significantly impeding the trustworthy AI applications in real-world scenarios like medical diagnosis and autonomous driving. Editing the internal activations of LLMs during inference has shown promising effectiveness in mitigating hallucinations with minimal cost. However, previous editing approaches neglect the query‑specific inference pathways that require tailored truthful steering vectors, resulting in suboptimal hallucination mitigation. To address these issues, we propose the Query-Routed Activation Editing (QRAE) framework, which comprises Divergence-sensitive Head Routing (DHR) and Truth-hierarchical Preference Steering (TPS), to fully leverage query-specific semantics for adaptive activation editing. Specifically, DHR is proposed to establish a query-aware head selection criterion, thereby dynamically routing to truth-critical attention heads. Subsequently, TPS introduces a query-specific steering vector calibration policy with the guidance of progressive truth-preferred optimization, enabling precise and adaptive editing for each distinct query. Extensive experiments on the widely recognized TruthfulQA benchmark demonstrate that QRAE outperforms SOTA methods by up to 13.2% in MC1. Meanwhile, QRAE demonstrates strong generalization to out-of-distribution TriviaQA and Natural Questions benchmarks.

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NeurIPS Conference 2025 Conference Paper

Conflict-Aware Knowledge Editing in the Wild: Semantic-Augmented Graph Representation for Unstructured Text

Zhange Zhang
Zhicheng Geng
Yuqing Ma
Tianbo Wang
Kai Lv
Xianglong Liu

Large Language Models (LLMs) have demonstrated broad applications but suffer from issues like hallucinations, erroneous outputs and outdated knowledge. Model editing emerges as an effective solution to refine knowledge in LLMs, yet existing methods typically depend on structured knowledge representations. However, real-world knowledge is primarily embedded within complex, unstructured text. Existing structured knowledge editing approaches face significant challenges when handling the entangled and intricate knowledge present in unstructured text, resulting in issues such as representation ambiguity and editing conflicts. To address these challenges, we propose a Conflict-Aware Knowledge Editing in the Wild (CAKE) framework, the first framework explicitly designed for editing knowledge extracted from wild unstructured text. CAKE comprises two core components: a Semantic-augmented Graph Representation module and a Conflict-aware Knowledge Editing strategy. The Semantic-augmented Graph Representation module enhances knowledge encoding through structural disambiguation, relational enrichment, and semantic diversification. Meanwhile, the Conflict-aware Knowledge Editing strategy utilizes a graph-theoretic coloring algorithm to disentangle conflicted edits by allocating them to orthogonal parameter subspaces, thereby effectively mitigating editing conflicts. Experimental results on the AKEW benchmark demonstrate that CAKE significantly outperforms existing methods, achieving a 15. 43\% improvement in accuracy on llama3 editing tasks. Our framework successfully bridges the gap between unstructured textual knowledge and reliable model editing, enabling more robust and scalable updates for practical LLM applications.

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IJCAI Conference 2025 Conference Paper

Continuous Diffusive Prediction Network for Multi-Station Weather Prediction

Chujie Xu
Yuqing Ma
Haoyuan Deng
Yajun Gao
Yudie Wang
Kai Lv
Xianglong Liu

Multi-station weather prediction provides weather forecasts for specific geographical locations, playing an important role in various aspects of daily life. Existing methods consider the relationships between individual stations discretely, making it difficult to model the continuous spatiotemporal processes of atmospheric motion, which results in suboptimal prediction outcomes. This paper proposes the Continuous Diffusive Prediction Network (CDPNet) to model the real-world continuous weather change process from discrete station observation data. CDPNet consists of two core modules: the Continuous Calibrated Initialization (CCI) and the Diffusive Difference Estimation (DDE). The CCI module interpolates data between observation stations to construct a spatially continuous physical field and ensures temporal continuity by integrating directional information from a global perspective. It accurately represents the current physical state and provides a foundation for future weather prediction. Moreover, the DDE module explicitly captures the spatial diffusion process and estimates the diffusive differences between consecutive time steps, effectively modeling spatio-temporally continuous atmospheric motion. Likewise, directional information on weather changes is introduced from the entire historical series to mitigate estimation uncertainty and improve the performance of weather prediction. Extensive experiments on the Weather2K and Global Wind/Temp datasets demonstrate that CDPNet outperforms state-of-the-art models.

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NeurIPS Conference 2025 Conference Paper

Empirical Study on Robustness and Resilience in Cooperative Multi-Agent Reinforcement Learning

Simin Li
Zihao Mao
Hanxiao Li
Zonglei Jing
Zhuohang bian
Jun Guo
Li Wang
Zhuoran Han

In cooperative Multi-Agent Reinforcement Learning (MARL), it is a common practice to tune hyperparameters in ideal simulated environments to maximize cooperative performance. However, policies tuned for cooperation often fail to maintain robustness and resilience under real-world uncertainties. Building trustworthy MARL systems requires a deep understanding of \emph{robustness}, which ensures stability under uncertainties, and \emph{resilience}, the ability to recover from disruptions—a concept extensively studied in control systems but largely overlooked in MARL. In this paper, we present a large-scale empirical study comprising over 82, 620 experiments to evaluate cooperation, robustness, and resilience in MARL across 4 real-world environments, 13 uncertainty types, and 15 hyperparameters. Our key findings are: (1) Under mild uncertainty, optimizing cooperation improves robustness and resilience, but this link weakens as perturbations intensify. Robustness and resilience also varies by algorithm and uncertainty type. (2) Robustness and resilience do not generalize across uncertainty modalities or agent scopes: policies robust to action noise for all agents may fail under observation noise on a single agent. (3) Hyperparameter tuning is critical for trustworthy MARL: surprisingly, standard practices like parameter sharing, GAE, and PopArt can hurt robustness, while early stopping, high critic learning rates, and Leaky ReLU consistently help. By optimizing hyperparameters only, we observe substantial improvement in cooperation, robustness and resilience across all MARL backbones, with the phenomenon also generalizing to robust MARL methods across these backbones.

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IJCAI Conference 2025 Conference Paper

From General Relation Patterns to Task-Specific Decision-Making in Continual Multi-Agent Coordination

Chang Yao
Youfang Lin
Shoucheng Song
Hao Wu
Yuqing Ma
Sheng Han
Kai Lv

Continual Multi-Agent Reinforcement Learning (Co-MARL) requires agents to address catastrophic forgetting issues while learning new coordination policies with the dynamics team. In this paper, we delve into the core of Co-MARL, namely Relation Patterns, which refer to agents’ general understanding of interactions. In addition to generality, relation patterns exhibit task-specificity when mapped to different action spaces. To this end, we propose a novel method called General Relation Patterns-Guided Task-specific Decision-Maker (RPG). In RPG, agents extract relation patterns from dynamic observation spaces using a relation capturer. These task-agnostic relation patterns are then mapped to different action spaces via a task-specific decision-maker generated by a conditional hypernetwork. To combat forgetting, we further introduce regularization items on both the relation capturer and the conditional hypernetwork. Results on SMAC and LBF demonstrate that RPG effectively prevents catastrophic forgetting when learning new tasks and achieves zero-shot generalization to unseen tasks.

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AAAI Conference 2024 Conference Paper

Fast and Controllable Post-training Sparsity: Learning Optimal Sparsity Allocation with Global Constraint in Minutes

Ruihao Gong
Yang Yong
Zining Wang
Jinyang Guo
Xiuying Wei
Yuqing Ma
Xianglong Liu

Neural network sparsity has attracted many research interests due to its similarity to biological schemes and high energy efficiency. However, existing methods depend on long-time training or fine-tuning, which prevents large-scale applications. Recently, some works focusing on post-training sparsity (PTS) have emerged. They get rid of the high training cost but usually suffer from distinct accuracy degradation due to neglect of the reasonable sparsity rate at each layer. Previous methods for finding sparsity rates mainly focus on the training-aware scenario, which usually fails to converge stably under the PTS setting with limited data and much less training cost. In this paper, we propose a fast and controllable post-training sparsity (FCPTS) framework. By incorporating a differentiable bridge function and a controllable optimization objective, our method allows for rapid and accurate sparsity allocation learning in minutes, with the added assurance of convergence to a predetermined global sparsity rate. Equipped with these techniques, we can surpass the state-of-the-art methods by a large margin, e.g., over 30\% improvement for ResNet-50 on ImageNet under the sparsity rate of 80\%. Our plug-and-play code and supplementary materials are open-sourced at https://github.com/ModelTC/FCPTS.

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AAAI Conference 2024 Conference Paper

Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection

Yunqian Fan
Xiuying Wei
Ruihao Gong
Yuqing Ma
Xiangguo Zhang
Qi Zhang
Xianglong Liu

Lane detection (LD) plays a crucial role in enhancing the L2+ capabilities of autonomous driving, capturing widespread attention. The Post-Processing Quantization (PTQ) could facilitate the practical application of LD models, enabling fast speeds and limited memories without labeled data. However, prior PTQ methods do not consider the complex LD outputs that contain physical semantics, such as offsets, locations, etc., and thus cannot be directly applied to LD models. In this paper, we pioneeringly investigate semantic sensitivity to post-processing for lane detection with a novel Lane Distortion Score. Moreover, we identify two main factors impacting the LD performance after quantization, namely intra-head sensitivity and inter-head sensitivity, where a small quantization error in specific semantics can cause significant lane distortion. Thus, we propose a Selective Focus framework deployed with Semantic Guided Focus and Sensitivity Aware Selection modules, to incorporate post-processing information into PTQ reconstruction. Based on the observed intra-head sensitivity, Semantic Guided Focus is introduced to prioritize foreground-related semantics using a practical proxy. For inter-head sensitivity, we present Sensitivity Aware Selection, efficiently recognizing influential prediction heads and refining the optimization objectives at runtime. Extensive experiments have been done on a wide variety of models including keypoint-, anchor-, curve-, and segmentation-based ones. Our method produces quantized models in minutes on a single GPU and can achieve 6.4\% F1 Score improvement on the CULane dataset. Code and supplementary statement can be found at https://github.com/PannenetsF/SelectiveFocus.

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AAAI Conference 2021 Conference Paper

Stratified Rule-Aware Network for Abstract Visual Reasoning

Sheng Hu
Yuqing Ma
Xianglong Liu
Yanlu Wei
Shihao Bai

Abstract reasoning refers to the ability to analyze information, discover rules at an intangible level, and solve problems in innovative ways. Raven’s Progressive Matrices (RPM) test is typically used to examine the capability of abstract reasoning. The subject is asked to identify the correct choice from the answer set to fill the missing panel at the bottom right of RPM (e. g. , a 3×3 matrix), following the underlying rules inside the matrix. Recent studies, taking advantage of Convolutional Neural Networks (CNNs), have achieved encouraging progress to accomplish the RPM test. However, they partly ignore necessary inductive biases of RPM solver, such as order sensitivity within each row/column and incremental rule induction. To address this problem, in this paper we propose a Stratified Rule-Aware Network (SRAN) to generate the rule embeddings for two input sequences. Our SRAN learns multiple granularity rule embeddings at different levels, and incrementally integrates the stratified embedding flows through a gated fusion module. With the help of embeddings, a rule similarity metric is applied to guarantee that SRAN can not only be trained using a tuplet loss but also infer the best answer efficiently. We further point out the severe defects existing in the popular RAVEN dataset for RPM test, which prevent from the fair evaluation of the abstract reasoning ability. To fix the defects, we propose an answer set generation algorithm called Attribute Bisection Tree (ABT), forming an improved dataset named Impartial-RAVEN (I-RAVEN for short). Extensive experiments are conducted on both PGM and I-RAVEN datasets, showing that our SRAN outperforms the state-of-the-art models by a considerable margin.

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IJCAI Conference 2020 Conference Paper

Few-shot Visual Learning with Contextual Memory and Fine-grained Calibration

Yuqing Ma
Wei Liu
Shihao Bai
Qingyu Zhang
Aishan Liu
Weimin Chen
Xianglong Liu

Few-shot learning aims to learn a model that can be readily adapted to new unseen classes (concepts) by accessing one or few examples. Despite the successful progress, most of the few-shot learning approaches, concentrating on either global or local characteristics of examples, still suffer from weak generalization abilities. Inspired by the inverted pyramid theory, to address this problem, we propose an inverted pyramid network (IPN) that intimates the human's coarse-to-fine cognition paradigm. The proposed IPN consists of two consecutive stages, namely global stage and local stage. At the global stage, a class-sensitive contextual memory network (CCMNet) is introduced to learn discriminative support-query relation embeddings and predict the query-to-class similarity based on the contextual memory. Then at the local stage, a fine-grained calibration is further appended to complement the coarse relation embeddings, targeting more precise query-to-class similarity evaluation. To the best of our knowledge, IPN is the first work that simultaneously integrates both global and local characteristics in few-shot learning, approximately imitating the human cognition mechanism. Our extensive experiments on multiple benchmark datasets demonstrate the superiority of IPN, compared to a number of state-of-the-art approaches.

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IJCAI Conference 2020 Conference Paper

Transductive Relation-Propagation Network for Few-shot Learning

Yuqing Ma
Shihao Bai
Shan An
Wei Liu
Aishan Liu
Xiantong Zhen
Xianglong Liu

Few-shot learning, aiming to learn novel concepts from few labeled examples, is an interesting and very challenging problem with many practical advantages. To accomplish this task, one should concentrate on revealing the accurate relations of the support-query pairs. We propose a transductive relation-propagation graph neural network (TRPN) to explicitly model and propagate such relations across support-query pairs. Our TRPN treats the relation of each support-query pair as a graph node, named relational node, and resorts to the known relations between support samples, including both intra-class commonality and inter-class uniqueness, to guide the relation propagation in the graph, generating the discriminative relation embeddings for support-query pairs. A pseudo relational node is further introduced to propagate the query characteristics, and a fast, yet effective transductive learning strategy is devised to fully exploit the relation information among different queries. To the best of our knowledge, this is the first work that explicitly takes the relations of support-query pairs into consideration in few-shot learning, which might offer a new way to solve the few-shot learning problem. Extensive experiments conducted on several benchmark datasets demonstrate that our method can significantly outperform a variety of state-of-the-art few-shot learning methods.

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IJCAI Conference 2019 Conference Paper

Coarse-to-Fine Image Inpainting via Region-wise Convolutions and Non-Local Correlation

Yuqing Ma
Xianglong Liu
Shihao Bai
Lei Wang
Dailan He
Aishan Liu

Recently deep neural networks have achieved promising performance for filling large missing regions in image inpainting tasks. They usually adopted the standard convolutional architecture over the corrupted image, where the same convolution filters try to restore the diverse information on both existing and missing regions, and meanwhile ignores the long-distance correlation among the regions. Only relying on the surrounding areas inevitably leads to meaningless contents and artifacts, such as color discrepancy and blur. To address these problems, we first propose region-wise convolutions to locally deal with the different types of regions, which can help exactly reconstruct existing regions and roughly infer the missing ones from existing regions at the same time. Then, a non-local operation is introduced to globally model the correlation among different regions, promising visual consistency between missing and existing regions. Finally, we integrate the region-wise convolutions and non-local correlation in a coarse-to-fine framework to restore semantically reasonable and visually realistic images. Extensive experiments on three widely-used datasets for image inpainting tasks have been conducted, and both qualitative and quantitative experimental results demonstrate that the proposed model significantly outperforms the state-of-the-art approaches, especially for the large irregular missing regions.

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AAAI Conference 2019 Conference Paper

Perceptual-Sensitive GAN for Generating Adversarial Patches

Aishan Liu
Xianglong Liu
Jiaxin Fan
Yuqing Ma
Anlan Zhang
Huiyuan Xie
Dacheng Tao

Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible perturbations mislead DNNs to incorrect results. Recently, adversarial patch, with noise confined to a small and localized patch, emerged for its easy accessibility in real-world. However, existing attack strategies are still far from generating visually natural patches with strong attacking ability, since they often ignore the perceptual sensitivity of the attacked network to the adversarial patch, including both the correlations with the image context and the visual attention. To address this problem, this paper proposes a perceptual-sensitive generative adversarial network (PS-GAN) that can simultaneously enhance the visual fidelity and the attacking ability for the adversarial patch. To improve the visual fidelity, we treat the patch generation as a patch-to-patch translation via an adversarial process, feeding any types of seed patch and outputting the similar adversarial patch with high perceptual correlation with the attacked image. To further enhance the attacking ability, an attention mechanism coupled with adversarial generation is introduced to predict the critical attacking areas for placing the patches, which can help producing more realistic and aggressive patches. Extensive experiments under semi-whitebox and black-box settings on two large-scale datasets GTSRB and ImageNet demonstrate that the proposed PS-GAN outperforms state-of-the-art adversarial patch attack methods.

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IJCAI Conference 2018 Conference Paper

Progressive Generative Hashing for Image Retrieval

Yuqing Ma
Yue He
Fan Ding
Sheng Hu
Jun Li
Xianglong Liu

Recent years have witnessed the success of the emerging hashing techniques in large-scale image retrieval. Owing to the great learning capacity, deep hashing has become one of the most promising solutions, and achieved attractive performance in practice. However, without semantic label information, the unsupervised deep hashing still remains an open question. In this paper, we propose a novel progressive generative hashing (PGH) framework to help learn a discriminative hashing network in an unsupervised way. Very different from existing studies, it first treats the hash codes as a kind of semantic condition for the similar image generation, and simultaneously feeds the original image and its codes into the generative adversarial networks (GANs). The real images together with the synthetic ones can further help train a discriminative hashing network based on a triplet loss. By iteratively inputting the learnt codes into the hash conditioned GANs, we can progressively enable the hashing network to discover the semantic relations. Extensive experiments on the widely-used image datasets demonstrate that PGH can significantly outperforms state-of-the-art unsupervised hashing methods.

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