OpenRaiser/Intern-Atlas huggingface dataset:
Insights and Implications of Evaluating Accessibility Compliance in AI-Generated Web Interfaces
Authors: Alexandra-Elena Gurita, Radu-Daniel Vatavu
Venue: WWW
Year: 2025

The recent availability of AI-driven user interface generation tools necessitates a proper understanding of their capabilities to produce accessible designs in accordance to established standards. To this end, this paper reports results from an evaluation of fifty user interfaces, generated using five publicly available AI design tools, against WCAG 2.1 criteria (e.g., text contrast and target size), comparing both accessibility-agnostic and accessibility-oriented text prompts. Our analysis reveals moderate violation severity (M=0.47) on a scale from 0, none to 4, critical), with text contrast (M=1.08) and target size (M=0.86) emerging as primary yet fixable violations. Contrary to our expectations, the accessibility-oriented prompt did not improve compliance in our dataset, but actually reduced it (M=0.54 vs. M=0.39). However, three of the examined AI tools showed improved results through dialogue and iterative refinement.
Developing a Stance-induced Epidemiological Model to Examine Polarized Information Contagion
Authors: Mayor Inna Gurung, Nitin Agarwal, Emmanuel Addai
Venue: WWW
Year: 2025

The spread of narratives in online environments, particularly those involving misinformation, poses significant challenges in under- standing and mitigating their impact. In this paper, we propose a novel stance-induced epidemiological model (SEIAIDZ) that incor- porates the stance of posts-an aspect often overlooked in previous models that primarily focused on metadata (such as hashtags) and single narratives in isolation. To demonstrate the utility of our model, we apply it to real-world data from X (formerly, Twitter) related to COVID-19 and 5G conspiracy theories, where some users spread misinformation (i.e., agreed with the narrative) while others actively debunked it (i.e., disagreed with the narrative), capturing the dynamic interactions between competing narratives. Our re- sults show that the proposed model outperforms the baseline SEIZ model by achieving lower error rates in predictive accuracy. Ad- ditionally, we identify two key parameters-β (transmission rate) and ψ (rate at which exposed individuals transition to the ''agree'', ''disagree'', and ''skeptic'' compartments)-as critical factors significantly influencing the basic reproduction number (ℜ0), a measure of transmission potential. These findings provide valuable insights into the role of stance and content dynamics in online misinforma- tion dissemination, paving the way for more effective strategies to model competing narratives and combat harmful ones, thereby informing better policymaking
LLM Shots: Best Fired at System or User Prompts?
Authors: Umut Halil, Jin Huang, Damien Graux, Jeff Z. Pan
Venue: WWW
Year: 2025

Over the past few years, the range of use cases involving Large Language Models (LLMs) has grown dramatically. Alongside this growth, many techniques have been established by the community to boost LLM performance. Among them, relying on the fact that LLMs excel at reproducing behaviors, practitioners have been charging their prompts with examples (a.k.a. shots) to guide or orientate the LLMs towards the correct directions given their main instruction. More recently, LLMs have evolved, allowing users to define overall roles by offering two inputs: system and user messages, based on the assumption that -in a sense- system instructions would be dedicated to admin/designer of chatbot interfaces. In such a setting: what is the best place to give example to the LLM so to improve its performances? In this study, we address this research question by systematically trying different shooting combinations with different popular benchmarks across a large set of LLMs. Our experiments show that it tends to be more beneficial to guide the LLMs through their system prompting mechanisms, leaving only the questions into their user messages.
Balancing Plasticity and Stability via Dual-Branch Learning in Online Continual Learning
Authors: Xu Han, Haodong Lu, Huiyi Wang, Dong Gong
Venue: WWW
Year: 2025

Deep learning models struggle with catastrophic forgetting in continual learning (CL), specifically online continual learning, requiring a balance between plasticity (adapting to new tasks) and stability (retaining prior knowledge). Memory replay addresses this challenge by integrating new data with stored memories, but existing methods of replaying the samples often fail to achieve an effective balance, overemphasizing either old or new knowledge. Previous works address this issue by enhancing replay buffer quality through sample selection or distilled images but overlook the model's learning process itself. To tackle this, we propose a novel dual-branch framework designed to manage the plasticity-stability trade-off in CL. The fast-updated branch achieves plasticity by quickly adapting to novel tasks, while the slow-updated branch maintains stability by gradually consolidating knowledge across all seen tasks only through the fast-updated branch, instead of directly learning from samples. To further stabilize the learning process, we introduce a consistency loss to align the two branches and prevent divergence. This dual-branch structure integrates new information while maintaining robust retention of prior knowledge. Extensive experiments on online continual learning benchmarks demonstrate superior performance and a well-balanced plasticity-stability trade-off. Open source code is available at https://github.com/han030927/BPS.
GL2GPU: Accelerating WebGL Applications via Dynamic API Translation to WebGPU
Authors: Yudong Han, Weichen Bi, Ruibo An, Deyu Tian, Qi Yang, Yun Ma
Venue: WWW
Year: 2025

WebGL has long been the prevalent API for GPU-accelerated graphics in web browsers, boosting 2D/3D graphical web applications. Despite widespread adoption, WebGL's programming model hinders its rendering performance on modern GPU hardware. To this end, WebGPU has been proposed as the next-generation API of GPU-accelerated processing in web browsers, exhibiting higher performance than WebGL. However, considering the complex logic of WebGL applications and the still-evolving WebGPU specification, statically migrating existing WebGL applications to WebGPU from source code is labor-intensive. To address this issue, we propose GL2GPU, an intermediate layer that dynamically translates WebGL to WebGPU at JavaScript runtime to improve rendering performance. GL2GPU addresses the inconsistencies between the WebGL and WebGPU programming models by emulating WebGL rendering states and leverages performance optimization mechanisms introduced by WebGPU to reduce the overhead of dynamic translation. Evaluation of three representative WebGL benchmarks shows that GL2GPU significantly enhances end-to-end rendering performance while maintaining visual consistency, achieving an average frame time reduction of 45.05% across different devices and operating systems.
Diverse Knowledge Selection for Enhanced Zero-shot Visual Question Answering
Authors: Seunghoon Han, Mingyu Choi, Hyewon Lee, SoYoung Park, Jong-Ryul Lee, Sungsu Lim, Tae-Ho Kim
Venue: WWW
Year: 2025

Visual Question Answering (VQA) is one of the important tasks that can help artificial intelligence understand the real world. Recently, with the growing popularity of zero-shot VQA, research has focused on utilizing external knowledge to tackle complex problems, especially those requiring common sense. However, existing studies that attempt to leverage external knowledge often use large amounts of knowledge without any selection process. Since some of this knowledge may not contribute to accurate predictions, the process of selecting relevant knowledge is essential. To address this issue, we propose Diverse Knowledge Selection for Enhanced Visual Question Answering (DKSVQA), which consists of three stages: Image-Context Generation, Similarity-based Knowledge Selection, and Query-Knowledge Graph-based Knowledge Selection. We demonstrate the superior performance of DKSVQA on two VQA benchmark datasets and compare it with zero-shot VQA baseline models. We highlight both the effectiveness and efficiency of DKSVQA through extensive experiments. For reproducibility, the source code is available at https://github.com/gooriiie/DKSVQA.
ABO: Abandon Bayer Filter for Adaptive Edge Offloading in Responsive Augmented Reality
Authors: Yongxuan Han, Shengzhong Liu, Fan Wu, Guihai Chen
Venue: WWW
Year: 2025

Bayer-patterned color filter array (CFA) has been the go-to solution for color image sensors. In augmented reality (AR), although color interpolation (i.e., demosaicing) of pre-demosaic RAW images facilitates a user-friendly rendering, it creates no benefits in offloaded DNN analytics but increases the image channels by 3x inducing higher transmission overheads. The potential optimization in frame preprocessing of DNN offloading is yet to be investigated. To that end, we propose ABO, an adaptive RAW frame offloading framework that parallelizes demosaicing with DNN computation. Its contributions are three-fold: First, we design a configurable tile-wise RAW image neural codec to compress frame sizes while sustaining downstream DNN accuracy under bandwidth constraints. Second, based on content-aware tiles-in-frame selection and runtime bandwidth estimation, a dynamic transmission controller adaptively calibrates codec configurations to maximize the DNN accuracy. Third, we further optimize the system pipelining to achieve lower end-to-end frame processing latency and higher throughput. Through extensive evaluations on a prototype platform, ABO consistently achieves 40% more frame processing throughput and 30% less end-to-end latency while improving the DNN accuracy by up to 15% than SOTA baselines. It also exhibits improved robustness against dim lighting and motion blur situations.
Automatic Instruction Data Selection for Large Language Models via Uncertainty-Aware Influence Maximization
Authors: Jindong Han, Hao Liu, Jun Fang, Naiqiang Tan, Hui Xiong
Venue: WWW
Year: 2025

Recent years have witnessed the prevalent integration of Large Language Models (LLMs) in various Web applications, such as search engines and recommender systems. As an emerging technique, instruction tuning aims to align pre-trained LLMs as capable chatbots that excel at following human instructions. Previous research indicates that selecting an appropriate subset of a large instruction dataset can enhance the capabilities of LLMs and reduce training costs. However, existing works tend to overlook external correlations between instruction examples during data selection process, which can introduce potential bias and lead to sub-optimal performance. To bridge this gap, we formalize this problem from graph influence maximization perspective and propose Uncertainty-aware influence Maximization (UniMax), a data selection framework that explicitly incorporates the complex inter-dependencies within instruction data. Specifically, we first define a latent instruction graph, treating each instruction example as a graph node and representing their implicit relations as graph edges. Instead of solely relying on heuristic metrics for graph construction, we develop a self-supervised graph learner to uncover the latent structure beyond surface-level feature correlations. After that, we propose an uncertainty-aware influence function to score each example on the instruction graph, allowing a simple greedy algorithm to select a valuable subset that embodies both high influence and uncertainty with an approximation guarantee. Extensive experiments on public datasets show that the proposed approach can significantly enhance model capabilities, underscoring the importance of exploiting data dependencies in instruction data selection.
Cross-Lingual Text Classification with Large Language Models
Authors: Bin Han, Sean T. Yang, Christopher LuVogt
Venue: WWW
Year: 2025

Cross-lingual text classification involves using a model trained on data in one language to classify text in another, which is crucial in global web applications where labeled data is scarce in certain languages. Although multilingual language models have been leveraged for such tasks, few studies investigate the ability of large language models in cross-lingual classification. In this paper, we evaluate the performance of four large language models and one smaller encoder-only model on cross-lingual text classification tasks using two benchmark datasets. We assess three task settings-direct-test without fine-tuning, direct-test with fine-tuning, and translate-test with fine-tuning. Our findings demonstrate that fine-tuning consistently improves performance, and the translate-test method slightly outperforms others. Furthermore, while large language models experience a slight performance drop on non-English languages, they outperform the baseline encoder-only model in many cases. This study provides insights into the effectiveness of large language models for cross-lingual classification in practical applications.
Unpacking the Layers: Exploring Self-Disclosure Norms, Engagement Dynamics, and Privacy Implications
Authors: Ehsan ul Haq, Shalini Jangra, Suparna De, Nishanth Sastry, Gareth Tyson
Venue: WWW
Year: 2025

This paper characterizes the self-disclosure behavior of Reddit users across 11 different types of self-disclosure. We find that at least half of the users share some type of disclosure in at least 10% of their posts, with half of these posts having more than one type of disclosure. We show that different types of self-disclosure are likely to receive varying levels of engagement. For instance, a Sexual Orientation disclosure garners more comments than other self-disclosures. We also explore confounding factors that affect future self-disclosure. We show that users who receive interactions from (self-disclosure) specific subreddit members are more likely to disclose in the future. We also show that privacy risks due to self-disclosure extend beyond Reddit users themselves to include their close contacts, such as family and friends, as their information is also revealed. We develop a browser plugin for end-users to flag self-disclosure in their content.
Perspectives from Ireland: The Psychological Harms of Cyber-Located Sexual Violence
Authors: Lorraine Hayman
Venue: WWW
Year: 2025

I share the results of a quantitative multiple-choice questionnaire conducted in the Republic of Ireland in which N=281 women revealed the multiple ways Cyber-located Sexual Violence (CLSV) victimization psychologically harms them. CLSV sits at the intersection of technology, cyberspace, and sexual violence, and includes cyberflashing, Image-based Sexual Abuse, cyberstalking, and unwanted threats, comments, and sexual requests (e-bile). While CLSV harms victim-survivors in multiple ways, including physically embodied and financial harms, the psychological harm of CLSV victimization is considerable for some victim-survivors, warranting a specific focus in this paper. The findings demonstrate that CLSV inflicts various psychological harms on the 281 women respondents, impacting their mental health negatively, their trust in others, and how safe they feel. To these findings, I add further depth by reflecting on my experiences of being psychologically harmed as a witness to the impact CLSV has on women and the threat of potential CLSV in my life. These reflections outline how CLSV is psychologically harmful not only to victim-survivors but also to their supporters, friends, family, communities, and wider societies. This paper contributes a consideration of the psychological harms of CLSV that existing literature on technology-facilitated abuses has not yet considered in the Irish context. As CLSV negatively impacts different women in different contexts and social locations, I recommend further intersectional research on the harms of CLSV in Ireland and beyond, specifically focusing on establishing similarities and differences in different contexts.
Efficient Vision Language Model Fine-tuning for Text-based Person Anomaly Search
Authors: Jiayi He, Shengeng Tang, Ao Liu, Lechao Cheng, Jingjing Wu, Yanyan Wei
Venue: WWW
Year: 2025

This paper presents the HFUT-LMC team's solution to the WWW 2025 challenge on Text-based Person Anomaly Search (TPAS). The primary objective of this challenge is to accurately identify pedestrians exhibiting either normal or abnormal behavior within a large library of pedestrian images. Unlike traditional video analysis tasks, TPAS significantly emphasizes understanding and interpreting the subtle relationships between text descriptions and visual data. The complexity of this task lies in the model's need to not only match individuals to text descriptions in massive image datasets but also accurately differentiate between search results when faced with similar descriptions. To overcome these challenges, we introduce the Similarity Coverage Analysis (SCA) strategy to address the recognition difficulty caused by similar text descriptions. This strategy effectively enhances the model's capacity to manage subtle differences, thus improving both the accuracy and reliability of the search. Our proposed solution demonstrated excellent performance in this challenge.
Motivation-Aware Session Planning over Heterogeneous Social Platforms
Authors: Chengkun He, Xiangmin Zhou, Yurong Cheng, Jie Shao, Guoren Wang, Iqbal Gondal, Zahir Tari
Venue: WWW
Year: 2025

With the explosive growth of online service platforms, an increasing number of people and enterprises are undertaking personal and professional tasks online. In real applications such as trip planning and online marketing, planning sessions for a sequence of activities or services will enable social users to receive the optimal services, improving their experience and reducing the cost of their activities. These online platforms are heterogeneous, including different types of services with different attributes. However, the problem of session planning over heterogeneous platforms has not been studied so far. In this paper, we propose a Motivation-Aware Session Planning (MASP) framework for session planning over heterogeneous social platforms. Specifically, we first propose a novel HeterBERT model to handle the heterogeneity of items at both type and attribute levels. Then, we propose to predict user preference using the motivations behind user activities. Finally, we propose an algorithm together with its optimisations for efficient session generation. The extensive tests prove the high effectiveness and efficiency of MASP.
FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement
Authors: Liam Hebert, Marialena Kyriakidi, Hubert Pham, Krishna Sayana, James Pine, Sukhdeep S. Sodhi, Ambarish Jash
Venue: WWW
Year: 2025

Recent proposals in recommender systems represent items with their textual description, using a large language model. They show better results on standard benchmarks compared to an item ID-only model, such as Bert4Rec. In this work, we revisit the often-used Bert4Rec baseline and show that with further tuning, Bert4Rec significantly outperforms previously reported numbers, and in some datasets, is competitive with state-of-the-art models. With revised baselines for item ID-only models, this paper also establishes new competitive results for architectures that combine IDs and textual descriptions. We demonstrate this with Flare (Fusing Language models and collaborative Architectures for Recommender Enhancement). Flare is a novel hybrid sequence recommender that integrates a language model with a collaborative filtering model using a Perceiver network. Prior studies focus evaluation on datasets with limited-corpus size, but many commercially-applicable recommender systems common on the web must handle larger corpora. We evaluate Flare on a more realistic dataset with a significantly larger item vocabulary, introducing new baselines for this setting. This paper also showcases Flare's inherent ability to support critiquing, enabling users to provide feedback and refine recommendations. We leverage critiquing as an evaluation method to assess the model's language understanding and its transferability to the recommendation task.
Graph Meets LLM for Review Personalization based on User Votes
Authors: Sharon Hirsch, Lilach Zitnitski, Slava Novgorodov, Ido Guy, Bracha Shapira
Venue: WWW
Year: 2025

Review personalization aims at presenting the most relevant reviews of a product according to the preferences of the individual user. Existing studies of review personalization use the reviews authored by the user as a proxy for their preferences, and henceforth as a means for learning and evaluating personalization quality. In this work, we suggest using review votes rather than authorship for personalization. We propose MAGLLM, an approach that leverages heterogeneous graphs for modeling the relationships among reviews, products, and users, with large language model (LLM) to enrich user representation on the graph. Our evaluation over a unique public dataset that includes user voting information indicates that the vote signal yields substantially higher personalization performance across a variety of recommendation methods and e-commerce domains. It also indicates that our graph-LLM approach outperforms comparative baselines and algorithmic alternatives. We conclude with concrete recommendations for e-commerce platforms seeking to enhance their review personalization experience.
Analyzing the Influence of the Pandemic on Seasonal Queries
Authors: Mandy Ho, Nicholas Largey, Behrooz Mansouri
Venue: WWW
Year: 2025

This study explores the impact of the pandemic on seasonal search trends by analyzing Google Trends data for seasonal queries. By evaluating mean seasonality and autocorrelation, this research assesses how the pandemic influenced the consistency of seasonal patterns. Results indicate that while mean seasonality remained largely stable, autocorrelation values increased for several queries when pandemic-period data were excluded, suggesting that COVID-19 introduced fluctuations in search behavior.
EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration
Authors: Minjie Hong, Yan Xia, Zehan Wang, Jieming Zhu, Ye Wang, Sihang Cai, Xiaoda Yang, Quanyu Dai, Zhenhua Dong, Zhimeng Zhang, Zhou Zhao
Venue: WWW
Year: 2025

Large language models (LLMs) are increasingly leveraged as foundational backbones in the development of advanced recommender systems, offering enhanced capabilities through their extensive knowledge and reasoning. Existing llm-based recommender systems (RSs) often face challenges due to the significant differences between the linguistic semantics of pre-trained LLMs and the collaborative semantics essential for RSs. These systems use pre-trained linguistic semantics but learn collaborative semantics from scratch via the llm-Backbone. However, LLMs are not designed for recommendations, leading to inefficient collaborative learning, weak result correlations, and poor integration of traditional RS features. To address these challenges, we propose EAGER-LLM, a decoder-only llm-based generative recommendation framework that integrates endogenous and exogenous behavioral and semantic information in a non-intrusive manner. Specifically, we propose 1) dual-source knowledge-rich item indices that integrates indexing sequences for exogenous signals, enabling efficient link-wide processing; 2) non-invasive multiscale alignment reconstruction tasks guide the model toward a deeper understanding of both collaborative and semantic signals; 3) an annealing adapter designed to finely balance the model's recommendation performance with its comprehension capabilities. We demonstrate EAGER-LLM's effectiveness through rigorous testing on three public benchmarks.