GenAI4ELab/papercli-papers huggingface dataset:
MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic Insights
Authors: Jingjing Hu; Dan Guo; Zhan Si; Deguang Liu; Yunfeng Diao; Jing Zhang; Jinxing Zhou; Meng Wang
Venue: AAAI 2025 (main)

Abstract: Molecular representation learning plays a crucial role in various downstream tasks, such as molecular property prediction and drug design. To accurately represent molecules, Graph Neural Networks (GNNs) and Graph Transformers (GTs) have shown potential in the realm of self-supervised pretraining. However, existing approaches often overlook the relationship between molecular structure and electronic information, as well as the internal semantic reasoning within molecules. This omission of fundamental chemical knowledge in graph semantics leads to incomplete molecular representations, missing the integration of structural and electronic data. To address these issues, we introduce MOL-Mamba, a framework that enhances molecular representation by combining structural and electronic insights. MOL-Mamba consists of an Atom & Fragment Mamba-Graph (MG) for hierarchical structural reasoning and a Mamba-Transformer (MT) fuser for integrating molecular structure and electronic correlation learning. Additionally, we propose a Structural Distribution Collaborative Training and E-semantic Fusion Training framework to further enhance molecular representation learning. Extensive experiments demonstrate that MOL-Mamba outperforms state-of-the-art baselines across eleven chemical-biological molecular datasets.
PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis
Authors: Xinlei Huang; Zhiqi Ma; Dian Meng; Yanran Liu; Shiwei Ruan; Qingqiang Sun; Xubin Zheng; Ziyue Qiao
Venue: AAAI 2025 (main)

Abstract: Spatial multi-modal omics technology, highlighted by Nature Methods as an advanced biological technique in 2023, plays a critical role in resolving biological regulatory processes with spatial context. Recently, graph neural networks based on K-nearest neighbor (KNN) graphs have gained prominence in spatial multi-modal omics methods due to their ability to model semantic relations between sequencing spots. However, the fixed KNN graph fails to capture the latent semantic relations hidden by the inevitable data perturbations during the biological sequencing process, resulting in the loss of semantic information. In addition, the common lack of spot annotation and class number priors in practice further hinders the optimization of spatial multi-modal omics models. Here, we propose a novel spatial multi-modal omics resolved framework, termed Prototype-aware Graph Adaptative Aggregation for Spatial Multi-modal Omics Analysis (PRAGA). PRAGA constructs a dynamic graph to capture latent semantic relations and comprehensively integrate spatial information and feature semantics. The learnable graph structure can also denoise perturbations by learning cross-modal knowledge. Moreover, a dynamic prototype contrastive learning is proposed based on the dynamic adaptability of Bayesian Gaussian Mixture Models to optimize the multi-modal omics representations for unknown biological priors. Quantitative and qualitative experiments on simulated and real datasets with 7 competing methods demonstrate the superior performance of PRAGA.
Social Recommendation via Graph-Level Counterfactual Augmentation
Authors: Yinxuan Huang; Ke Liang; Yanyi Huang; Xiang Zeng; Kai Chen; Bin Zhou
Venue: AAAI 2025 (main)

Abstract: Traditional recommendation system focus more on the correlations between users and items (user-item relationships),
while research on user-user relationships has received significant attention these years, which is also known as social recommendation. Graph-based models have achieved a great success in this task by utilizing the complex topological information of the social networks. However, these models still face the insufficient expressive and overfitting problems. Counterfactual approaches are proven effective as information augmentation strategies towards above issues in various scenarios, but not fully utilized in social recommendations. To this end, we propose a novel social recommendation method, termed SR-GCA, via a plug-and-play Graph-Level Counterfactual Augmentation mechanism. Specifically, we first generate counterfactual social and item links by constructing a counterfactual matrix for data aug-
mentation. Then, we employ a supervised learning strategy to refine data both factual and counterfactual links. Thirdly, we
enhance representations learning between users via an alignment and self-supervised optimization techniques. Extensive
experiments demonstrate the promising capacity of our model from five aspects, including superiority, effectively, transfer-
ability, complexity, sensitively. In particular, the transferability is well-proven by extending our GCA module to three typical social recommendation models.
DF-MIA: A Distribution-Free Membership Inference Attack on Fine-Tuned Large Language Models
Authors: Zhiheng Huang; Yannan Liu; Daojing He; Yu Li
Venue: AAAI 2025 (main)

Abstract: Membership Inference Attack (MIA) aims to determine if a specific sample is present in the training dataset of a target machine learning model. 
Previous MIAs against fine-tuned Large Language Models (LLMs) either fail to address the unique challenges in the fine-tuned setting or rely on strong assumption of the training data distribution.
This paper proposes a distribution-free MIA framework tailored for fine-tuned LLMs, named DF-MIA. 
We recognize that samples await to test can serve as a valuable reference dataset for fine-tuning reference models. 
By enhancing the signals of non-member samples within this reference dataset, we can achieve a more reliable and practical calibration of probabilities, improving the differentiation between members and non-members.
Leveraging these insights, we have developed a two-stage framework that employs specially designed data augmentation and perturbation techniques to prioritize the significance of non-members and mitigate the influence of potential members within the reference dataset.
We evaluate our method on three representative LLM models ranging from 1B to 8B on three datasets. The results demonstrate that the DF-MIA significantly enhances the performance of MIA.
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations
Authors: Pengcheng Jiang; Cao Xiao; Tianfan Fu; Parminder Bhatia; Taha Kass-Hout; Jimeng Sun; Jiawei Han
Venue: AAAI 2025 (main)

Abstract: Molecular representation learning is vital for various downstream applications, including the analysis and prediction of molecular properties and side effects. While Graph Neural Networks (GNNs) have been a popular framework for modeling molecular data, they often struggle to capture the full complexity of molecular representations. In this paper, we introduce a novel method called Gode, which accounts for the dual-level structure inherent in molecules. Molecules possess an intrinsic graph structure and simultaneously function as nodes within a broader molecular knowledge graph. Gode integrates individual molecular graph representations with multi-domain biochemical data from knowledge graphs. By pre-training two GNNs on different graph structures and employing contrastive learning, Gode effectively fuses molecular structures with their corresponding knowledge graph substructures. This fusion yields a more robust and informative representation, enhancing molecular property predictions by leveraging both chemical and biological information. When fine-tuned across 11 chemical property tasks, our model significantly outperforms existing benchmarks, achieving an average ROC-AUC improvement of 12.7% for classification tasks and an average RMSE/MAE improvement of 34.4% for regression tasks. Notably, Gode surpasses the current leading model in property prediction, with advancements of 2.2% in classification and 7.2% in regression tasks.
Learning Generalized Residual Exchange-Correlation-Uncertain Functional for Density Functional Theory
Authors: Sizhuo Jin; Shuo Chen; Jianjun Qian; Ying Tai; Jun Li
Venue: AAAI 2025 (main)

Abstract: Density Functional Theory (DFT) stands as a widely used and efficient approach for addressing the many-electron Schrödinger equation across various domains such as physics, chemistry, and biology. However, a core challenge that persists over the long term pertains to refining the exchange-correlation (XC) approximation. This approximation significantly influences the triumphs and shortcomings observed in DFT applications. Nonetheless, a prevalent issue among XC approximations is the presence of systematic errors, stemming from deviations from the mathematical properties of the exact XC functional. For example, although both B3LYP and DM21 (DeepMind 21) exhibit improvements over previous benchmarks, there is still potential for further refinement. In this paper, we propose a strategy for enhancing XC approximations by estimating the neural uncertainty of the XC functional, named Residual XC-Uncertain Functional. Specifically, our approach involves training a neural network to predict both the mean and variance of the XC functional, treating it as a Gaussian distribution. To ensure stability in each sampling point, we construct the mean by combining traditional XC approximations with our neural predictions, mitigating the risk of divergence or vanishing values. It is crucial to highlight that our methodology excels particularly in cases where systematic errors are pronounced. Empirical outcomes from three benchmark tests substantiate the superiority of our approach over existing state-of-the-art methods. Our approach not only surpasses related techniques but also significantly outperforms both the popular B3LYP and the recent DM21 methods, achieving average RMSE improvements of 62% and 37%, respectively, across the three benchmarks: W4-17, G21EA, and G21IP.
IWRN:A Robust Blind Watermarking Method for Artwork Image Copyright Protection Against Noise Attack
Authors: Feifei Kou; Yuhan Yao; Siyuan Yao; Jiahao Wang; Lei Shi; Yawen Li; Xuejing Kang
Venue: AAAI 2025 (main)

Abstract: Adding imperceptible watermarks to artwork images, such as paintings and photographs, can effectively safeguard the copyright of these images without compromising their usability. However, existing blind watermarking techniques encounter two major challenges in addressing this task: imperceptibility and robustness, particularly when subjected to various noise attacks. In this paper, we propose a blind watermarking method for artwork image copyright protection, IWRN, which can ensure both the Imperceptibility of the Watermark and Robustness against Noise attacks. For imperceptibility, we design a Learnable Wavelet Network (LWN) to adaptively embed the watermark into the high-frequency region  where the watermark has better invisibility.
    For robustness, we establish a Deform-Attention based Invertible Neural Network (DA-INN) with a decoding optimization, which offers the advantage of computational reversion, and combines the deform-attention mechanism and decoding optimization to enhance the model's resistance against noises. Additionally, we design a Joint Contrast Learning (JCL) mechanism  to improve imperceptibility and robustness simultaneously. Experiments show that our IWRN outperforms other state-of-the-art blind watermarking methods, achieves an average performance of 41.55 PSNR and 99.57% accuracy on the Coco2017, Wikiart, and Div2k datasets when facing 12 kinds of noise attacks.
AnalogCoder: Analog Circuit Design via Training-Free Code Generation
Authors: Yao Lai; Sungyoung Lee; Guojin Chen; Souradip Poddar; Mengkang Hu; David Z. Pan; Ping Luo
Venue: AAAI 2025 (main)

Abstract: Analog circuit design is a significant task in modern chip technology, focusing on the selection of component types, connectivity, and parameters to ensure proper circuit functionality. Despite advances made by Large Language Models (LLMs) in digital circuit design, the complexity and scarcity of data in analog circuitry pose significant challenges. To mitigate these issues, we introduce AnalogCoder, the first training-free LLM agent for designing analog circuits through Python code generation. Firstly, AnalogCoder incorporates a feedback-enhanced flow with tailored domain-specific prompts, enabling the automated and self-correcting design of analog circuits with a high success rate. Secondly, it proposes a circuit tool library to archive successful designs as reusable modular sub-circuits, simplifying composite circuit creation. Thirdly, extensive experiments on a benchmark designed to cover a wide range of analog circuit tasks show that AnalogCoder outperforms other LLM-based methods. It has successfully designed 20 circuits, 5 more than standard GPT-4o. We believe AnalogCoder can significantly improve the labor-intensive chip design process, enabling non-experts to design analog circuits efficiently.
Political Actor Agent: Simulating Legislative System for Roll Call Votes Prediction with Large Language Models
Authors: Hao Li; Ruoyuan Gong; Hao Jiang
Venue: AAAI 2025 (main)

Abstract: Predicting roll call votes through modeling political actors has emerged as a focus in quantitative political science and computer science. Widely used embedding-based methods generate vectors for legislators from diverse data sets to predict legislative behaviors. However, these methods often contend with challenges such as the need for manually predefined features, reliance on extensive training data, and a lack of interpretability. Achieving more interpretable predictions under flexible conditions remains an unresolved issue. This paper introduces the Political Actor Agent (PAA), a novel agent-based framework that utilizes Large Language Models to overcome these limitations. By employing role-playing architectures and simulating legislative system, PAA provides a scalable and interpretable paradigm for predicting roll-call votes. Our approach not only enhances the accuracy of predictions but also offers multi-view, human-understandable decision reasoning, providing new insights into political actor behaviors. We conducted comprehensive experiments using voting records from the 117-118th U.S. House of Representatives, validating the superior performance and interpretability of PAA. This study not only demonstrates PAA's effectiveness but also its potential in political science research.
Simulate and Eliminate: Revoke Backdoors for Generative Large Language Models
Authors: Haoran Li; Yulin Chen; Zihao Zheng; Qi Hu; Chunkit Chan; Heshan Liu; Yangqiu Song
Venue: AAAI 2025 (main)

Abstract: With rapid advances, generative large language models (LLMs) dominate various Natural Language Processing (NLP) tasks from understanding to reasoning. Yet, language models' inherent vulnerabilities may be exacerbated due to increased accessibility and unrestricted model training on massive data. A malicious adversary may publish poisoned data online and conduct backdoor attacks on the victim LLMs pre-trained on the poisoned data. Backdoored LLMs behave innocuously for normal queries and generate harmful responses when the backdoor trigger is activated. Despite significant efforts paid to LLMs' safety issues, LLMs are still struggling against backdoor attacks. As Anthropic recently revealed, existing safety training strategies, including supervised fine-tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), fail to revoke the backdoors once the LLM is backdoored during the pre-training stage. In this paper, we present Simulate and Eliminate (SANDE) to erase the undesired backdoored mappings for generative LLMs. We initially propose Overwrite Supervised Fine-tuning (OSFT) for effective backdoor removal when the trigger is known. Then, to handle scenarios where trigger patterns are unknown, we integrate OSFT into our two-stage framework, SANDE. Unlike other works that assume access to cleanly trained models, our safety-enhanced LLMs are able to revoke backdoors without any reference. Consequently, our safety-enhanced LLMs no longer produce targeted responses when the backdoor triggers are activated. We conduct comprehensive experiments to show that our proposed SANDE is effective against backdoor attacks while bringing minimal harm to LLMs' powerful capability.
Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron Subtomogram Segmentation with Denoised Pseudo-Labeling
Authors: Haoran Li; Xingjian Li; Jiahua Shi; Huaming Chen; Bo Du; Daisuke Kihara; Johan Barthelemy; Jun Shen; Min Xu
Venue: AAAI 2025 (main)

Abstract: Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that facilitates the study of macromolecular structures at near-atomic resolution. Recent volumetric segmentation approaches on cryo-ET images have drawn widespread interest in the biological sector. However, existing methods heavily rely on manually labeled data, which requires highly professional skills, thereby hindering the adoption of fully-supervised approaches for cryo-ET images. Some unsupervised domain adaptation (UDA) approaches have been designed to enhance the segmentation network performance using unlabeled data. However, applying these methods directly to cryo-ET image segmentation tasks remains challenging due to two main issues: 1) the source dataset, usually obtained through simulation, contains a fixed level of noise, while the target dataset, directly collected from raw-data from the real-world scenario, have unpredictable noise levels. 2) the source data used for training typically consists of known macromoleculars. In contrast, the target domain data are often unknown, causing the model to be biased towards those known macromolecules, leading to a domain shift problem. To address such challenges, in this work, we introduce a voxel-wise unsupervised domain adaptation approach, termed Vox-UDA, specifically for cryo-ET subtomogram segmentation. Vox-UDA incorporates a noise generation module to simulate target-like noises in the source dataset for cross-noise level adaptation. Additionally, we propose a denoised pseudo-labeling strategy based on the improved Bilateral Filter to alleviate the domain shift problem. More importantly, we construct the first UDA cryo-ET subtomogram segmentation benchmark on three experimental datasets. Extensive experimental results on multiple benchmarks and newly curated real-world datasets demonstrate the superiority of our proposed approach compared to state-of-the-art UDA methods.
ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area
Authors: Junxian Li; Di Zhang; Xunzhi Wang; Zeying Hao; Jingdi Lei; Qian Tan; Cai Zhou; Wei Liu; Yaotian Yang; Xinrui Xiong; Weiyun Wang; Zhe Chen; Wenhai Wang; Wei Li; Mao Su; Shufei Zhang; Wanli Ouyang; Yuqiang Li; Dongzhan Zhou
Venue: AAAI 2025 (main)

Abstract: Large Language Models (LLMs) have achieved remarkable success and have been applied across various scientific fields, including chemistry. However, many chemical tasks require the processing of visual information, which cannot be successfully handled by existing chemical LLMs. This brings a growing need for models capable of integrating multimodal information in the chemical domain. In this paper, we introduce ChemVLM, an open-source chemical multimodal large language model specifically designed for chemical applications. ChemVLM is trained on a carefully curated bilingual multimodal dataset that enhances its ability to understand both textual and visual chemical information, including molecular structures, reactions, and chemistry examination questions. We develop three datasets for comprehensive evaluation, tailored to Chemical Optical Character Recognition (OCR), Multimodal Chemical Reasoning (MMCR), and Multimodal Molecule Understanding tasks. We benchmark ChemVLM against a range of open-source and proprietary multimodal large language models on various tasks. Experimental results demonstrate that ChemVLM achieves competitive performance across all evaluated tasks.
Critical Forgetting-Based Multi-Scale Disentanglement for Deepfake Detection
Authors: Kai Li; Wenqi Ren; Jianshu Li; Wei Wang; Xiaochun Cao
Venue: AAAI 2025 (main)

Abstract: Recent face forgery detection methods based on disentangled representation learning utilize paired images for cross-reconstruction, aiming to extract forgery-relevant attributes and forgery-irrelevant content. However, there still exist the following issues that may comprise the detector performance: 1) using information-dense images as the decoupling targets increases the decoupling difficulty; 2) the extracted attribute features are reconstruction-irrelevant rather than forgery-relevant, and single-scale forgery representation decoupling cannot capture sufficient discriminative information; 3) the generalization performance of decoupled attribute features is poor as the detector focuses on learning specific artifact types in the training set. To address these issues, we propose a novel disentangled representation learning framework for deepfake detection. First, we extract features by partitioning the dense information within the image, focusing independently on texture, color, or edges. These features are then used as the decoupling targets rather than the images themselves, which could mitigate the decoupling difficulty. Second, we extend reconstruction loss from image-level to feature-level, thus extending the forgery representation decoupling from single-scale to multi-scale. Third, we propose a critical forgetting mechanism that forces the detector to forget the most salient features during training, which correspond to specific forgery artifact types in the training set. Extensive experimental results validate the efficacy of the proposed method.
Learning Complex Heterogeneous Multimodal Fake News via Social Latent Network Inference
Authors: Mingxin Li; Yuchen Zhang; Haowei Xu; Xianghua Li; Chao Gao; Zhen Wang
Venue: AAAI 2025 (main)

Abstract: With the diversification of online social platforms, news dissemination has become increasingly complex, heterogeneous, and multimodal, making the fake news detection task more challenging and crucial.
Previous works mainly focus on obtaining social relationships of news via retweets, limiting the accurate detection when real cascades are inaccessible. Given the proven assessment of the spreading influence of events, this paper proposes a method called HML (Complex Heterogeneous Multimodal Fake News Detection method via Latent Network Inference). Specifically, an improved social latent network inference strategy is designed to estimate the maximum likelihood of news influences under the same event. Meanwhile, a novel heterogeneous graph is built based on social attributes for multimodal news under different events. Further, to better aggregate the relationships among heterogeneous multimodal features, this paper proposes a self-supervised-based multimodal content learning strategy, to enhance, align, fuse and compare heterogeneous modal contents. Based above, a personalized heterogeneous graph representation learning is designed to classify fake news. Extensive experiments demonstrate that the proposed method outperforms the SOTA in real social media news datasets.