OpenRaiser/Intern-Atlas huggingface dataset:
Node2binary: Compact Graph Node Embeddings using Binary Vectors
Authors: Niloy Talukder, Croix Gyurek, Mohammad Al Hasan
Venue: WWW
Year: 2025

With the adoption of deep learning models to low-power, small-memory edge devices, energy consumption and storage usage of such models have become a key concern. The problem exacerbates even further with ever-growing data and equally-matched bulkier models. This concern is particularly pronounced for graph data due to its quadratic storage, irregular (non-grid) geometry, and very large size. Typical graph data, such as road networks, infrastructure networks, and social networks, easily exceeds millions of nodes, and several gigabytes of storage is needed just to store the node embedding vectors, let alone the model parameters. In recent years, the memory issue has been addressed by moving away from memory-intensive double precision floating-point arithmetic towards single-precision or even half-precision, often by trading-off marginally small performance. Along this effort, we propose Node2Binary, which embeds graph nodes in as few as 128 binary bits, thereby reducing the memory footprint of vertex embedding vectors by several orders of magnitude. Node2Binary. leverages a fast community detection algorithm to convert the given graph into a hierarchical partition tree and then find embeddings of graph vertices in binary space by solving a combinatorial optimization (CO) task over the tree edges. CO is NP-hard, but Node2Binary uses an innovative combination of discrete gradient descent and randomization to solve this task effectively and efficiently. Extensive experiments over four real-world graphs show that Node2Binary achieves competitive performance compared to the state-of-the art graph embedding methods in both node classification and link prediction tasks.
Meta-Agent-Workflow: Streamlining Tool Usage in LLMs through Workflow Construction, Retrieval, and Refinement
Authors: Xiaoyu Tan, Bin Li, Xihe Qiu, Chao Qu, Wei Chu, Yinghui Xu, Yuan Qi
Venue: WWW
Year: 2025

Large language models (LLMs) have recently shown significant advancements and are increasingly used as key components in automated agents for various web-based tasks. Typically, this agentization is achieved by carefully prompting LLMs to guide their behavior in using tools for specific tasks. However, this approach can be limited by the complexity of tasks and the inherent capabilities of LLMs. To enhance task-specific performance, a pre-defined workflow approach can be employed, reducing repetitive and error-prone planning for particular tasks. This workflow-driven process is especially well-suited for industrial applications, where task-specific agents can be easily configured using visual interfaces supported by various open-source platforms. In this paper, we introduce a novel framework called Meta-Agent-Workflowto create, retrieve, and refine agent workflows. Experiments on ToolBench demonstrate that our framework effectively transforms LLM tool-reasoning processes into task-specific workflows, retrieves workflows for different tasks based on various queries, and updates them based on execution feedback. We also open-source our code and follow the workflow architecture of an open-source agent platform (e.g., Dify) to facilitate further industrial and community use. The Meta-Agent-Workflow will be open-sourced in https://github.com/testlbin/meta_agent_workflows.
Large Language Model Empowered Logical Relations Mining for Personalized Recommendation
Authors: Yanchao Tan, Wanzi Shao, Guofang Ma, Carl Yang
Venue: WWW
Year: 2025

In personalized recommendations, users often express complex logical requirements, involving the intersection of multiple preferences over heterogeneous graphs containing users, items, and external knowledge. Existing methods for mining logical relations face challenges in scalability and often overlook the semantics of relations, which are essential for uncovering higher order connections and addressing incomplete relations within the graph. To tackle these challenges, we propose RelRec, a novel approach that leverages large language models (LLMs) to mine logical relations and satisfy users' logical requirements in personalized recommendation tasks. Specifically, the framework begins with the extraction of user-driven logical relations, followed by a rule-based logical relation mining module that integrates both semantic and structural information using the capabilities of LLMs. By uncovering higher-order logical relations, our approach effectively refines the heterogeneous graph for reasoning capacity and recommendation accuracy. Extensive experiments on real-world datasets demonstrate that RelRec significantly outperforms existing methods.
Luoling: An Immersive Cross-Modal Interactive Poem Creation Factory through Multi-Agent Collaboration
Authors: Tao Tan, Quan Tu, Songhao Wu, Hongda Sun, Chuanqi Cheng, Weikai Xu, Rui Yan
Venue: WWW
Year: 2025

Chinese poetry creation holds significant cultural and artistic value, but traditional methods are limited by attribute, text-only, or image-only inputs, often failing to capture the creator's detailed intents. Besides, the single-step generation will result in inconsistencies between the intents and the generated poem. In this work, we introduce Luoling, an immersive, cross-modal interactive poem creation factory with fine-grained, controllable attributes, driven by a multi-agent collaboration system. Luoling integrates text, image, and attribute fusion as inputs, enabling a richer, more expressive creative process. The interactive poem creation process is first introduced, where a large language model engages in proactive dialogue with the creator, guiding precise intent expression. Luoling is powered by a multi-agent collaboration system, including a poem writer, adviser, and rhythm checker, to refine the poem. Finally, the poem is delivered with the poet's tone using style-based text-to-speech technology, providing an authentic auditory experience. Luoling offers a more immersive environment, motivating creators to express their physical and spiritual feelings through compositions.
Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning
Authors: Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Xin Yuan, Wenjie Zhang
Venue: WWW
Year: 2025

Large Language Models (LLMs) have achieved impressive results in various tasks but struggle with hallucination problems and lack of relevant knowledge, especially in deep complex reasoning and knowledge-intensive tasks. Knowledge Graphs (KGs), which capture vast amounts of facts in a structured format, offer a reliable source of knowledge for reasoning. However, existing KG-based LLM reasoning methods face challenges like handling multi-hop reasoning, multi-entity questions, and effectively utilizing graph structures. To address these issues, we propose Paths-over-Graph (PoG), a novel method that enhances LLM reasoning by integrating knowledge reasoning paths from KGs, improving the interpretability and faithfulness of LLM outputs. PoG tackles multi-hop and multi-entity questions through a three-phase dynamic multi-hop path exploration, which combines the inherent knowledge of LLMs with factual knowledge from KGs. In order to improve the efficiency, PoG prunes irrelevant information from the graph exploration first and introduces efficient three-step pruning techniques that incorporate graph structures, LLM prompting, and a pre-trained language model (e.g., SBERT) to effectively narrow down the explored candidate paths. This ensures all reasoning paths contain highly relevant information captured from KGs, making the reasoning faithful and interpretable in problem-solving. PoG innovatively utilizes graph structure to prune the irrelevant noise and represents the first method to implement multi-entity deep path detection on KGs for LLM reasoning tasks. Comprehensive experiments on five benchmark KGQA datasets demonstrate PoG outperforms the state-of-the-art method ToG across GPT-3.5-Turbo and GPT-4, achieving an average accuracy improvement of 18.9%. Notably, PoG with GPT-3.5-Turbo surpasses ToG with GPT-4 by up to 23.9%.
A Framework to Assess Multilingual Vulnerabilities of LLMs
Authors: Likai Tang, Niruth Bogahawatta, Yasod Ginige, Jiarui Xu, Shixuan Sun, Surangika Ranathunga, Suranga Seneviratne
Venue: WWW
Year: 2025

Large Language Models (LLMs) are acquiring a wider range of capabilities, including understanding and responding in multiple languages. While they undergo safety training to prevent them from answering illegal questions, imbalances in training data and human evaluation resources can make these models more susceptible to attacks in low-resource languages (LRL). This paper proposes a framework to automatically assess the multilingual vulnerabilities of commonly used LLMs. Using our framework, we evaluated six LLMs across eight languages representing varying levels of resource availability. We validated the assessments generated by our automated framework through human evaluation in two languages, demonstrating that the framework's results align with human judgments in most cases. Our findings reveal vulnerabilities in LRL; however, these may pose minimal risk as they often stem from the model's poor performance, resulting in incoherent responses.
LREF: A Novel LLM-based Relevance Framework for E-commerce Search
Authors: Tian Tang, Zhixing Tian, Zhenyu Zhu, Chenyang Wang, Haiqing Hu, Guoyu Tang, Lin Liu, Sulong Xu
Venue: WWW
Year: 2025

Query and product relevance prediction is a critical component for ensuring a smooth user experience in e-commerce search. Traditional studies mainly focus on BERT-based models to assess the semantic relevance between queries and products. However, the discriminative paradigm and limited knowledge capacity of these approaches restrict their ability to comprehend the relevance between queries and products fully. With the rapid advancement of Large Language Models (LLMs), recent research has begun to explore their application to industrial search systems, as LLMs provide extensive world knowledge and flexible optimization for reasoning processes. Nonetheless, directly leveraging LLMs for relevance prediction tasks introduces new challenges, including a high demand for data quality, the necessity for meticulous optimization of reasoning processes, and an optimistic bias that can result in over-recall. To overcome the above problems, this paper proposes a novel framework called the LLM-based RElevance Framework(LREF) aimed at enhancing e-commerce search relevance. The framework comprises three main stages: supervised fine-tuning (SFT) with Data Selection, Multiple Chain of Thought (Multi-CoT) tuning, and Direct Preference Optimization (DPO) for de-biasing. We evaluate the performance of the framework through a series of offline experiments on large-scale real-world datasets, as well as online A/B testing. The results indicate significant improvements in both offline and online metrics. Ultimately, the model was deployed in a well-known e-commerce application, yielding substantial commercial benefits.
Dual Pairwise Pre-training and Prompt-tuning with Aligned Prototypes for Interbank Credit Rating
Authors: Jiehao Tang, Wenjun Wang, Dawei Cheng, Hui Zhao, Changjun Jiang
Venue: WWW
Year: 2025

In the global financial market, assessing bank credit ratings is essential for evaluating financial health, managing risk, and safeguarding systemic stability. While risk can transmit rapidly within the interbank lending network, timely incorporation of the latest financial disclosures to update bank ratings is vital in the swiftly evolving financial markets. However, existing approaches primarily conduct credit rating tasks using end-to-end models trained on historical financial data, thereby overlooking the staggered timing of financial disclosure from banks. Limited excavation of the credit rating records and the temporal distribution shifts existed in different financial periods still pose challenges to improving the accuracy of the credit rating tasks. To address these challenges, in this work we propose a Dual Pairwise pre-training and prompt Tuning framework with Aligned Prototypes (DPTAP) for interbank credit rating, which enables dynamic credit updates. Specifically, the dual pairwise pre-training strategy allows the framework to capture direction and distance discrepancies between rating categories. To alleviate the adverse impact of temporal distribution shifts in quarters, the latest financial features are prompted to dynamically map the patterns of the corresponding banks in the last quarter. Furthermore, we integrate rating guides from two consecutive quarters into a set of aligned prototypes to enhance supervision during the prompt tuning process. We conducted extensive experiments on a real-world bank dataset globally in the latest 8 years. The results demonstrate the superiority of our proposed framework over various competitive models, highlighting its notable capabilities in early warning and risk contagion forecasting.
TimeChain: A Secure and Decentralized Off-chain Storage System for IoT Time Series Data
Authors: Yixiao Teng, Jiamei Lv, Ziping Wang, Yi Gao, Wei Dong
Venue: WWW
Year: 2025

Blockchain-based distributed storage systems offer enhanced security, transparency, and lower costs compared to traditional centralized storage, making them ideal for peer-to-peer collaboration. However, with the trend towards the Web of Things (WoT), lower transaction speeds and higher computational requirements limit their access to high-density data such as IoT. To address this, we propose TimeChain, an efficient off-chain blockchain storage system for IoT time series data. TimeChain batches discrete time series data, storing only the hash value of each batch on-chain while keeping the complete data off-chain. This significantly reduces storage overhead on the blockchain and storage latency by 37.4 times. TimeChain adopts an adaptive packaging mechanism to reduce the additional latency in range queries by converting the batch processing problem into a graph partitioning problem. To reduce the overhead of node selection, TimeChain integrates a node selection mechanism based on consensus protocol, combining node selection and consensus processes together. TimeChain also proposes a Locality-Sensitive Hashing tree-based data integrity verification mechanism to reduce transmission size. Our evaluation shows a reduction in query latency by 64.6% and storage latency by 35.3% compared to existing systems.
Cross Platform MultiModal Retrieval Augmented Distillation for Code-Switched Content Understanding
Authors: Surendrabikram Thapa, Hariram Veeramani, Imran Razzak, Roy Ka-Wei Lee, Usman Naseem
Venue: WWW
Year: 2025

In the era of digital communication and social media, the proliferation of multimodal content, such as code-switched memes, has become a ubiquitous form of expression. This phenomenon is especially significant for low-resource languages like Nepali, where the need for sentiment analysis and hate speech detection remains unmet due to the unavailability of publicly available datasets. To address this gap, we provide ENeMeme, an annotated dataset of 4,211 code-switched memes in the Nepali-English language for sentiment and hate speech. While the previous state-of-the-art methods of meme analysis particularly focus on high-resource language, they fail to perform well in low-resource language. To bridge this gap, our paper also builds on existing literature to adapt a novel multimodal model, MM-RAD, designed to understand code-switched Nepali-English memes, leveraging both textual and visual content. The model's effectiveness is analyzed across various retrieval platforms. Our proposed MM-RAD demonstrates superior performance in sentiment analysis and hate speech detection compared to individual baseline models. The dataset can be availed through https://github.com/therealthapa/crossplatform
Towards Collaborative Anti-Money Laundering Among Financial Institutions
Authors: Zhihua Tian, Yuan Ding, Xiang Yu, Enchao Gong, Jian Liu, Kui Ren
Venue: WWW
Year: 2025

Money laundering is the process that intends to legalize the income derived from illicit activities, thus facilitating their entry into the monetary flow of the economy without jeopardizing their source. It is crucial to identify such activities accurately and reliably in order to enforce anti-money laundering (AML). Despite considerable efforts to AML, a large number of such activities still go undetected. Rule-based methods were first introduced and are still widely used in current detection systems. With the rise of machine learning, graph-based learning methods have gained prominence in detecting illicit accounts through the analysis of money transfer graphs. Nevertheless, these methods generally assume that the transaction graph is centralized, whereas in practice, money laundering activities usually span multiple financial institutions. Due to regulatory, legal, commercial, and customer privacy concerns, institutions tend not to share data, restricting their utility in practical usage. In this paper, we propose the first algorithm that supports performing AML over multiple institutions while protecting the security and privacy of local data. To evaluate, we construct Alipay-ECB, a real-world dataset comprising digital transactions from Alipay, the world's largest mobile payment platform, alongside transactions from E-Commerce Bank (ECB). The dataset includes over 200 million accounts and 300 million transactions, covering both intra-institution transactions and those between Alipay and ECB. This makes it the largest real-world transaction graph available for analysis. The experimental results demonstrate that our methods can effectively identify cross-institution money laundering subgroups. Additionally, experiments on synthetic datasets also demonstrate that our method is efficient, requiring only a few minutes on datasets with millions of transactions. Our code and dataset are available on https://github.com/zhihuat/Collaborative-AML.
Towards Explainable Search Results in E-commerce
Authors: Xianyang Tian, Xiang Xu, Chao Wang, Tong Ruan, Baohua Wu, Maofei Que, Shenghua Ni, Zhuoran Zhuang, Jingping Liu
Venue: WWW
Year: 2025

Search result explanations are essential in E-commerce, helping users understand the relevance of the returned results. Existing methods primarily focus on explaining relevance based on either product content or behavioral data. However, we argue that combining both content and behavior data can provide more comprehensive and accurate explanations. In this paper, we propose a novel approach to generate relevance explanations. First, we utilize the content data to train a domain-specific large language model (LLM) that generates relevance labels and reasoning processes for queries and items. Then, we introduce the BehaviorRAG framework to retrieve behavioral data related to queries and items, allowing the model to generate explainable reasons for their relevance. Finally, the LLM integrates outputs from both the content- and behavior-based modules to produce a final explanation. To evaluate the effectiveness of our methods, we conduct extensive experiments on both our built dataset and publicly available datasets. The results demonstrate that our method outperforms current state-of-the-art baselines in predicting relevance and generating explainable reasons. Furthermore, online A/B testing on the Fliggy app demonstrates that incorporating explanations generated by our approach into the search results leads to a 2.30% increase in the Unique Visitors List to Order. The codes are publicly available at https://github.com/tonnyaudio/explainable.
sQuIrRel: Large-Scale Evaluation of E-commerce Query Classification Models
Authors: Anna Tigunova, Ghadir Eraisha, Damla Ezgi Akçora
Venue: WWW
Year: 2025

Query-to-Product Type (Q2PT) is a crucial e-commerce query understanding signal, which directly influences search results relevance and customer UX experience. This imposes high standards on the industrial Q2PT classification models, which have to be regularly monitored for quality among all predicted product types and use cases at scale. Existing solutions for such Q2PT model evaluation involve human-labeled datasets, which are usually small-scale and are costly to collect and refresh. Moreover, it is unrealistic to create ample human annotations for all e-commerce product categories, which can span several thousands. To address these drawbacks, we propose a method sQuIrRel (Query Intent from Relevance) to automatically collect an evaluation dataset for monitoring e-commerce query classification models, which ensures large-scale analysis and full coverage of all existing category labels. sQuIrRel is constructed using distant supervision from a high-precision query-item relevance classifier, allowing to quickly collect and refresh query labels at scale. While sQuIrRel method can be applied to any query classification task, across various e-commerce stores, our study focuses on using sQuIrRel for Q2PT prediction. We provide comparisons with alternative dataset collection methods and show how the obtained dataset can be used to analyze the performance of a commercial Q2PT model.
BeyondFacts 2025: 5th International Workshop on Computational Methods for Online Discourse Analysis
Authors: Konstantin Todorov, Pavlos Fafalios, Stefan Dietze, Dimitar Dimitrov
Venue: WWW
Year: 2025

This workshop explores the intersection of computational and interdisciplinary approaches to analyzing online discourse, including claims, arguments, and opinions on controversial topics. With the rise of mis- and disinformation, bias, and echo chambers, NLP-based methods such as argument mining, stance detection, and fact verification have become essential. However, these tasks require robust conceptual foundations across fields like communication studies, computational linguistics, and computer science. BeyondFacts fosters collaboration among diverse research communities-including social sciences, political science, computational journalism, and computer science-to enhance machine-interpretation and analysis of societal debates using techniques from Web mining, AI, and NLP.
Ploutos: Towards Explainable Stock Movement Prediction with Financial Large Language Model
Authors: Hanshuang Tong, Jun Li, Ning Wu, Ming Gong, Dongmei Zhang, Qi Zhang
Venue: WWW
Year: 2025

Recent advancements in large language models (LLMs) have opened new pathways for many domains. However, the full potential of LLMs in financial investments remains largely untapped. There are two main challenges for typical deep learning-based methods for quantitative finance. First, they struggle to fuse textual and numerical information flexibly for stock movement prediction. Second, traditional methods lack clarity and explainability, which impedes their application in scenarios where the justification for predictions is essential. To solve the above challenges, we propose Ploutos, a novel financial LLM framework that consists of PloutosGen and PloutosGPT. The PloutosGen contains multiple primary experts that can analyze different modal data, such as text and numbers, and provide quantitative strategies from different perspectives. Then PloutosGPT combines their insights and predictions and generates explainable rationales. To generate accurate and faithful rationales, the training strategy of PloutosGPT leverages a rearview-mirror prompting mechanism to guide GPT-4 to generate rationales and a dynamic token weighting mechanism to finetune LLM by detecting and emphasizing key tokens in rationales. Extensive experiments show our framework outperforms the state-of-the-art methods on both prediction accuracy and explainability.