OpenRaiser/Intern-Atlas huggingface dataset:
Suppressing Gradient Conflict for Generalizable Deepfake Detection
Venue: arXiv cs.CV 2025
Year: 2025

Robust deepfake detection models must be capable of generalizing to ever-evolving manipulation techniques beyond training data. A promising strategy is to augment the training data with online synthesized fake images containing broadly generalizable artifacts. However, in the context of deepfake detection, it is surprising that jointly training on both original and online synthesized forgeries may result in degraded performance. This contradicts the common belief that incorporating more source-domain data should enhance detection accuracy. Through empirical analysis, we trace this degradation to gradient conflicts during backpropagation which force a trade-off between source domain accuracy and target domain generalization. To overcome this issue, we propose a Conflict-Suppressed Deepfake Detection (CS-DFD) framework that explicitly mitigates the gradient conflict via two synergistic modules. First, an Update Vector Search (UVS) module searches for an alternative update vector near the initial gradient vector to reconcile the disparities of the original and online synthesized forgeries. By further transforming the search process into an extremum optimization problem, UVS yields the uniquely update vector, which maximizes the simultaneous loss reductions for each data type. Second, a Conflict Gradient Reduction (CGR) module enforces a low-conflict feature embedding space through a novel Conflict Descent Loss. This loss penalizes misaligned gradient directions and guides the learning of representations with aligned, non-conflicting gradients. The synergy of UVS and CGR alleviates gradient interference in both parameter optimization and representation learning. Experiments on multiple deepfake benchmarks demonstrate that CS-DFD achieves state-of-the-art performance in both in-domain detection accuracy and cross-domain generalization.
Can We End the Cat-and-Mouse Game? Simulating Self-Evolving Phishing Attacks with LLMs and Genetic Algorithms
Venue: arXiv cs.CR 2025
Year: 2025

Anticipating emerging attack methodologies is crucial for proactive cybersecurity. Recent advances in Large Language Models (LLMs) have enabled the automated generation of phishing messages and accelerated research into potential attack techniques. However, predicting future threats remains challenging due to reliance on existing training data. To address this limitation, we propose a novel framework that integrates LLM-based phishing attack simulations with a genetic algorithm in a psychological context, enabling phishing strategies to evolve dynamically through adversarial interactions with simulated victims. Through simulations using Llama 3.1, we demonstrate that (1) self-evolving phishing strategies employ increasingly sophisticated psychological manipulation techniques, surpassing naive LLM-generated attacks, (2) variations in a victim's prior knowledge significantly influence the evolution of attack strategies, and (3) adversarial interactions between evolving attacks and adaptive defenses create a cat-and-mouse dynamic, revealing an inherent asymmetry in cybersecurity -- attackers continuously refine their methods, whereas defenders struggle to comprehensively counter all evolving threats. Our approach provides a scalable, cost-effective method for analyzing the evolution of phishing strategies and defenses, offering insights into future social engineering threats and underscoring the necessity of proactive cybersecurity measures.
VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation
Venue: arXiv cs.IR 2025
Year: 2025

Recommendation systems often suffer from data sparsity caused by limited user-item interactions, which degrade their performance and amplify popularity bias in real-world scenarios. This paper proposes a novel data augmentation framework that leverages Large Language Models (LLMs) and item textual descriptions to enrich interaction data. By few-shot prompting LLMs multiple times to rerank items and aggregating the results via majority voting, we generate high-confidence synthetic user-item interactions, supported by theoretical guarantees based on the concentration of measure. To effectively leverage the augmented data in the context of a graph recommendation system, we integrate it into a graph contrastive learning framework to mitigate distributional shift and alleviate popularity bias. Extensive experiments show that our method improves accuracy and reduces popularity bias, outperforming strong baselines.
SafeDriveRAG: Towards Safe Autonomous Driving with Knowledge Graph-based Retrieval-Augmented Generation
Venue: arXiv cs.AI 2025
Year: 2025

In this work, we study how vision-language models (VLMs) can be utilized to enhance the safety for the autonomous driving system, including perception, situational understanding, and path planning. However, existing research has largely overlooked the evaluation of these models in traffic safety-critical driving scenarios. To bridge this gap, we create the benchmark (SafeDrive228K) and propose a new baseline based on VLM with knowledge graph-based retrieval-augmented generation (SafeDriveRAG) for visual question answering (VQA). Specifically, we introduce SafeDrive228K, the first large-scale multimodal question-answering benchmark comprising 228K examples across 18 sub-tasks. This benchmark encompasses a diverse range of traffic safety queries, from traffic accidents and corner cases to common safety knowledge, enabling a thorough assessment of the comprehension and reasoning abilities of the models. Furthermore, we propose a plug-and-play multimodal knowledge graph-based retrieval-augmented generation approach that employs a novel multi-scale subgraph retrieval algorithm for efficient information retrieval. By incorporating traffic safety guidelines collected from the Internet, this framework further enhances the model's capacity to handle safety-critical situations. Finally, we conduct comprehensive evaluations on five mainstream VLMs to assess their reliability in safety-sensitive driving tasks. Experimental results demonstrate that integrating RAG significantly improves performance, achieving a +4.73% gain in Traffic Accidents tasks, +8.79% in Corner Cases tasks and +14.57% in Traffic Safety Commonsense across five mainstream VLMs, underscoring the potential of our proposed benchmark and methodology for advancing research in traffic safety. Our source code and data are available at https://github.com/Lumos0507/SafeDriveRAG.
Emerging Trends in Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation with Image-Level Supervision
Venue: arXiv cs.CV 2025
Year: 2025

Unlike fully supervised semantic segmentation, weakly supervised semantic segmentation (WSSS) relies on weaker forms of supervision to perform dense prediction tasks. Among the various types of weak supervision, WSSS with image level annotations is considered both the most challenging and the most practical, attracting significant research attention. Therefore, in this review, we focus on WSSS with image level annotations. Additionally, this review concentrates on mainstream research directions, deliberately omitting less influential branches. Given the rapid development of new methods and the limitations of existing surveys in capturing recent trends, there is a pressing need for an updated and comprehensive review. Our goal is to fill this gap by synthesizing the latest advancements and state-of-the-art techniques in WSSS with image level labels. Basically, we provide a comprehensive review of recent advancements in WSSS with image level labels, categorizing existing methods based on the types and levels of additional supervision involved. We also examine the challenges of applying advanced methods to domain specific datasets in WSSS,a topic that remains underexplored. Finally, we discuss the current challenges, evaluate the limitations of existing approaches, and outline several promising directions for future research. This review is intended for researchers who are already familiar with the fundamental concepts of WSSS and are seeking to deepen their understanding of current advances and methodological innovations.
A Tree-Shaped Tableau for Checking the Satisfiability of Signal Temporal Logic with Bounded Temporal Operators
Venue: arXiv cs.LO 2025
Year: 2025

Signal Temporal Logic (STL) is a widely recognized formal specification language to express rigorous temporal requirements on mixed analog signals produced by cyber-physical systems (CPS). A relevant problem in CPS design is how to efficiently and automatically check whether a set of STL requirements is logically consistent. This problem reduces to solving the STL satisfiability problem, which is decidable when we assume that our system operates in discrete time steps dictated by an embedded system's clock. This paper introduces a novel tree-shaped, one-pass tableau method for satisfiability checking of discrete-time STL with bounded temporal operators. Originally designed to prove the consistency of a given set of STL requirements, this method has a wide range of applications beyond consistency checking. These include synthesizing example signals that satisfy the given requirements, as well as verifying or refuting the equivalence and implications of STL formulas. Our tableau exploits redundancy arising from large time intervals in STL formulas to speed up satisfiability checking, and can also be employed to check Mission-Time Linear Temporal Logic (MLTL) satisfiability. We compare our tableau with Satisfiability Modulo Theories (SMT) and First-Order Logic encodings from the literature on a benchmark suite, partly collected from the literature, and partly provided by an industrial partner. Our experiments show that, in many cases, our tableau outperforms state-of-the-art encodings.
StaffPro: an LLM Agent for Joint Staffing and Profiling
Venue: arXiv cs.AI 2025
Year: 2025

Large language model (LLM) agents integrate pre-trained LLMs with modular algorithmic components and have shown remarkable reasoning and decision-making abilities. In this work, we investigate their use for two tightly intertwined challenges in workforce management: staffing, i.e., the assignment and scheduling of tasks to workers, which may require team formation; and profiling, i.e., the continuous estimation of workers' skills, preferences, and other latent attributes from unstructured data. We cast these problems in a formal mathematical framework that links scheduling decisions to latent feature estimation, and we introduce StaffPro, an LLM agent that addresses staffing and profiling jointly. Differently from existing staffing solutions, StaffPro allows expressing optimization objectives using natural language, accepts textual task descriptions and provides high flexibility. StaffPro interacts directly with humans by establishing a continuous human-agent feedback loop, ensuring natural and intuitive use. By analyzing human feedback, our agent continuously estimates the latent features of workers, realizing life-long worker profiling and ensuring optimal staffing performance over time. A consulting firm simulation example demonstrates that StaffPro successfully estimates workers' attributes and generates high quality schedules. With its innovative design, StaffPro offers a robust, interpretable, and human-centric solution for automated personnel management.
GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection
Venue: arXiv cs.CR 2025
Year: 2025

Modern in-vehicle networks face various cyber threats due to the lack of encryption and authentication in the Controller Area Network (CAN). To address this security issue, this paper presents GUARD-CAN, an anomaly detection framework that combines graph-based representation learning with time-series modeling. GUARD-CAN splits CAN messages into fixed-length windows and converts each window into a graph that preserves message order. To detect anomalies in the timeaware and structure-aware context at the same window, GUARD-CAN takes advantage of the overcomplete Autoencoder (AE) and Graph Convolutional Network (GCN) to generate graph embedding vectors. The model groups these vectors into sequences and feeds them into the Gated Recurrent Unit (GRU) to detect temporal anomaly patterns across the graphs. GUARD-CAN performs anomaly detection at both the sequence level and the window level, and this allows multi-perspective performance evaluation. The model also verifies the importance of window size selection through an analysis based on Shannon entropy. As a result, GUARD-CAN shows that the proposed model detects four types of CAN attacks (flooding, fuzzing, replay and spoofing attacks) effectively without relying on complex feature engineering.
Hyperbolic Genome Embeddings
Venue: arXiv cs.LG 2025
Year: 2025

Current approaches to genomic sequence modeling often struggle to align the inductive biases of machine learning models with the evolutionarily-informed structure of biological systems. To this end, we formulate a novel application of hyperbolic CNNs that exploits this structure, enabling more expressive DNA sequence representations. Our strategy circumvents the need for explicit phylogenetic mapping while discerning key properties of sequences pertaining to core functional and regulatory behavior. Across 37 out of 42 genome interpretation benchmark datasets, our hyperbolic models outperform their Euclidean equivalents. Notably, our approach even surpasses state-of-the-art performance on seven GUE benchmark datasets, consistently outperforming many DNA language models while using orders of magnitude fewer parameters and avoiding pretraining. Our results include a novel set of benchmark datasets--the Transposable Elements Benchmark--which explores a major but understudied component of the genome with deep evolutionary significance. We further motivate our work by exploring how our hyperbolic models recognize genomic signal under various data-generating conditions and by constructing an empirical method for interpreting the hyperbolicity of dataset embeddings. Throughout these assessments, we find persistent evidence highlighting the potential of our hyperbolic framework as a robust paradigm for genome representation learning. Our code and benchmark datasets are available at https://github.com/rrkhan/HGE.
AI Literacy as a Key Driver of User Experience in AI-Powered Assessment: Insights from Socratic Mind
Venue: arXiv cs.HC 2025
Year: 2025

As Artificial Intelligence (AI) tools become increasingly embedded in higher education, understanding how students interact with these systems is essential to supporting effective learning. This study examines how students' AI literacy and prior exposure to AI technologies shape their perceptions of Socratic Mind, an interactive AI-powered formative assessment tool. Drawing on Self-Determination Theory and user experience research, we analyze relationships among AI literacy, perceived usability, satisfaction, engagement, and perceived learning effectiveness. Data from 309 undergraduates in Computer Science and Business courses were collected through validated surveys. Partial least squares structural equation modeling showed that AI literacy - especially self-efficacy, conceptual understanding, and application skills - significantly predicts usability, satisfaction, and engagement. Usability and satisfaction, in turn, strongly predict perceived learning effectiveness, while prior AI exposure showed no significant effect. These findings highlight that AI literacy, rather than exposure alone, shapes student experiences. Designers should integrate adaptive guidance and user-centered features to support diverse literacy levels, fostering inclusive, motivating, and effective AI-based learning environments.
Collaborative State Machines: A Better Programming Model for the Cloud-Edge-IoT Continuum
Venue: arXiv cs.DC 2025
Year: 2025

The development of Cloud-Edge-IoT applications requires robust programming models. Existing models often struggle to manage the dynamic and stateful nature of these applications effectively. This paper introduces the Collaborative State Machines (CSM) programming model to address these complexities. CSM facilitates the development of reactive, event-driven, and stateful applications targeting the Cloud-Edge-IoT continuum. Applications built with CSM are composed of state machines that collaborate autonomously and can be distributed across different layers of the continuum. Key features of CSM include (i) a sophisticated collaboration mechanism among state machines utilizing events and persistent data; (ii) encapsulation of state through the inherent state of state machines and persistent data; (iii) integration of actions and service invocations within states and state transitions, thereby decoupling complex application logic from compute and data processing services; and (iv) an advanced data model that supports the processing of local, static, and persistent data with defined scope and lifetime. In addition to introducing the CSM programming model, we present a runtime system and a comprehensive evaluation of our approach. This evaluation is based on three use cases: a stress test on a large-scale infrastructure, a surveillance system application, and a complex smart factory scenario, all deployed on the Grid'5000 testbed. Our results demonstrate a 12x increase in throughput through novel language features in the stress test. Compared to Serverless Workflow, a state-of-the-art baseline system, we show a 2.3x improvement in processing time per processed image in a surveillance system use case, a 55x reduction in total processing time for a smart factory use case, and an overall improvement in productivity across these use cases.
A Multi-Agent Generative AI Framework for IC Module-Level Verification Automation
Venue: arXiv cs.AR 2025
Year: 2025

As large language models demonstrate enormous potential in the field of Electronic Design Automation (EDA), generative AI-assisted chip design is attracting widespread attention from academia and industry. Although these technologies have made preliminary progress in tasks such as code generation, their application in chip verification -- a critical bottleneck in the chip development cycle -- remains at an exploratory stage. This paper proposes an innovative Multi-Agent Verification Framework (MAVF) aimed at addressing the limitations of current single-LLM approaches in complex verification tasks. Our framework builds an automated transformation system from design specifications to testbench through the collaborative work of multiple specialized agents, including specification parsing, verification strategy generation, and code implementation. Through verification experiments on multiple chip modules of varying complexity, results show that MAVF significantly outperforms traditional manual methods and single-dialogue generative AI approaches in verification document parsing and generation, as well as automated testbench generation. This research opens new directions for exploring generative AI applications in verification automation, potentially providing effective approaches to solving the most challenging bottleneck issues in chip design.
PREIG: Physics-informed and Reinforcement-driven Interpretable GRU for Commodity Demand Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Accurately forecasting commodity demand remains a critical challenge due to volatile market dynamics, nonlinear dependencies, and the need for economically consistent predictions. This paper introduces PREIG, a novel deep learning framework tailored for commodity demand forecasting. The model uniquely integrates a Gated Recurrent Unit (GRU) architecture with physics-informed neural network (PINN) principles by embedding a domain-specific economic constraint: the negative elasticity between price and demand. This constraint is enforced through a customized loss function that penalizes violations of the physical rule, ensuring that model predictions remain interpretable and aligned with economic theory. To further enhance predictive performance and stability, PREIG incorporates a hybrid optimization strategy that couples NAdam and L-BFGS with Population-Based Training (POP). Experiments across multiple commodities datasets demonstrate that PREIG significantly outperforms traditional econometric models (ARIMA,GARCH) and deep learning baselines (BPNN,RNN) in both RMSE and MAPE. When compared with GRU,PREIG maintains good explainability while still performing well in prediction. By bridging domain knowledge, optimization theory and deep learning, PREIG provides a robust, interpretable, and scalable solution for high-dimensional nonlinear time series forecasting in economy.
Knowledge-Guided Memetic Algorithm for Capacitated Arc Routing Problems with Time-Dependent Service Costs
Venue: arXiv cs.NE 2025
Year: 2025

The capacitated arc routing problem with time-dependent service costs (CARPTDSC) is a challenging combinatorial optimization problem that arises from winter gritting applications. CARPTDSC has two main challenges about time consumption. First, it is an NP-hard problem. Second, the time-dependent service costs of tasks require frequent evaluations during the search process, significantly increasing computational effort. These challenges make it difficult for existing algorithms to perform efficient searches, often resulting in limited efficiency. To address these issues, this paper proposes a knowledge-guided memetic algorithm with golden section search and negatively correlated search (KGMA-GN), where two knowledge-guided strategies are introduced to improve search efficiency. First, a knowledge-guided initialization strategy (KGIS) is proposed to generate high-quality initial solutions to speed up convergence. Second, a knowledge-guided small-step-size local search strategy (KGSLSS) is proposed to filter out invalid moves, thereby reducing unnecessary evaluations and saving the computation time. Experimental results on five benchmark test sets, including both small- and larger-scale instances, demonstrate that KGMA-GN achieves higher search efficiency than the state-of-the-art methods. Moreover, the ablation study further confirms that the knowledge-guided local search operators in KGSLSS can significantly reduce runtime compared to traditional operators, especially for the knowledge-guided swap operator, which achieves more than a tenfold improvement in speed.
Adversarial Defence without Adversarial Defence: Enhancing Language Model Robustness via Instance-level Principal Component Removal
Venue: arXiv cs.CL 2025
Year: 2025

Pre-trained language models (PLMs) have driven substantial progress in natural language processing but remain vulnerable to adversarial attacks, raising concerns about their robustness in real-world applications. Previous studies have sought to mitigate the impact of adversarial attacks by introducing adversarial perturbations into the training process, either implicitly or explicitly. While both strategies enhance robustness, they often incur high computational costs. In this work, we propose a simple yet effective add-on module that enhances the adversarial robustness of PLMs by removing instance-level principal components, without relying on conventional adversarial defences or perturbing the original training data. Our approach transforms the embedding space to approximate Gaussian properties, thereby reducing its susceptibility to adversarial perturbations while preserving semantic relationships. This transformation aligns embedding distributions in a way that minimises the impact of adversarial noise on decision boundaries, enhancing robustness without requiring adversarial examples or costly training-time augmentation. Evaluations on eight benchmark datasets show that our approach improves adversarial robustness while maintaining comparable before-attack accuracy to baselines, achieving a balanced trade-off between robustness and generalisation.