OpenRaiser/Intern-Atlas huggingface dataset:
LLM-Powered Information Extraction for the Dairy Financial Domain: Tackling Data Scarcity and Ambiguity
Authors: Chunyan An, Yuying Huang, Qiang Yang, Siyu Yuan, Zhixu Li
Venue: CIKM
Year: 2025

Information extraction is a critical technology for intelligent analysis and risk assessment in the dairy financial domain. However, real-world applications face three major challenges: the complexity and diversity of entity-relation types, significant data imbalance, and ambiguity in textual expressions. Traditional methods often fail to capture rare patterns, struggle with vague mentions, and exhibit poor generalization in low-resource settings. To address these issues, we propose a novel framework that integrates large language models (LLMs) with targeted data augmentation and agent-based retrieval-augmented generation (RAG). Our approach builds on the BaiChuan2 model, which is first adapted to the dairy finance domain via secondary pretraining. We introduce a two-stage data augmentation strategy: the first stage uses ChatGPT to generate pseudo-samples for rare types, and the second stage refines model weaknesses based on prediction-guided feedback. These augmented datasets are used to fine-tune the model through prompt-based supervised learning with LoRA. To further enhance robustness, we incorporate an agent-based RAG module for completing vague or underspecified entities by retrieving external contextual knowledge. Extensive experiments demonstrate that our framework achieves state-of-the-art performance, with the improved metric, i.e., F1+ scores, of 0.876 and 0.824 for entity recognition and relation extraction, respectively. The RAG component boosts entity completion accuracy to 0.802 while reducing retrieval latency by over 6x, showcasing both the effectiveness and practicality of our method in real-world dairy financial applications.
White Rabbit: Demonstrating Online KG Pathfinding Using Embeddings
Authors: Panagiotis Antivasis, Giannis Vassiliou, Georgios Tsamis, Paraskevi Zacharia, Eleftheria Barka, Julian Gini, Argyri Kyriakaki, Giorgos Andreadakis, Ioannis Christodoulakis, Nikos Papadakis, Haridimos Kondylakis
Venue: CIKM
Year: 2025

The paper introduces White Rabbit, a novel method for discovering high-quality, meaningful paths between entities in online Knowledge Graphs (KGs). Traditional exploration methods, such as SPARQL endpoints, struggle due to the large size and complexity of KGs. The proposed approach addresses this by introducing the problem of context-aware path finding, ensuring that retrieved paths are coherent and involve highly relevant entities. White Rabbit uses embeddings to score entity neighbors, a queue-based prioritization mechanism, and an iterative refinement process to improve efficiency and relevance. The system is demonstrated live, allowing participants to test it and compare against baseline methods (structural approaches, pretrained embeddings, and large language models). Results show that White Rabbit enhances both the efficiency of exploration and the quality of discovered paths.
Building Trustworthy Peer Review Quality Assessment Systems
Authors: Negar Arabzadeh, Sajad Ebrahimi, Ali Ghorbanpour, Soroush Sadeghian, Sara Salamat, Muhan Li, Hai Son Le, Mahdi Bashari, Ebrahim Bagheri
Venue: CIKM
Year: 2025

Peer review is foundational to academic publishing, yet the quality of reviews remains difficult to assess at scale due to subjectivity, inconsistency, and the lack of standardized evaluation mechanisms. This talk presents our experience developing and deploying a scalable framework for assessing review quality in operational settings. We combine two complementary approaches: interpretable machine learning models built on quantifiable review- and reviewer-level features, and the application of large language models (LLMs), including Qwen, Phi, and GPT-4o, in zero- and few-shot configurations for textual quality evaluation. We also explore the fine-tuning of LLMs on expert-annotated datasets to examine their upper-bound capabilities. To benchmark these methods, we constructed a dataset of over 700 paper-review pairs labeled by domain experts across multiple quality dimensions. Our findings demonstrate that transparent, feature-based models consistently outperform LLMs in reliability and generalization, particularly when evaluating conceptual depth and argumentative structure. The talk will highlight key engineering choices, deployment challenges, and broader implications for integrating automated review evaluation into scholarly workflows.
RELINK: Edge Activation for Closed Network Influence Maximization via Deep Reinforcement Learning
Authors: Shivvrat Arya, Smita Ghosh, Bryan Maruyama, Venkatesh Srinivasan
Venue: CIKM
Year: 2025

Influence Maximization aims to select a subset of elements in a social network to maximize information spread under a diffusion model. While existing work primarily focuses on selecting influential nodes, these approaches assume unrestricted message propagation-an assumption that fails in closed social networks, where content visibility is constrained and node-level activations may be infeasible. Motivated by the growing adoption of privacy-focused platforms such as Signal, Discord, Instagram, and Slack, our work addresses the following fundamental question: How can we learn effective edge activation strategies for influence maximization in closed networks? To answer this question we introduce Reinforcement Learning for Link Activation (RELINK), the first DRL framework for edge-level influence maximization in privacy-constrained networks. It models edge selection as a Markov Decision Process, where the agent learns to activate edges under budget constraints. Unlike prior node-based DRL methods, RELINK uses an edge-centric Q-learning approach that accounts for structural constraints and constrained information propagation. Our framework combines a rich node embedding pipeline with an edge-aware aggregation module. The agent is trained using an n-step Double DQN objective, guided by dense reward signals that capture marginal gains in influence spread. Extensive experiments on real-world networks show that RELINK consistently outperforms existing edge-based methods, achieving up to 15% higher influence spread and improved scalability across diverse settings.
Dynamic Triangulation-Based Graph Rewiring for Graph Neural Networks
Authors: Hugo Attali, Thomas Papastergiou, Nathalie Pernelle, Fragkiskos D. Malliaros
Venue: CIKM
Year: 2025

Graph Neural Networks (GNNs) have emerged as the leading paradigm for learning over graph-structured data. However, their performance is limited by issues inherent to graph topology, most notably oversquashing and oversmoothing. Recent advances in graph rewiring aim to mitigate these limitations by modifying the graph topology to promote more effective information propagation. In this work, we introduce TRIGON, a novel framework that constructs enriched, non-planar triangulations by learning to select relevant triangles from multiple graph views. By jointly optimizing triangle selection and downstream classification performance, our method produces a rewired graph with markedly improved structural properties such as reduced diameter, increased spectral gap, and lower effective resistance compared to existing rewiring methods. Empirical results demonstrate that TRIGON outperforms state-of-the-art approaches on node classification tasks across a range of homophilic and heterophilic benchmarks.
Extreme Multi-Label Completion for Semantic Document Tagging with Taxonomy-Aware Parallel Learning
Authors: Julien Audiffren, Christophe Broillet, Ljiljana Dolamic, Philippe Cudré-Mauroux
Venue: CIKM
Year: 2025

The objective of Extreme Multi-Label Completion (XMLCo) is to predict missing document labels drawn from a very large collection. Together with Extreme Multi-Label Classification (XMLC), XMLCo is arguably one of the most challenging document classification tasks, as the number of potential labels is generally very large compared to the number of labeled documents. The collection of labels is often structured in a taxonomy that encodes relationships between labels, and many methods have been proposed to leverage this hierarchy to improve XMLCo algorithms. In this paper, we propose a new approach to this problem: TAMLEC (Taxonomy-Aware Multi-task Learning for Extreme multi-label Completion). TAMLEC divides the problem into several Taxonomy-Aware Tasks, i.e. into specific subsets of the labels drawn from paths in the taxonomy, and trains on these tasks using a dynamic Parallel Feature sharing approach where parts of the model are shared between tasks while others are task-specific. Then, at inference time, TAMLEC uses the labels available in a document to predict missing labels, using the Weak-Semilattice structure that is naturally induced by the tasks. Our empirical evaluation on real-world datasets shows that TAMLEC substantially outperforms the state-of-the-art in XMLCo. Furthermore, additional experiments show that TAMLEC is particularly suited for few-shot settings, where new tasks or labels are introduced with only few examples after initial training.
Compressed Concatenation of Small Embedding Models
Authors: Ben Ayad Mohamed Ayoub, Michael Dinzinger, Kanishka Ghosh Dastidar, Jelena Mitrovic, Michael Granitzer
Venue: CIKM
Year: 2025

Embedding models are central to dense retrieval, semantic search, and recommendation systems, but their size often makes them impractical to deploy in resource-constrained environments such as browsers or edge devices. While smaller embedding models offer practical advantages, they typically underperform compared to their larger counterparts. To bridge this gap, we demonstrate that concatenating the raw embedding vectors of multiple small models can outperform a single larger baseline on standard retrieval benchmarks. To overcome the resulting high dimensionality of naive concatenation, we introduce a lightweight unified decoder trained with a Matryoshka Representation Learning (MRL) loss. This decoder maps the high-dimensional joint representation to a low-dimensional space, preserving most of the original performance without fine-tuning the base models. We also show that while concatenating more base models yields diminishing gains, the robustness of the decoder's representation under compression and quantization improves. Our experiments show that, on a subset of MTEB retrieval tasks, our concat-encode-quantize pipeline recovers 89% of the original performance with a 48× compression factor when the pipeline is applied to a concatenation of four small embedding models.
Relation-Faceted Graph Pooling with LLM Guidance for Dynamic Span-Aware Information Extraction
Authors: Hye-Yoon Baek, Jinho Choi, Jimyeung Seo, Xiongnan Jin, Dongcheon Lee, Byungkook Oh
Venue: CIKM
Year: 2025

Joint information extraction aims to convert unstructured text into structured knowledge by identifying entities and their relations. However, existing methods often rely on static span formation and relation-agnostic validation, limiting their ability to capture dynamic, context-sensitive semantics. We present RePooL, a hierarchical validation framework that performs fine-grained token-level filtering followed by coarse-grained span-level validation, enabling robust multi-granular semantic modeling. RePooL constructs a dual-view knowledge graph that models tokens and relations as distinct node types. It leverages auxiliary structural relations to encode token-relation semantic compatibility via subject and object roles and to compose multi-token spans dynamically, thereby enabling relation-aware validation across multiple granularities. To further strengthen semantic grounding, RePooL incorporates LLM-guided alignment, which evaluates candidate triples against the input text to specifically reinforce coherent extractions. Extensive experiments on standard IE benchmarks show that RePooL achieves superior performance, demonstrating its effectiveness in modeling fine-grained entity-relation interactions.
MUFFIN: Mixture of User-Adaptive Frequency Filtering for Sequential Recommendation
Authors: Ilwoong Baek, Mincheol Yoon, Seongmin Park, Jongwuk Lee
Venue: CIKM
Year: 2025

Sequential recommendation (SR) aims to predict users' subsequent interactions by modeling their sequential behaviors. Recent studies have explored frequency domain analysis, which effectively models periodic patterns in user sequences. However, existing frequency-domain SR models still face two major drawbacks: (i) limited frequency band coverage, often missing critical behavioral patterns in a specific frequency range, and (ii) lack of personalized frequency filtering, as they apply an identical filter for all users regardless of their distinct frequency characteristics. To address these challenges, we propose a novel frequency-domain model, Mixture of User-adaptive Frequency FIlteriNg (MUFFIN ), operating through two complementary modules. (i) The global filtering module (GFM) handles the entire frequency spectrum to capture comprehensive behavioral patterns. (ii) The local filtering module (LFM) selectively emphasizes important frequency bands without excluding information from other ranges. (iii) In both modules, the user-adaptive filter (UAF) is adopted to generate user-specific frequency filters tailored to individual unique characteristics. Finally, by aggregating both modules, MUFFIN captures diverse user behavioral patterns across the full frequency spectrum. Extensive experiments show that MUFFIN consistently outperforms state-of-the-art frequency-domain SR models over five benchmark datasets. The source code is available at https://github.com/ilwoong100/MUFFIN.
Subclass-Aware Inclusive Classifier via Repulsive Hidden Strata
Authors: Namita Bajpai, Jiaul H. Paik, Sudeshna Sarkar
Venue: CIKM
Year: 2025

Classification models in machine learning are typically trained using coarse-grained class labels. Although these models often achieve strong overall accuracy, their performance is asymmetric across the subclasses that arise out of a common phenomenon called hidden stratification. Generally, the latent subclasses within each class differ substantially in distribution and characteristics, resulting in poor generalization for underrepresented groups. Moreover, imbalanced subclass distributions lead to majority subclasses dominating training, resulting in biased and less reliable models, especially for safety-critical applications (such as medical). To address these challenges, we propose a novel framework that attempts to uncover hidden subclasses via a repulsive point process. Our approach then leverages these fine-grained labels to make the classifier more inclusive across the subclasses. Our approach identifies subclasses without requiring additional supervision, thereby promoting diversity and reducing sensitivity to subclass imbalance. Extensive experiments on four benchmark datasets demonstrate consistent and significant improvements over state-of-the-art baselines across both balanced and imbalanced subclass distributions, underscoring the effectiveness and generalizability of our approach.
ReDSM5: A Reddit Dataset for DSM-5 Depression Detection
Authors: Eliseo Bao, Anxo Pérez, Javier Parapar
Venue: CIKM
Year: 2025

Depression is a pervasive mental health condition that affects hundreds of millions of individuals worldwide, yet many cases remain undiagnosed due to barriers in traditional clinical access and pervasive stigma. Social media platforms, and Reddit in particular, offer rich, user-generated narratives that can reveal early signs of depressive symptomatology. However, existing computational approaches often label entire posts simply as depressed or not depressed, without linking language to specific criteria from the DSM-5, the standard clinical framework for diagnosing depression. This limits both clinical relevance and interpretability. To address this gap, we introduce ReDSM5, a novel Reddit corpus comprising 1484 long-form posts, each exhaustively annotated at the sentence level by a licensed psychologist for the nine DSM-5 depression symptoms. For each label, the annotator also provides a concise clinical rationale grounded in DSM-5 methodology. We conduct an exploratory analysis of the collection, examining lexical, syntactic, and emotional patterns that characterize symptom expression in social media narratives. Compared to prior resources, ReDSM5 uniquely combines symptom-specific supervision with expert explanations, facilitating the development of models that not only detect depression but also generate human-interpretable reasoning. We establish baseline benchmarks for both multi-label symptom classification and explanation generation, providing reference results for future research on detection and interpretability.
SQuAI: Scientific Question-Answering with Multi-Agent Retrieval-Augmented Generation
Authors: Ines Besrour, Jingbo He, Tobias Schreieder, Michael Färber
Venue: CIKM
Year: 2025

We present SQuAI (https://squai.scads.ai/), a scalable and trustworthy multi-agent retrieval-augmented generation (RAG) framework for scientific question answering (QA) with large language models (LLMs). SQuAI addresses key limitations of existing RAG systems in the scholarly domain, where complex, open-domain questions demand accurate answers, explicit claims with citations, and retrieval across millions of scientific documents. Built on over 2.3 million full-text papers from arXiv.org, SQuAI employs four collaborative agents to decompose complex questions into sub-questions, retrieve targeted evidence via hybrid sparse-dense retrieval, and adaptively filter documents to improve contextual relevance. To ensure faithfulness and traceability, SQuAI integrates in-line citations for each generated claim and provides supporting sentences from the source documents. Our system improves faithfulness, answer relevance, and contextual relevance by up to +0.088 (12%) over a strong RAG baseline. We further release a benchmark of 1,000 scientific question-answer-evidence triplets to support reproducibility. With transparent reasoning, verifiable citations, and domain-wide scalability, SQuAI demonstrates how multi-agent RAG enables more trustworthy scientific QA with LLMs.
International Workshop on Multimodal Generative Search and Recommendation (MMGenSR@CIKM 2025)
Authors: Yi Bin, Haoxuan Li, Haokai Ma, Yang Zhang, Wenjie Wang, Yunshan Ma, Yang Yang, Tat-Seng Chua
Venue: CIKM
Year: 2025

Recent breakthroughs in generative Artificial Intelligence (AI) have ignited a revolutionary wave across information retrieval and recommender systems. This workshop serves as a premier interdisciplinary platform to explore how generative models, particularly Large Language Models (LLMs) and Large Multimodal Models (LMMs), are transforming multimodal search and recommendation paradigms [3, 6, 9, 10, 12-14]. We aim to convene researchers and practitioners to discuss innovative architectures, methodologies, and evaluation strategies spanning generative document retrieval [5, 8] generative image retrieval [ 7, 16], grounded answer generation [17], generative recommendation [2, 4, 11], and related tasks involving multiple modalities [1,15]. The workshop will facilitate discussions on improving algorithms, generating personalized content, evolving user-system interactions, enhancing trustworthiness, and refining evaluation methodologies for these cutting-edge systems. This timely workshop seeks to identify promising research directions, address key challenges, and foster collaborations towards the development of next-generation intelligent systems.
Safeguarding Generative AI Applications in Preclinical Imaging through Hybrid Anomaly Detection
Authors: Jakub Binda, Valentina Paneta, Vasileios Eleftheriadis, Hongkyou Chung, Panagiotis G. Papadimitroulas, Neo Christopher Chung
Venue: CIKM
Year: 2025

Generative artificial intelligence (GenAI) holds great potentials to automate and enhance data synthesis in nuclear medicine. However, the high-stakes nature of biomedical imaging necessitates robust mechanisms to detect and manage unexpected or erroneous model behavior. We report development and implementation of a hybrid anomaly detection framework to safeguard GenAI models in BIOEMTECH's eyes™ systems. Two applications are demonstrated: Pose2Xray, which generates synthetic X-rays from photographic mouse images, and DosimetrEYE, which estimates 3D radiation dose maps from 2D SPECT/CT scans. In both cases, our outlier detection (OD) enhances reliability, reduces manual oversight, and supports real-time quality control. This approach strengthens the industrial viability of GenAI in preclinical settings by increasing robustness, scalability, and regulatory compliance.
Uncovering Corporate Influence: A First Scalable Method for Qualifying Holdings Computation
Authors: Livia Blasi, Matteo Brandetti, Costanza Catalano, Andrea Gentili, Davide Magnanimi
Venue: CIKM
Year: 2025

Distributed logic-based reasoning has recently emerged as a powerful, explainable, and auditable approach for solving complex problems across a wide range of domains. Based on our experience in building company ownership graphs at the Bank of Italy, this paper presents a distributed reasoning solution to the qualifying holding problem. Qualifying holdings measure an entity's influence over a bank or financial intermediary and serve as a key metric in banking supervision. Their calculation is particularly challenging due to the large scale of real-world ownership networks and their structural complexity---the latter often not fully addressed by existing regulatory frameworks. To our knowledge, no standardised, efficient computational approach that also faithfully reflects regulatory interpretations has ever been proposed, leaving a significant gap between legal requirements and practical capabilities. We fill this gap by proposing the first mathematical formalisation of the qualifying holding problem, proving it to be inherently hard (#P-complete). Despite its general intractability, we develop a logic-based reasoning algorithm based on Datalog± that enables efficient and parallel computation of qualifying holdings across real-world scenarios. Extensive experiments confirm its practical effectiveness and scalability.
hopwise: A Python Library for Explainable Recommendation based on Path Reasoning over Knowledge Graphs
Authors: Ludovico Boratto, Gianni Fenu, Mirko Marras, Giacomo Medda, Alessandro Soccol
Venue: CIKM
Year: 2025

Explainability is becoming central to the development of responsible recommender systems, especially as path reasoning over knowledge graphs saw increased adoption for extracting structured, semantic user-item connections. However, reproducible research in such field remains limited due to fragmented implementations, missing utilities, and the lack of standardized evaluation pipelines. In this paper, we propose hopwise, an open-source library that supports the full life-cycle of explainable path reasoning recommendation methods over knowledge graphs, from knowledge graph preparation to explanation path delivery and evaluation. Rather than creating a new library from scratch, hopwise builds upon the modular and widely adopted RecBole ecosystem, enriching it with more knowledge graphs, path sampling utilities, path reasoning methods, and metrics for evaluating explanation path utility, coverage, and diversity. We show the framework's utility by means of a benchmark including two knowledge graphs and several recommendation methods. Code and Data: https://github.com/tail-unica/hopwise.