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.
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Dynamic link prediction aims to predict whether two nodes will interact at a future time point in a dynamic graph based on their historical interactions. Existing sampling based methods, which can be considered as a compressor, generally select a subset of one-hop neighbors from the entire interaction sequence for efficiency. It inevitably makes them lose rich context such as high order structural information and temporal patterns, and often underperform on nodes with sparse historical interaction with other nodes. To this aim, we propose a novel context construction based approach named PeNS that adopts Pseudo Neighbor Augmentation Sampling Strategy for more accurate dynamic link prediction. Specifically, PeNS tries to augment neighbor sequences with three complementary types of pseudo neighbors, injecting structural and temporal cues to provide rich context information especially in sparse neighbor scenarios. We further introduce a Streaming Neighbor Memory Module for efficient pseudo neighbor construction and a dual-stream fusion mechanism for robust representation learning. Extensive experiments demonstrate the effectiveness and generalizability of PeNS.
Single-cell RNA sequencing (scRNA-seq) serves a pivotal role in characterizing gene expression at the cellular level, enabling the identification of cell types and advancing the understanding of cellular heterogeneity. Despite the significant progress in scRNA-seq data clustering, we argue that current methods always ignore the sparsity and noise, as well as the complex intercellular structural information inherent in scRNA-seq data. Toward this end, in this paper, we propose a novel single-cell RNA-seq clustering framework via deep Siamese Graph Transformer Network (termed scGTN), which explicitly integrates gene expression profile and intercellular structural dependencies for cell clustering. In particular, we formulate scRNA-seq data as a graph and construct two augmented graph views that serve as dual views to capture complementary intercellular information. Then, a Siamese graph transformer network is employed to explicitly incorporate shortest-path information and node-wise distances for capturing richer structural relationships between cells. Finally, we employ an optimal transport strategy to guide the cell clustering in a self-supervised manner. Extensive experiments on multiple benchmark scRNA-seq datasets demonstrate that our scGTN consistently outperforms existing methods. Our code is available at https://github.com/W-RMSL/scGTN.
Graph pre-training and prompt tuning provide an effective route to label-efficient node classification by learning transferable backbones and adapting them with lightweight prompts. However, existing pre-train-and-prompt pipelines often generalize poorly across graphs with diverse homophily due to two fundamental limitations: (i) spectral bias, where learned representations overemphasize a single frequency band, and (ii) prompt misalignment, where spatial-domain prompts cannot explicitly modulate critical spectral components and may even disrupt frequency-aware backbones. In this paper, we propose SCA-GPPT, a spectrum-aware framework that unifies Spectrum-based Graph Pre-training and Cluster-Augmented Prompt Tuning. First, we present a systematic spectral analysis of graph prompting, revealing the inherent limitations of spatial-domain prompts in capturing attenuated high-frequency signals. Second, we explicitly learn complementary low- and high-frequency spectral filters via parameterized Chebyshev polynomials and employ band-aware contrastive learning to model diverse spectral patterns during pre-training. For downstream adaptation, we freeze the pre-trained backbone and introduce cluster-augmented prompts that directly adapt Chebyshev coefficients while enforcing global structural consistency, enabling stable few-shot transfer across varying homophily regimes. Extensive experiments on real-world benchmarks demonstrate that SCA-GPPT consistently outperforms strong baselines under both transductive and inductive settings. Our code is available at https://anonymous.4open.science/r/SCA-GPPT-17B2.
Due to proliferation of vehicle trajectory data from advanced sensing technologies, path representation learning has become a pivotal task in intelligent transportation systems. Although existing self-supervised approaches work to some extent, their dependence on deterministic contrastive learning paradigms and handcrafted view augmentation strategies inherently restricts cross-scenario generalization capabilities. To address these limitations, we present DGCPath – an innovative Distribution-aware Generative Contrastive learning framework for Path representation. This architecture establishes a synergistic connection between generative modeling and distributional contrastive learning, enabling the acquisition of robust and transferable feature embeddings. Specifically, our framework incorporates: (1) a diffusion-based view generator that autonomously produces semantically coherent yet diversified trajectory views from Gaussian noise distributions; (2) a variational contrastive mechanism enforcing latent feature alignment at the distribution level, transcending conventional instance-wise consistency; and (3) a novel generative cross-supervision module that reinforces view-level consistency through cross-view reconstruction learning. Comprehensive evaluations on three real-world trajectory datasets demonstrate DGCPath's superior performance over state-of-the-art baselines in two distinct downstream tasks, validating its enhanced generalization capacity and representation effectiveness.
Recommendation system technology faces the fundamental challenge of data sparsity. Although incorporating auxiliary textual data works to enrich interactions, lengthy and unstructured item descriptions often fail to depict user interests, as they primarily describe item attributes. Furthermore, the sparse interactions provide incomplete interest patterns, leading to biased recommendations. To address these issues, this work proposes an LLM-summarized interest distillation (SID) model to enrich interactions by interest summarization and retrieval augmentation. Interest summarization leverages large language model to improve user interest descriptions and filters out interest-irrelevant details, generating compact, user-centric interest texts. Retrieval augmentation utilizes semantic neighbors as multiple teachers to adaptively transfer their multimodal collaborative knowledge to augment behaviorally sparse student users, thereby enriching interest learning for recommendation. It enhances interactions both qualitatively, through improved interest descriptions, and quantitatively, through expanded semantic neighbors. Extensive experiments show the superior performance of SID over the state-of-the-art models, demonstrating its effectiveness of interest summarization and retrieval augmentation to improve recommendations.
Hyperspectral image (HSI) clustering facilitates the unsupervised discrimination of complex surface materials but traditionally relies on the idealized assumption of centralized data availability. In real-world scenarios, however, this assumption clashes with data privacy regulations and the physical distribution of data across isolated silos. While Federated Learning offers a decentralized solution, existing frameworks in remote sensing are predominantly confined to supervised paradigms and struggle to address the heavy reliance on annotations and Non-IID distributions inherent among clients. To overcome these limitations, we propose a novel framework named Robust Federated HSI Clustering(RFHC) that enables collaborative unsupervised learning without requiring raw data exchange. Specifically, we design a dual-encoder architecture that incorporates a Federated Model Weight Augmentation strategy(FMWA), which generates consistent views through local-global network interactions to mitigate the spectral distortion introduced by traditional data augmentation. Furthermore, we develop a hybrid optimization objective that synergizes prototype relationship optimization with contrastive learning. This mechanism utilizes global prototypes to guide local training, effectively stabilizing feature learning against data heterogeneity while ensuring intra-cluster compactness. Extensive experiments on three benchmark HSI datasets demonstrate the effectiveness and superiority of the proposed method against state-of-the-art model(SOTA) federated approaches.
Dynamic Graph Neural Networks (DyGNNs) are facing challenges with distribution shifts between training and test data that are similar but not identical. Existing DyGNNs for out-of-distribution scenarios primarily focus on discovering invariant patterns in the spatial domain, overlooking its impact on the structural properties in the spectral domain. In this paper, we propose a spectral-based graph augmentation framework designed to investigate and improve generalization behavior in dynamic graphs under distribution shifts. Specifically, we augment the input graph spectra into a mixture of shift components by maximizing the variance of spectral distance and propose an efficient approximation to reduce the computational cost brought by eigen-decomposition. Building on this, we develop a multi-encoder architecture in which each encoder targets a specific spectral shift component to generate referential representations. Our method adopts a new learning objective to encourage the model to rely on robust spectral properties for better generalization under distribution shifts. Extensive experiments on both real-world and synthetic datasets demonstrate that our proposed method significantly outperforms state-of-the-art approaches in node classification and link prediction tasks. Source codes are available at https://github.com/SSQiana/DSPA.
Edge addition is commonly considered risky in Graph Anomaly Detection (GAD), as random edge addition may induce anomaly–normal connectivity. Consequently, most existing augmentation strategies focus on feature perturbation, edge removal, or subgraph sampling, leaving edge addition largely unexplored. In contrast, we empirically find that moderate and targeted structural completion among normal nodes consistently improves GAD performance, revealing guided edge addition as an overlooked yet effective augmentation dimension. Motivated by this observation, we introduce two adjacency-centered edge generation strategies with complementary mechanisms. One performs a training-free structural completion scheme via spectrum-aware Langevin dynamics, enriching graph connectivity while preserving node features. The other models the joint evolution of node features and graph structure through a stochastic differential equation–based diffusion process, producing structurally coherent and anomaly-aware complementary graphs. Extensive experiments on 12 benchmark datasets with 7 state-of-the-art GAD models demonstrate consistent and substantial improvements in both AUROC and AUPRC. Code and appendices are available at https://github.com/GaoHaokai222/LangGen-JointSDE
Contrastive learning has become a dominant paradigm for learning time series representations from large-scale unlabeled data. However, current methods are often adapted from computer vision and rely on random time-domain augmentations (e.g., jittering and cropping). Such augmentations can unpredictably disrupt the natural frequency structure of signals, leading to representations failing to capture crucial patterns in the data. To address this, we propose a framework of Frequency-Aware Augmentation and Alignment for Time Series Contrastive Learning (FACL), which comprises two key innovations. First, FACL employs a novel frequency-structured augmentation mechanism based on wavelet transforms. The mechanism constructs controlled and interpretable contrastive views by the structured attenuation and recombination of specific wavelet components. Second, FACL introduces a multi-level contrastive objective that incorporates a subspace alignment strategy. This objective explicitly aligns representations within their corresponding frequency subspaces. Experiments across six forecasting and four classification benchmarks show that FACL achieves superior performance compared to recent baselines. Ablation studies and model analysis highlight the contribution of each component in FACL. Furthermore, low-sample semi-supervised learning experiments confirm the robustness and generalization of FACL.
We present Magnol.AI Copilot, an extension of the Magnol.AI digital biomarker platform that integrates multimodal large language models (LLMs) to transform digital health technology (DHT) trial dashboards into conversational systems. Copilot augments the platform with a multi-agent orchestration layer and vision-enabled LLMs that interpret visualizations, tabular summaries, and textual metadata. The system enables natural language queries and automatic generation of contextual insights, allowing researchers to interact with wearable data through dialogue rather than static inspection. A case study with an actigraphy device demonstrates Copilot’s ability to identify nightly compliance gaps and provide contextual explanations, reducing cognitive load compared to manual dashboard review. This work presents a novel integration of IoMT infrastructure with multimodal LLMs, advancing digital biomarker research toward conversational and accessible DHT trial platforms.
Capturing expertise and enabling efficient information retrieval are critical in the energy sector, where high staff turnover can lead to significant knowledge loss. Retrieval Augmented Generation (RAG) offers a solution by grounding Large Language Model (LLM) outputs in documented sources, but its effectiveness is limited by reliance on general-purpose embeddings. We present Wikatoni, an agentic AI system for energy engineering workflows that integrates a novel domain-specific embedding model. Wikatoni combines fine-tuned embeddings with agentic RAG, metadata filtering, and hybrid retrieval to improve document search, automated reporting, and workflow efficiency. Evaluation on internal enterprise offshore energy data shows that the domain-adapted embedding improves recall by 10%, and Wikatoni agentic RAG further increases answer accuracy by 14% compared to vanilla RAG with the base embedding model, achieving the best overall performance in context recall, faithfulness, and answer accuracy.
We present DS SERVE, a framework that transforms large-scale text datasets—comprising half a trillion tokens—into a high-performance neural retrieval system. DS SERVE offers both a web interface and API endpoints, achieving low latency with modest memory overhead on a single node. The framework also supports inference-time tradeoffs between latency, accuracy, and result diversity. We anticipate that DS SERVE will be broadly useful for a range of applications such as large-scale retrieval-augmented generation (RAG), training data attribution, training a search agent, and beyond.
This work presented a web-based system which introduces an active-listening strategy in a spoken dialogue for self-disclosure to support mental health of a campus user. To enhance the system usability and safety, this demo is developed to conduct the bilingual (Mandarin/English) spoken dialogue where a high-risk dialogue detection during speech interaction is reliably augmented. In particular, a prompt-driven GPT classifier identifies the utterances indicating self-harm or suicide intent and triggers safety alerts with help center and counselor notification. We also integrate a TTS module for Taiwanese Mandarin and standard English, and redesign the user interface to automatically pop up alert messages when high-risk dialogue is detected. In addition, we collect speech data under diverse mental dialogue scenarios with bilingual speech to enable system analysis, evaluation and refinement. Overall, these extensions build a framework that promotes empathetic interactions, enables timely alert in critical cases, and improves the accessibility for diverse users.
Retrieval-Augmented Generation (RAG) has become the standard approach for integrating domain knowledge into Large Language Models (LLMs). However, fair comparison of RAG pipelines remains difficult: data preparation is often ad hoc, subsampling methods are opaque, parameters vary across implementations, and evaluation is fragmented. We present In-Situ Eval, a unified and reproducible framework that operationalizes the full RAG pipeline with configurable subsampling strategies and both RAG-specific and generic evaluation metrics. The platform supports two execution modes: an offline Dataset mode for evaluating precomputed outputs, and a live Retrieval mode for benchmarking RAG variants with state-of-the-art LLMs. Users can flexibly select datasets, retrieval techniques, models, and metrics, enabling side-by-side comparisons, ablations, and targeted analyses. This holistic approach reduces computational costs, clarifies the impact of subsampling techniques, and provides actionable insights for real-world deployments. By facilitating transparent, customizable, and interactive benchmarking, In-Situ Eval empowers both researchers and practitioners to make informed decisions in adapting RAG pipelines to domain-specific needs.
Sequential recommendation (SR) aims to model users' dynamic preferences from their historical interaction sequences to provide personalized recommendations. However, data sparsity remains a core bottleneck limiting the performance of sequential recommendation models. Existing mixup methods face two major challenges: 1) They cannot effectively address the data sparsity dilemma in long-tail scenarios. 2) It is difficult to maintain the Semantic structure of augmented samples during the random mixing process. To address these challenges, this study proposes the Semantic-Aware Data Augmentation (SADA) framework, which utilizes large language models (LLMs) to generate semantic embeddings. This framework allows for the fusion of both collaborative and semantic signals, alleviating the representation deficiency of long-tail items. Additionally, through semantic-guided mixup, the framework preserves semantic structure consistency at both the user and item levels, thereby avoiding semantic structure degradation caused by traditional random mixing. This approach is expected to significantly improve recommendation performance and generalization ability across multiple datasets and application scenarios. In a broader context, this research aims to drive the evolution of data augmentation in sequential recommendation from heuristic methods to a semantic-driven paradigm, helping to build more personalized, accurate, and socially valuable recommendation services.
PANDA: Empowering Small Language Models for Proactive Dialogue Through Agent-Based Synthesis (Student Abstract)
PDF ↗Proactive dialogue systems, which are designed to guide conversations toward predetermined goals. However, contemporary LLMs predominantly function as passive assistants, mechanically executing human instructions. A key challenge contributing to this limitation is the inherent difficulty in acquiring and annotating high-quality training data for proactive dialogue. Consequently, the scarcity of such data results in a notable deficiency in the proactive conversational capabilities of current LLMs.In this paper, we introduce PANDA (Proactive Agent-based Negotiation Dialogue Augmentation), a method designed to generate accurate, complex, and diverse proactive dialogue data for a challenging task—financial dispute mediation—where a LLM acts as the mediator. PANDA leverages a novel self-evolving synthesis process to manage a pool of user profiles and generate dialogues through structured interactions between multiple LLM-driven agents. To ensure data fidelity, we propose a comprehensive evaluation framework and build a two-level validation system combining automated and expert human verification. Our experiments demonstrate that an 8B-parameter model, trained on our synthesized dataset, achieves state-of-the-art results in the task's evaluation framework. Its performance rivals top closed-source models guided by heavily engineered prompts, even when provided with only essential information.
GSAG-CDGAN: A Generalizable Small-Sample Attention-Guided GAN for Remote Sensing Change Detection (Student Abstract)
PDF ↗Remote sensing change detection (RSCD) is crucial for ur- ban monitoring, environmental protection, and disaster as- sessment, but small-sample scenarios often lead to overfitting and inaccurate predictions on unseen data. To address this, we propose GSAG-CDGAN, an end-to-end framework integrat- ing Selective Noise Augmentation (SNA) to mitigate overfit- ting, an Attention-Guided Adversarial Network (AGAN) to enhance structural consistency, and a Perceptual Loss Mod- ule (PLM) to preserve semantic consistency. Experiments on CDData-50 show that GSAG-CDGAN improves F1-Score from 0.6954 to 0.8851, with notable gains in Recall and IoU, demonstrating enhanced robustness under small-sample con- ditions. Further evaluation on the WHU-CD dataset yields an F1-Score of 0.9502, confirming strong cross-dataset general- ization and the method’s effectiveness in diverse scenarios.
Semantic Embedding and Synthetic Augmentation for Longitudinal Survey Prediction (Student Abstract)
PDF ↗Longitudinal surveys are a crucial component of behavioral research. Such surveys, however, face significant gaps in the data created by item and unit non-responses as well as semantic gaps resulting from questionnaires, assessed trends, and data collection methods evolving over time. Using 15 waves of vaccination surveys as a test-bed, we demonstrate how modern AI techniques can bridge both item and unit gaps, originating from non-response, and semantic gaps, originating from instrument evolution. We address these gaps through a two-component framework. We leverage LLM-generated semantic embeddings of survey questions to encode question meaning, enabling a Deep & Cross Network used for imputation to jointly model responses across item semantics, individual characteristics, and temporal dynamics. This structure directly addresses survey evolution by operating in learned semantic space. To overcome data scarcity, we use cluster-informed synthetic data generation via hierarchical prompting that produces synthetic responses preserving distributional properties and empirical cluster structure. Our approach achieves a strong improvement in semantic gap tasks and 80-90% synthetic data fidelity, providing practical solutions for evolving longitudinal studies.
BRI-MH: Behavioral Risk Index for Mental Health — An Interpretable Multimodal LLM-Augmented Framework (Student Abstract)
PDF ↗Mental health monitoring faces challenges from fragmented data and opaque risk scores. We present BRI-MH, an in- terpretable multimodal framework combining behavioral sig- nals with cognitive features from large language models to produce a weekly Behavioral Risk Index. Unlike prior work with isolated or black-box scores, BRI-MH offers transpar- ent, actionable insights and links continuous monitoring to adaptive feedback and therapeutic support, bridging digital phenotyping and clinical care.