OpenRaiser/Intern-Atlas huggingface dataset:
Next-Generation Price Recommendation with LLM-Augmented Graph Transformers
Authors: Hadi Mohammadzadeh Abachi, Amin Beheshti, Milad Mosharraf, Pooyan Asgari, Majid Namazi
Venue: CIKM
Year: 2025

Dynamic pricing on two-sided platforms such as Airbnb presents complex challenges due to the heterogeneity of listings, user behaviours, and contextual variables. In this work, we propose a robust and interpretable pricing framework that leverages Large Language Models (LLMs) and prompt engineering to automate the generation of high-level meta-features from unstructured and structured listing data. These meta-features are designed to capture nuanced semantic features that are often overlooked by traditional feature engineering pipelines. We further integrate these representations into a Transformer-based Graph Neural Network (GNN), which models the relational and spatial dependencies between listings in a data-driven and several relation-construction manner. By combining prompt-driven embeddings with graph-aware contextual learning, our framework significantly enhances price recommendation accuracy while offering transparency through assortativity analysis. Extensive experiments on real-world Airbnb datasets demonstrate our approach's performance in both prediction and unseen data across neighbourhoods and output interpretability. This work highlights the potential of unifying LLMs, structured graph learning, and interpretable AI for next-generation dynamic pricing systems.
ADMP-GNN: Adaptive Depth Message Passing GNN
Authors: Yassine Abbahaddou, Fragkiskos D. Malliaros, Johannes F. Lutzeyer, Michalis Vazirgiannis
Venue: CIKM
Year: 2025

Graph Neural Networks (GNNs) have proven to be highly effective in various graph learning tasks. A key characteristic of GNNs is their use of a fixed number of message-passing steps for all nodes in the graph, regardless of each node's diverse computational needs and characteristics. Through empirical real-world data analysis, we demonstrate that the optimal number of message-passing layers varies for nodes with different characteristics. This finding is further supported by experiments conducted on synthetic datasets. To address this, we propose Adaptive Depth Message Passing GNN (ADMP-GNN), a novel framework that dynamically adjusts the number of message passing layers for each node, resulting in improved performance. This approach applies to any model that follows the message passing scheme. We evaluate ADMP-GNN on the node classification task and observe performance improvements over baseline GNN models. Our code is publicly available at: https://github.com/abbahaddou/ADMP-GNN
RerankArena: A Unified Platform for Evaluating Retrieval, Reranking and RAG with Human and LLM Feedback
Authors: Abdelrahman Abdallah, Mahmoud Abdalla, Bhawna Piryani, Jamshid Mozafari, Mohammed Ali, Adam Jatowt
Venue: CIKM
Year: 2025

Evaluating the quality of retrieval-augmented generation (RAG) and document reranking systems remains challenging due to the lack of scalable, user-centric, and multi-perspective evaluation tools. We introduce RankArena, a unified platform for comparing and analysing the performance of retrieval pipelines, rerankers, and RAG systems using structured human and LLM-based feedback as well as for collecting such feedback. RankArena supports multiple evaluation modes: direct reranking visualisation, blind pairwise comparisons with human or LLM voting, supervised manual document annotation, and end-to-end RAG answer quality assessment. It captures fine-grained relevance feedback through both pairwise preferences and full-list annotations, along with auxiliary metadata such as movement metrics, annotation time, and quality ratings. The platform also integrates LLM-as-a-judge evaluation, enabling comparison between model-generated rankings and human ground truth annotations. All interactions are stored as structured evaluation datasets that can be used to train rerankers, reward models, judgment agents, or retrieval strategy selectors. Our platform is publicly available at https://rankarena.ngrok.io/, and the Demo video is provided. https://youtu.be/jIYAP4PaSSI.
EnhanceMyPrompt: Rewriting Chat Queries for Effective Response Generation from LLMs
Authors: Tushar Abhishek, Manas Jain, Shishir Hardia, Shreevignesh Suriyanarayanan, Sandra Anil, Rushabh Gandhi, Manish Gupta
Venue: CIKM
Year: 2025

Short and ambiguous queries in chat interfaces like Microsoft Copilot often lead to vague or irrelevant LLM responses, increasing task completion time. Hence, we introduce a novel problem: semi-automatically enhancing such queries/prompts into specific, well-formed ones with clear intent. Unlike prompt optimization, our approach adds relevant sub-intents or constraints rather than just rewording for brevity. We propose EnhanceMyPrompt, which uses small language models (SLMs) to enrich prompts by adding sub-intents/constraints, suggesting placeholders, and recommending popular values. We also introduce metrics to measure prompt improvement, user effort, and LLM response quality. Experiments on a proprietary Microsoft Copilot and LMSYS+NQ datasets with four SLMs show effectiveness: EnhanceMyPrompt predicts user intents up to 3 turns ahead in ~23% of conversations, enabling efficient sessions. Code, prompts, data, and models for LMSYS+NQ are publicly available.
Towards Rational Pesticide Design with Graph Machine Learning Models for Ecotoxicology
Authors: Jakub Adamczyk
Venue: CIKM
Year: 2025

This research focuses on rational pesticide design, using graph machine learning to accelerate the development of safer, eco-friendly agrochemicals, inspired by in silico methods in drug discovery. With an emphasis on ecotoxicology, the initial contributions include the creation of ApisTox, the largest curated dataset on pesticide toxicity to honey bees. We conducted a broad evaluation of machine learning (ML) models for molecular graph classification, including molecular fingerprints, graph kernels, GNNs, and pretrained transformers. The results show that methods successful in medicinal chemistry often fail to generalize to agrochemicals, underscoring the need for domain-specific models and benchmarks. Future work will focus on developing a comprehensive benchmarking suite and designing ML models tailored to the unique challenges of pesticide discovery.
FR-LoRA: Fisher Regularized LoRA for Multilingual Continual Learning
Authors: Sayanta Adhikari, Sanjay Agrawal, Vivek Sembium
Venue: CIKM
Year: 2025

Relevance in e-commerce product search is critical to ensuring that results accurately reflect customer intent. While large language models (LLMs) have recently advanced natural language processing capabilities, their high inference latency and significant infrastructure demands make them less suitable for real-time e-commerce applications. Consequently, transformer-based encoder models are widely adopted for relevance classification tasks. These models typically evaluate the relevance of a product to a given query by encoding the query and product title as input features. As e-commerce stores expand into new marketplaces, the need for language- and region-specific relevance models grows, often resulting in the sequential development and maintenance of separate models per marketplace. To address this challenge, we introduce a multilingual continual learning (CL) framework that mitigates catastrophic forgetting. Our proposed method, FR-LoRA (Fisher Regularized LoRA), integrates Elastic Weight Consolidation (EWC) with marketplace-specific LoRA modules, where each LoRA is regularized using the Fisher information matrix. FR-LoRA retains the same inference-time footprint as the base model, ensuring zero additional latency while enabling frequent, scalable updates. Empirically, our approach achieves a ~3% ROC-AUC improvement over single-marketplace baselines and outperforms several recent CL baselines on both proprietary and public datasets.
Autoregressive Generative Retrieval for Industrial-Scale Recommendations at Pinterest
Authors: Prabhat Agarwal, Anirudhan Badrinath, Laksh Bhasin, Jaewon Yang, Jiajing Xu, Charles Rosenberg
Venue: CIKM
Year: 2025

Generative retrieval methods utilize generative sequential modeling techniques, such as transformers, to generate candidate items for recommender systems. These methods have demonstrated promising results in academic benchmarks, surpassing traditional retrieval models like two-tower architectures. However, current generative retrieval methods lack the scalability required for industrial recommender systems, and they are insufficiently flexible to satisfy the multiple metric requirements of modern systems. This talk introduces PinRec, a novel generative retrieval model developed for applications at Pinterest. PinRec utilizes outcome-conditioned generation, enabling modelers to specify how to balance various outcome metrics, such as the number of saves and clicks, to effectively align with business goals and user exploration. Additionally, PinRec incorporates multi-token generation to enhance output diversity while optimizing generation. Our experiments demonstrate that PinRec can successfully balance performance, diversity, and efficiency, delivering a significant positive impact to users using generative models. This talk presents the first in-depth study on productionizing generative retrieval. Our experiments demonstrate that PinRec can successfully balance performance, diversity, and efficiency, delivering a significant positive impact such as +2% sitewide clicks and +4% search repins. This paper marks a significant milestone in generative retrieval, as it presents, to our knowledge, the first rigorous study on implementing generative retrieval at the scale of Pinterest.
EvenOddML: Even and Odd Aggregation with Multi-Level Contrastive Learning for Bipartite Graph
Authors: Manasvi Aggarwal, Jahnavi Methukumalli, Deepanshu Bagotia, Suhas Powar
Venue: CIKM
Year: 2025

Bipartite graphs, which model relationships between two distinct entity types, are common in various applications. Existing bipartite graph neural networks (GNNs) often fail to capture both local and global structures and inadequately consider the indirect and direct influences from same-type nodes, leading to suboptimal performance. To address these issues, we propose the EvenOddML model, a contrastive learning based node representation learning module for bipartite graphs. The model comprises an Even-Odd encoder (an even-N-odd aggregation module) that aggregates information from immediate neighbors as well as 2-hop neighbors, both directly and indirectly. We also introduce a novel three-level contrastive learning framework (Layer Level, Type-Global Level, and Network- Global Level) that hierarchically maximizes mutual information by integrating local and global information at various scales. We evaluate EvenOddML on recommendation and link prediction tasks, showing its effectiveness over state-of-the-art methods in bipartite graph representation learning.
Mixed data k-Anonymization by Consistent Maximal Association and Microaggregation
Authors: Julien Ah-Pine, Nathaniel Gbenro
Venue: CIKM
Year: 2025

This paper addresses the challenge of anonymizing mixed data, comprising both categorical (qualitative) and numerical (continuous) variables, while preserving data utility. The inherent heterogeneity of such data complicates the use of traditional anonymization methods. To overcome this limitation, we propose a novel microaggregation-based framework for k-anonymization that integrates statistical association measures applicable to both variable types, ensuring a coherent and consistent treatment. Our approach, called Mix-R 2, relies on a unified set of core concepts grounded in analysis of variance, enabling the application of a common methodology to both categorical and numerical attributes. By leveraging these consistent association measures, the framework improves the robustness of the k-anonymization process, delivering strong privacy protection while maintaining high data utility. Numerical experiments on benchmark datasets demonstrate the effectiveness and advantages of our method, highlighting its contribution to privacy-preserving analysis of mixed-type data.
Quantization Aware Matryoshka Adaptation: Leveraging Matryoshka Learning, Quantization, and Bitwise Operations for Reduced Storage and Improved Retrieval Speed
Authors: Faizan Ahemad
Venue: CIKM
Year: 2025

We introduce Quantization Aware Matryoshka Adaptation (QAMA), a unified framework for creating compact yet semantically rich embeddings through Matryoshka Representation Learning and multi-level quantization. Our approach learns nested embeddings that gracefully shrink to smaller dimensional subsets and leverages bitwise operations (XOR, NOT, POPCOUNT) for efficient retrieval. By augmenting transformer-based encoders with lightweight feedforward layers and specialized regularization (Matryoshka Loss, Orthogonality, Information Bottleneck, and Quantization Loss), we produce quantization-friendly representations that preserve essential information in early dimensions. We explore 0.5-bit, 1-bit, 1.5-bit, and 2-bit quantization levels, as well as a Hybrid Quantization scheme that adaptively allocates higher precision to dimensions with greater information content. Across extensive evaluations on Modern BERT (MB) and MiniLM models, 2-bit quantization and Hybrid Quant consistently recover up to 95--98% of the original full-precision (FP32) performance while reducing memory usage by over 90%. Even at low embedding dimensions (e.g., 96--192), QAMA's hierarchical training ensures that performance remains surprisingly robust, highlighting the effectiveness of our bit-level expansions and nested representation learning. Our proposed end-to-end training with quantization-aware loss yields embeddings that map cleanly into discrete levels, supporting rapid Hamming distance calculations for semantic similarity search. % using bitwise operations. Our framework QAMA offers a practical means to optimize embedding storage and retrieval speed for information retrieval systems.
Efficient Knowledge Transfer from Large to Small Language Models via Low-Overhead Query Mechanism
Authors: Faizan Ahemad
Venue: CIKM
Year: 2025

Small language models offer computational efficiency but often lack the performance of larger models. We introduce a novel query mechanism enabling small models to efficiently extract knowledge from large models during inference. Our approach executes the large model on a single vector prompt, significantly reducing computational overhead compared to full model execution. Using a 1B-parameter Llama 3.2 as the small model and 3B/8B Llama models as knowledge sources, we evaluate on 20 diverse benchmarks spanning reasoning, factual recall, and reading comprehension tasks. Our best method achieves substantial improvements over the small model baseline, with particularly strong gains on factual memory tasks (average +114.9% relative improvement). Notable results include improvements on TriviaQA (74.4% vs 35.4% baseline), Freebase Questions (42.5% vs 14.6%), and Natural Questions (34.9% vs 12.6%). Our approach consistently outperforms traditional fine-tuning methods while maintaining efficiency, achieving improvements of over 41% across multiple tasks while incurring only 31% additional compute over the 1B baseline.
The Geometry of Knowledge and Computational Discovery
Authors: Yong-Yeol Ahn
Venue: CIKM
Year: 2025

Modern neural networks transform vast datasets into continuous embedding spaces, translating semantic relationships into geometric structures. The key to unlocking their full potential lies in making these representations interpretable. This talk presents a simple theory underlining the interpretability of the embedding space and how this principle can allow us to analyze the data, design new metrics, and model the dynamics of the system, moving beyond black-box models to data-driven interpretable insights.
Explicit Path CGR: Maintaining Sequence Fidelity in Geometric Representations
Authors: Sarwan Ali
Venue: CIKM
Year: 2025

We present a novel information-preserving Chaos Game Representation (CGR) method, also called Reverse-CGR (R-CGR), for biological sequence analysis that addresses the fundamental limitation of traditional CGR approaches - the loss of sequence information during geometric mapping. Our method introduces complete sequence recovery through explicit path encoding combined with rational arithmetic precision control, enabling perfect sequence reconstruction from stored geometric traces. Unlike purely geometric approaches, our reversibility is achieved through comprehensive path storage that maintains both positional and character information at each step. We demonstrate the effectiveness of R-CGR on biological sequence classification tasks, achieving competitive performance compared to traditional sequence-based methods while providing interpretable geometric visualizations. The approach generates feature-rich images suitable for deep learning while maintaining complete sequence information through explicit encoding, opening new avenues for interpretable bioinformatics analysis where both accuracy and sequence recovery are essential.
IARD: Intruder Activity Recognition Dataset for Threat Detection
Authors: Shehzad Ali, Md Tanvir Islam, Ik Hyun Lee, Saeed Anwar, Javier Del Ser, Khan Muhammad
Venue: CIKM
Year: 2025

Home security and surveillance systems are rapidly evolving, with Artificial Intelligence (AI) playing a transformative role in enhancing safety and threat detection. While several AI methods and datasets for intruder-related risk assessment exist, they predominantly focus on face detection and recognition, leaving a significant gap in addressing high-risk scenarios involving malicious intent, such as theft or harm. The lack of dedicated datasets for recognizing complex intruder activities, such as carrying weapons or engaging in destructive actions like kicking doors or breaking locks, limits the development of robust solutions. This work bridges this gap by introducing the Intruder Activity Recognition Dataset (IARD), a video dataset specifically designed to recognize four critical intruder activities: Armed Intruder, Door Kick, Intruder Inside and Lock Breaking. Leveraging IARD, we thoroughly benchmark various state-of-the-art methods, among which a Vision Transformer is found to achieve an impressive 93.3% accuracy in recognizing intruder actions. Our contribution highlights the potential of IARD in advancing AI-driven surveillance systems, providing a foundational dataset and benchmark for recognizing complex intruder activities.
A Robust Clustered Federated Learning Approach for Non-IID Data with Quantity Skew
Authors: Michael Ben Ali, Imen Megdiche, André Péninou, Olivier Teste
Venue: CIKM
Year: 2025

Federated Learning (FL) is a decentralized paradigm that enables a client-server architecture to collaboratively train a global Artificial Intelligence model without sharing raw data, thereby preserving privacy. A key challenge in FL is Non-IID data. Quantity Skew (QS) is a particular problem of Non-IID, where clients hold highly heterogeneous data volumes. Clustered Federated Learning (CFL) is an emergent variant of FL that presents a promising solution to Non-IID problem. It improves models' performance by grouping clients with similar data distributions into clusters. CFL methods generally fall into two operating strategies. In the first strategy, clients select the cluster that minimizes the local training loss. In the second strategy, the server groups clients based on local model similarities. However, most CFL methods lack systematic evaluation under QS but present significant challenges because of it. In this paper, we present two main contributions. The first one is an evaluation of state-of-the-art CFL algorithms under various Non-IID settings, applying multiple QS scenarios to assess their robustness. Our second contribution is a novel iterative CFL algorithm, named CORNFLQS, which proposes an optimal coordination between both operating strategies of CFL. Our approach is robust against the different variations of QS settings. We conducted intensive experiments on six image classification datasets, resulting in 270 Non-IID configurations. The results show that CORNFLQS achieves the highest average ranking in both accuracy and clustering quality, as well as strong robustness to QS perturbations. Overall, our approach outperforms actual CFL algorithms.
Towards Trustworthy AI: Enhancing Factuality, Bias, and Compliance in LLMs
Authors: Pratuat Amatya
Venue: CIKM
Year: 2025

Large Language Models (LLMs) have demonstrated impressive capabilities across natural language tasks, yet critical concerns persist regarding their factual reliability, societal bias, and alignment with regulatory norms. Central to addressing these challenges is the ability to systematically extract, normalize, and rank the claims made by LLMs-whether factual, normative, or policy-relevant. However, existing approaches often assume that claims are self-contained within individual sentences, overlooking the reality that many important claims emerge only through multi-sentence context. This leads to fragmented analysis and underestimates the complexity of model behavior. Furthermore, current methods are limited in scope, often relying on narrow domains and fixed knowledge sources, and struggle to identify or prioritize claims with potential for social harm or legal noncompliance. By advancing methods for context-aware claim extraction, standardization across sensitive attributes and regulatory categories, and risk-informed ranking, this research aims to provide a more comprehensive foundation for evaluating and auditing LLM outputs. Such a framework is essential for building systems that are not only factually grounded, but also fair, transparent, and compliant in high-stakes applications.
AI Planning for Data Exploration
Authors: Sihem Amer-Yahia
Venue: CIKM
Year: 2025

Data Exploration is an incremental process that helps users ex- press what they want through a conversation with the data. A large body of work focused on automating data exploration (e.g., to ex- plore very large galaxy data in SDSS [6, 7], to summarize large datasets [8, 9], or to explore ratings [2, 3] and search for prod- ucts [5]). Reinforcement Learning (RL) is one of the most notable approaches to automate data exploration and several solutions have been proposed. With the advent of Large Language Models and their ability to reason sequentially, it has become legitimate to ask the question: would LLMs and, more generally AI planning, outperform a customized RL policy in data exploration [1]? More specifically, would LLMs help circumvent retraining for new tasks and strike a balance between specificity and generality [4]? This talk will attempt to answer this question by reviewing RL training and policy reusability for data exploration. This talk will start with an overview of exploratory data analysis and the various uses of RL to automate this online decision-making process. Then I will introduce AI planning and the need for policy reusability in RL. The last part of the talk will discuss pressing questions on AI planning applied to data exploration, including memory management, evaluation, and responsible deployment.