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Peng Jiang

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19 papers

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AAAI Conference 2026 Conference Paper

Align³GR: Unified Multi-Level Alignment for LLM-based Generative Recommendation

Wencai Ye
Mingjie Sun
Shuhang Chen
Wenjin Wu
Peng Jiang

Large Language Models (LLMs) demonstrate significant advantages in leveraging structured world knowledge and multi-step reasoning capabilities. However, fundamental challenges arise when transforming LLMs into real-world recommendation systems due to semantic and behavioral misalignment. To bridge this gap, we propose Align³GR, a novel framework that unifies token-level, behavior modeling-level, and preference-level alignment. Our approach introduces: Dual tokenization fusing user-item semantic and collaborative signals. Enhanced behavior modeling with bidirectional semantic alignment. Progressive DPO strategy combining self-play (SP-DPO) and real-world feedback (RF-DPO) for dynamic preference adaptation. Experiments show Align³GR outperforms the SOTA baseline by +17.8% in Recall@10 and +20.2% in NDCG@10 on the public dataset, with significant gains in online A/B tests and full-scale deployment on an industrial large-scale recommendation platform.

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AAAI Conference 2026 Conference Paper

Fairness-Aware Design for Contextual Experiments: Guaranteeing Reliability and Equity in Heterogeneous Subgroups

Guangyan Gan
Ling Zhang
Yanhua Cheng
Yongxiang Tang
kaiyuan Li
Xialong Liu
Peng Jiang

Experimental design is critical for evidence-based decision-making in healthcare, marketing, and public policy. However, designing efficient experiments across heterogeneous subgroups presents significant challenges. Existing methods often optimize for statistical power or overall sample efficiency, overlooking crucial fairness considerations across these different subgroups. To address this gap, we introduce a Fairness-Aware Contextual Track-and-Stop Design (F-CTSD) algorithm. The proposed F-CTSD algorithm provides statistical guarantees on subgroup fairness while minimizing required sample sizes. We quantify the fairness-efficiency trade-off and derive the sample complexity bound for the proposed F-CTSD algorithm under its fairness constraints. We further theoretically prove that the proposed F-CTSD algorithm consistently produces accurate treatment effect estimates even under fairness requirements, enhancing statistical reliability. Numerical experiments show that the proposed F-CTSD algorithm outperforms existing methods, achieving higher sample efficiency while reducing subgroup fairness violations by 4.95%.

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AAAI Conference 2026 Conference Paper

Generic Adversarial Attack Framework Against Graph-based Vertical Federated Learning

Yimin Liu
Peng Jiang
Qi Liu
Liehuang Zhu

Graph-based vertical federated learning (GVFL) enables multiple parties to collaboratively train and infer over aligned nodes, where each party contributes its own local embedding derived from different attributes and adjacency relations. Adversarial inputs injected by an attacker can skew the joint prediction toward its desired outcomes while diminishing the influence of benign parties and undermining contribution. However, most attacks typically have pre-set assumptions, such as access to the server architecture, model queries, or in-domain auxiliary graphs. In this paper, we propose SGAC, an attack framework that enables domination of joint inference without relying on above assumptions. SGAC learns label-indicative embeddings and class-transferable probabilities to generate a surrogate that closely mimics the server-side classification behavior by exploiting auxiliary graphs from non-training domains. SGAC then leverages saliency over node attributes and edges on the auxiliary graphs to construct a diverse set of shadow inputs resembling highly influential test instances. With the surrogate fidelity and input diversity, SGAC crafts transferable contribution-monopoly adversarial inputs that hijack GVFL incentives. Extensive experiments across diverse model architectures validate SGAC's effectiveness.

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AAAI Conference 2025 Conference Paper

D&M: Enriching E-commerce Videos with Sound Effects by Key Moment Detection and SFX Matching

Jingyu Liu
Minquan Wang
Ye Ma
Bo Wang
Aozhu Chen
Quan Chen
Peng Jiang
Xirong Li

Videos showcasing specific products are increasingly important for E-commerce. Key moments naturally exist as the first appearance of a specific product, presentation of its distinctive features, the presence of a buying link, etc. Adding proper sound effects (SFX) to such moments, or video decoration with SFX (VDSFX), is crucial for enhancing user engaging experience. Previous work adds SFX to videos by video-to-SFX matching at a holistic level, lacking the ability of adding SFX to a specific moment. Meanwhile, previous studies on video highlight detection or video moment retrieval consider only moment localization, leaving moment to SFX matching untouched. By contrast, we propose in this paper D&M, a unified method that accomplishes key moment detection and moment-to-SFX matching simultaneously. Moreover, for the new VDSFX task we build a large-scale dataset SFX-Moment from an E-commerce video creation platform. For a fair comparison, we build competitive baselines by extending a number of current video moment detection methods to the new task. Extensive experiments on SFX-Moment show the superior performance of the proposed method over the baselines.

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IJCAI Conference 2025 Conference Paper

Generic Adversarial Attack Framework Against Vertical Federated Learning

Yimin Liu
Peng Jiang

Vertical federated learning (VFL) enables feature-level collaboration by incorporating scattered attributes from aligned samples, and allows each party to contribute its personalized input to joint training and inference. The injection of adversarial inputs can mislead the joint inference towards the attacker’s will, forcing other benign parties to make negligible contributions and losing rewards regarding the importance of their contributions. However, most attacks require server model queries, subsets of complete test samples, or labeled auxiliary images from the training domain. These extra requirements are not practical for real-world VFL applications. In this paper, we propose PGAC, a novel and practical attack framework for crafting adversarial inputs to dominate joint inference, which does not rely on the above requirements. PGAC advances prior attacks by requiring only access to auxiliary images from non-training domains. PGAC learns generalized label-indicative embeddings and estimates class-transferable probabilities across domains to generate a proxy model that closely approximates the server model. PGAC then augments images by emphasizing salient regions with class activation maps, creating a diverse shadow input set that resembles influential test inputs. With proxy fidelity and input diversity, PGAC crafts transferable adversarial inputs. Evaluation on diverse model architectures confirms the effectiveness of PGAC.

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AAAI Conference 2025 Conference Paper

LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application

Jian Jia
Yipei Wang
Yan Li
Honggang Chen
Xuehan Bai
Zhaocheng Liu
Jian Liang
Quan Chen

Contemporary recommendation systems predominantly rely on ID embedding to capture latent associations among users and items. However, this approach overlooks the wealth of semantic information embedded within textual descriptions of items, leading to suboptimal performance and poor generalizations. Leveraging the capability of large language models to comprehend and reason about textual content presents a promising avenue for advancing recommendation systems. To achieve this, we propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge. We address computational complexity concerns by utilizing pretrained LLMs as item encoders and freezing LLM parameters to avoid catastrophic forgetting and preserve open-world knowledge. To bridge the gap between the open-world and collaborative domains, we design a twin-tower structure supervised by the recommendation task and tailored for practical industrial application. Through experiments on the real large-scale industrial dataset and online A/B tests, we demonstrate the efficacy of our approach in industry application. We also achieve state-of-the-art performance on six Amazon Review datasets to verify the superiority of our method.

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AAAI Conference 2025 Conference Paper

Learning Multiple User Distributions for Recommendation via Guided Conditional Diffusion

Cheng Wu
Liang Su
Chaokun Wang
Shaoyun Shi
Ziqian Zhang
Ziyang Liu
Wang Peng
Wenjin Wu

Recommender systems are increasingly prevalent to provide personalized suggestions and enhance user satisfaction. Typical recommendation models encode users and items as embeddings, and generate recommendations by assessing the similarity between these embeddings. Despite their effectiveness, these embedding-based models struggle with modeling user uncertainty and capturing diverse user interests using a single fixed user embedding. Recent studies have begun to explore a user-distribution paradigm to learn distributions for users. However, this approach employs a single distribution per user, which fails to effectively delineate semantic boundaries, resulting in sub-optimal recommendations. To this end, we propose GCDR, a Guided Conditional Diffusion Recommender model, to learn multiple distributions for each user in this paper. Specifically, GCDR addresses two major challenges: 1) learning disentangled distributions, and 2) learning personalized distributions. GCDR captures inter-user and intra-user distribution properties through conditional and guided diffusion, respectively. It maintains user-specific embeddings to encode long-term interests for conditional diffusion, while for guided diffusion, it incorporates short-term interests encoded from recent interactions with category preferences. To align the diffusion model with the recommendation task, we train GCDR with three loss functions, included the user loss, the recommendation loss and the diffusion loss. Extensive experiments on four real-world datasets show that GCDR is able to learn effective user distributions and is superior to thirteen state-of-the-art baseline methods.

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AAAI Conference 2025 Conference Paper

LLM-Powered User Simulator for Recommender System

Zijian Zhang
Shuchang Liu
Ziru Liu
Rui Zhong
Qingpeng Cai
Xiangyu Zhao
Chunxu Zhang
Qidong Liu

User simulators can rapidly generate a large volume of timely user behavior data, providing a testing platform for reinforcement learning-based recommender systems, thus accelerating their iteration and optimization. However, prevalent user simulators generally suffer from significant limitations, including the opacity of user preference modeling and the incapability of evaluating simulation accuracy. In this paper, we introduce an LLM-powered user simulator to simulate user engagement with items in an explicit manner, thereby enhancing the efficiency and effectiveness of reinforcement learning-based recommender systems training. Specifically, we identify the explicit logic of user preferences, leverage LLMs to analyze item characteristics and distill user sentiments, and design a logical model to imitate real human engagement. By integrating a statistical model, we further enhance the reliability of the simulation, proposing an ensemble model that synergizes logical and statistical insights for user interaction simulations. Capitalizing on the extensive knowledge and semantic generation capabilities of LLMs, our user simulator faithfully emulates user behaviors and preferences, yielding high-fidelity training data that enrich the training of recommendation algorithms. We establish quantifying and qualifying experiments on five datasets to validate the simulator's effectiveness and stability across various recommendation scenarios.

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NeurIPS Conference 2025 Conference Paper

Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation

Wei Yang
Rui Zhong
Yiqun Chen
Chi Lu
Peng Jiang

Multimodal recommendation aims to integrate collaborative signals with heterogeneous content such as visual and textual information, but remains challenged by modality-specific noise, semantic inconsistency, and unstable propagation over user–item graphs. These issues are often exacerbated by naive fusion or shallow modeling strategies, leading to degraded generalization and poor robustness. While recent work has explored the frequency domain as a lens to separate stable from noisy signals, most methods rely on static filtering or reweighting, lacking the ability to reason over spectral structure or adapt to modality-specific reliability. To address these challenges, we propose a Structured Spectral Reasoning (SSR) framework for frequency-aware multimodal recommendation. Our method follows a four-stage pipeline: (i) Decompose graph-based multimodal signals into spectral bands via graph-guided transformations to isolate semantic granularity; (ii) Modulate band-level reliability with spectral band masking, a training-time masking with representation-consistency objective that suppresses brittle frequency components; (iii) Fuse complementary frequency cues using hyperspectral reasoning with low-rank cross-band interaction; and (iv) Align modality-specific spectral features via contrastive regularization to promote semantic and structural consistency. Experiments on three real-world benchmarks show consistent gains over strong baselines, particularly under sparse and cold-start settings. Additional analyses indicate that structured spectral modeling improves robustness and provides clearer diagnostics of how different bands contribute to performance.

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EAAI Journal 2024 Journal Article

An adaptive lightweight small object detection method for incremental few-shot scenarios of unmanned surface vehicles

Bo Wang
Peng Jiang
Zhuoyan Liu
Yueming Li
Jian Cao
Ye Li

Real-time and accurate detection of sea surface objects has become an important research topic for unmanned surface vehicles (USVs). During the execution of tasks, USVs sometimes need to upgrade their model to detect new categories, and the initial data is often limited. This requires quick adaptation to new categories under few-shot scenarios. We propose a lightweight neural network for detecting small sea surface objects named Shuffle-High-Resolution-Net (SHRDet), which integrates the enhanced Shuffle Block based on High-Resolution-Net (HRNet), lightweight feature fusion module, and Focal Efficient Intersection over Union loss. Based on SHRDet, a fast adaptation method named SHRDet-N for incremental few-shot categories is proposed. It generates category enhancement features through a cross-attention mechanism, and introduces elastic weight consolidation and feature distance to solve catastrophic forgetting when learning incremental few-shot categories. The algorithms have been applied to an intelligent USV platform for various surface missions, such as security patrol, ocean investigation, and marine engineering. The experimental results on public datasets indicate that SHRDet achieves 80. 7 % mean Average Precision (mAP) on the Water Surface Object Detection Dataset (WSODD) with only 0. 69 M parameters and less calculation quantity, and SHRDet is significantly superior to state-of-the-art methods in terms of lightweight and accuracy. Moreover, SHRDet-N effectively solves the learning problem of incremental few-shot categories. When the sample number of a new category is set to 20, the mAP of the base categories is 82. 5 % and that of the new category is 63. 8 %, which is 2. 8 % and 4. 2 % higher than that of state-of-the-art models like Sylph.

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EAAI Journal 2024 Journal Article

Interpretable detector for cervical cytology using self-attention and cell origin group guidance

Peng Jiang
Juan Liu
Jing Feng
Hua Chen
Yuqi Chen
Cheng Li
Baochuan Pang
Dehua Cao

Deep learning has advanced the development of automated cervical cytology, yet limited studies have delved into methods for incorporating medical domain knowledge, and model interpretability has not been thoroughly investigated. To address this issue, this paper proposes a novel, explainable detection method for abnormal cervical cells, called dual-stream self-attention based feature fusion and origin grouping network (DSA-FFOGNet). To encourage the model to focus more on lesion cells and cell nuclei of diagnostic significance, the dual-stream self-attention (DSA) module is introduced to enhance the learning of lesion-specific features. In view of the complex background, cell dense distribution, cell overlap, or clumps existing in the actual cervical cytology images, multi-scale features are extracted and fused by using the path aggregation network (PAN) to enhance the feature representation ability. By integrating biomedical insights regarding cell provenance and formulating an origin grouping loss, DSA-FFOGNet adjusts the penalties for cervical cells originating from different groups, thereby enhancing the optimization of the model training process. To further improve the detection performance, the classification and localization tasks are decoupled via the use of double detection heads. Extensive experiments validate the robustness of the proposed DSA-FFOGNet. The visualization of class activation maps (CAMs) showcases the model’s interpretability. The proposed approach advances the application and development of explainable artificial intelligence (XAI) models in cervical cytology and inspires further research in automated cervical cytology.

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NeurIPS Conference 2023 Conference Paper

KuaiSim: A Comprehensive Simulator for Recommender Systems

Kesen Zhao
Shuchang Liu
Qingpeng Cai
Xiangyu Zhao
Ziru Liu
Dong Zheng
Peng Jiang
Kun Gai

Reinforcement Learning (RL)-based recommender systems (RSs) have garnered considerable attention due to their ability to learn optimal recommendation policies and maximize long-term user rewards. However, deploying RL models directly in online environments and generating authentic data through A/B tests can pose challenges and require substantial resources. Simulators offer an alternative approach by providing training and evaluation environments for RS models, reducing reliance on real-world data. Existing simulators have shown promising results but also have limitations such as simplified user feedback, lacking consistency with real-world data, the challenge of simulator evaluation, and difficulties in migration and expansion across RSs. To address these challenges, we propose KuaiSim, a comprehensive user environment that provides user feedback with multi-behavior and cross-session responses. The resulting simulator can support three levels of recommendation problems: the request level list-wise recommendation task, the whole-session level sequential recommendation task, and the cross-session level retention optimization task. For each task, KuaiSim also provides evaluation protocols and baseline recommendation algorithms that further serve as benchmarks for future research. We also restructure existing competitive simulators on the Kuairand Dataset and compare them against KuaiSim to future assess their performance and behavioral differences. Furthermore, to showcase KuaiSim's flexibility in accommodating different datasets, we demonstrate its versatility and robustness when deploying it on the ML-1m dataset. The implementation code is available online to ease reproducibility \footnote{https: //github. com/Applied-Machine-Learning-Lab/KuaiSim}.

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NeurIPS Conference 2023 Conference Paper

State Regularized Policy Optimization on Data with Dynamics Shift

Zhenghai Xue
Qingpeng Cai
Shuchang Liu
Dong Zheng
Peng Jiang
Kun Gai
Bo An

In many real-world scenarios, Reinforcement Learning (RL) algorithms are trained on data with dynamics shift, i. e. , with different underlying environment dynamics. A majority of current methods address such issue by training context encoders to identify environment parameters. Data with dynamics shift are separated according to their environment parameters to train the corresponding policy. However, these methods can be sample inefficient as data are used \textit{ad hoc}, and policies trained for one dynamics cannot benefit from data collected in all other environments with different dynamics. In this paper, we find that in many environments with similar structures and different dynamics, optimal policies have similar stationary state distributions. We exploit such property and learn the stationary state distribution from data with dynamics shift for efficient data reuse. Such distribution is used to regularize the policy trained in a new environment, leading to the SRPO (\textbf{S}tate \textbf{R}egularized \textbf{P}olicy \textbf{O}ptimization) algorithm. To conduct theoretical analyses, the intuition of similar environment structures is characterized by the notion of homomorphous MDPs. We then demonstrate a lower-bound performance guarantee on policies regularized by the stationary state distribution. In practice, SRPO can be an add-on module to context-based algorithms in both online and offline RL settings. Experimental results show that SRPO can make several context-based algorithms far more data efficient and significantly improve their overall performance.

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NeurIPS Conference 2022 Conference Paper

Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training

Peng Jiang
Lihan Hu
Shihui Song

Sparse training is a popular technique to reduce the overhead of training large models. Although previous work has shown promising results for nonstructured sparse models, it is still unclear whether a sparse model with structural constraints can be trained from scratch to high accuracy. In this work, we study the dynamic sparse training for a class of sparse models with shuffled block structures. Compared to nonstructured models, such fine-grained structured models are more hardware-friendly and can effectively accelerate the training process. We propose an algorithm that keeps adapting the sparse model while maintaining the active parameters in shuffled blocks. We conduct experiments on a variety of networks and datasets and obtain positive results. In particular, on ImageNet, we achieve dense accuracy for ResNet50 and ResNet18 at 0. 5 sparsity. On CIFAR10/100, we show that dense accuracy can be recovered at 0. 6 sparsity for various models. At higher sparsity, our algorithm can still match the accuracy of nonstructured sparse training in most cases, while reducing the training time by up to 5x due to the fine-grained block structures in the models.

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AAAI Conference 2022 Conference Paper

GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-supervised Learning and Explicit Policy Injection

Wanwei He
Yinpei Dai
Yinhe Zheng
Yuchuan Wu
Zheng Cao
Dermot Liu
Peng Jiang
Min Yang

Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems. However, current pre-training methods mainly focus on enhancing dialog understanding and generation tasks while neglecting the exploitation of dialog policy. In this paper, we propose GALAXY, a novel pre-trained dialog model that explicitly learns dialog policy from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised learning. Specifically, we introduce a dialog act prediction task for policy optimization during pre-training and employ a consistency regularization term to refine the learned representation with the help of unlabeled dialogs. We also implement a gating mechanism to weigh suitable unlabeled dialog samples. Empirical results show that GALAXY substantially improves the performance of task-oriented dialog systems, and achieves new state-of-the-art results on benchmark datasets: In-Car, MultiWOZ2. 0 and Multi- WOZ2. 1, improving their end-to-end combined scores by 2. 5, 5. 3 and 5. 5 points, respectively. We also show that GALAXY has a stronger few-shot ability than existing models under various low-resource settings. For reproducibility, we release the code and data at https: //github. com/siat-nlp/GALAXY.

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TCS Journal 2021 Journal Article

Generic construction for tightly-secure signatures from discrete log

Jianchang Lai
Ge Wu
Peng Jiang
Zhen Zhao
Willy Susilo
Fuchun Guo

Tightly secure signature plays a significant role in the research of cryptography and has been studied extensively in the literature. In this paper, we present a generic construction for tightly-secure signatures from the discrete log (DL) assumption in the existential-unforgeability against key only attacks (EUF-KOA) security model, where the adversary is allowed to obtain only the public key, but not any sample signature. Moreover, the generic construction can also be extended into the multi-user setting with corruptions (MU-C) model. Roughly speaking, given any signature scheme, we can efficiently convert it into a signature scheme that features tight security under the DL assumption in the MU-EUF-KOA-C security model with random oracles. Our transformation shows it is easy to construct a DL-equivalent signature in the EUF-KOA security model, although many known DL-based signatures are not equivalent to DL. If the given signature scheme is key-re-randomizable, the transformed scheme is also key-re-randomizable. Hence, our result provides a supplement to Bader et al. 's work (EUROCRYPT 2016).

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TCS Journal 2019 Journal Article

Efficient identity-based broadcast encryption with keyword search against insider attacks for database systems

Peng Jiang
Fuchun Guo
Yi Mu

Public key encryption with keyword search (PEKS) is a promising technique for data confidentiality and utilization. In cloud database systems, the user (sender) could want to share his data with multiple users (receivers), while the ciphertext complexity increases linearly with the number of receivers if directly employing PEKS. Also, the security of PEKS is weakly defined in presence of outsider attacks, where the untrusted server infers the keyword in trapdoor by insider attacks. Therefore, how to reduce the size of the ciphertext and resist insider attacks remain unresolved. In this paper, we propose the first efficient identity-based broadcast encryption with keyword search against insider attacks (IBEKS-IA) for cloud database systems. We build the IBEKS-IA framework to provide data retrieval for multiple receivers and resist insider attacks. Based on this framework, we construct an IBEKS-IA scheme featuring with constant size ciphertext, which is proved to be semantically secure against the chosen keyword attack and the server keyword guessing attack. We further give an extended version featuring with both the constant size ciphertext and constant size online trapdoor. Performance evaluation presents the practicality of the proposal in the cloud database systems.

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NeurIPS Conference 2018 Conference Paper

A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication

Peng Jiang
Gagan Agrawal

The large communication overhead has imposed a bottleneck on the performance of distributed Stochastic Gradient Descent (SGD) for training deep neural networks. Previous works have demonstrated the potential of using gradient sparsification and quantization to reduce the communication cost. However, there is still a lack of understanding about how sparse and quantized communication affects the convergence rate of the training algorithm. In this paper, we study the convergence rate of distributed SGD for non-convex optimization with two communication reducing strategies: sparse parameter averaging and gradient quantization. We show that $O(1/\sqrt{MK})$ convergence rate can be achieved if the sparsification and quantization hyperparameters are configured properly. We also propose a strategy called periodic quantized averaging (PQASGD) that further reduces the communication cost while preserving the $O(1/\sqrt{MK})$ convergence rate. Our evaluation validates our theoretical results and shows that our PQASGD can converge as fast as full-communication SGD with only $3\%-5\%$ communication data size.

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NeurIPS Conference 2018 Conference Paper

DifNet: Semantic Segmentation by Diffusion Networks

Peng Jiang
Fanglin Gu
Yunhai Wang
Changhe Tu
Baoquan Chen

Deep Neural Networks (DNNs) have recently shown state of the art performance on semantic segmentation tasks, however, they still suffer from problems of poor boundary localization and spatial fragmented predictions. The difficulties lie in the requirement of making dense predictions from a long path model all at once since details are hard to keep when data goes through deeper layers. Instead, in this work, we decompose this difficult task into two relative simple sub-tasks: seed detection which is required to predict initial predictions without the need of wholeness and preciseness, and similarity estimation which measures the possibility of any two nodes belong to the same class without the need of knowing which class they are. We use one branch network for one sub-task each, and apply a cascade of random walks base on hierarchical semantics to approximate a complex diffusion process which propagates seed information to the whole image according to the estimated similarities. The proposed DifNet consistently produces improvements over the baseline models with the same depth and with the equivalent number of parameters, and also achieves promising performance on Pascal VOC and Pascal Context dataset. OurDifNet is trained end-to-end without complex loss functions.

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