GenAI4ELab/papercli-papers huggingface dataset:
GenAL: Generative Agent for Adaptive Learning
Authors: Rui Lv; Qi Liu; Weibo Gao; Haotian Zhang; Junyu Lu; Linbo Zhu
Venue: AAAI 2025 (main)

Abstract: Adaptive learning, also known as adaptive teaching, relies on learning path recommendations that sequentially suggest personalized learning items (such as lectures and exercises) to meet the unique needs of each learner. Despite the extensive research in this field, previous approaches have primarily modeled the interaction sequences between learners and items using simple indexing, leading to three issues: (1) The utilization of information from both learners and items is not sufficient. For instance, these models are unable to leverage the semantic information contained within the textual content of the items. (2) Models need to be retrained on different datasets separately, which makes it difficult to adapt to the continuously expanding item pool in online educational scenarios. 
(3) The existing recommendation paradigm based on trained reinforcement learning frameworks, suffers from unstable recommendation performance in sparse learning logs.
To address these challenges, we propose a generalized Generative Agent for Adaptive Learning (GenAL), which integrates educational tools with LLMs' semantic understanding to enable effective and generalizable learning path recommendations across diverse data distributions. Specifically, our framework consists of two components: the Global Thinking Agent, which updates the learner profile and reflects on recommendation outcomes based on the learner's historical learning records. The other is the Local Teaching Agent, which recommends items using educational prior knowledge. Leveraging the LLM's robust semantic understanding, our framework does not rely on item indexing but instead extracts relevant information from the textual content. We evaluated our approach on three real-world datasets, and the experimental results demonstrate that our GenAL not only consistently outperforms all baselines but also exhibits strong generalization ability.
M²N: A Progressive Macro-to-Micro 3D Modeling Scheme for Unveiling Drug-Target Affinity
Authors: Tianxu Lv; Jie Zhu; Jinyi Liu; Shiyun Nie; Hongnian Tian; Yang Xiao; Yuan Liu; Llihua Li; Xiang Pan
Venue: AAAI 2025 (main)

Abstract: Accurate drug-target affinity (DTA) prediction holds significant potential in the field of artificial intelligence (AI)-based drug discovery. However, existing methods primarily operate at a single scale, specifically at the macro (residue) scale for target proteins and the micro (atom) scale for drugs, which limits their ability to provide information at micro (atom) scale for targets and macro (functional group, FG) scale for drugs. This limitation hinders a comprehensive understanding of the binding patterns and properties of drug-target pairs. In this paper, we propose a progressive Macro-to-Micro 3D Modeling Network (M²N) that enables macro (residue/FG) to micro (atom) scale unified modeling, termed cross-scale, to predict DTA. Specifically, M²N operates drugs by learning their chemical properties and structural characteristics from a 3D FG graph to a 3D atom graph. Correspondingly, M²N encodes proteins from a 3D residue graph to a 3D atom graph to exploit their sequence, evolutionary, and geometric representations. Such cross-scale 3D modeling scheme allows for coarse-to-fine embedding optimization, followed by an adaptive fusion module to dynamically integrate the refined features by end-to-end learning. Extensive experiments on two datasets indicate that M²N not only outperforms state-of-the-art methods under various conditions, but also provides a new paradigm for target and drug unified modeling.
Number Theoretic Accelerated Learning of Physics-Informed Neural Networks
Authors: Takashi Matsubara; Takaharu Yaguchi
Venue: AAAI 2025 (main)

Abstract: Physics-informed neural networks solve partial differential equations by training neural networks. Since this method approximates infinite-dimensional PDE solutions with finite collocation points, minimizing discretization errors by selecting suitable points is essential for accelerating the learning process. Inspired by number theoretic methods for numerical analysis, we introduce good lattice training and periodization tricks, which ensure the conditions required by the theory. Our experiments demonstrate that GLT requires 2-7 times fewer collocation points, resulting in lower computational cost, while achieving competitive performance compared to typical sampling methods.
Defense Against Model Stealing Based on Account-Aware Distribution Discrepancy
Authors: Jian-Ping Mei; Weibin Zhang; Jie Chen; Xuyun Zhang; Tiantian Zhu
Venue: AAAI 2025 (main)

Abstract: Malicious users attempt to replicate commercial models functionally at low cost by training a clone model with query responses. It is challenging to timely prevent such model-stealing attacks to achieve strong protection and maintain utility. In this paper, we propose a novel non-parametric detector called Account-aware Distribution Discrepancy (ADD) to recognize queries from malicious users by leveraging account-wise local dependency. We formulate each class as a Multivariate Normal distribution (MVN) in the feature space and measure the malicious score as the sum of weighted class-wise distribution discrepancy. The ADD detector is combined with random-based prediction poisoning to yield a plug-and-play defense module named D-ADD for image classification models. Results of extensive experimental studies show that D-ADD achieves strong defense against different types of attacks with little interference in serving benign users for both soft and hard-label settings.
Multi-modal Deepfake Detection via Multi-task Audio-Visual Prompt Learning
Authors: Hui Miao; Yuanfang Guo; Zeming Liu; Yunhong Wang
Venue: AAAI 2025 (main)

Abstract: With the malicious use and dissemination of multi-modal deepfake videos, researchers start to investigate multi-modal deepfake detection. Unfortunately, most of the existing methods tune all the parameters of the deep network with limited speech video datasets and are trained under coarse-grained consistency supervision, which hinders their generalization ability in practical scenarios. To solve these problems, in this paper, we propose the first multi-task audio-visual prompt learning method for multi-modal deepfake video detection, by exploiting multiple foundation models. Specifically, we construct a two-stream multi-task learning architecture and propose sequential visual prompts and short-time audio prompts to extract multi-modal features, which are aligned at the frame level and utilized in subsequent fine-grained feature matching and fusion. Due to the natural alignment of visual content and audio signal in real data, we propose a frame-level cross-modal feature matching loss function to learn the fine-grained audio-visual consistency. Comprehensive experiments demonstrate the effectiveness and superior generalization ability of our method against the state-of-the-art methods.
GoBERT: Gene Ontology Graph Informed BERT for Universal Gene Function Prediction
Authors: Yuwei Miao; Yuzhi Guo; Hehuan Ma; Jingquan Yan; Feng Jiang; Rui Liao; Junzhou Huang
Venue: AAAI 2025 (main)

Abstract: Exploring the functions of genes and gene products is crucial to a wide range of fields, including medical research, evolutionary biology, and environmental science. However, discovering new functions largely relies on expensive and exhaustive wet lab experiments. Existing methods of automatic function annotation or prediction mainly focus on protein function prediction with sequence, 3D-structures or protein family information. In this study, we propose to tackle the gene function prediction problem by exploring Gene Ontology graph and annotation with BERT (GoBERT) to decipher the underlying relationships among gene functions. Our proposed novel function prediction task utilizes existing functions as inputs and generalizes the function prediction to gene and gene products. Specifically, two pre-train tasks are designed to jointly train GoBERT to capture both explicit and implicit relations of functions. Neighborhood prediction is a self-supervised multi-label classification task that captures the explicit function relations. Specified masking and recovering task helps GoBERT in finding implicit patterns among functions. The pre-trained GoBERT possess the ability to predict novel functions for various gene and gene products based on known functional annotations. Extensive experiments, biological case studies, and ablation studies are conducted to demonstrate the superiority of our proposed GoBERT.
SLRL: Semi-Supervised Local Community Detection Based on Reinforcement Learning
Authors: Li Ni; Rui Ye; Wenjian Luo; Yiwen Zhang; Lei Zhang; Victor S. Sheng
Venue: AAAI 2025 (main)

Abstract: Most existing semi-supervised community detection algorithms leverage known communities to learn community structures, subsequently identifying communities that align with these learned community structures. However, differences in community structures may render the community structures learned by these methods inappropriate for the community containing the given node of interest. As a result, the identified community may exclude the given node or be of poor quality. Inspired by the success of reinforcement learning, we propose a Semi-supervised Local community detection method based on Reinforcement Learning, named SLRL, which only explores parts of the network surrounding the given node.  It first extracts the local structure around a given node with an extractor, followed by selecting communities that are similar to this local structure to distill useful communities. These selected communities are employed to train the expander, which expands the community containing a given node. Experimental results demonstrate that SLRL outperforms state-of-the-art algorithms on five real-world datasets.
Robust Heterogeneous Graph Classification for Molecular Property Prediction with Information Bottleneck
Authors: Zhibin Ni; Chang Liu; Hai Wan; Xibin Zhao
Venue: AAAI 2025 (main)

Abstract: Heterogeneous Graph Neural Networks (HGNNs) have achieved state-of-the-art performance in classifying molecular graphs, capitalizing on their ability to capture rich semantics. However, HGNNs for molecule property prediction exhibit significant susceptibility to adversarial attacks—a challenge that prior research has entirely overlooked. To fill this gap, this paper introduces the first study focused on robust graph-level representation learning tailored for heterogeneous molecular graphs. To achieve this goal, we propose a comprehensive Robust Heterogeneous Graph Classification (RHGC) framework grounded in the Information Bottleneck principle, which aims to identify the most informative and least noisy heterogeneous subgraphs to derive robust, holistic representations.  This is specifically accomplished through a dedicated Node Semantic Purifier, which enhances node-level and semantic-level robustness by eliminating label-irrelevant interference using graph stochastic attention and the Hilbert-Schmidt Independence Criterion, along with a Global Graph Disentanglement method, which improves graph-level robustness by addressing information leak. Experiments on three molecular benchmarks demonstrate that RHGC enhances accuracy by an average of 5.06% under all three attack settings and meanwhile by 4.33% on clean data.
Counterexample Guided Program Repair Using Zero-Shot Learning and MaxSAT-based Fault Localization
Authors: Pedro Orvalho; Mikoláš Janota; Vasco M. Manquinho
Venue: AAAI 2025 (main)

Abstract: Automated Program Repair (APR) for introductory programming assignments (IPAs) is motivated by the large number of student enrollments in programming courses each year. Since providing feedback on programming assignments requires substantial time and effort from faculty, personalized automated feedback often involves suggesting repairs to students' programs. Symbolic semantic repair approaches, which rely on Formal Methods (FM), check a program's  execution against a test suite or reference solution, are effective but limited. These tools excel at identifying buggy parts but can only fix programs if the correct implementation and the faulty one share the same control flow graph. Conversely, Large Language Models (LLMs) are used for program repair but often make extensive rewrites instead of minimal adjustments. This tends to lead to more invasive fixes, making it harder for students to learn from their mistakes.
In summary, LLMs excel at completing strings, while FM-based fault localization excel at identifying buggy parts of a program.

In this paper, we propose a novel approach that combines the strengths of both FM-based fault localization and LLMs, via zero-shot learning, to enhance APR for IPAs. Our method uses MaxSAT-based fault localization to identify buggy parts of a program, then presents the LLM with a program sketch devoid of these buggy statements. This hybrid approach follows a Counterexample Guided Inductive Synthesis (CEGIS) loop to iteratively refine the program. We ask the LLM to synthesize the missing parts, which are then checked against a test suite. If the suggested program is incorrect, a counterexample from the test suite is fed back to the LLM for revised synthesis.
Our experiments on 1,431 incorrect student programs show that our counterexample guided approach, using MaxSAT-based bug-free program sketches, significantly improves the repair capabilities of all six evaluated LLMs. This method allows LLMs to repair more programs and produce smaller fixes, outperforming other configurations and state-of-the-art symbolic program repair tools.
MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware Classification
Authors: Jimin Park; AHyun Ji; Minji Park; Mohammad Saidur Rahman; Se Eun Oh
Venue: AAAI 2025 (main)

Abstract: Continual Learning (CL) for malware classification tackles the rapidly evolving nature of malware threats and the frequent emergence of new types. Generative Replay (GR)-based CL systems utilize a generative model to produce synthetic versions of past data, which are then combined with new data to retrain the primary model. Traditional machine learning techniques in this domain often struggle with catastrophic forgetting, where a model's performance on old data degrades over time.

In this paper, we introduce a GR-based CL system that employs Generative Adversarial Networks (GANs) with feature matching loss to generate high-quality malware samples. Additionally, we implement innovative selection schemes for replay samples based on the model’s hidden representations.

Our comprehensive evaluation across Windows and Android malware datasets in a class-incremental learning scenario -- where new classes are introduced continuously over multiple tasks -- demonstrates substantial performance improvements over previous methods. For example, our system achieves an average accuracy of 55% on Windows malware samples, significantly outperforming other GR-based models by 28%. This study provides practical insights for advancing GR-based malware classification systems.
Exploring Query Efficient Data Generation Towards Data-Free Model Stealing in Hard Label Setting
Authors: Gaozheng Pei; Shaojie Lyu; Ke Ma; Pinci Yang; Qianqian Xu; Yingfei Sun
Venue: AAAI 2025 (main)

Abstract: Data-free model stealing involves replicating the functionality of a target model into a substitute model without accessing the target model's structure, parameters, or training data. Instead, the adversary can only access the target model's predictions for generated samples. Once the substitute model closely approximates the behavior of the target model, attackers can exploit its white-box characteristics for subsequent malicious activities, such as adversarial attacks. Existing methods within cooperative game frameworks often produce samples with high confidence for the prediction of the substitute model, which makes it difficult for the substitute model to replicate the behavior of the target model. This paper presents a new data-free model stealing approach called Query Efficient Data Generation (QEDG). We introduce two distinct loss functions to ensure the generation of sufficient samples that closely and uniformly align with the target model's decision boundary across multiple classes. Building on the limitation of current methods, which typically yield only one piece of supervised information per query, we propose the query-free sample augmentation that enables the acquisition of additional supervised information without increasing the number of queries.  Motivated by theoretical analysis, we adopt the consistency rate metric, which more accurately evaluates the similarity between the substitute and target models. We conducted extensive experiments to verify the effectiveness of our proposed method, which achieved better performance with fewer queries compared to the state-of-the-art methods on the real MLaaS scenario and five datasets.
A Simple and Comprehensive Benchmark for Single-Cell Transcriptomics
Authors: Jiaxin Qi; Yan Cui; Kailei Guo; Xiaomin Zhang; Jianqiang Huang; Gaogang Xie
Venue: AAAI 2025 (main)

Abstract: Single-cell transcriptomics describes complex molecular features at the individual cell level, serving various roles in biological research, such as enhancing gene expression and predicting drug responses. Due to transcriptomic data structurally resembling sequential data, many researchers have trained numerous transformers on extensive transcriptomic datasets. However, they have consistently neglected to explore the intrinsic properties of the data and the appropriateness of their chosen model architecture. In this paper, we carefully investigate the nature of transcriptomics, identifying three overlooked problems: 1) long-tailed data problem, 2) model selection problem, and 3) evaluation problem. Consequently, by applying the weighted sampling strategy, we address the long-tailed data problem and achieve consistent improvement across all settings. By adapting different model structures to transcriptomic data, we discover that transformers are not the only option. By developing three downstream tasks and fair evaluation metrics, we establish a simple and comprehensive benchmark to validate the effectiveness of models for transcriptomics. Through extensive experiments, we clarify the misunderstandings in the traditional methods and provide competitive baselines, thereby paving the way for future research in this field.
Dual-Channel Interactive Graph Transformer for Traffic Classification with Message-Aware Flow Representation
Authors: Xing Qiu; Guang Cheng; Weizhou Zhu; Dandan Niu; Nan Fu
Venue: AAAI 2025 (main)

Abstract: Traffic classification is crucial for network management and security. Recently, deep learning-based methods have demonstrated good performance in traffic classification. However, they primarily capture features from raw packet bytes, overlooking the significance of inter-packet correlations within flows from a global perspective. Additionally, effectively handling both packet-length and temporal information, while extracting the structural relationships from a graph into the model, remains a challenge for enhancing the performance of traffic prediction. In this paper, we propose DigTraffic, a novel dual-channel interactive graph transformer to address these limitations. DigTraffic employs a message-level graph-structured flow representation combined with message-aware structural aggregation. To learn intrinsic flow representations, DigTraffic constructs traffic interaction graphs, by incorporating three well-designed heterogeneous types of edges to capture client-server interactions. After that, we separately encode lengthy and temporal flow sequences using a dual-channel network and fuse these modalities within a Transformer architecture. Furthermore, DigTraffic introduces a message-aware Graph Transformer that leverages both node embeddings and edge spatial relations to capture complex graph structures and rich structural information.
Experimental results demonstrate that our method significantly outperforms the state-of-the-art methods on four real-world traffic datasets.
Revisiting Tampered Scene Text Detection in the Era of Generative AI
Authors: Chenfan Qu; Yiwu Zhong; Fengjun Guo; Lianwen Jin
Venue: AAAI 2025 (main)

Abstract: The rapid advancements of generative AI have fueled the potential of generative text image editing, meanwhile escalating the threat of misinformation spreading.  However, existing forensics methods struggle to detect unseen forgery types that they have not been trained on, underscoring the need for a model capable of generalized detection of tampered scene text.  To tackle this, we propose a novel task: open-set tampered scene text detection, which evaluates forensics models on their ability to identify both seen and previously unseen forgery types. We have curated a comprehensive, high-quality dataset, featuring the texts tampered by eight text editing models, to thoroughly assess the open-set generalization capabilities. Further, we introduce a novel and effective pre-training paradigm that subtly alters the texture of selected texts within an image and trains the model to identify these regions. This approach not only mitigates the scarcity of high-quality training data but also enhances models' fine-grained perception and open-set generalization abilities. Additionally, we present DAF, a novel framework that improves open-set generalization by distinguishing between the features of authentic and tampered text, rather than focusing solely on the tampered text's features. Our extensive experiments validate the remarkable efficacy of our methods. For example, our zero-shot performance can even beat the previous state-of-the-art full-shot model by a large margin.
Towards Better Robustness Against Natural Corruptions in Document Tampering Localization
Authors: Huiru Shao; Kaizhu Huang; Wei Wang; Xiaowei Huang; Qiufeng Wang
Venue: AAAI 2025 (main)

Abstract: Marvelous advances have been exhibited in recent document tampering localization (DTL) systems. However, confronted with corrupted tampered document images, their vulnerability is fatal in real-world scenarios. While robustness against adversarial attack has been extensively studied by adversarial training (AT), the robustness on natural corruptions remains under-explored for DTL. In this paper, to overcome forensic dependency, we propose the adversarial forensic regularization (AFR) based on min-max optimization to improve robustness. Specifically, we adopt mutual information (MI) to represent forensic dependency between two random variable over tampered and authentic pixels spaces, where the MI can be approximated by Jensen-Shannon-Divergence (JSD) with empirical sampling. To further enable a trade-off between predictive representations in clean tampered document pixels and robust ones in corrupted pixels, an additional regularization term is formulated with divergence between clean and perturbed pixels distribution (DDR). Following min-max optimization framework, our method can also work well against adversarial attacks. To evaluate our proposed method, we collect a dataset (i.e., TSorie-CRP) for evaluating robustness against natural corruptions in real scenarios. Extensive experiments demonstrate the effectiveness of our method against natural corruptions. Without any surprise, our method also achieves good performance against adversarial attack on DTL benchmark datasets.