OpenRaiser/Intern-Atlas huggingface dataset:
Angular Distance-Guided Neighbor Selection for Graph-Based Approximate Nearest Neighbor Search
Authors: Sungjun Jung, Yongsang Park, Haeun Lee, Young H. Oh, Jae W. Lee
Venue: WWW
Year: 2025

Graph-based approximate nearest neighbor search (ANNS) algorithms are widely used to identify the most similar vectors to a given query vector. Graph-based ANNS consists of two stages: constructing a graph and searching on the graph for a given query vector. While reducing the query response time is of great practical importance, less attention has been paid to improving the online search method than the offline graph construction method. This paper provides an extensive experimental analysis on the popular greedy search and other search optimization strategies. We also propose a novel angular distance-guided search method for graph-based ANNS (ADA-NNS) to improve search efficiency. The key innovation of ADA-NNS is introducing a low-cost neighbor selection mechanism based on approximate similarity score derived from angular distance estimation, which effectively filters out less relevant neighbors. We compare state-of-the-art search techniques, including FINGER, on six datasets using different similarity metrics. It provides a comprehensive perspective on their tradeoffs in terms of throughput, latency, and recall. Our evaluation shows that ADA-NNS achieves 34%-107% higher queries per second (QPS) than the greedy search at 95% recall@10 on HNSW, one of the most popular graph structures for ANNS.
Agent-Initiated Interaction in Phone UI Automation
Authors: Noam Kahlon, Guy Rom, Anatoly Efros, Filippo Galgani, Omri Berkovitch, Sapir Caduri, William E. Bishop, Oriana Riva, Ido Dagan
Venue: WWW
Year: 2025

Phone automation agents aim to autonomously perform a given natural-language user request, such as scheduling appointments or booking a hotel. While much research effort has been devoted to screen understanding and action planning, complex tasks often necessitate user interaction for successful completion. Aligning the agent with the user's expectations is crucial for building trust and enabling personalized experiences. This requires the agent to proactively engage the user when necessary, avoiding actions that violate their preferences while refraining from unnecessary questions where a default action is expected. We argue that such subtle agent-initiated interaction with the user deserves focused research attention. To promote such research, this paper introduces a task formulation for detecting the need for user interaction and generating appropriate messages. We thoroughly define the task, including aspects like interaction timing and the scope of the agent's autonomy. Using this definition, we derived annotation guidelines and created a diverse dataset for the task, leveraging an existing UI automation dataset. We tested several text-based and multimodal baseline models for the task, finding that it is very challenging for current LLMs. We suggest that our task formulation, dataset, baseline models and analysis will be valuable for future UI automation research, specifically in addressing this crucial yet often overlooked aspect of agent-initiated interaction. This work provides a needed foundation to allow personalized agents to properly engage the user when needed, within the context of phone UI automation.
Preference-based Learning with Retrieval Augmented Generation for Conversational Question Answering
Authors: Magdalena Kaiser, Gerhard Weikum
Venue: WWW
Year: 2025

Conversational Question Answering (ConvQA) involves multiple subtasks, i) to understand incomplete questions in their context, ii) to retrieve relevant information, and iii) to generate answers. This work presents PRAISE, a pipeline-based approach for ConvQA that trains LLM adapters for each of the three subtasks. As labeled training data for individual subtasks is unavailable in practice, PRAISE learns from its own generations using the final answering performance as feedback signal without human intervention and treats intermediate information, like relevant evidence, as weakly labeled data. We apply Direct Preference Optimization by contrasting successful and unsuccessful samples for each subtask. In our experiments, we show the effectiveness of this training paradigm: PRAISE shows improvements per subtask and achieves new state-of-the-art performance on a popular ConvQA benchmark, by gaining 15.5 percentage points increase in precision over baselines.
Mathematical AI-Driven Insights into Societal Dynamics and Resilience
Authors: Md Sarwar Kamal, Md. Rafiqul Islam
Venue: WWW
Year: 2025

The behavioral patterns of individuals within a society can serve as a reflection of its overall conditions and status. By examining these patterns, we can identify critical societal issues such as poverty, hunger, crime, sadness, underdevelopment, premature mortality, and social structure. Our research aims to understand the factors associated with these problems by leveraging societal data analytics. We explore societal behavior and resilience by analyzing Hindi cinema from 1951 onwards and comparing it with contemporary community data from developed countries. This is achieved through integrated interpretive and mathematical artificial intelligence (MAI) techniques, alongside natural language processing (NLP). The primary goal is to uncover and analyze the underlying societal norms, resilience patterns, and behavioral dynamics depicted in Hindi movies, and to validate these findings against real-world community data. The methodology begins with language processing techniques, such as term frequency analysis and contextual thematic analysis, to extract and quantify thematic variables from song lyrics. These variables capture fundamental societal issues like wealth disparity, human suffering, and societal despair. Additionally, Fourier and Laplace transforms are used for time-series analysis of audio signals and thematic continuity in video sequences. Game theory models are also applied to study decision-making processes and social interactions portrayed in the films. To ensure the transparency and interpretability of the MAI-driven insights, we employ explainable AI (XAI) approaches, including counterfactual explanations, feature visualization, and concept activation vectors (CAVs).From both the outcomes of MAI and XAI, we have noticed that there are some key features showing from both the data set and these are hunger, crimes, underdevelopment, poverty, and social structure. By treating multimedia content as a reflection of societal views and comparing it with empirical data, this research provides a core analysis of societal dynamics across different cultural contexts. Our proposed methods demonstrate higher accuracy compared to state-of-the-art approaches which ensure our findings more explainable and insightful.
Explainable Vision-Language Model for Personalized Medicine
Authors: Md Sarwar Kamal, Sonia Farhana Nimmy, Wafa Alharbi
Venue: WWW
Year: 2025

Recent advancements in computer vision (CV) and natural language processing (NLP) have led to the emergence of Vision-Language Models (VLMs), which excel in interpreting complex multimodal information by seamlessly integrating visual and textual data. This paper proposes a novel, interpretable framework that combines VLMs with specific mathematical transforms-namely, the Fast Fourier Transform (FFT) for efficient computation of frequency domains, and the Bilateral Laplace Transform for enhanced stability analysis in nonlinear systems-to enhance drug discovery and personalized medicine. The interpretable application of FFT identifies periodic patterns in temporal gene expression data from genes such as TP53 and EGFR, crucial for understanding circadian influences on drug metabolism. The Bilateral Laplace Transform, also applied in an interpretable manner, assesses system stability and response under various therapeutic interventions, focusing on genes like BRCA1 and PTEN for short-term treatment outcomes. This integrated model leverages the strengths of VLMs to synthesize and contextualize the transformed data, providing a robust and interpretable analytical tool for predicting individual drug responses and optimizing treatment strategies. Validation of the proposed framework on multimodal datasets comprising clinical imaging, genomic data, and textual descriptions confirms its potential in significantly improving the precision of personalized treatment plans. The outcomes of this research advances our understanding of complex drug interactions within the human body and also pave the way for developing a user-friendly and interpretable tool that assists clinicians in real-time decision-making, ultimately enhancing patient outcomes in clinical settings. We have made all resources available on GitHub to support and encourage future studies and advancements based on our findings. You can access them at https://github.com/Sarwar-UTS/Interpretable-VLMs-for-Medicine.
Explainable Medical Image Captioning
Authors: Md Sarwar Kamal, Sonia Farhana Nimmy, Md. Rafiqul Islam, Usman Naseem
Venue: WWW
Year: 2025

In the age of current information superhighways, it is essential to apply Large Language Models Using Multi-modal Data for User Modeling in medical data analysis to enhance accuracy, contextual understanding, and interpretability. This research aims to transform the landscape of medical imaging by introducing an interpretable approach for generating precise medical captions from X-ray images and associated data. Current methods in this domain often have some limitations such as insufficient accuracy, lack of contextual understanding, and limited ability to provide explainable results. Addressing these challenges, our work emphasizes the importance of explainable AI (XAI), which is crucial for enhancing trust and reliability in automated medical diagnostics. We propose a novel framework that integrates advanced deep learning techniques with explainable AI. Initially, the framework employs a U-net architecture for pre-processing the medical images to identify the regions of interests (ROIs), effectively isolating and enhancing relevant features. The Vision Transformer (ViT), known for its effectiveness in handling image data, extracts critical features and generates tokens. To further enhance the contextual understanding of the medical images, we construct a comprehensive medical language graph (MLG). This graph is formed by integrating the tokens generated by the ViT with corpus of medical tokens. This integration enables a deeper understanding of the medical context, significantly improving the accuracy and relevance of the generated captions. For the final step, a language decoder is employed to translate the rich, context-aware features into coherent and medically accurate captions. This step is pivotal in transforming complex medical data into understandable and actionable insights. To ensure that our model's decisions are transparent and interpretable, we apply Layer-wise Relevance Propagation (LRP) and Local Interpretable Model-agnostic Explanations (LIME) for the image and text data, respectively. These explainability techniques enable us to provide clear insights into the model's reasoning process, thereby making it more trustworthy and accessible to medical professionals.
Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalance
Authors: Nikos Kanakaris, Heng Ping, Xiongye Xiao, Nesreen K. Ahmed, Luca Luceri, Emilio Ferrara, Paul Bogdan
Venue: WWW
Year: 2025

Detecting organized political campaigns, commonly known as astroturf campaigns, is of paramount importance in fighting against disinformation on social media. Existing approaches for the identification of such organized actions employ techniques mostly from network science, graph machine learning and natural language processing. Their ultimate goal is to analyze the relationships and interactions (e.g. re-posting) among users and the textual similarities of their posts. Despite their effectiveness in recognizing astroturf campaigns, these methods face significant challenges, notably the class imbalance in available training datasets. To mitigate this issue, recent methods usually resort to data augmentation or increasing the number of positive samples, which may not always be feasible or sufficient in real-world settings. Following a different path, in this paper, we propose a novel framework for identifying astroturf campaigns based solely on large language models (LLMs), introducing a Balanced Retrieval-Augmented Generation (Balanced RAG) component. Our approach first gives both textual information concerning the posts (in our case tweets) and the user interactions of the social network as input to a language model. Then, through prompt engineering and the proposed Balanced RAG method, it effectively detects coordinated disinformation campaigns on 𝕏 (Twitter). The proposed framework does not require any training or fine-tuning of the language model. Instead, by strategically harnessing the strengths of prompt engineering and Balanced RAG, it facilitates LLMs to overcome the effects of class imbalance and effectively identify coordinated political campaigns. The experimental results demonstrate that by incorporating the proposed prompt engineering and Balanced RAG methods, our framework outperforms the traditional graph-based baselines, achieving 2×-3× improvements in terms of precision, recall and F1 scores.
Continual Learning based on Memory Update Model for Multimodal User Modeling
Authors: Kyuchang Kang, Changseok Bae, Dong-oh Kang
Venue: WWW
Year: 2025

Recently, various artificial intelligence applications that utilize advanced machine learning models have been developed to address real-world problems. However, as we are well aware, such applications require large-scale training data, complex network architectures, and significant time for the training process. This paper proposes a model that supports online real-time learning using an accumulative memory structure as a way to overcome these limitations of existing machine learning models. The proposed model extracts latent vectors in feature space from stimuli received from the external environment and converts them into sparse distribution representations through scalar encoding and spatial pooling. Sparse distribution representations are known to mimic the structures used by the neural systems of living organisms for memory. Whenever new stimuli are received, the accumulated results of sparse distribution representations derived from each element of the latent vector are used to form memory through hierarchical clustering, reflecting morphological similarities. During the continuous updating process of this memory, the similarity recalculation, incorporating weights derived from the cumulative distributions of each cluster, enhances the performance of cluster formation based on the morphological characteristics of the stimuli. Experimental results using the MNIST dataset confirm that the model can achieve appropriate clustering even for inputs that are morphologically difficult to distinguish. The proposed continuous learning model, through memory updates, is also applicable to multimodal user modeling.
RTC: Road Marking Thinking for Scenario Reasoning and Decision Support in Vigilant Driving
Authors: Jieun Kang, Subi Kim, Yongik Yoon
Venue: WWW
Year: 2025

To create a reliable autonomous driving environment, advanced artificial intelligence technologies, such as object recognition and path prediction, are being applied. However, the road environment is unpredictable and dynamic, with real-time challenges. If the focus is only on recognizing objects and predicting their movements and paths, there are limitations in complex situational reasoning, real-time attention, alertness to surroundings, and proactive driving. This paper proposes the Roadmarking Thinking Causality Architecture (RTC Architecture), which enables the derivation of various potential driving scenarios and appropriate responses through the interpretation of the intrinsic meaning of road markings. The RTC Architecture employs the CLIP model to transform road marking images into interpretable text. The text-based road markings are processed by a GPT-based model, which generates complex scenarios and driving response strategies for each , allowing for the derivation of the Road Marking Thinking Causality to explain the causal relationship between road markings and potential outcomes. Rather than relying on the movement of dynamic objects, RTC Architecture infers driving situations through the interpretation of the intrinsic meaning of road markings, allowing for the extraction of complex information from the driving environment. By understanding the causality between potential scenarios and corresponding response strategies from road markings and interpretable explanation, RTC Architecture supports reliable decision-making and recognizing potential risks proactively vigilant driving.
Commonsense AI in the History of the Web
Authors: Mayank Kejriwal, Deborah L. McGuinness, Henry Lieberman
Venue: WWW
Year: 2025

Machine common sense (MCS)-the challenge of enabling computers to grasp everyday human knowledge-has been a grand challenge in Artificial Intelligence (AI) since the 1950s. While recent advances in large language models have led to impressive progress, there is still no consensus on how much common sense today's AI actually possesses. In this brief review, we revisit the historical development of MCS in the context of the Web, examining how the Web's evolution-from early knowledge representation efforts to knowledge graphs, the Semantic Web, and crowdsourcing-has shaped MCS research. We argue that key breakthroughs in Web technologies were instrumental in addressing longstanding challenges of scale and coverage in commonsense reasoning. At the same time, MCS research has influenced the development of core Web applications, including intelligent agents, plausibility-based reasoning, and robust evaluation of black-box AI systems.
InteractRank: Personalized Web-Scale Search Pre-Ranking with Cross Interaction Features
Authors: Sujay Khandagale, Bhawna Juneja, Prabhat Agarwal, Aditya Subramanian, Jaewon Yang, Yuting Wang
Venue: WWW
Year: 2025

Modern search systems use a multi-stage architecture to deliver personalized results efficiently. Key stages include retrieval, pre-ranking, full ranking, and blending, which refine billions of items to top selections. The pre-ranking stage, vital for scoring and filtering hundreds of thousands of items down to a few thousand, typically relies on two tower models due to their computational efficiency, despite often lacking in capturing complex interactions. While query-item cross interaction features are paramount for full ranking, integrating them into pre-ranking models presents efficiency-related challenges. In this paper, we introduce InteractRank, a novel two tower pre-ranking model with robust cross interaction features used at Pinterest. By incorporating historical user engagement-based query-item interactions in the scoring function along with the two tower dot product, InteractRank significantly boosts pre-ranking performance with minimal latency and computation costs. In real-world A/B experiments at Pinterest, InteractRank improves the online engagement metric by 6.5% over a BM25 baseline and by 3.7% over a vanilla two tower baseline. We also highlight other components of InteractRank, like real-time user-sequence modeling, and analyze their contributions through offline ablation studies. The code for InteractRank is available at https://github.com/pinterest/atg-research/tree/main/InteractRank.
SparseNet: Sparse Tweet Network for Classification of Informative Posts using Graph Convolutional Network
Authors: Parul Khandelwal, Parag Rachchh, Smit Patel, Parshva Gang, Preety Singh, Rajbir Kaur
Venue: WWW
Year: 2025

A major challenge during critical events is to differentiate useful content from the noise spread through posts on social media. This paper presents the use of a Graph Convolutional Network (GCN) to classify tweets as informative or non-informative on a new crisis dataset. To reduce the computational complexity, we evaluate the performance of the GCN by varying the degree of sparseness of the input tweet graph. Our experimental results show that the classification results improve when the input graph is sparse and contains edges only between highly similar tweets compared to a complete graph. We show the efficacy of our approach by achieving an accuracy of 88.40% and an F1-score of 88.05% despite using a small-sized dataset.
UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification
Authors: Siddhant Kharbanda, Devaansh Gupta, Gururaj K, Pankaj Malhotra, Amit Singh, Cho-Jui Hsieh, Rohit Babbar
Venue: WWW
Year: 2025

Extreme Multi-label Classification (XMC) involves predicting a subset of relevant labels from an extremely large label space, given an input query and labels with textual features. Models developed for this problem have conventionally made use of dual encoder (DE) to embed the queries and label texts and one-vs-all (OvA) classifiers to rerank the shortlisted labels by the DE. While such methods have shown empirical success, a major drawback is their computational cost, often requiring upto 16 GPUs to train on the largest public dataset. Such a high cost is a consequence of calculating the loss over the entire label space. While shortlisting strategies have been proposed for classifiers, we aim to study such methods for the DE framework. In this work, we develop UniDEC, a loss-independent, end-to-end trainable framework which trains the DE and classifier together in a unified manner with a multi-class loss, while reducing the computational cost by 4--16x. This is done via the proposed pick-some-label (PSL) reduction, which aims to compute the loss on only a subset of positive and negative labels. These labels are carefully chosen in-batch so as to maximise their supervisory signals. Not only does the proposed framework achieve state-of-the-art results on datasets with labels in the order of millions, it is also computationally and resource efficient in achieving this performance on a single GPU. Code is made available at https://github.com/the-catalyst/UniDEC.
Policies for Refugee Mental Health Using Generative Agents
Authors: Aparup Khatua
Venue: WWW
Year: 2025

In recent times, mental health has attracted the attention of researchers, and the extant literature has explored digital platforms to identify and analyze individuals' expressions of distress on social media platforms, such as depressive tweets, to probe various aspects of users' mental health. This unidirectional perspective often overlooks the societal context that may impact mental health. In the context of refugees' mental health, we propose a novel dual-perspective approach by examining both self-reflective tweets expressing emotional distress and hostile tweets from host country citizens. We employ large language models to extract and summarize crucial concerns from vast unstructured user-generated data, using them as input for our agent-based simulations for policy intervention. The proposed simulation-based approach suggests comprehensive policies to address displacement-related trauma and societal hostility faced by refugees in host countries.