OpenRaiser/Intern-Atlas huggingface dataset:
SuiGPT MAD: Move AI Decompiler to Improve Transparency and Auditability on Non-Open-Source Blockchain Smart Contract
Authors: Eason Chen, Xinyi Tang, Zimo Xiao, Chuangji Li, Shizhuo Li, Tingguan Wu, Siyun Wang, Kostas Kryptos Chalkias
Venue: WWW
Year: 2025

The vision of Web3 is to improve user control over data and assets, but one challenge that complicates this vision is the prevalence of non-transparent, scam-prone applications and vulnerable smart contracts that put Web3 users at risk. While code audits are one solution to this problem, the lack of smart contracts source code on many blockchain platforms, such as Sui, hinders the ease of auditing. A promising approach to this issue is the use of a decompiler to reverse-engineer smart contract bytecode. However, existing decompilers for Sui produce code that is difficult to understand and cannot be directly recompiled. To address this, we developed the SuiGPT Move AI Decompiler (MAD), a Large Language Model (LLM)-powered web application that decompiles smart contract bytecodes on Sui into logically correct, human-readable, and re-compilable source code with prompt engineering. Our evaluation shows that MAD's output successfully passes original unit tests and achieves a 73.33% recompilation success rate on real-world smart contracts. Additionally, newer models tend to deliver improved performance, suggesting that MAD's approach will become increasingly effective as LLMs continue to advance. In a user study involving 12 developers, we found that MAD significantly reduced the auditing workload compared to using traditional decompilers. Participants found MAD's outputs comparable to the original source code, improving accessibility for understanding and auditing non-open-source smart contracts. Through qualitative interviews with these developers and Web3 projects, we further discussed the strengths and concerns of MAD. MAD has practical implications for blockchain smart contract transparency, auditing, and education. It empowers users to easily and independently review and audit non-open-source smart contracts, fostering accountability and decentralization. Moreover, MAD's methodology could potentially extend to other smart contract languages, like Solidity, further enhancing Web3 transparency.
Query Design for Crowdsourced Clustering: Effect of Cognitive Overload and Contextual Bias
Authors: Yi Chen, Ramya Korlakai Vinayak
Venue: WWW
Year: 2025

Crowdsourced clustering leverages human input to group items into clusters. The design of tasks for crowdworkers, specifically the number of items presented per query, impacts answer quality and cognitive load. This work investigates the trade-off between query size and answer accuracy, revealing diminishing returns beyond 4-5 items per query. Crucially, we identify contextual bias in crowdworker responses - the likelihood of grouping items depends not only on their similarity but also on the other items present in the query. This structured noise contradicts assumptions made in existing noise models. Our findings underscore the need for more nuanced noise models that account for the complex interplay between items and query context in crowdsourced clustering tasks.
A Districting System for Sales Territory Design
Authors: Peilin Chen, Xulin Wang, Xin Shu, Yong Liu, Pan Wang
Venue: WWW
Year: 2025

The rapid growth of the Online-to-Offline (O2O) economy necessitates precise operational management, particularly in supply-side tasks such as merchant acquisition and maintenance. A critical challenge is the division of urban areas into districts for sales representatives, known as the Sales Territory Design Problem (STDP). The STDP involves partitioning a city into connected, balanced, and compact districts, promoting equitable workload distribution for sales teams. We address this challenge by formulating it as the Balanced Connected (k)-Partition Problem (BCPk ), a well-known NP-hard problem, with an added compactness objective. Existing approaches often struggle to scale effectively in large cities with large k. In this paper, we propose an efficient districting system for STDP comprising a hierarchy-aware map segmentation module and a block aggregation module. The map segmentation module partitions large geographic areas into small blocks using hierarchical road networks and other physical boundaries. The block aggregation module introduces a novel Mixed-Integer Programming (MIP) formulation of O(|V|) size, incorporating a global Polsby-Popper score to enhance district compactness. Our system demonstrates substantial improvements in computational efficiency and solution quality, and has been effectively deployed in real-world production environments.
Creator-Side Recommender System: Challenges, Designs, and Applications
Authors: Xiaoshuang Chen, Yibo Wang, Yao Wang, Husheng Liu, Kaiqiao Zhan, Ben Wang, Kun Gai
Venue: WWW
Year: 2025

Users and creators are two crucial components of recommender systems. Typical recommender systems focus on the user side, providing the most suitable items based on each user's request. In such scenarios, a few items receive a majority of exposures, while many items receive very few. This imbalance leads to poorer experiences and decreased activity among the creators receiving less feedback, harming the recommender system in the long term. To this end, we develop a creator-side recommender system, called DualRec, to answer the following question: How to find the most suitable users for each item to enhance the creators' experience? We show that typical user-side recommendation algorithms, such as retrieval and ranking algorithms, can be adapted into the creator-side versions with just a few modifications. This greatly simplifies algorithm design in DualRec. Moreover, we discuss a unique challenge in DualRec: the user availability issue, which is not present in user-side recommender systems. To tackle this issue, we incorporate a user availability calculation (UAC) module to effectively enhance DualRec's performance. DualRec has already been implemented in Kwai, a short video recommendation system with over 100 million users and over 10 million creators, significantly improving the experience for creators.
SheetAgent: Towards a Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Models
Authors: Yibin Chen, Yifu Yuan, Zeyu Zhang, Yan Zheng, Jinyi Liu, Fei Ni, Jianye Hao, Hangyu Mao, Fuzheng Zhang
Venue: WWW
Year: 2025

Spreadsheets are ubiquitous across the World Wide Web, playing a critical role in enhancing work efficiency across various domains. Large language model (LLM) has been recently attempted for automatic spreadsheet manipulation but has not yet been investigated in complicated and realistic tasks where reasoning challenges exist (e.g., long horizon manipulation with multi-step reasoning and ambiguous requirements). To bridge the gap with the real-world requirements, we introduce SheetRM, a benchmark featuring long-horizon and multi-category tasks with reasoning-dependent manipulation caused by real-life challenges. To mitigate the above challenges, we further propose SheetAgent, a novel autonomous agent that utilizes the power of LLMs. SheetAgent consists of three collaborative modules: Planner, Informer, and Retriever, achieving both advanced reasoning and accurate manipulation over spreadsheets without human interaction through iterative task reasoning and reflection. Extensive experiments demonstrate that SheetAgent delivers 20--40% pass rate improvements on multiple benchmarks over baselines, achieving enhanced precision in spreadsheet manipulation and demonstrating superior table reasoning abilities. More details and visualizations are available at the https://sheetagent.github.io/. The datasets and source code are available at https://anonymous.4open.science/r/SheetAgent.
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
Authors: Xuanze Chen, Jiajun Zhou, Shanqing Yu, Qi Xuan
Venue: WWW
Year: 2025

Graph neural networks excel at graph representation learning but struggle with heterophilous data and long-range dependencies. And graph transformers address these issues through self-attention, yet face scalability and noise challenges on large-scale graphs. To overcome these limitations, we propose GNNMoE, a universal model architecture for node classification. This architecture flexibly combines fine-grained message-passing operations with a mixture-of-experts mechanism to build feature encoding blocks. Furthermore, by incorporating soft and hard gating layers to assign the most suitable expert networks to each node, we enhance the model's expressive power and adaptability to different graph types. In addition, we introduce adaptive residual connections and an enhanced FFN module into GNNMoE, further improving the expressiveness of node representation. Extensive experimental results demonstrate that GNNMoE performs exceptionally well across various types of graph data, effectively alleviating the over-smoothing issue and global noise, enhancing model robustness and adaptability, while also ensuring computational efficiency on large-scale graphs.
Knowledge-Decoupled Synergetic Learning: An MLLM based Collaborative Approach to Few-shot Multimodal Dialogue Intention Recognition
Authors: Bin Chen, Yu Zhang, Hongfei Ye, Yizi Huang, Hongyang Chen
Venue: WWW
Year: 2025

Few-shot multimodal dialogue intention recognition is a critical challenge in the e-commerce domainn. Previous methods have primarily enhanced model classification capabilities through post-training techniques. However, our analysis reveals that training for few-shot multimodal dialogue intention recognition involves two interconnected tasks, leading to a seesaw effect in multi-task learning. This phenomenon is attributed to knowledge interference stemming from the superposition of weight matrix updates during the training process. To address these challenges, we propose Knowledge-Decoupled Synergetic Learning (KDSL), which mitigates these issues by utilizing smaller models to transform knowledge into interpretable rules, while applying the post-training of larger models. By facilitating collaboration between the large and small multimodal large language models for prediction, our approach demonstrates significant improvements. Notably, we achieve outstanding results on two real Taobao datasets, with enhancements of 6.37% and 6.28% in online weighted F1 scores compared to the state-of-the-art method, thereby validating the efficacy of our framework.
SCOOT: SLO-Oriented Performance Tuning for LLM Inference Engines
Authors: Ke Cheng, Zhi Wang, Wen Hu, Tiannuo Yang, Jianguo Li, Sheng Zhang
Venue: WWW
Year: 2025

As large language models (LLMs) are gaining increasing popularity across a wide range of web applications, it is of great importance to optimize service-level objectives (SLOs) for LLM inference services to enhance user satisfaction and improve the competitiveness of cloud vendors. In this paper, we observe that adjusting the parameters of LLM inference engines can improve service performance, and the optimal parameter configurations of different services are different. Therefore, we propose SCOOT, an automatic performance tuning system to optimize SLOs for each LLM inference service by tuning the parameters of the inference engine. SCOOT jointly exploits single-objective and multiple-objective Bayesian optimization (BO) techniques to handle various optimization objectives via exploration and exploitation. Moreover, SCOOT prunes the search space with known constraints and adopts a random forest to learn hidden constraints during the tuning process to mitigate invalid exploration. To improve the tuning efficiency, SCOOT utilizes the parallel suggestion to accelerate the tuning process. Extensive experiments demonstrate that SCOOT considerably outperforms existing tuning techniques in SLO optimization while greatly improving the tuning efficiency. Moreover, SCOOT is universally applicable to various LLM inference engines including vLLM and TensorRT-LLM. Currently, SCOOT has already been implemented in the production environment at Ant Group.
Backdoor Attack against Log Anomaly Detection Models
Authors: He Cheng, Depeng Xu, Shuhan Yuan
Venue: WWW
Year: 2025

Detecting anomalies in system logs has been an active research topic because of its importance in detecting system faults and novel attacks. Recently, many log anomaly detection approaches equipped with deep learning techniques have demonstrated great success. However, the vulnerability to backdoor attacks of these approaches is under-exploited. In this paper, we study how to inject a backdoor into self-supervised log anomaly detection models, i.e., making abnormal logs evade detection. To ensure stealth, we first design a trigger pattern without including any abnormal log entries. Then, we revise a learning objective that can inject the trigger into anomaly detection models. After deployment, if abnormal logs are hidden within the trigger, the backdoored log anomaly detection models could classify them as normal. We conduct backdoor attacks against two well-established self-supervised log anomaly detection models, DeepLog and LogBERT. Experimental results demonstrate the effectiveness of our method in making these models predict abnormal log entries as normal ones.
SQLord: A Robust Enterprise Text-to-SQL Solution via Reverse Data Generation and Workflow Decomposition
Authors: Song Cheng, Qiannan Cheng, Linbo Jin, Lei Yi, Guannan Zhang
Venue: WWW
Year: 2025

Transforming natural language into SQL queries (NL2SQL) is crucial for data-driven business applications. Existing frameworks, trained on open-source datasets, struggle with complex business logic and lack domain-specific data for fine-tuning. Additionally, evaluation methods often require annotated data and executable database environments, which are scarce in real-world scenarios. To address these challenges, we propose SQLord, an enterprise-level NL2SQL framework. First, SQLord introduces a data reverse generation approach to convert raw SQL statements into annotated data for supervised fine-tuning (SFT). Second, it proposes a decomposition method for complex queries using an automated workflow generator. Additionally, SQLord features a comprehensive GPT-Judge evaluation framework, including Execution Evaluation (EXE), Query-SQL Evaluation (QSE), and SQL-SQL Evaluation (SSE), tailored to diverse scenarios. Offline tests significantly outperform state-of-the-art baselines, and online accuracy consistently exceeds 90, highlighting SQLord's advantages and effectiveness in complex real-world scenarios. SQLord has been successfully applied across multiple scenarios on the world's largest B2B e-commerce platform.
Inferring Directed Acyclic Graphs from Event Sequences via Learning Gromov-Wasserstein-Regularized Hawkes Processes
Authors: Haoran Cheng, Dixin Luo
Venue: WWW
Year: 2025

Inferring directed acyclic graphs (DAGs) of event types based on observed event sequences aims to discover the causal relationships hidden in events' temporal behaviors, which is crucial in many applications such as earthquake prediction and epidemic spreading. In this study, we propose a novel method to infer DAGs from event sequences, introducing an optimal transport-based regularizer into the maximum likelihood estimation framework of Hawkes process. In particular, the proposed method infers DAGs based on the infectivity matrices associated with Hawkes processes. A Gromov-Wasserstein distance-based regularizer is applied to penalize the discrepancy between the infectivity matrices and adaptive lower-triangular matrices, ensuring the graphs corresponding to the learned infectivity matrices are directed acyclic. Experiments demonstrate that learning Gromov-Wasserstein-regularized Hawkes processes (HP-GWR) can infer the DAGs robustly from event sequences, whose performance is competitive to state-of-the-art DAG inference methods.
HOLMES & WATSON: A Robust and Lightweight HTTPS Website Fingerprinting through HTTP Version Parallelism
Authors: Yifei Cheng, Yujia Zhu, Baiyang Li, Peishuai Sun, Yong Ding, Xinhao Deng, Qingyun Liu
Venue: WWW
Year: 2025

Website Fingerprinting (WF) is a traffic analysis technique that aims to identify websites visited by users through the analysis of encrypted traffic patterns. Existing approaches often exhibit limited robustness against network variability and concept drift, resulting in significant performance degradation under real-world HTTPS conditions. Moreover, these methods typically require large-scale training datasets and substantial computational resources, which further increases the complexity of deployment. In this paper, we propose HOLMES, a novel approach that exploits HTTP version parallelism to extract enhanced application-layer features. These features, including the number of web resources transmitting in various HTTP versions, expose up to 4.28 bits of information-surpassing 98% of previously reported features and demonstrate increased stability across varying network conditions. Complementary to this, we introduce WATSON, a lightweight classification method based on lazy learning, which substantially reduces the dependency on large training datasets. To further enhance the identification accuracy, we incorporate two fingerprint-specific distance metrics that ensure high intra-class similarity. Our experimental evaluation demonstrates that HOLMES & WATSON significantly enhance both robustness and efficiency, achieving an average accuracy of 87.7% with only a single sample per website, marking an improvement of over 15% compared to state-of-the-art methods.
Bridging the Scientific Knowledge Gap and Reproducibility: A Survey of Provenance, Assertion and Evidence Ontologies
Authors: Tek Raj Chhetri, Yaroslav O. Halchenko, Dorota Jarecka, Puja Trivedi, Satrajit S. Ghosh, Patrick Ray, Lydia Ng
Venue: WWW
Year: 2025

The rapid growth of scientific publications and evolving experimental paradigms create significant challenges in staying up-to-date with current advances. Assertions are often unstructured and have limited provenance, which hinders reproducibility. Ontologies and knowledge graphs (KGs) offer structured solutions by capturing assertions, evidence, and provenance to support reproducibility. This paper reviews 23 ontologies -- 13 focused on assertions and evidence and 10 on provenance -- providing an overview of the current landscape while highlighting key challenges and opportunities for improvement.
Pre-Finetuning with Impact Duration Awareness for Stock Movement Prediction
Authors: Cheng-Chih Chiu, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: WWW
Year: 2025

Understanding the duration of news events' impact on the stock market is crucial for effective time-series forecasting, yet this facet is largely overlooked in current research. This paper addresses this research gap by introducing a novel dataset, the Impact Duration Estimation Dataset (IDED), specifically designed to estimate impact duration based on investor opinions. Our research establishes that pre-finetuning language models with IDED can enhance performance in text-based stock movement predictions. In addition, we juxtapose our proposed pre-finetuning task with sentiment analysis pre-finetuning, further affirming the significance of learning impact duration. Our findings highlight the promise of this novel research direction in stock movement prediction, offering a new avenue for financial forecasting. We also provide the IDED and pre-finetuned language models under the CC BY-NC-SA 4.0 license for academic use, fostering further exploration in this field.
Kronecker Generative Models for Power-Law Patterns in Real-World Hypergraphs
Authors: Minyoung Choe, Jihoon Ko, Taehyung Kwon, Kijung Shin, Christos Faloutsos
Venue: WWW
Year: 2025

Do real-world hypergraphs obey any patterns? Are power laws fundamental in hypergraphs as they are in real-world graphs? What generator can reproduce these patterns? A hypergraph is a generalization of a conventional graph, and it consists of nodes and hyperedges, with each hyperedge joining any number of nodes. Hypergraphs are adept at representing group interactions where two or more entities interact simultaneously, such as collaborative research and group discussions. In a wide range of real-world hypergraphs, we discover power-law or log-logistic distributions in eight structural properties. To simulate these observed patterns, we introduce HyRec, a tractable and realistic generative model leveraging the Kronecker product. We mathematically demonstrate that HyRec accurately reproduces both the patterns we observed and typical evolutionary trends found in real-world hypergraphs. To fit the parameters of HyRec to large-scale hypergraphs, we design SingFit, a fast and space-efficient algorithm successfully applied to eleven real-world hypergraphs with up to one million nodes and hyperedges. This paper makes the following contributions: (a) Discoveries: we identify multiple patterns that real-world hypergraphs obey, (b) Model: we propose HyRec, a tractable and realistic model capable of reproducing real-world hypergraphs efficiently (spec., with fewer than 1,000 parameters) with the support of SingFit, and (c) Proofs: we prove that HyRec adheres to these patterns.
Exploring the Potential of Graph Neural Networks-based Methods for General Linear Programs
Authors: Yung-Cheng Chuang, Ruihong Qiu
Venue: WWW
Year: 2025

Linear Programming (LP) and Integer Programming (IP) problems have been pivotal in optimization processes, finding applications across diverse industries like resource management, finance, and logistics. It is also used for internet network traffic optimization. Traditionally, LP solvers have consistently provided optimal solutions for problems formulated linearly. However, with more data and larger problems to solve, these LP solvers become computationally heavy and time-consuming. One solution is to provide better close-to-optimal initial solutions for these solvers, which previously many heuristics have attempted. Recently, researchers have successfully trained Graph Neural Networks to infer close-to-optimal initial solutions as a quick start for specific LP/IP problem types. Instead, this paper explores the potential of GNN models to predict close-to-optimal initial solutions for diverse problems. Specifically, the Smart Initial Basis (SIB), Learn To Pivot (LTP), and Interior-Point Message-passing GNN (IPMGNN) were investigated. Furthermore, an interactive website was developed releasing these models for readers to experiment with. A video demonstration can be accessed https://www.youtube.com/watch?v=7hCr92SnNak&ab_channel=boomer2boom, the repository for the website is open-sourced https://github.com/boomer3boom/Exploring-the-Potential-of-Graph-Neural-Networks-based-Methods-for-General-Linear-Program-Website-, and the model learning code is open-sourced https://github.com/boomer3boom/Exploring-the-Potential-of-Graph-Neural-Networks-based-Methods-for-General-Linear-Program.