GenAI4ELab/papercli-papers huggingface dataset:
Scalable Graph Classification via Random Walk Fingerprints (Extended Abstract)
Authors: Peiyan Li; Honglian Wang; Christian Böhm
Venue: IJCAI 2025 (main)

Abstract: We design a lightweight structural feature extraction technique for graph classification. It leverages node subsets and connection strength reflected by random-walk-based heuristics, presenting a scalable, unsupervised, and easily interpretable alternative. We provide theoretical insights into our technical design and establish a relation between the extracted structural features and the graph spectrum. We show our method achieves high levels of computational efficiency while maintaining robust classification accuracy.
A Relaxed Symmetric Non-negative Matrix Factorization Approach for Community Discovery (Extended Abstract)
Authors: Zhigang Liu; Hao Yan; Yurong Zhong; Weiling Li
Venue: IJCAI 2025 (main)

Abstract: Community discovery is a prominent issue in com-plex network analysis. Symmetric non-negative matrix factorization (SNMF) is frequently adopted to tackle this issue. The use of a single feature matrix can depict network symmetry, but it limits its ability to learn node representations. To break this limitation, we present a novel Relaxed Symmetric NMF (RSN) approach to boost an SNMF-based community detector. It works by 1) expanding the representational space and its degrees of freedom with multiple feature factors; 2) integrating the well-designed equality-constraints to make the model well-aware of the network’s intrinsic symmetry; 3) employing graph regularization to pre-serve the local geometric invariance of the network structure; and 4) separating constraints from decision variables for efficient optimization via the principle of alternating-direction-method of multi-pliers. RSN’s effectiveness is verified through empirical studies on six real social networks, show-casing superior precision in community discovery over existing models and baselines.
How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging (Extended Abstract)
Authors: Qianou Ma; Hua Shen; Ken Koedinger; Tongshuang Wu
Venue: IJCAI 2025 (main)

Abstract: Large Language Models (LLMs) excel at generating content at impeccable speeds. However, they are imperfect and still make various mistakes. In Computer Science education, as LLMs are widely recognized as "AI pair programmers," it becomes increasingly important to train students on evaluating and debugging LLM-generated codes. In this work, we introduce HypoCompass, a novel system to facilitate deliberate practice on debugging, where human novices play the role of Teaching Assistants and help LLM-powered teachable agents debug code. We enable effective task delegation between students and LLMs in this learning-by-teaching environment: students focus on hypothesizing the cause of code errors, while adjacent skills like code completion are offloaded to LLM-agents. Our evaluations demonstrate that HypoCompass generates high-quality training materials (e.g., bugs and fixes), outperforming human counterparts fourfold in efficiency, and significantly improves student performance on debugging by 12% in the pre-to-post test.
Data Void Exploits: Tracking & Mitigation Strategies (Extended Abstract)
Authors: Miro Mannino; Junior Garcia; Reem Hazim; Azza Abouzied; Paolo Papotti
Venue: IJCAI 2025 (main)

Abstract: In the evolving landscape of online information, disinformation is a growing concern. A concept central to this challenge is the "data void", a situation where there is a lack of relevant information online regarding certain search terms. This creates an opportunity for misleading or false narratives to fill the gap, often influencing public perception. In this work, we present methods to track and mitigate data voids in Web search settings.
Decoupled Search for the Masses: A Novel Task Transformation for Classical Planning (Extended Abstract)
Authors: David Speck; Daniel Gnad
Venue: IJCAI 2025 (main)

Abstract: Classical planning provides a framework for solving sequential decision-making problems, i.e., finding a sequence of actions that transforms the current state of the world into a state that satisfies a desired goal condition. Planning tasks are modeled in a logic that describes the environment and its dynamics. It is well known that the specific problem formulation can significantly affect the performance of planning systems solving problems like the Rubik's Cube or finding algorithms for matrix multiplication. In this work, we propose a domain-general problem reformulation that embodies decoupled search, a search-reduction technique from classical planning and model checking. Decoupled search decomposes a given problem to exploit its structure, achieving exponential reductions over other search techniques. We show that decoupled search can be captured exactly as a task reformulation and that, on many benchmark domains, it performs as good and sometimes even better than a native decoupled-search implementation.
CAM-Based Methods Can See through Walls (Extended Abstract)
Authors: Magamed Taimeskhanov; Ronan Sicre; Damien Garreau
Venue: IJCAI 2025 (main)

Abstract: CAM-based methods are widely-used post-hoc interpretability methods that produce a saliency map to explain the decision of an image classification model. The saliency map highlights the important areas of the image relevant to the prediction. In this paper, we show that most of these methods can incorrectly attribute an important score to parts of the image that the model cannot see. We show that this phenomenon occurs both theoretically and experimentally. On the theory side, we analyze the behavior of GradCAM on a simple masked CNN model at initialization. Experimentally, we train a VGG-like model constrained to not use the lower part of the image and nevertheless observe positive scores in the unseen part of the image. This behavior is evaluated quantitatively on two new datasets. We believe that this is problematic, potentially leading to mis-interpretation of the model's behavior.
Interpreting Pretrained Language Models via Concept Bottlenecks (Extended Abstract)
Authors: Zhen Tan; Lu Cheng; Song Wang; Yuan Bo; Jundong Li; Huan Liu
Venue: IJCAI 2025 (main)

Abstract: Pretrained language models (PLMs) achieve state-of-the-art results but often function as ``black boxes'', hindering interpretability and responsible deployment. While methods like attention analysis exist, they often lack clarity and intuitiveness. We propose interpreting PLMs through high-level, human-understandable concepts using Concept Bottleneck Models (CBMs). This extended abstract introduces C3M (ChatGPT-guided Concept augmentation with Concept-level Mixup), a novel framework for training Concept-Bottleneck-Enabled PLMs (CBE-PLMs). C3M leverages Large Language Models (LLMs) like ChatGPT to augment concept sets and generate noisy concept labels, combined with a concept-level MixUp mechanism to enhance robustness and effectively learn from both human-annotated and machine-generated concepts. Empirical results show our approach provides intuitive explanations, aids model diagnosis via test-time intervention, and improves the interpretability-utility trade-off, even with limited or noisy concept annotations. This is an concise version of [Tan et al., 2024b], recipient of the Best Paper Award at PAKDD 2024. Code and data are released at https://github.com/Zhen-Tan-dmml/CBM_NLP.git.
Credulous Acceptance in High-Order Argumentation Frameworks with Necessities: An Incremental Approach (Abstract Reprint)
Authors: Gianvincenzo Alfano; Andrea Cohen; Sebastian Gottifredi; Sergio Greco; Francesco Parisi; Guillermo R. Simari
Venue: IJCAI 2025 (main)

Abstract: Argumentation is an important research area in the field of AI. There is a substantial amount of work on different aspects of Dung's abstract Argumentation Framework (AF). Two relevant aspects considered separately so far are: i) extending the framework to account for recursive attacks and supports, and ii) considering dynamics, i.e., AFs evolving over time. In this paper, we jointly deal with these two aspects. We focus on High-Order Argumentation Frameworks with Necessities (HOAFNs) which allow for attack and support relations (interpreted as necessity) not only between arguments but also targeting attacks and supports at any level. We propose an approach for the incremental evaluation of the credulous acceptance problem in HOAFNs, by “incrementally” computing an extension (a set of accepted arguments, attacks and supports), if it exists, containing a given goal element in an updated HOAFN. In particular, we are interested in monitoring the credulous acceptance of a given argument, attack or support (goal) in an evolving HOAFN. Thus, our approach assumes to have a HOAFN Δ, a goal ϱ occurring in Δ, an extension E for Δ containing ϱ, and an update u establishing some changes in the original HOAFN, and uses the extension for first checking whether the update is relevant; for relevant updates, an extension of the updated HOAFN containing the goal is computed by translating the problem to the AF domain and leveraging on AF solvers. We provide formal results for our incremental approach and empirically show that it outperforms the evaluation from scratch of the credulous acceptance problem for an updated HOAFN.
Scalable Primal Heuristics Using Graph Neural Networks for Combinatorial Optimization (Abstract Reprint)
Authors: Furkan Cantürk; Taha Varol; Reyhan Aydoğan; Okan Örsan Özener
Venue: IJCAI 2025 (main)

Abstract: By examining the patterns of solutions obtained for various instances, one can gain insights into the structure and behavior of combinatorial optimization (CO) problems and develop efficient algorithms for solving them. Machine learning techniques, especially Graph Neural Networks (GNNs), have shown promise in parametrizing and automating this laborious design process. The inductive bias of GNNs allows for learning solutions to mixed-integer programming (MIP) formulations of constrained CO problems with a relational representation of decision variables and constraints. The trained GNNs can be leveraged with primal heuristics to construct high-quality feasible solutions to CO problems quickly. However, current GNN-based end-to-end learning approaches have limitations for scalable training and generalization on larger-scale instances; therefore, they have been mostly evaluated over small-scale instances. Addressing this issue, our study builds on supervised learning of optimal solutions to the downscaled instances of given large-scale CO problems. We introduce several improvements on a recent GNN model for CO to generalize on instances of a larger scale than those used in training. We also propose a two-stage primal heuristic strategy based on uncertainty-quantification to automatically configure how solution search relies on the predicted decision values. Our models can generalize on 16x upscaled instances of commonly benchmarked five CO problems. Unlike the regressive performance of existing GNN-based CO approaches as the scale of problems increases, the CO pipelines using our models offer an incremental performance improvement relative to CPLEX. The proposed uncertainty-based primal heuristics provide 6-75% better optimality gap values and 45-99% better primal gap values for the 16x upscaled instances and brings immense speedup to obtain high-quality solutions. All these gains are achieved through a computationally efficient modeling approach without sacrificing solution quality.
The Human in Interactive Machine Learning: Analysis and Perspectives for Ambient Intelligence (Abstract Reprint)
Authors: Kevin Delcourt; Sylvie Trouilhet; Jean-Paul Arcangeli; Françoise Adreit
Venue: IJCAI 2025 (main)

Abstract: As the vision of Ambient Intelligence (AmI) becomes more feasible, the challenge of designing effective and usable human-machine interaction in this context becomes increasingly important. Interactive Machine Learning (IML) offers a set of techniques and tools to involve end-users in the machine learning process, making it possible to build more trustworthy and adaptable ambient systems. In this paper, our focus is on exploring approaches to effectively integrate and assist human users within ML-based AmI systems. Through a survey of key IML-related contributions, we identify principles for designing effective human-AI interaction in AmI applications. We apply them to the case of Opportunistic Composition, which is an approach to achieve AmI, to enhance collaboration between humans and Artificial Intelligence. Our study highlights the need for user-centered and context-aware design, and provides insights into the challenges and opportunities of integrating IML techniques into AmI systems.
On Measuring Inconsistency in Graph Databases with Regular Path Constraints (Abstract Reprint)
Authors: John Grant; Francesco Parisi
Venue: IJCAI 2025 (main)

Abstract: Real-world data are often inconsistent. Although a substantial amount of research has been done on measuring inconsistency, this research concentrated on knowledge bases formalized in propositional logic. Recently, inconsistency measures have been introduced for relational databases. However, nowadays, real-world information is always more frequently represented by graph-based structures which offer a more intuitive conceptualization than relational ones. In this paper, we explore inconsistency measures for graph databases with regular path constraints, a class of integrity constraints based on a well-known navigational language for graph data. In this context, we define several inconsistency measures dealing with specific elements contributing to inconsistency in graph databases. We also define some rationality postulates that are desirable properties for an inconsistency measure for graph databases. We analyze the compliance of each measure with each postulate and find various degrees of satisfaction; in fact, one of the measures satisfies all the postulates. Finally, we investigate the data and combined complexity of the calculation of all the measures as well as the complexity of deciding whether a measure is lower than, equal to, or greater than a given threshold. It turns out that for a majority of the measures these problems are tractable, while for the other different levels of intractability are exhibited.
CADS: A Systematic Literature Review on the Challenges of Abstractive Dialogue Summarization (Abstract Reprint)
Authors: Frederic Kirstein; Jan Philip Wahle; Bela Gipp; Terry Ruas
Venue: IJCAI 2025 (main)

Abstract: Abstractive dialogue summarization is the task of distilling conversations into informative and concise summaries. Although focused reviews have been conducted on this topic, there is a lack of comprehensive work that details the core challenges of dialogue summarization, unifies the differing understanding of the task, and aligns proposed techniques, datasets, and evaluation metrics with the challenges. This article summarizes the research on Transformer-based abstractive summarization for English dialogues by systematically reviewing 1262 unique research papers published between 2019 and 2024, relying on the Semantic Scholar and DBLP databases. We cover the main challenges present in dialog summarization (i.e., language, structure, comprehension, speaker, salience, and factuality) and link them to corresponding techniques such as graph-based approaches, additional training tasks, and planning strategies, which typically overly rely on BART-based encoder-decoder models. Recent advances in training methods have led to substantial improvements in language-related challenges. However, challenges such as comprehension, factuality, and salience remain difficult and present significant research opportunities. We further investigate how these approaches are typically analyzed, covering the datasets for the subdomains of dialogue (e.g., meeting, customer service, and medical), the established automatic metrics (e.g., ROUGE), and common human evaluation approaches for assigning scores and evaluating annotator agreement. We observe that only a few datasets (i.e., SAMSum, AMI, DialogSum) are widely used. Despite its limitations, the ROUGE metric is the most commonly used, while human evaluation, considered the gold standard, is frequently reported without sufficient detail on the inter-annotator agreement and annotation guidelines. Additionally, we discuss the possible implications of the recently explored large language models and conclude that our described challenge taxonomy remains relevant despite a potential shift in relevance and difficulty.
Confidence-based Estimators for Predictive Performance in Model Monitoring (Abstract Reprint)
Authors: Juhani Kivimäki; Jakub Białek; Jukka K. Nurminen; Wojtek Kuberski
Venue: IJCAI 2025 (main)

Abstract: After a machine learning model has been deployed into production, its predictive performance needs to be monitored. Ideally, such monitoring can be carried out by comparing the model’s predictions against ground truth labels. For this to be possible, the ground truth labels must be available relatively soon after inference. However, there are many use cases where ground truth labels are available only after a significant delay, or in the worst case, not at all. In such cases, directly monitoring the model’s predictive performance is impossible. Recently, novel methods for estimating the predictive performance of a model when ground truth is unavailable have been developed. Many of these methods leverage model confidence or other uncertainty estimates and are experimentally compared against a naive baseline method, namely Average Confidence (AC), which estimates model accuracy as the average of confidence scores for a given set of predictions. However, until now the theoretical properties of the AC method have not been properly explored. In this paper, we bridge this gap by reviewing the AC method and show that under certain general assumptions, it is an unbiased and consistent estimator of model accuracy. We also augment the AC method by deriving valid confidence intervals for the estimates it produces. These contributions elevate AC from an ad-hoc estimator to a principled one, encouraging its use in practice. We complement our theoretical results with empirical experiments, comparing AC against more complex estimators in a monitoring setting under covariate shift. We conduct our experiments using synthetic datasets, which allow for full control over the nature of the shift. Our experiments with binary classifiers show that the AC method is able to beat other estimators in many cases. However, the comparative quality of the different estimators is found to be heavily case-dependent.
CureGraph: Contrastive Multi-Modal Graph Representation Learning for Urban Living Circle Health Profiling and Prediction (Abstract Reprint)
Authors: Jinlin Li; Xiao Zhou
Venue: IJCAI 2025 (main)

Abstract: The early detection and prediction of health status decline among the elderly at the neighborhood level are of great significance for urban planning and public health policymaking. While existing studies affirm the connection between living environments and health outcomes, most rely on single data modalities or simplistic feature concatenation of multi-modal information, limiting their ability to comprehensively profile the health-oriented urban environments. To fill this gap, we propose CureGraph, a contrastive multi-modal representation learning framework for urban health prediction that employs graph-based techniques to infer the prevalence of common chronic diseases among the elderly within the urban living circles of each neighborhood. CureGraph leverages rich multi-modal information, including photos and textual reviews of residential areas and their surrounding points of interest, to generate urban neighborhood embeddings. By integrating pre-trained visual and textual encoders with graph modeling techniques, CureGraph captures cross-modal spatial dependencies, offering a comprehensive understanding of urban environments tailored to elderly health considerations. Extensive experiments on real-world datasets demonstrate that CureGraph improves the best baseline by 28% on average in terms of across elderly disease risk prediction tasks. Moreover, the model enables the identification of stage-wise chronic disease progression and supports comparative public health analysis across neighborhoods, offering actionable insights for sustainable urban development and enhanced quality of life. The code is publicly available at https://github.com/jinlin2021/CureGraph.
A Semantic Framework for Neurosymbolic Computation (Abstract Reprint)
Authors: Simon Odense; Artur Garcez
Venue: IJCAI 2025 (main)

Abstract: The field of neurosymbolic AI aims to benefit from the combination of neural networks and symbolic systems. A cornerstone of the field is the translation or encoding of symbolic knowledge into neural networks. Although many neurosymbolic methods and approaches have been proposed, and with a large increase in recent years, no common definition of encoding exists that can enable a precise, theoretical comparison of neurosymbolic methods. This paper addresses this problem by introducing a semantic framework for neurosymbolic AI. We start by providing a formal definition of semantic encoding, specifying the components and conditions under which a knowledge-base can be encoded correctly by a neural network. We then show that many neurosymbolic approaches are accounted for by this definition. We provide a number of examples and correspondence proofs applying the proposed framework to the neural encoding of various forms of knowledge representation. Many, at first sight disparate, neurosymbolic methods, are shown to fall within the proposed formalization. This is expected to provide guidance to future neurosymbolic encodings by placing them in the broader context of semantic encodings of entire families of existing neurosymbolic systems. The paper hopes to help initiate a discussion around the provision of a theory for neurosymbolic AI and a semantics for deep learning.
Human-AI Coevolution (Abstract Reprint)
Authors: Dino Pedreschi; Luca Pappalardo; Emanuele Ferragina; Ricardo Baeza-Yates; Albert-László Barabási; Frank Dignum; Virginia Dignum; Tina Eliassi-Rad; Fosca Giannotti; János Kertész; Alistair Knott; Yannis Ioannidis; Paul Lukowicz; Andrea Passarella; Alex Sandy Pentland; John Shawe-Taylor; Alessandro Vespignani
Venue: IJCAI 2025 (main)

Abstract: Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.