GenAI4ELab/papercli-papers huggingface dataset:
MolHFCNet: Enhancing Molecular Graph Representations with Hierarchical Feature Combining and Hybrid Pretraining
Authors: Duy-Long Nguyen; Duc-Luong Ho-Viet; Anh-Thu Ngo-Tran; Quang H. Nguyen; Binh P. Nguyen
Venue: IJCAI 2025 (main)

Abstract: Efficient molecular property prediction is crucial in bioinformatics and cheminformatics, with applications in drug discovery, materials science, and chemical engineering. This paper introduces MolHFCNet, a graph neural network designed to enhance molecular representation learning. At its core, the n-Hierarchical Features Combining (n-HFC) module aggregates information across multiple hierarchical feature spaces, effectively capturing both local and global graph structures. Unlike conventional models, n-HFC maintains computational complexity comparable to a single full-dimensional graph layer while supporting either 2D or 3D molecular graphs, ensuring flexibility across tasks. Furthermore, we propose a novel graph pretraining strategy that integrates predictive and contrastive learning, enabling the model to capture local chemical interactions and global molecular contexts for robust embeddings. Experimental results on benchmark datasets demonstrate MolHFCNet’s superior accuracy and efficiency compared to state-of-the-art methods, highlighting the potential of high-order hierarchical feature learning for advancing molecular graph analysis. Our code is available at https://github.com/ndlongvn/MolHFCNet.
AI4TRT: Automatic Simulation of Teeth Restoration Treatment
Authors: Feihong Shen; Yuer Ye
Venue: IJCAI 2025 (main)

Abstract: Visualizing restoration treatments is a crucial task in dentistry. Traditionally, dentists drag the standard template tooth line onto the inner image from the front view to simulate the outcome of the restoration. This process lacks the precision needed for patient presentation. We find that calculating the camera pose and the relative positions of the upper and lower jaws can enhance visualization accuracy and efficiency while assisting dentists in treatment design. In this work, we leverage the optical flow model and a customized point renderer to help dentists show the treatment outcome to the patient. Specifically, we take the 3D scan model and the intraoral image pair as input. Our framework automatically outputs the camera pose and the relative position of the upper and lower jaws. With these parameters, dentists can directly design the restoration treatment on the 3D scan model without caring about the 2D visualization. Then the designed tooth line and other simulation modalities can be rendered on the intraoral image with our customized renderer. Our framework relieves the labor of dentists and shows the case precisely.
NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelines
Authors: Chathurangi Shyalika; Renjith Prasad; Fadi El Kalach; Revathy Venkataramanan; Ramtin Zand; Ramy Harik; Amit Sheth
Venue: IJCAI 2025 (main)

Abstract: In modern assembly pipelines, identifying anomalies is crucial in ensuring product quality and operational efficiency. Conventional single-modality methods fail to capture the intricate relationships required for precise anomaly prediction in complex predictive environments with abundant data and multiple modalities. This paper proposes a neurosymbolic AI and fusion-based approach for multimodal anomaly prediction in assembly pipelines. We introduce a time series and image-based fusion model that leverages decision-level fusion techniques. Our research builds upon three primary novel approaches in multimodal learning: time series and image-based decision-level fusion modeling, transfer learning for fusion, and knowledge-infused learning. We evaluate the novel method using our derived and publicly available multimodal dataset and conduct comprehensive ablation studies to assess the impact of our preprocessing techniques and fusion model compared to traditional baselines. The results demonstrate that a neurosymbolic AI-based fusion approach that uses transfer learning can effectively harness the complementary strengths of time series and image data, offering a robust and interpretable approach for anomaly prediction in assembly pipelines with enhanced performance. \noindent The datasets, codes to reproduce the results, supplementary materials, and demo are available at https://github.com/ChathurangiShyalika/NSF-MAP.
COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement
Authors: Zhihao Sui; Liang Hu; Jian Cao; Usman Naseem; Zhongyuan Lai; Qi Zhang
Venue: IJCAI 2025 (main)

Abstract: Large deep learning models have achieved significant success in various tasks. However, the performance of a model can significantly degrade if it is needed to train on datasets with noisy labels with misleading or ambiguous information. To date, there are limited investigations on how to restore performance when model degradation has been incurred by noisy label data. Inspired by the "forgetting mechanism" in neuroscience, which enables accelerating the relearning of correct knowledge by unlearning the wrong knowledge, we propose a robust model restoration and refinement (MRR) framework COLUR, namely Confidence-Oriented Learning, Unlearning and Relearning. Specifically, we implement COLUR with an efficient co-training architecture to unlearn the influence of label noise, and then refine model confidence on each label for relearning. Extensive experiments are conducted on four real datasets and all evaluation results show that COLUR consistently outperforms other SOTA methods after MRR.
Horae: A Domain-Agnostic Language for Automated Service Regulation
Authors: Yutao Sun; Mingshuai Chen; Tiancheng Zhao; Kangjia Zhao; He Li; Jintao Chen; Zhongyi Wang; Liqiang Lu; Xinkui Zhao; Shuiguang Deng; Jianwei Yin
Venue: IJCAI 2025 (main)

Abstract: Artificial intelligence is rapidly encroaching on the field of service regulation. However, existing AI-based regulation techniques are often tailored to specific application domains and thus are difficult to generalize in an automated manner. This paper presents Horae, a unified specification language for modeling (multimodal) regulation rules across a diverse set of domains. We showcase how Horae facilitates an intelligent service regulation pipeline by further exploiting a fine-tuned large language model named RuleGPT that automates the Horae modeling process, thereby yielding an end-to-end framework for fully automated intelligent service regulation. The feasibility and effectiveness of our framework are demonstrated over a benchmark of various real-world regulation domains. In particular, we show that our open-sourced, fine-tuned RuleGPT with 7B parameters suffices to outperform GPT-3.5 and perform on par with GPT-4o.
CoFinDiff: Controllable Financial Diffusion Model for Time Series Generation
Authors: Yuki Tanaka; Ryuji Hashimoto; Takehiro Takayanagi; Zhe Piao; Yuri Murayama; Kiyoshi Izumi
Venue: IJCAI 2025 (main)

Abstract: The generation of synthetic financial data is a critical technology in the financial domain, addressing challenges posed by limited data availability. Traditionally, statistical models have been employed to generate synthetic data. However, these models fail to capture the stylized facts commonly observed in financial data, limiting their practical applicability. Recently, machine learning models have been introduced to address the limitations of statistical models; however, controlling synthetic data generation remains challenging. We propose CoFinDiff (Controllable Financial Diffusion model), a synthetic financial data generation model based on conditional diffusion models that accept conditions about the synthetic time series. By incorporating conditions derived from price data into the conditional diffusion model via cross-attention, CoFinDiff learns the relationships between the conditions and the data, generating synthetic data that align with arbitrary conditions. Experimental results demonstrate that: (i) synthetic data generated by CoFinDiff capture stylized facts; (ii) the generated data accurately meet specified conditions for trends and volatility; (iii) the diversity of the generated data surpasses that of the baseline models; and (iv) models trained on CoFinDiff-generated data achieve improved performance in deep hedging task.
Map2Traj: Street Map Piloted Zero-shot Trajectory Generation Method for Wireless Network Optimization
Authors: Zhenyu Tao; Wei Xu; Xiaohu You
Venue: IJCAI 2025 (main)

Abstract: In modern wireless networks, user mobility modeling plays a pivotal role in learning-based network optimization, particularly in tasks such as user association and resource allocation. Traditional random mobility models, e.g., random waypoint and Gauss Markov model, often fail to accurately capture the distribution patterns of users within real-world areas. While trace-based mobility models and advanced learning-based trajectory generation methods offer improvements, they are frequently limited by the scarcity of real-world trajectory data in target areas, primarily due to privacy concerns. This paper introduces Map2Traj, a novel zero-shot trajectory generation method that leverages the diffusion model to capture the intrinsic relationship between street maps and user mobility. With solely the street map of an unobserved area, Map2Traj generates synthetic user trajectories that closely resemble the real-world ones in trajectory pattern and spatial distribution. This enables the creation of high-fidelity individual user channel states and an accurate representation of the overall network user distribution, facilitating effective wireless network optimization. Extensive experiments across multiple regions in Xi'an and Chengdu, China demonstrate the effectiveness of our proposed method for zero-shot trajectory generation. A case study applying Map2Traj to user association and load balancing in wireless networks is also presented to validate its efficacy in network optimization.
DeCo: Defect-Aware Modeling with Contrasting Matching for Optimizing Task Assignment in Online IC Testing
Authors: Lo Pang-Yun Ting; Yu-Hao Chiang; Yi-Tung Tsai; Hsu-Chao Lai; Kun-Ta Chuang
Venue: IJCAI 2025 (main)

Abstract: In the semiconductor industry, integrated circuit (IC) processes play a vital role, as the rising complexity and market expectations necessitate improvements in yield. Identifying IC defects and assigning IC testing tasks to the right engineers improves efficiency and reduces losses. While current studies emphasize fault localization or defect classification, they overlook the integration of defect characteristics, historical failures, and the insights from engineer expertise, which restrains their effectiveness in improving IC handling. To leverage AI for these challenges, we propose DeCo, an innovative approach for optimizing task assignment in IC testing. DeCo constructs a novel defect-aware graph from IC testing reports, capturing co-failure relationships to enhance defect differentiation, even with scarce defect data. Additionally, it formulates defect-aware representations for engineers and tasks, reinforced by local and global structure modeling on the defect-aware graph. Finally, a contrasting-based assignment mechanism pairs testing tasks with QA engineers by considering their skill level and current workload, thus promoting an equitable and efficient job dispatch. Experiments on a real-world dataset demonstrate that DeCo achieves the highest task-handling success rates in different scenarios, exceeding 80%, while also maintaining balanced workloads on both scarce or expanded defect data. Moreover, case studies reveal that DeCo can assign tasks to potentially capable engineers, even for their unfamiliar defects, highlighting its potential as an AI-driven solution for the real-world IC failure analysis and task handling.
Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
Authors: Satyavrat Wagle; Akshay Malhotra; Shahab Hamidi-Rad; Aditya Sant; David J. Love; Christopher G. Brinton
Venue: IJCAI 2025 (main)

Abstract: In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML systems for wireless communications is the availability of realistic wireless channel datasets, which are extremely resource-intensive to produce. To this end, the generation of realistic wireless channels plays a key role in the subsequent design of effective ML algorithms for wireless communication systems. Generative models have been proposed to synthesize channel matrices, but outputs produced by such methods may not correspond to geometrically viable channels and do not provide any insight into the scenario being generated. In this work, we aim to address both these issues by integrating established parametric, physics-based geometric channel (PPGC) modeling frameworks with generative methods to produce realistic channel matrices with interpretable representations in the parameter domain. We show that the generative model converges to prohibitively suboptimal stationary points when learning the underlying prior directly over the parameters due to the non-convex PPGC model. To address this limitation, we propose a linearized reformulation of the problem to ensure smooth gradient flow during generative model training, while also providing insights into the underlying physical environment. We evaluate our model against prior baselines by comparing the generated, scenario-specific samples in terms of the 2-Wasserstein distance and through its utility when used for downstream compression tasks.
Learning Dynamical Coupled Operator For High-dimensional Black-box Partial Differential Equations
Authors: Yichi Wang; Tian Huang; Dandan Huang; Zhaohai Bai; Xuan Wang; Lin Ma; Haodi Zhang
Venue: IJCAI 2025 (main)

Abstract: The deep operator networks (DON), a class of neural operators that learn mappings between function spaces, have recently emerged as surrogate models for parametric partial differential equations (PDEs). However, their full potential for accurately approximating general black-box PDEs remains underexplored due to challenges in training stability and performance, primarily arising from difficulties in learning mappings between low-dimensional inputs and high-dimensional outputs. Furthermore, inadequate encoding of input functions and query positions limits the generalization ability of DONs. To address these challenges, we propose the Dynamical Coupled Operator (DCO), which incorporates temporal dynamics to learn coupled functions, reducing information loss and improving training robustness. Additionally, we introduce an adaptive spectral input function encoder based on empirical mode decomposition to enhance input function representation, as well as a hybrid location encoder to improve query location encoding. We provide theoretical guarantees on the universal expressiveness of DCO, ensuring its applicability to a wide range of PDE problems. Extensive experiments on real-world, high-dimensional PDE datasets demonstrate that DCO significantly outperforms DONs.
SpaceDet: A Large-scale Space-based Image Dataset and RSO Detection for Space Situational Awareness
Authors: Jiaping Xiao; Rangya Zhang; Yuhang Zhang; Lu Bai; Qianlei Jia; Mir Feroskhan
Venue: IJCAI 2025 (main)

Abstract: Space situational awareness (SSA) plays an imperative role in maintaining safe space operations, especially given the increasingly congested space traffic around the Earth. Space-based SSA offers a flexible and lightweight solution compared to traditional ground-based SSA. With advanced machine learning approaches, space-based SSA can extract features from high-resolution images in space to detect and track resident space objects (RSOs). However, existing spacecraft image datasets, such as SPARK, fall short of providing realistic camera observations, rendering the derived algorithms unsuitable for real SSA systems. In this work, we introduce SpaceDet, a large-scale realistic space-based image dataset for SSA. We consider accurate space orbit dynamics and a physical camera model with various noise distributions, generating images at the photon level. To extend the available observation window, four overlapping cameras are simulated with a fixed rotation angle. SpaceDet includes images of RSOs observed from 19 km to 63,000 km, captured by a tracker operating in LEO, MEO, and GEO orbits over a period of 5,000 seconds. Each image has a resolution of 4418 x 4418 pixels, providing detailed features for developing advanced SSA approaches. We split the dataset into three subsets: SpaceDet-100, SpaceDet-5000, and SpaceDet-full, catering to various image processing applications. The SpaceDet-full corpus includes a comprehensive dataloader with 781.5 GB of images and 25.9 MB of ground truth labels. Furthermore, we adapted detection and tracking algorithms on the collected dataset using a specified splitting method to accelerate the training process. The trained model can detect RSOs from real-world space observations with zero-shot capability.
Multi-Hierarchical Fine-Grained Feature Mapping Driven by Feature Contributions for Molecular Odor Prediction
Authors: Hongxin Xie; Jiande Sun; Fanfu Xue; Zifei Han; Shanshan Feng; Qi Chen
Venue: IJCAI 2025 (main)

Abstract: Molecular odor prediction involves using a molecule's structure to estimate its odor. While accurate prediction remains challenging, AI models can suggest potential odors. Existing methods, however, often rely on basic descriptors or handcrafted fingerprints, which lack expressive power and hinder effective learning. Furthermore, these methods suffer from severe class imbalance, limiting the training effectiveness of AI models. To address these challenges, we propose a Feature Contribution-driven Hierarchical Multi-Feature Mapping Network (HMFNet). Specifically, we introduce a fine-grained, Local Multi-Hierarchy Feature Extraction module (LMFE) that performs deep feature extraction at the atomic level, capturing detailed features crucial for odor prediction. To enhance the extraction of discriminative atomic features, we integrate a Harmonic Modulated Feature Mapping (HMFM). This module dynamically learns feature importance and frequency modulation, improving the model's capability to capture relevant patterns. Additionally, a Global Multi-Hierarchy Feature Extraction module (GMFE) is designed to learn global features from the molecular graph topology, enabling the model to fully leverage global information and enhance its discriminative power for odor prediction. To further mitigate the issue of class imbalance, we propose a Chemically-Informed Loss (CIL). Experimental results demonstrate that our approach significantly improves performance across various deep learning models, highlighting its potential to advance molecular structure representation and accelerate the development of AI-driven technologies.
Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques
Authors: Xiaofei Xu; Xiuzhen Zhang; Ke Deng
Venue: IJCAI 2025 (main)

Abstract: Fake news and misinformation poses a significant threat to society, making efficient mitigation essential. However, manual fact-checking is costly and lacks scalability. Large Language Models (LLMs) offer promise in automating counter-response generation to mitigate misinformation, but a critical challenge lies in their tendency to hallucinate non-factual information. Existing models mainly rely on LLM self-feedback to reduce hallucination, but this approach is computationally expensive. In this paper, we propose MisMitiFact, Misinformation Mitigation grounded in Facts, an efficient framework for generating fact-grounded counter-responses at scale. MisMitiFact generates simple critique feedback to refine LLM outputs, ensuring responses are grounded in evidence. We develop lightweight, fine-grained critique models trained on data sourced from readily available fact-checking sites to identify and correct errors in key elements such as numerals, entities, and topics in LLM generations. Experiments show that MisMitiFact generates counter-responses of comparable quality to LLMs' self-feedback while using significantly smaller critique models. Importantly, it achieves ~5x increase in feedback generation throughput, making it highly suitable for cost-effective, large-scale misinformation mitigation. Code and additional results are available at https://github.com/xxfwin/MisMitiFact.
KGCL: Knowledge-Enhanced Graph Contrastive Learning for Retrosynthesis Prediction Based on Molecular Graph Editing
Authors: Fengqin Yang; Dekui Zhao; Haoxuan   Qiu; Yifei Li; Zhiguo Fu
Venue: IJCAI 2025 (main)

Abstract: Retrosynthesis, which predicts the reactants of a given target molecule, is an essential task for drug discovery. Retrosynthesis prediction based on molecular graph editing has garnered widespread attention due to excellent interpretability. Existing methods fail to effectively incorporate the chemical knowledge when learning molecular representations. To address this issue, we propose a Knowledge-enhanced Graph Contrastive Learning model (KGCL), which retrieve functional group embeddings from a chemical knowledge graph and integrate them into the atomic embeddings of the product molecule using an attention mechanism. Furthermore, we introduce a graph contrastive learning strategy that generates augmented samples using graph edits to improve the molecular graph encoder. Our proposed method outperforms the strong baseline method Graph2Edits by 1.6% and 3.2% in terms of the top-1 accuracy and top-1 round-trip accuracy on the USPTO-50K dataset, respectively, and also achieves a new state-of-the-art performance among semi-template-based methods on the USPTO-FULL dataset.
HygMap: Representing All Types of Map Entities via Heterogeneous Hypergraph
Authors: Yifan Yang; Jingyuan Wang; Xie Yu; Yibang  Tang
Venue: IJCAI 2025 (main)

Abstract: Maps are crucial for various smart city applications as a core component of city geographic information systems (GIS). Developing effective Map Entity Representation Learning methods can extract semantic information for downstream tasks like crime rate prediction and land use classification, with significant application potential. A map comprises three entity types: land parcels, road segments, and points of interest. Most existing methods focus on a single entity type, losing inter-entity relationships and weakening representation effectiveness for real-world applications. Thus, jointly modelling and representing multiple map entity types is essential. However, designing a unified framework is challenging due to map data's unstructured, complex, and heterogeneous nature. We propose a novel method, HygMap, to represent all map entity types. We model the map as a heterogeneous hypergraph, design an encoder for map entities, and introduce a hybrid self-supervised training scheme. This architecture comprehensively captures the heterogeneous relationships among map entities at different levels. Experiments on nine downstream tasks with two real-world datasets show that our framework outperforms all baselines, with good computational efficiency and scalability.