CT-to-PET synthesis aims to synthesize PET images from the widely available and lower-cost CT scans to address the high cost and additional radiation exposure associated with PET scanning. However, CT-to-PET synthesis faces two key challenges due to the sequential correlation of volumetric imaging: preserving smooth transition between adjacent slices and ensuring style consistency across long-range slices. To overcome these limitations, we propose the Style-aware Bidirectional Stream Diffusion Model, which ensures both inter-slice continuity and global style consistency with low computational cost. Specifically, we first utilize a Vector Quantized Variational Autoencoder to encode bidirectional adjacent CT slices into latent codes. A Neighbor Attention module is then introduced to capture transition patterns among these latent codes, ensuring structural continuity. To enhance style consistency, we further design a Prototype Prompting mechanism to construct a feature pool from long-range slices. Global style prototypes are extracted from the feature pool and dynamically integrated into the generation process, guiding the model’s attention toward consistent stylistic features across slices. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods.
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Existing spatial domain identification methods primarily use graph neural networks to model spatial and transcriptional relationships. However, their performance is highly sensitive to noisy affinity graphs. Moreover, graph autoencoders tend to over-constrain latent representations, which limits their ability to capture global variability in spatial multi-omics data. To overcome these issues, we propose a dual-flow latent refinement framework that simultaneously integrates structure-aware and structure-free transformations. Specifically, a graph normalizing flow is employed to enforce relational consistency, while a parallel vanilla normalizing flow preserves global distributional flexibility. Features learned by the two flows are then adaptively fused to obtain a robust and unified latent representations. In addition, we introduce a grouping belief-based affinity refinement strategy to suppress unreliable connections and strengthen confident neighborhood relationships, which provides a more stable structural prior for representation learning. Extensive experiments on multiple spatial multi-omics datasets show that the proposed method consistently outperforms state-of-the-art approaches and achieves more accurate and robust spatial domain identification. The supplementary material is publicly available at https://github.com/LiangSDNULab/GBFlow.
Masked autoencoder have demonstrated excellent performance in point cloud representation learning and received widespread attention. However, due to the unstructured nature of point clouds, existing methods struggle to effectively model both local geometric information and global topological features, while generally neglecting positional information, resulting in insufficient model representational capacity. To address these challenges, we propose a Position-Assigned Graph Masked Autoencoder (PA-GMAE) framework for point cloud representation learning. Specifically, we first explicitly transform the unstructured point clouds into a graph structure and assigns it positional information, leveraging the structured properties of graph to mine the intrinsic correlations among points. During the encoding stage, global contextual are dynamically injected into node features, endowing the model with capabilities of global awareness. In the decoding stage, collaborative work across multiple decoding tasks fully captures the global topology and positional dependencies of the point cloud, enhancing the quality of learned representations. Extensive experiments are conducted on three authoritative benchmark datasets, namely ScanObjectNN, ModelNet40, and ShapeNetPart, across typical downstream tasks including point cloud classification and part segmentation, the results demonstrate the effectiveness and superiority of the proposed framework.
Medication recommendation plays a critical role in clinical decision-making by supporting personalized and safe treatment planning. Existing methods rely heavily on historical co-occurrence patterns and primarily optimize discrete prescription prediction objectives, limiting generalization in rare or emerging disease settings. We propose MGRec, a framework that shifts learning from discrete prescription prediction to condition-aware modeling in a continuous molecular representation space. MGRec treats molecular structure as the primary modeling target and employs a Therapeutic--Safety Factorized Conditional Variational Autoencoder to disentangle therapeutic and safety-related factors in a condition-aware molecular latent space. The model infers treatment-relevant molecular representations conditioned on current patient-specific clinical context, which are mapped to clinically approved medications for final recommendation. To improve clinical safety, we further introduce a DDI (drug-drug interaction)-guided latent regularization to integrate drug interaction knowledge at the representation level. Experiments on two real-world benchmarks demonstrate that MGRec achieves state-of-the-art accuracy and reduced interaction risk, particularly in data-sparse scenarios.
Inverse reinforcement learning (IRL) has made significant progress in recovering reward functions from expert demonstrations. However, a key challenge remains: how to extract reward functions that generalize across related but distinct tasks. In this paper, we address this by focusing on transferable IRL, learning intrinsic rewards that can drive effective behavior in unseen but structurally aligned environments. Our method leverages a variational autoencoder to learn an abstract representation of the state space shared across multiple source tasks. This abstracted space captures high-level features that are invariant across tasks, enabling the learning of a unified abstract reward function. The learned reward is then used to train policies in a separate, previously unseen target task without requiring new demonstrations in the target task. We evaluate our approach on multiple environments from Gymnasium and AssistiveGym, demonstrating that the learned abstract rewards consistently support successful policy learning in novel task settings.
Social bots threaten online platforms by spreading disinformation and manipulating public discourse. Graph neural networks have emerged as effective tools for bot detection by modeling user interactions, yet two fundamental challenges limit their practical deployment: severe class imbalance where bots constitute a small minority of users, and camouflaged edges where bots forge deceptive connections to humans to evade detection. Class imbalance causes decision boundaries to shift toward the majority class, while camouflaged edges corrupt neighborhood aggregation through spurious message passing. We present BotVA, a unified framework addressing both challenges through variational feature augmentation and adversarial graph learning. Our approach makes three key contributions: (i) a conditional variational autoencoder that models minority class distributions and synthesizes semantically coherent features, effectively expanding minority support in representation space; (ii) an adversarial training paradigm where a generator simulates camouflage by injecting deceptive edges while a graph transformer discriminator with semantic attention learns to identify and downweight such perturbations; and (iii) a two-stage training strategy that pretrains the variational module before alternating generator-discriminator optimization to ensure stable convergence. Experiments on three benchmarks demonstrate that BotVA achieves state-of-the-art accuracy and F1-score, exhibits strong robustness under camouflage perturbations, and maintains competitive performance with limited supervision.
Large Language Models (LLMs) powerful generative capabilities also pose significant risks, underscoring the need for effective detoxification methods to ensure safer deployment. Due to the polysemantic nature of LLM neurons, recent neuron intervention methods inevitably entangle unrelated concepts, compromising generation quality and interpretability. Sparse Autoencoders (SAEs) have opened new horizons for decomposing model activations into monosemantic features, offering interpretability and targeted feature-level steering. Empirical findings reveal that, despite capturing interpretable features, indiscriminate interventions on toxicity-related features expose the fragility of LLMs, achieving toxicity mitigation at the cost of degraded fluency. Building upon this finding, we propose DeLFE, a lightweight controlled detoxification approach that identifies specific toxic features across model layers and performs targeted interventions on them. DeLFE learns toxicity subspaces from label-guided SAE feature subsets to characterize toxic v.s. non-toxic activation patterns. When auto-completing a response token-by-token, DeLFE tracks the toxicity-triggering risks and steers toxic features away from the subspace via a flow-matching feature transformation. We further design three feature-level strategies that adjust intervention timing and strength to reconstruct the target model’s original activations. Extensive experiments demonstrate that our method achieves strong detoxification effectiveness while maintaining high generation quality across models of varying sizes and diverse base LLMs.
Graph Masked AutoEncoders (GMAEs) have achieved notable success in diverse downstream tasks. However, their superior performance usually relies on reliable and unperturbed input graphs. In real-world scenarios, graphs are vulnerable to adversarial attacks (e.g., perturbations on node features or topology). As a result, existing GMAEs tend to overfit these perturbed inputs, leading to poor robustness. Motivated by this, we propose an adversarial masked graph modeling for robust graph autoencoders, named ArmorGAE. Specifically, we first design a dual-consistency graph augmentation method that enriches the graph topology to mitigate structural vulnerabilities. Theoretically, we establish a connection between ArmorGAE and contrastive learning. It demonstrates that our augmentation strategy in GMAEs is equivalent to effectively expanding the set of high-quality positive samples in contrastive learning, providing richer and more reliable supervisory signals. We then present an adversarial masked autoencoder to mitigate model overfitting to the perturbed graph. The generator and discriminator are jointly trained under an adversarial paradigm, where the former aims to recover the underlying true data distribution, while the latter distinguishes reconstructed and real edges. Experimental results on five datasets demonstrate that ArmorGAE outperforms state-of-the-art methods in terms of classification accuracy on clean graphs and adversarial robustness under 16 distinct adversarial scenarios. The codes are publicly available at https://github.com/ZZY-GraphMiningLab/ArmorGAE.
Multi-view clustering has garnered significant attention for its ability to integrate heterogeneous data and uncover underlying categorical structures. However, prevailing autoencoder-based methods often yield indiscriminative embeddings, rendering them prone to trivial solutions. While diffusion models have shown promise in modeling complex data distributions, their generative processes remain inherently unstable. More critically, the resulting latent representations often lack sufficient intra-class compactness and inter-class separability, leading to suboptimal clustering performance. In this paper, we propose SCD-MVC, a Stable Conditional Diffusion-driven framework for Multi-View Clustering. Specifically, it integrates conditional diffusion generation into the latent encoder to generate a target view conditioned on the remaining views, thereby facilitating robust joint distribution modeling. To align the generative process with clustering objectives, we introduce a clustering-guided semantic alignment module. This module regulates the diffusion path to enforce the learning of representations that are both view-consistent and discriminative. Furthermore, we introduce a temporal consistency regularization that enforces alignment between representations of adjacent diffusion steps. This stabilizes training and guides the optimization toward clustering-friendly representations. Extensive experiments on nine benchmark datasets demonstrate that SCD-MVC consistently outperforms eight state-of-the-art methods, achieving performance gains ranging from 6.68% to 13.09%. The source code is available at https://github.com/Knighttt0011/SCD-MVC.
Probabilistic time series forecasting seeks to quantify the uncertainty of future observations. While recent works introduce latent variables to alleviate the spurious dependencies caused by hidden confounders, thereby reducing overly wide confidence intervals, simply incorporating latent factors is not sufficient. When the underlying latent dynamics evolve at multiple temporal scales, existing methods may entangle temporally coarse and fine latent dynamics, which introduces spurious latent transitions and in turn amplifies predictive uncertainty. Therefore, disentangling temporally coarse and fine latent dynamics is essential for achieving sharper and more reliable probabilistic forecasting results. Building on this insight, we propose COFE (COarse And FinE latent dynamics disentanglement), a variational autoencoder–based framework that models the temporal distribution by disentangling and modeling the rapidly changing and slowly varying latent dynamics simultaneously. In particular, the proposed COFE harnesses the independence of estimated noises between adjacent latent states as well as a sparsity constraint on estimated noise to disentangle latent dynamics across different scales effectively. More specifically, we show that the multi-scale latent dynamics are disentangled with rigorous theoretical guarantees. Extensive experiments on 15 benchmark datasets, compared against 12 state-of-the-art baselines, demonstrate that COFE achieves superior uncertainty quantification, validating its effectiveness in a wide range of real-world scenarios. Code is available at https://github.com/polars8948/COFE
Generative models continue to advance rapidly in both fidelity and diversity, posing increasing challenges for reliably distinguishing generated images from real ones. Existing reconstruction-based detection methods often rely on assumptions tied to specific generative models, which leads to limited cross-model generalization and substantial computational overhead. In this paper, we propose General Feature Reconstruction Error (GFRE), a fast and generalizable detection paradigm that leverages reconstruction behavior in a general-purpose representation space. Our key insight is that real and generated images exhibit consistently different reconstruction stability when projected into universal visual representations. Instead of tracing generator-specific artifacts, GFRE employs a lightweight autoencoder to model the reconstructability of image representations, producing a reconstruction signal that is inherently generator-agnostic and transferable across diverse generative processes. Extensive experiments on images synthesized by 18 different generative models demonstrate that GFRE consistently outperforms existing state-of-the-art methods, achieving a 4.70% improvement in detection accuracy and an 8.20% gain in cross-model generalization. Moreover, by avoiding costly generator inversion and diffusion-based reconstruction, GFRE reduces reconstruction error extraction time by up to 150x, enabling efficient and scalable deployment.
Recent efforts have successfully scaled plain Vision Transformers (ViTs) to unprecedented sizes, ranging from 6B to 22B parameters. However, hierarchical ViTs, which are inherently better suited for multi-scale representation and dense prediction tasks, have largely remained constrained to under 2B parameters. This scaling gap prevents the community from leveraging high-capacity, multi-scale foundation models for complex visual scenes. To bridge this gap, we propose EHV, an Efficient Hierarchical ViT architecture designed for massive scaling. Our dense models scale from 200M to 5B parameters, and we further introduce a Sparse Mixture-of-Experts (SMoE) variant, pushing the scale to an industry-leading 30B parameters. The training pipeline follows a two-stage pretraining strategy: (1) MAE Stage: self-supervised pretraining on ImageNet-21K using a Masked Autoencoder (MAE); and (2) Distillation Stage: distilling knowledge from multiple state-of-the-art foundation models on a 27M-image dataset. With only 6.7B active parameters, EHV-5B-MoE achieves an exceptional 89.0% linear accuracy on ImageNet-1K, outperforming much larger models like EVA-CLIP-18B and DINOv3-7B. These results demonstrate that EHV effectively learns high-quality, generalizable, and linearly separable features across image classification, video analysis, and dense prediction tasks.
Prometheus: Unsupervised Discovery of Phase Transitions and Order Parameters in the 2D Ising Model Using Variational Autoencoders (Student Abstract)
PDF ↗Phase transitions in condensed matter systems traditionally require prior knowledge of order parameters for identification. We present Prometheus, a variational autoencoder framework for unsupervised discovery of phase transitions and order parameters in the two-dimensional Ising model without prior physical knowledge. Our approach combines convolutional neural networks with beta-variational autoencoders to learn compressed representations that naturally separate ordered and disordered phases. Experimental validation demonstrates automatic discovery of the order parameter with 0.85 correlation to theoretical magnetization and critical temperature detection within 0.27% of the theoretical value, achieving 89% improvement over principal component analysis while requiring no supervision.
Guided Latent Spaces for Controllable Multi-Scenario Generation in Autonomous Driving (Student Abstract)
PDF ↗Scenario-based testing is an important approach for the development and validation of autonomous driving systems, as it enables evaluation across different driving situations. Safety-critical scenarios are especially relevant, but they occur rarely in real-world data, which creates the need for generation methods. In this paper, we present a scalable AI-based approach based on a variational autoencoder that unifies the generation of different types of critical scenarios while introducing controllability through a structured latent space. The integration of unified generation and latent space control advances AI-based scenario generation towards practical use, thereby supporting the requirements of industrial validation pipelines.
Steering Sparse Autoencoder Latents to Control Dynamic Head Pruning in Vision Transformers (Student Abstract)
PDF ↗Dynamic head pruning in Vision Transformers (ViTs) improves efficiency by removing redundant attention heads, but existing pruning policies are often difficult to interpret and control. In this work, we propose a novel framework by integrating Sparse Autoencoders (SAEs) with dynamic pruning, leveraging their ability to disentangle dense embeddings into interpretable and controllable sparse latents. Specifically, we train an SAE on the final-layer residual embedding of the ViT and amplify the sparse latents with different strategies to alter pruning decisions. Among them, per-class steering reveals compact, class-specific head subsets that preserve accuracy. For example, bowl improves accuracy (76%→82%) while reducing head usage (0.72→0.33) via heads h2 and h5. These results show that sparse latent features enable class-specific control of dynamic pruning, effectively bridging pruning efficiency and mechanistic interpretability in ViTs.
Lithology-Aware Conditional Variational Autoencoder for Synthetic Well Log Generation in Petroleum Reservoirs (Student Abstract)
PDF ↗Machine learning applications in reservoir modeling are hindered by the limited availability of well log data, a common challenge in the oil and gas industry. We propose VAEc-tMC, a domain-informed Conditional Variational Autoencoder that generates synthetic well-log data conditioned on rock type. Addressing a critical gap by existing generative models that rely solely on statistical reconstruction, our model embeds geological domain knowledge into the latent space, and optimizes a modified objective with an adaptive Student-t reconstruction loss and a beta-weighted KL regularizer, improving stability under heavy-tailed data. When used for data augmentation, the synthetic samples preserve inter-log dependencies and substantially enhance downstream classification, accuracy 39→63%, F1-score 36→68%, AUC 0.46→0.80 on a held-out well. Beyond the geological context, the proposed approach illustrates a generalizable strategy where domain-aware generative models with adaptive loss functions provide a robust solution for data-efficient learning in scientific domains facing data scarcity, noise, and heavy-tailed distributions.
Fusing Time-Domain and Constellation Views: A Multimodal MAE for Wireless Signals (Student Abstract)
PDF ↗This paper introduces a multi-modal masked autoencoder (MMAE) that jointly denoises and classifies signals by fusing time-domain IQ sequences and constellation diagrams within a cross-attentive transformer. This approach treats noise as a learnable modality to enhance robustness, a dynamic masking curriculum combined with domain regularization training and a hybrid loss function to promote domain-invariant features. Experimentation on the RadioML 2018.01A and RadioML22 datasets demonstrates superior accuracy across different SNR levels while using substantially less labeled data than state-of-the-art approaches.
Tabular data synthesis presents unique challenges, with Transformer models remaining underexplored despite the applications of Variational Autoencoders and Generative Adversarial Networks. To address this gap, we propose the Transformer-based Tabular Variational AutoEncoder (TTVAE), leveraging the attention mechanism for capturing complex data distributions. The inclusion of the attention mechanism enables our model to understand complex relationships among heterogeneous features, a task often difficult for traditional methods. TTVAE facilitates the integration of interpolation within the latent space during the data generation process. Specifically, TTVAE is trained once, establishing a low-dimensional representation of real data, and then various latent interpolation methods can efficiently generate synthetic latent points. Through extensive experiments on diverse datasets, TTVAE consistently achieves state-of-the-art performance, highlighting its adaptability across different feature types and data sizes. This innovative approach, empowered by the attention mechanism and the integration of interpolation, addresses the complex challenges of tabular data synthesis, establishing TTVAE as a powerful solution.
Effective crime linkage analysis is crucial for identifying serial offenders and enhancing public safety. To address the limitations of traditional crime linkage methods when handling high-dimensional, sparse, and heterogeneous data, this paper proposes a Siamese Autoencoder framework to learn meaningful latent representations and uncover correlations in highly complex data. Using a dataset from the Violent Crime Linkage Analysis System—a database maintained by the Serious Crime Analysis Section of the UK’s National Crime Agency—our approach mitigates signal dilution in high-dimensional sparse data through decoder-stage integration of geographic-temporal features. This integration amplifies learned behavioral representations rather than allowing them to be overwhelmed at the input stage, leading to consistent improvements over baseline methods across multiple metrics. We further examine how different data reduction strategies based on domain-expert can impact model performance, offering practical insights into preprocessing for crime linkage. Our solution shows that advanced machine learning approaches can enhance linkage accuracy, improving AUC by up to 9% over traditional methods and providing insights to support human decision-making in crime investigation.
MIRNet: Integrating Constrained Graph-Based Reasoning with Pre-training for Diagnostic Medical Imaging
PDF ↗Automated interpretation of medical images demands robust modeling of complex visual-semantic relationships while addressing annotation scarcity, label imbalance, and clinical plausibility constraints. We introduce MIRNet (Medical Image Reasoner Network), a novel framework that integrates self-supervised pre-training with constrained graph-based reasoning. Tongue image diagnosis is a particularly challenging domain that requires fine-grained visual and semantic understanding. Our approach leverages self-supervised masked autoencoder (MAE) to learn transferable visual representations from unlabeled data; employs graph attention networks (GAT) to model label correlations through expert-defined structured graphs; enforces clinical priors via constraint-aware optimization using KL divergence and regularization losses; and mitigates imbalance using asymmetric loss (ASL) and boosting ensembles. To address annotation scarcity, we also introduce TongueAtlas-4K, a comprehensive expert-curated benchmark comprising 4,000 images annotated with 22 diagnostic labels–representing the largest public dataset in tongue analysis. Validation shows our method achieves state-of-the-art performance. While optimized for tongue diagnosis, the framework readily generalizes to broader diagnostic medical imaging tasks.