OpenRaiser/Intern-Atlas huggingface dataset:
AI Model Modulation with Logits Redistribution
Authors: Zihan Wang, Zhongkui Ma, Xinguo Feng, Zhiyang Mei, Ethan Ma, Derui Wang, Minhui Xue, Guangdong Bai
Venue: WWW
Year: 2025

Large-scale models are typically adapted to meet the diverse requirements of model owners and users. However, maintaining multiple specialized versions of the model is inefficient. In response, we propose AIM, a novel model modulation paradigm that enables a single model to exhibit diverse behaviors to meet the specific end requirements. AIM enables two key modulation modes: utility and focus modulations. The former provides model owners with dynamic control over output quality to deliver varying utility levels, and the latter offers users precise control to shift model's focused input features. AIM introduces a logits redistribution strategy that operates in a training data-agnostic and retraining-free manner. We establish a formal foundation to ensure AIM's regulation capability, based on the statistical properties of logits ordering via joint probability distributions. Our evaluation confirms AIM's practicality and versatility for AI model modulation, with tasks spanning image classification, semantic segmentation and text generation, and prevalent architectures including ResNet, SegFormer and Llama.
Gamblers or Delegatees: Identifying Hidden Participant Roles in Crypto Casinos
Authors: Jiaxin Wang, Qian&apos, ang Mao, Hongliang Sun, Jiaqi Yan
Venue: WWW
Year: 2025

With the development of blockchain technology, crypto gambling has gained popularity due to its high level of anonymity. However, similar to traditional casinos, crypto casinos are controlled by a few internal Delegatees, making it impossible for them to achieve complete transparency and fairness. These delegatees are hidden among gamblers and are difficult to identify and distinguish in anonymous and large-scale blockchain transaction networks. This paper proposes an unsupervised dual-stage role identification method to adaptively identify key roles and hidden delegatees in label-sparse crypto casinos. Specifically, inspired by voting-style transaction patterns, we propose a novel voting influence metric for key node identification. This metric is based on one-dimensional structural entropy to capture global dissemination capability. Subsequently, we develop a multi-view graph neural network framework enhanced with two-dimensional global structural entropy minimization and self-supervised contrastive learning to improve the robustness and interpretability of hidden role partitioning. Experiments on real-world cases of the most mainstream blockchains-Ethereum, TRON, and Arbitrum-demonstrate that our proposed method effectively reveals distinct role compositions and collusion patterns, distinguishing between gamblers and delegatees. Our results achieve a higher match with identities confirmed by judicial authorities than existing methods, indicating the effectiveness and generalizability of our approach in enhancing security and regulation oversight.
Poisoning Attacks and Defenses to Federated Unlearning
Authors: Wenbin Wang, Qiwen Ma, Zifan Zhang, Yuchen Liu, Zhuqing Liu, Minghong Fang
Venue: WWW
Year: 2025

Federated learning allows multiple clients to collaboratively train a global model with the assistance of a server. However, its distributed nature makes it susceptible to poisoning attacks, where malicious clients can compromise the global model by sending harmful local model updates to the server. To unlearn an accurate global model from a poisoned one after identifying malicious clients, federated unlearning has been introduced. Yet, current research on federated unlearning has primarily concentrated on its effectiveness and efficiency, overlooking the security challenges it presents. In this work, we bridge the gap via proposing BadUnlearn, the first poisoning attacks targeting federated unlearning. In BadUnlearn, malicious clients send specifically designed local model updates to the server during the unlearning process, aiming to ensure that the resulting unlearned model remains poisoned. To mitigate these threats, we propose UnlearnGuard, a robust federated unlearning framework that is provably robust against both existing poisoning attacks and our BadUnlearn. The core concept of UnlearnGuard is for the server to estimate the clients' local model updates during the unlearning process and employ a filtering strategy to verify the accuracy of these estimations. Theoretically, we prove that the model unlearned through UnlearnGuard closely resembles one obtained by train-from-scratch. Empirically, we show that BadUnlearn can effectively corrupt existing federated unlearning methods, while UnlearnGuard remains secure against poisoning attacks.
AutoDive+: An Adaptive Model Enhanced Multimodal Online Annotation Tool
Authors: Ludi Wang, Dongze Song, Qiang Cui, Xueqing Chen, Yuanchun Zhou, Wenjuan Cui, Yi Du
Venue: WWW
Year: 2025

The demand for data by machine learning models, especially pre-trained large-scale models, has surged dramatically in recent times, resulting in an urgent need for efficient data annotation. Despite the prevalence of annotation tools, they grapple with challenges such as high data conversion costs, limited scalability, and inefficiencies in annotating multimodal data. To tackle these challenges, this paper introduces AutoDive+, an advanced data annotation tool empowered by active learning mechanisms and integrated automatic extraction models. Expanding upon the capabilities of its predecessor, AutoDive, which excels in direct text annotation within PDF documents, AutoDive+ undergoes extensive architectural enhancements. It not only exploits high-value resource ranking modules to boost annotation efficiency across entity and content annotation but also boasts scalable interfaces for seamless integration of new annotation modes and modalities. Furthermore, our novel model community concept facilitates adaptable integration of appropriate automatic extraction and annotation models. We substantiate the effectiveness of our proposed modules through user studies spanning diverse domains. Our vision entails the establishment of an annotation task-driven open community, bolstered by the support of AutoDive+. A live demo of Autodive+ is available at http://autodive.sciwiki.cn/, and a video demo http://autodive.sciwiki.cn/introVideo/introduce-v2.0.mp4. The source code is available at https://github.com/Autodive.
AIArena: A Blockchain-Based Decentralized AI Training Platform
Authors: Zhipeng Wang, Rui Sun, Elizabeth Lui, Tuo Zhou, Yizhe Wen, Jiahao Sun
Venue: WWW
Year: 2025

The rapid advancement of AI has underscored critical challenges in its development and implementation, largely due to centralized control by a few major corporations. This concentration of power intensifies biases within AI models, resulting from inadequate governance and oversight mechanisms. Additionally, it limits public involvement and heightens concerns about the integrity of model generation. Such monopolistic control over data and AI outputs threatens both innovation and fair data usage, as users inadvertently contribute data that primarily benefits these corporations. In this work, we propose AIArena, a blockchain-based decentralized AI training platform designed to democratize AI development and alignment through on-chain incentive mechanisms. AIArena fosters an open and collaborative environment where participants can contribute models and computing resources. Its on-chain consensus mechanism ensures fair rewards for participants based on their contributions. We instantiate and implement AIArena on the public Base blockchain Sepolia testnet, and the evaluation results demonstrate the feasibility of AIArena in real-world applications. Our project page is available at https://train.flock.io/explore.
TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning
Authors: Weiqi Wang, Zhiyi Tian, An Liu, Shui Yu
Venue: WWW
Year: 2025

With the increasing prevalence of Web-based platforms handling vast amounts of user data, machine unlearning has emerged as a crucial mechanism to uphold users' right to be forgotten, enabling individuals to request the removal of their specified data from trained models. However, the auditing of machine unlearning processes remains significantly underexplored. Although some existing methods offer unlearning auditing by leveraging backdoors, these backdoor-based approaches are inefficient and impractical, as they necessitate involvement in the initial model training process to embed the backdoors. In this paper, we propose a TAilored Posterior diffErence (TAPE) method to provide unlearning auditing independently of original model training. We observe that the process of machine unlearning inherently introduces changes in the model, which contains information related to the erased data. TAPE leverages unlearning model differences to assess how much information has been removed through the unlearning operation. Firstly, TAPE mimics the unlearned posterior differences by quickly building unlearned shadow models based on first-order influence estimation. Secondly, we train a Reconstructor model to extract and evaluate the private information of the unlearned posterior differences to audit unlearning. Existing privacy reconstructing methods based on posterior differences are only feasible for model updates of a single sample. To enable the reconstruction effective for multi-sample unlearning requests, we propose two strategies, unlearned data perturbation and unlearned influence-based division, to augment the posterior difference. Extensive experimental results indicate the significant superiority of TAPE over the state-of-the-art unlearning verification methods, at least 4.5x efficiency speedup and supporting the auditing for broader unlearning scenarios.
ShapeShifter: Workload-Aware Adaptive Evolving Index Structures Based on Learned Models
Authors: Hui Wang, Xin Wang, Jiake Ge, Lei Liang, Peng Yi
Venue: WWW
Year: 2025

In real-world tasks like data management and Web search, index operations often exhibit strong skewness, unlike standard benchmarks with uniform data distribution. While learned indexes improve query and update efficiency, they typically fail to address the skewed workload access, often prioritizing a single performance metric at the cost of overall index effectiveness. Additionally, the full reliance on learned models can increase vulnerability to attacks, compromising system stability. To address these challenges, we propose ShapeShifter, an adaptive evolutionary structure based on traditional indexes, capable of dynamically adjusting node structures according to the workload. ShapeShifter introduces a node evolution strategy with workload-skew-aware policies to adaptively adjust and optimize the partial index structure, leveraging a hybrid mechanism that combines traditional and learned structures for robust performance and optimal time-space tradeoff under skewed workloads and extreme data conditions. The evaluation results show that ShapeShifter achieves the optimal tradeoff while maintaining robustness.
P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Network
Authors: Zheng Wang, Wanwan Wang, Yimin Huang, Zhaopeng Peng, Ziqi Yang, Ming Yao, Cheng Wang, Xiaoliang Fan
Venue: WWW
Year: 2025

In recent years, graph neural networks (GNNs) have been commonly utilized for social recommendation systems. However, real-world scenarios often present challenges related to user privacy and business constraints, inhibiting direct access to valuable social information from other platforms. While many existing methods have tackled matrix factorization-based social recommendations without direct social data access, developing GNN-based federated social recommendation models under similar conditions remains largely unexplored. To address this issue, we propose a novel vertical federated social recommendation method leveraging privacy-preserving two-party graph convolution networks (P4GCN) to enhance recommendation accuracy without requiring direct access to sensitive social information. First, we introduce a Sandwich-Encryption module to ensure comprehensive data privacy during the collaborative computing process. Second, we provide a thorough theoretical analysis of the privacy guarantees, considering the participation of both curious and honest parties. Extensive experiments on four real-world datasets demonstrate that P4GCN outperforms state-of-the-art methods in terms of recommendation accuracy.
NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized Recommendation Systems
Authors: Shuli Wang, Xue Wei, Senjie Kou, Chi Wang, Wenshuai Chen, Qi Tang, Yinhua Zhu, Xiong Xiao, Xingxing Wang
Venue: WWW
Year: 2025

Reranking plays a crucial role in modern multi-stage recommender systems by rearranging the initial ranking list. Due to the inherent challenges of combinatorial search spaces, some current research adopts an evaluator-generator paradigm, with a generator generating feasible sequences and an evaluator selecting the best sequence based on the estimated list utility. However, these methods still face two issues. Firstly, due to the goal inconsistency problem between the evaluator and generator, the generator tends to fit the local optimal solution of exposure distribution rather than combinatorial space optimization. Secondly, the strategy of generating target items one by one is difficult to achieve optimality because it ignores the information of subsequent items. To address these issues, we propose a utilizing Neighbor Lists model for Generative Reranking (NLGR), which aims to improve the performance of the generator in the combinatorial space. NLGR follows the evaluator-generator paradigm and improves the generator's training and generating methods. Specifically, we use neighbor lists in combination space to enhance the training process, making the generator perceive the relative scores and find the optimization direction. Furthermore, we propose a novel sampling-based non-autoregressive generation method, which allows the generator to jump flexibly from the current list to any neighbor list. Extensive experiments on public and industrial datasets validate NLGR's effectiveness and we have successfully deployed NLGR on the Meituan food delivery platform.
Hierarchical Prompt Decision Transformer: Improving Few-Shot Policy Generalization with Global and Adaptive Guidance
Authors: Zhe Wang, Haozhu Wang, Yanjun Qi
Venue: WWW
Year: 2025

Decision transformers recast reinforcement learning as a conditional sequence generation problem, offering a simple but effective alternative to traditional value or policy-based methods. A recent key development in this area is the integration of prompting in decision transformers to facilitate few-shot policy generalization. However, current methods mainly use static prompt segments to guide rollouts, limiting their ability to provide context-specific guidance. Addressing this, we introduce a hierarchical prompting approach enabled by retrieval augmentation. Our method learns two layers of soft tokens as guiding prompts: (1) global tokens encapsulating task-level information about trajectories, and (2) adaptive tokens that deliver focused, timestep-specific instructions. The adaptive tokens are dynamically retrieved from a curated set of demonstration segments, ensuring context-aware guidance. Experiments across seven benchmark tasks in the MuJoCo and MetaWorld environments demonstrate the proposed approach consistently outperforms all baseline methods, suggesting that hierarchical prompting for decision transformers is an effective strategy to enable few-shot policy generalization.
Unlearning Incentivizes Learning under Privacy Risk
Authors: Qiyuan Wang, Ruiling Xu, Shibo He, Randall Berry, Meng Zhang
Venue: WWW
Year: 2025

While federated learning enables intelligent services and personalized user experiences, it raises privacy concerns due to regulatory requirements and user demands for data protection. Federated unlearning offers a potential solution to these issues. However, despite increasing demand for its practical implementation driven by right-to-be-forgotten regulations, the economic implications of federated unlearning on user behavior and platform profitability remain underexplored, potentially hindering its adoption. In this paper, we formulate a set of contract design problems for both unlearning-disabled and unlearning-enabled scenarios. Challenges arise when the unlearning-enabled platform jointly designs compensation for both learning and unlearning to incentivize users' sequential decisions to balance the expected revenue and unlearning cost. We first conduct a questionnaire survey that reveals that federated unlearning increases users' willingness to participate in federated learning. We then provide a necessary condition for maximizing the surplus of an unlearning-enabled platform, enabling the point-wise decomposition for the optimal contract design problem, based on which we minimize the incentive cost and maximize the surplus for the platform. Our further analysis reveals that i) the incentive effects of unlearning grow quadratically with users' privacy sensitivity, and ii) enabling unlearning may even profit more than disabling it when the training cost increases at a faster rate than the probability of privacy leakage as effort levels rise. Our numerical results show that the platform's profitability is primarily influenced by users' privacy sensitivity. When users have a relatively high privacy sensitivity, enabling unlearning can significantly improve profitability.
Unleashing the Potential of Two-Tower Models: Diffusion-Based Cross-Interaction for Large-Scale Matching
Authors: Yihan Wang, Fei Xiong, Zhexin Han, Qi Song, Kaiqiao Zhan, Ben Wang
Venue: WWW
Year: 2025

Two-tower models are widely adopted in the industrial-scale matching stage across a broad range of application domains, such as content recommendations, advertisement systems, and search engines. This model efficiently handles large-scale candidate item screening by separating user and item representations. However, the decoupling network also leads to a neglect of potential information interaction between the user and item representations. Current state-of-the-art (SOTA) approaches include adding a shallow fully connected layer(i.e., COLD), which is limited by performance and can only be used in the ranking stage. For performance considerations, another approach attempts to capture historical positive interaction information from the other tower by regarding them as the input features(i.e., DAT). Later research showed that the gains achieved by this method are still limited because of lacking the guidance on the next user intent. To address the aforementioned challenges, we propose a "cross-interaction decoupling architecture" within our matching paradigm. This user-tower architecture leverages a diffusion module to reconstruct the next positive intention representation and employs a mixed-attention module to facilitate comprehensive cross-interaction. During the next positive intention generation, we further enhance the accuracy of its reconstruction by explicitly extracting the temporal drift within user behavior sequences. Experiments on two real-world datasets and one industrial dataset demonstrate that our method outperforms the SOTA two-tower models significantly, and our diffusion approach outperforms other generative models in reconstructing item representations.
Privacy-Friendly Cross-Domain Recommendation via Distilling User-irrelevant Information
Authors: Cheng Wang, Wenchao Xu, Haozhao Wang, Wei Liu, Ruixuan Li
Venue: WWW
Year: 2025

Privacy-preserving Cross-Domain Recommendation (CDR) has been extensively studied to address the cold-start problem using auxiliary source domains while simultaneously protecting sensitive information. However, existing privacy-preserving CDR methods rely heavily on transferring sensitive user embeddings or behaviour logs, which leads to adopt privacy methods to distort the data patterns before transferring it to the target domain. The distorted information can compromise overall performance during the knowledge transfer process. To overcome these challenges, our approach differs from existing privacy-preserving methods that focus on safeguarding user-sensitive information. Instead, we concentrate on distilling transferable knowledge from insensitive item embeddings, which we refer to as prototypes. Specifically, we propose a conditional model inversion mechanism to accurately distill prototypes for individual users. We have designed a new data format and corresponding learning paradigm for distilling transferable prototypes from traditional recommendation models using model inversion. These prototypes facilitate bridging the domain shift between distinct source and target domains in a privacy-friendly manner. Additionally, they enable the identification of top-k users in the target domain to substitute for cold-start users prediction. We conduct extensive experiments across large real-world datasets, and the results substantiate the effectiveness of PFCDR https://github.com/walcheng/PFCDR..
MHG-Predictor: A Multi-Layer Heterogeneous Graph-based Predictor for Next Activity in Complex Business Processes
Authors: Jiaxing Wang, Yifeng Yu, Ning Fang, Bin Cao, Jing Fan, Ji Zhang
Venue: WWW
Year: 2025

Accurate prediction of the next activity in business processes is crucial for operational efficiency and decision making in modern enterprise systems. Existing methods often fail to capture the multi-layered structural dependencies inherent in event logs. To address this limitation, we propose the Multi-layer Heterogeneous Gaph-based Predictor (MHG-Predictor), where the key innovation lies in our multi-layer heterogeneous graph that decomposes process knowledge into four interconnected layers: global process dynamics, instance-level patterns, activities, and contextual attributes, thereby capturing both temporal and structural dependencies in process execution. To leverage this rich structural information, we develop a multi-layer graph convolutional network (GCN) that combines GatedGraphConv and HeteroSAGE, unified through the multi-layer perceptrons (MLPs) for next activity prediction. Extensive experiments on real-world datasets demonstrate that MHG-Predictor outperforms existing methods, with an average improvement of more than 2% across all evaluation metrics compared to the best baseline method. Furthermore, the four-layer heterogeneous graph has been successfully deployed in the Remote Banking Center's intelligent ticket generation system, achieving an accuracy of up to 80%, representing an approximately 5% improvement over the previous system. The source code is available at https://github.com/Joker3993/MHG-Predictor.
CubeRobot: Grounding Language in Rubik's Cube Manipulation via Vision-Language Model
Authors: Feiyang Wang, Xiaomin Yu, Wangyu Wu
Venue: WWW
Year: 2025

Proving Rubik's Cube theorems at the high level represents a notable milestone in human-level spatial imagination and logic thinking and reasoning. Traditional Rubik's Cube robots, relying on complex vision systems and fixed algorithms, often struggle to adapt to complex and dynamic scenarios. To overcome this limitation, we introduce CubeRobot, a novel vision-language model (VLM) tailored for solving 3x3 Rubik's Cubes, empowering embodied agents with multimodal understanding and execution capabilities. We used the CubeCoT image dataset, which contains multiple-level tasks (43 subtasks in total) that humans are unable to handle, encompassing various cube states. We incorporate a dual-loop VisionCoT architecture and Memory Stream, a paradigm for extracting task-related features from VLM-generated planning queries, thus enabling CubeRobot to independent planning, decision-making, reflection and separate management of high- and low-level Rubik's Cube tasks. Furthermore, in low-level Rubik's Cube restoration tasks, CubeRobot achieved a high accuracy rate of 100%, similar to 100% in medium-level tasks, and achieved an accuracy rate of 80% in high-level tasks.