OpenRaiser/Intern-Atlas huggingface dataset:
MERA: Mixture of Experts with Retrieval-Augmented Representation for Modeling Diversified Stock Patterns
Authors: YuJun Liu, Chen-Hui Song, Peiyuan Liu, Naiqi Li, Tao Dai, Jigang Bao, Yong Jiang, Shu-Tao Xia
Venue: WWW
Year: 2025

Successful quantitative investment relies on accurate predictions of the future stock price. Deep learning-based solutions have recently demonstrated a superior ability to capture the intricate and nonlinear interactions among various market variables. However, most existing methods use the same parameters to fit all samples, without considering that the real stock market often exhibits multiple patterns. To alleviate this issue, we propose a novel module called Mixture of Experts with Retrieval-Augmented Representation (MERA). Essentially, MERA consists of a set of independent experts for differentiated modeling as well as a GateNet that dynamically allocates data of different patterns to the most suitable experts. The model backbone is responsible for learning the coarse-grained representations for all stock patterns. Then, each expert in the MERA module focuses on the specific pattern and performs a more fine-grained analysis. However, accurate data allocation remains challenging due to the lack of explicit pattern identifiers. To overcome this, MERA retrieves relevant samples using high-level representations from self-supervised pre-training. The label information of neighbor samples is promising discriminative signals to indicate the target stock pattern. Extensive experiments on real-world stock markets show significant improvements. Our code is released at https://github.com/chenchen1104/MERA.
Transfer Rule Learning over Large Knowledge Graphs
Authors: Hong Liu, Zhe Wang, Kewen Wang, Xiaowang Zhang, Zhiyong Feng
Venue: WWW
Year: 2025

Logical rules have been widely used for expressing schema knowledge in various practical applications. It is infeasible to handcraft rules from large knowledge graphs (KGs) and thus many methods have been proposed for learning rules automatically from KGs. However, it is largely ignored how to extract rules in a (target) KG from rules that already exist in some other (source) KGs. In this paper, we propose a framework for KG rule learning based on transfer learning. A major challenge for establishing such a framework is that a suitable alignment mechanism is required for mapping certain subgraph structures between predicates in the source KG and the target KG. Hence, our framework provides a new method for predicate mapping based on graph-structural similarity. The proposed framework can be used as a standalone rule learner but more importantly, it paves a new way for enhancing the state-of-the-art rule learners for large KGs. Extensive experiments are conducted to evaluate the new approach to rule learning, which shows that rules in smaller KGs can be effectively transferred to a large KG.
Know When to Fold: Futility-Aware Early Termination in Online Experiments
Authors: Yu Liu, Runzhe Wan, Yian Huang, James McQueen, Doug Hains, Jinxiang Gu, Rui Song
Venue: WWW
Year: 2025

As the demand for online A/B testing continues to rises for tech companies, the opportunity cost of conducting these experiments becomes increasingly significant. Consequently, there is a rising need for an efficient continuous monitoring system capable of early terminating experiments when necessary. Existing literature and tools primarily focuses on early terminating experiments with evidently significant results (demonstrated efficacy). However, for example, among the tens of thousands of online experiments conducted every year in Amazon, only a small proportion will meet launch criterion. To improve innovation efficiency and allow terminating experiments for futility, in this paper, we present a comprehensive literature review, propose new methods, and conduct a large-scale meta-analysis using historical online experiments in Amazon. This is the first such kind of study in the literature. We also delve into empirical challenges and explore various empirical strategies to handle them that we met while deploying these methods at Amazon. This paper is based on our work to develop the first such service for the largest online experiment platform at Amazon. Launched in 2024, this product is now available to thousands of labs on the platform each year and sends automatic notifications to experimenters with early termination recommendations. The product saves time for around 10% of labs, cuts about 2 weeks for each terminated lab, and reduces negative impact by several dozen basis points for ineffective or negative treatments.
Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models
Authors: Qi Liu, Bo Wang, Nan Wang, Jiaxin Mao
Venue: WWW
Year: 2025

Recent studies have demonstrated the effectiveness of using large language language models (LLMs) in passage ranking. The listwise approaches, such as RankGPT, have become new state-of-the-art in this task. However, the efficiency of RankGPT models is limited by the maximum context length and relatively high latency of LLM inference. To address these issues, in this paper, we propose PE-Rank, leveraging the single passage embedding as a good context compression for efficient listwise passage reranking. By treating each passage as a special token, we can directly input passage embeddings into LLMs, thereby reducing input length. Additionally, we introduce an inference method that dynamically constrains the decoding space to these special tokens, accelerating the decoding process. For adapting the model to reranking, we employ listwise learning to rank loss for training. Evaluation results on multiple benchmarks demonstrate that PE-Rank significantly improves efficiency in both prefilling and decoding, while maintaining competitive ranking effectiveness. The code is available at https://github.com/liuqi6777/pe_rank
FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities
Authors: Yi Liu, Cong Wang, Xingliang Yuan
Venue: WWW
Year: 2025

The Web of Things (WoT) enhances interoperability across web-based and ubiquitous computing platforms while complementing existing IoT standards. The multimodal Federated Learning (FL) paradigm has been introduced to enhance WoT by enabling the fusion of multi-source mobile sensing data while preserving privacy. However, a key challenge in mobile sensing systems using multimodal FL is modality incompleteness, where some modalities may be unavailable or only partially captured, potentially degrading the system's performance and reliability. Current multimodal FL frameworks typically train multiple unimodal FL subsystems or apply interpolation techniques on the node side to approximate missing modalities. However, these approaches overlook the shared latent feature space among incomplete modalities across different nodes and fail to discriminate against low-quality nodes. To address this gap, we present FedMobile, a new knowledge contribution-aware multimodal FL framework designed for robust learning despite missing modalities. FedMobile prioritizes local-to-global knowledge transfer, leveraging cross-node multimodal feature information to reconstruct missing features. It also enhances system performance and resilience to modality heterogeneity through rigorous node contribution assessments and knowledge contribution-aware aggregation rules. Empirical evaluations on five widely recognized multimodal benchmark datasets demonstrate that FedMobile maintains robust learning even when up to 90% of modality information is missing or when data from two modalities are randomly missing, outperforming state-of-the-art baselines. Our code and data are available at the https://doi.org/10.5281/zenodo.14802364 link.
Whitening-based Adversarial Bi-Regressor for Cross-platform Insurance Claims Prediction
Authors: Xiaoyu Liu, Yiqing Wu, Dingyuan Zhu, Yifan Wu, Daixin Wang, Xiaobo Qin, Fuzhen Zhuang
Venue: WWW
Year: 2025

Insurance claims prediction aims to predict claim amounts for auto-adjusting premiums. Deep learning has shown great potential in this field but typically requires large amounts of labeled data. In our cross-platform insurance claims business, obtaining labeled data is highly challenging due to privacy and confidentiality concerns. Our business focuses on providing trained models to partners (other insurance companies) without accessing their data labels. This creates a challenge: applying models trained on our private data directly to partners' data. This problem falls under Unsupervised Domain Adaptation (UDA). Additionally, since we aim to predict claim amounts, it specifically belongs to the Unsupervised Domain Adaptation Regression (UDAR) task.
Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains
Authors: Yunhui Liu, Qizhuo Xie, Jinwei Shi, Jiaxu Shen, Tieke He
Venue: WWW
Year: 2025

Heterogeneous Text-Attributed Graphs (HTAGs), where different types of entities are not only associated with texts but also connected by diverse relationships, have gained widespread popularity and application across various domains. However, current research on text-attributed graph learning predominantly focuses on homogeneous graphs, which feature a single node and edge type, thus leaving a gap in understanding how methods perform on HTAGs. One crucial reason is the lack of comprehensive HTAG datasets that offer original textual content and span multiple domains of varying sizes. To this end, we introduce a collection of challenging and diverse benchmark datasets for realistic and reproducible evaluation of machine learning models on HTAGs. Our HTAG datasets are multi-scale, span years in duration, and cover a wide range of domains, including movie, community question answering, academic, literature, and patent networks. We further conduct benchmark experiments on these datasets with various graph neural networks. All source data, dataset construction codes, processed HTAGs, data loaders, benchmark codes, and evaluation setup are publicly available at https://github.com/Cloudy1225/HTAG and https://huggingface.co/datasets/Cloudy1225/HTAG.
Federated Graph Anomaly Detection via Disentangled Representation Learning
Authors: Zhengyang Liu, Hang Yu, Xiangfeng Luo
Venue: WWW
Year: 2025

Graph anomaly detection plays a crucial role in identifying nodes that deviate significantly from normal patterns within a graph, with applications spanning various domains such as detection of authorship fraud and rumor propagation. Traditional methods primarily focus on aggregating information from neighboring nodes and reconstructing the central node based on these aggregated features. The anomaly degree is then calculated by comparing the reconstructed features with the original ones. Despite their effectiveness, these methods face limitations due to the constraints of device performance and the need to protect user privacy. In reality, graph data is often partitioned and distributed across different local clients, which leads to isolated client subgraphs. This partitioning results in incomplete feature aggregation, as the connections between subgraphs are missing, ultimately reducing the performance of anomaly detection models. To overcome these challenges, a federated graph anomaly detection approach based on disentangled representation learning is proposed. This method separates node features into two distinct components: intrinsic features and subgraph style features. By identifying outliers within the subgraph style features, a set of pseudo-nodes is generated and shared across the entire graph. These pseudo-nodes simulate connections between otherwise isolated subgraphs, which enables more comprehensive aggregation of intrinsic features from neighboring nodes. In addition, conditional variational autoencoders (CVAE) are employed alongside contrastive learning strategies to alleviate class imbalance and achieve effective feature disentanglement. These techniques help ensure that anomalous nodes are detected more accurately despite the inherent challenges of federated graph systems. Extensive experiments conducted on six diverse datasets provide compelling evidence of the proposed method's superior performance in federated graph anomaly detection, highlighting its ability to effectively handle incomplete graph structures while maintaining data privacy.
Towards Distributionally Balanced End-to-End Generative Retrieval
Authors: Yuxuan Liu, Tianchi Yang, Zihan Zhang, Minghui Song, Haizhen Huang, Weiwei Deng, Feng Sun, Qi Zhang
Venue: WWW
Year: 2025

Generative retrieval represents a promising paradigm in information retrieval, using seq2seq models to encode document features as parameters and decode relevant document identifiers (IDs) in response to search queries. Current generative retrieval methods typically rely on a pre-processing stage to assign static document IDs, which can introduce a semantic gap between ID assignments and retrieval objectives. However, optimizing both ID assignments and retrieval end-to-end is challenging, particularly due to the long-tailed distribution of real-world data that often leads to inefficient and unbalanced ID utilization. To address these challenges, we propose ASI++, an end-to-end generative retrieval approach that jointly optimizes balanced ID assignments and retrieval accuracy. ASI++ builds on the framework of vanilla ASI and introduces three novel criteria: a distribution balancing criterion to improve ID space utilization, a representation bottleneck criterion applied to the encoder to produce more distinguishable dense representations, and an information consistency criterion that aligns information retention with retrieval goals within an information-theoretic framework. We also examine various structures for learning ID assignments, such as neural quantization, differentiable product quantization, and residual quantization. Extensive offline experiments on public and industrial datasets demonstrate that ASI++ improves both retrieval performance and ID balance, with online results highlighting its significant business value in Microsoft Bing sponsored search.
One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction
Authors: Ben Liu, Jihai Zhang, Fangquan Lin, Xu Jia, Min Peng
Venue: WWW
Year: 2025

Large language models (LLMs) have been increasingly employed in various intelligent educational systems, simulating human tutors to facilitate effective human-machine interaction. However, previous studies often overlook the significance of recognizing and adapting to individual learner characteristics. Such adaptation is crucial for enhancing student engagement and learning efficiency, particularly in mathematics instruction, where diverse learning styles require personalized strategies to promote comprehension and enthusiasm. In this paper, we propose a PersonAlized Conversational tutoring agEnt (PACE) for mathematics instruction. PACE simulates students' learning styles based on the Felder and Silverman learning style model, aligning with each student's persona. In this way, our PACE can effectively assess the personality of students, allowing to develop individualized teaching strategies that resonate with their unique learning styles. To further enhance students' comprehension, PACE employs the Socratic teaching method to provide instant feedback and encourage deep thinking. By constructing personalized teaching data and training models, PACE demonstrates the ability to identify and adapt to the unique needs of each student, significantly improving the overall learning experience and outcomes. Moreover, we establish multi-aspect evaluation criteria and conduct extensive analysis to assess the performance of personalized teaching. Experimental results demonstrate the superiority of our model in personalizing the educational experience and motivating students compared to existing methods.
Fully Anonymous Decentralized Identity Supporting Threshold Traceability with Practical Blockchain
Authors: Yizhong Liu, Zedan Zhao, Boyu Zhao, Feiang Ran, Xun Lin, Dawei Li, Zhenyu Guan
Venue: WWW
Year: 2025

Decentralized identity (DID) holds significant potential for applications in Web3, such as digital markets and financial systems. Traditional DID paradigms offer a degree of privacy but struggle to prevent the link analysis on user behaviours and repeated public key usage. Anonymity is not fully achieved, as users' real identities or public keys are exposed to the issuing authority, while introducing high public key management complexity. Besides, existing anonymous credential schemes lack effective mechanisms for threshold traceability, not meeting Web3's distributed governance requirements. In this paper, we propose FADID-TT, a Fully Anonymous DID system supporting Threshold Tracing with practical blockchain, to tackle the above challenges. Firstly, we propose a distributed identity registration scheme based on secret sharing. A committee composed of distributed issuing authorities is responsible for issuing user's secret key shares and no single entity in the system can obtain a user's real identity or public key, achieving anonymity to authority. Moreover, we design a fully anonymous DID system combined with anonymous signatures and decentralized anonymous credentials (DAC). A service provider can only use the committee public key to verify a user identity, eliminating the need for user public keys, fully resisting link attacks, and reducing the user public key management complexity from O(n) to O(1). Furthermore, we design a publicly verifiable threshold tracing mechanism that enables committee members to collaboratively trace the identity of a malicious user without compromising privacy guarantees. FADID-TT realizes publicly verifiable tracing via zero-knowledge proofs. Finally, we give comprehensive security analysis and concrete performance evaluation. In addition to evaluate each part of proposal, we also deploy FADID-TT on two well-known blockchain platforms including Hyperledger Fabric (permissioned) and Ethereum (permissionless) to demonstrate the practical feasibility of FADID-TT.
Diversity-Augmented Diffusion Network With LLM Assistance For Radiology Report Generation
Authors: Jieting Long, Zhiyuan Li, Jianan Fan, Zhuonan Liang, Ao Ma, Henning Müller, Weidong Cai
Venue: WWW
Year: 2025

Radiology report generation (RRG) is a demanding yet challenging task that involves producing multi-sentence diagnostic narratives, requiring long-form text with high diversity while addressing inherent data bias. Sentence-level diversity is therefore crucial for capturing varying diagnostic details across multiple regions of interest (ROIs) within a single report, yet it remains underexplored in the field. In this paper, we propose DADNET, a novel diffusion-based framework that leverages the inherent ability of diffusion models to generate diverse text. We make the first attempt to integrate large language models (LLMs) to bridge the inherent training-inference gap in diffusion models. Specifically, LLMs are used to draft a preliminary report, which is subsequently incorporated into the diffusion process to enhance report diversity. Additionally, we introduce a bias equalization technique using domain-specific priors to mitigate data distribution biases, improving the quality and reliability of generated reports under various scenarios. Experimental results demonstrate that DADNET outperforms existing approaches under the same non-autoregressive (NAR) mechanism and sets a new benchmark for diversity in RRG. This work underscores the importance of diversity in RRG and establishes DADNET as a pioneering framework for addressing this challenge with NAR methods.
Bridging Culture and Finance: A Multimodal Analysis of Memecoins in the Web3 Ecosystem
Authors: Hou-Wan Long, Nga-Man Wong, Wei Cai
Venue: WWW
Year: 2025

Memecoins, driven by social media engagement and cultural narratives, have rapidly grown within the Web3 ecosystem. Unlike traditional cryptocurrencies, they are shaped by humor, memes, and community sentiment. This paper introduces the Coin-Meme dataset, an open-source collection of visual, textual, community, and financial data from the Pump.fun platform on the Solana blockchain. We also propose a multimodal framework to analyze memecoins, uncovering patterns in cultural themes, community interaction, and financial behavior. Through clustering, sentiment analysis, and word cloud visualizations, we identify distinct thematic groups centered on humor, animals, and political satire. Additionally, we provide financial insights by analyzing metrics such as Market Entry Time and Market Capitalization, offering a comprehensive view of memecoins as both cultural artifacts and financial instruments within Web3. The Coin-Meme dataset is publicly available at https://github.com/hwlongCUHK/Coin-Meme.git.
Simulating Question-answering Correctness with a Conditional Diffusion
Authors: Ting Long, Li&apos, ang Yin, Yi Chang, Wei Xia, Yong Yu
Venue: WWW
Year: 2025

A question-answering (QA) simulator is a model that simulates human students QA behaviors. By leveraging QA history to estimate the probability of correctly answering a newly recommended question, the simulator enables the educational recommender systems to be trained in a simulated environment, protecting human students from the potential negative impact of low-quality recommendations. Despite its significant importance, the construction of QA simulators has not been thoroughly explored in the research domain of AI. Previous methods mainly rely on existing knowledge tracing (KT) models to construct such a simulator. However, due to the discrepancy between the KT task and the simulation task, those KT-based simulators suffer from severe bias accumulation, which limits the effectiveness of the simulation. In this paper, we propose a method called Diffusion-based Simulator (DSim), which takes advantage of diffusion to alleviate the bias accumulation. To our knowledge, DSim is the first to focus on building a QA simulator.
EDGAR-CRAWLER: From Raw Web Documents to Structured Financial NLP Datasets
Authors: Lefteris Loukas, Fabian Billert, Manos Fergadiotis, Prodromos Malakasiotis, Ion Androutsopoulos
Venue: WWW
Year: 2025

Acquiring data for resource-limited domains like financial NLP presents numerous challenges. One rich web resource of textual financial data is EDGAR, the Electronic Data Gathering, Analysis, and Retrieval system, where U.S. legislation requires publicly traded companies to upload economic reports for investor transparency. However, automating the process of downloading EDGAR filings is not enough. These long-form documents, often exceeding 100 pages each, include noisy text, images, and tables, requiring significant manual preprocessing. Moreover, researchers often require only specific sections of these lengthy documents to solve specific NLP tasks, further complicating the use of these filings. To address these challenges, we present EDGAR-CRAWLER, the first Open-Source Software for automatically downloading and preprocessing EDGAR filings. Equipped with a variety of document mining options, EDGAR-CRAWLER can convert lengthy raw financial documents into clean and section-specific JSON files, which can be integrated seamlessly into NLP workflows. Supporting multiple filing types and offering extensive filtering options, EDGAR-CRAWLER facilitates the creation of structured large-scale datasets for financial research. Serving as a free alternative to premium web service data providers, EDGAR-CRAWLER has been widely adopted by both practitioners and academic researchers. Our software is available at https://github.com/lefterisloukas/edgar-crawler.