Traditional sequential recommendation (SR) models learn low-dimensional item ID embeddings from user-item interactions, often overlooking textual information such as item titles or descriptions. Recent advances in Large Language Models (LLMs) have inspired a surge of research that encodes item textual information with high-dimensional semantic embeddings, and designs transformation methods to inject such embeddings into SR models. These embedding transformation strategies can be categorized into two types, both of which exhibits notable drawbacks: 1) adapter-based methods suffer from pronounced dimension collapse, concentrating information into a few dominant dimensions; 2) SVD-based methods are rigid and manual, considering only a few principal spectral components while discarding rich information in the remaining spectrum.

The rapid advancement of multimodal large language models has enabled agents to operate mobile devices by directly interacting with graphical user interfaces, opening new possibilities for mobile automation. However, real-world mobile tasks are often complex and allow for multiple valid solutions. This contradicts current mobile agent evaluation standards: offline static benchmarks can only validate a single predefined ''golden path'', while online dynamic testing is constrained by the complexity and non-reproducibility of real devices, making both approaches inadequate for comprehensively assessing agent capabilities. To bridge the gap between offline and online evaluation and enhance testing stability, this paper introduces a novel graph-structured benchmarking framework. By modeling the finite states observed during real-device interactions, it achieves static simulation of dynamic behaviors. Building on this, we develop ColorBench, a benchmark focused on complex long-horizon tasks. It supports evaluation of multiple valid solutions, subtask completion rate statistics, and atomic-level capability analysis. ColorBench contains 175 tasks (74 single-app, 101 cross-app) with an average length of over 13 steps. Each task includes at least two correct paths and several typical error paths, enabling quasi-dynamic interaction.

Interpretability of point cloud (PC) models becomes imperative given their deployment in safety-critical scenarios such as autonomous vehicles. We focus on attributing PC model outputs to interpretable critical concepts, defined as meaningful subsets of the input point cloud. To enable human-understandable diagnostics of model failures, an ideal critical subset should be faithful (preserving points that causally influence predictions) and conceptually coherent (forming semantically meaningful structures that align with human perception). We propose InfoCons, an explanation framework that applies information-theoretic principles to decompose the point cloud into 3D concepts, enabling the examination of their causal effect on model predictions with learnable priors. We evaluate InfoCons on synthetic datasets for classification, comparing it qualitatively and quantitatively with four baselines. We further demonstrate its scalability and flexibility on two real-world datasets and in two applications that utilize critical scores of PC.

Image watermarking embeds imperceptible signals into AI-generated images for deepfake detection and provenance verification. Although recent semantic-level watermarking methods demonstrate strong resistance against conventional pixel-level removal attacks, their robustness against more advanced removal strategies remains underexplored, raising concerns about their reliability in practical scenarios. Existing removal attacks primarily operate in the pixel domain without altering image semantics, which limits their effectiveness against semantic-level watermarks. In this paper, we propose Next Frame Prediction Attack (NFPA), the first semantic-level removal attack. Unlike pixel-level attacks, NFPA formulates watermark removal as a video generation task: it treats the watermarked image as the initial frame and aims to subtly manipulate the image semantics to generate the next-frame image, i.e., the unwatermarked image. We conduct a comprehensive evaluation on eight state-of-the-art image watermarking schemes, demonstrating that NFPA consistently outperforms thirteen removal attack baselines in terms of the trade-off between watermark removal and image quality. Our results reveal the vulnerabilities of current image watermarking methods and highlight the urgent need for more robust watermarks.

Time series classification (TSC) is a cornerstone of modern web applications, powering tasks such as financial data analysis, network traffic monitoring, and user behavior analysis. In recent years, deep neural networks (DNNs) have greatly enhanced the performance of TSC models in these critical domains. However, DNNs are vulnerable to backdoor attacks, where attackers can covertly implant triggers into models to induce malicious outcomes. Existing backdoor attacks targeting DNN-based TSC models remain elementary. In particular, early methods borrow trigger designs from computer vision, which are ineffective for time series data. More recent approaches utilize generative models for trigger generation, but at the cost of significant computational complexity.

With the rapid advancement of intelligent connected vehicles (ICVs), in-vehicle networks (IVNs) have increasingly become active targets for cyberattacks. The controller area network (CAN), a widely used IVN, lacks security mechanisms, making it vulnerable to attacks that may lead to system failures and even endanger passenger safety. Existing intrusion detection models depend on centralized data processing and limited real attack data, which raises privacy concerns and restricts detection capabilities. Therefore, we propose a novel federated learning (FL)-based solution, called FedGAN-ID, which aims to generate realistic attack data to enhance the intrusion detection performance using generative adversarial networks (GANs) in ICVs. This article generates CAN message graph within given interval based on CAN ID, message content, and timestamps, enabling comprehensive detection of various attacks that existing models can only partially detect, such as DoS, spoofing, fuzzy, replay, and masquerade attacks. Considering there are few known real attack signatures for IVNs, this article develops a two-level cascade detector. Specifically, we first propose a novel GAN model that is trained to mimic the normal data distribution to enable detection of both known and unknown attacks. Subsequently, we introduce a FL scheme in which each vehicle generates differentially private synthetic data and uploads these data to the cloud server to improve the attack classification ability of FedGAN-ID without sharing real data. Extensive experiments have been conducted on three real vehicles supported by XPeng, demonstrating that FedGAN-ID can accurately detect all these attacks in 1.96 milliseconds.

Deep neural networks have demonstrated remarkable performance in point cloud classification. However, pre-vious works show they are vulnerable to adversarial per-turbations that can manipulate their predictions. Given the distinctive modality of point clouds, various attack strategies have emerged, posing challenges for existing defenses to achieve effective generalization. In this study, we for the first time introduce causal modeling to enhance the robustness of point cloud classification models. Our insight is from the observation that adversarial examples closely re-semble benign point clouds from the human perspective. In our causal modeling, we incorporate two critical variables, the structural information, (standing for the key feature leading to the classification) and the hidden confounders, (standing for the noise interfering with the classification). The resulting overall framework CausalPC consists of three sub-modules to identify the causal effect for robust classification. The framework is model-agnostic and adaptable for integration with various point cloud classifiers. Our approach significantly improves the adversarial robustness of three mainstream point cloud classification models on two benchmark datasets. For instance, the classification accuracy for DGCNN on ModelNet40 increases from 29.2% to 72.0% with CausalPC, whereas the best-performing base-line achieves only 42.4%.