Deep hashing offers efficient storage and fast retrieval capabilities. As a result, it has been extensively applied to large‑scale retrieval tasks. To alleviate the dependence on high-quality annotated data, recent research has focused on unsupervised domain adaptive hashing methods, which aim to transfer knowledge from a label-rich source domain to a label-scarce target domain. However, in open-world scenarios, source domain labels are often inevitably noisy, which tends to undermine the quality of learned hash codes and induce considerable performance deterioration. To this end, we introduce a novel Robust Domain Adaptive Hashing (RDAH) method to jointly mitigate the adverse effects of label noise and domain discrepancy. Specifically, we first model the loss distribution of training samples using a two-component Gaussian mixture model to estimate each sample’s confidence, based on which the data is partitioned. Subsequently, we introduce a neighbor consistency-guided correction strategy, which leverages the semantic structure of high-confidence neighbors to perform weighted correction on noisy samples. Moreover, we design a dual-level cross-domain alignment mechanism that jointly mitigates domain shift from two complementary perspectives. Extensive experimental results validate the effectiveness and robustness of RDAH across multiple benchmark datasets.
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Point-of-Interest (POI) recommendation plays a pivotal role in location-based services by guiding users to discover new and relevant places. While graph-based methods have shown promising results, effectively modeling the diversity and dynamics of user preferences remains a key challenge. Addressing this requires richer representations of both POIs and user interests, as well as more adaptive learning strategies. In this work, we propose TMHKG, a Task-aware Meta-learning framework with a Heterogeneous Knowledge Graph for POI recommendation. To enhance representation learning, TMHKG constructs a dual-view POI knowledge graph that integrates geographical proximity and user-aware category transitions, and models users' evolving interests from sequential visit histories. On top of enriched features, TMHKG adopts a task-aware meta-learning paradigm, treating each user's recommendation task as a separate meta-task. A generalizable recommendation policy is first learned from diverse training tasks and then quickly adapted to each user's unique behavior, enabling highly personalized predictions. Extensive experiments on two real-world datasets demonstrate that TMHKG consistently outperforms state-of-the-art baselines, highlighting its effectiveness in capturing complex user-POI interactions.
TOP-RL: Task-Optimized Progressive Token Pruning with Reinforcement Learning for Vision Language Models
PDF ↗In recent years, Large Vision-Language Models (LVLMs) have significantly advanced multimodal tasks. However, their inference requires intensive processing of numerous visual tokens and incurs substantial computational overhead. Existing methods typically compress visual tokens either at the input stage or in early model layers, ignoring variations across tasks and depths. To address these limitations, we introduce TOP-RL, a Task-Optimized Progressive token pruning framework based on Reinforcement Learning. TOP-RL formulates visual token pruning as a multi-stage Markov Decision Process (MDP). It employs an agent trained with dense and fine-grained reward signals to progressively generate differentiable binary masks. This enables TOP-RL to adaptively select crucial visual tokens tailored to each task, effectively balancing accuracy and computational efficiency. Extensive experiments on leading multimodal datasets and advanced LVLMs validate that TOP-RL effectively learns task-optimized pruning policies, significantly boosting inference efficiency while preserving robust performance. For instance, LLaVA-NeXT equipped with TOP-RL achieves a 1.9x speedup in inference time and a 9.3x reduction in FLOPs, with 96% performance preserved.
Multi-modal knowledge graph completion (MMKGC) aims to infer missing entities of triples by leveraging heterogeneous information in knowledge graph (KG). However, existing approaches often struggle with inconsistent modality alignment, limited reasoning depth, and insufficient negative sample quality. In this work, we propose HFR-MKGC, a novel framework that integrates hierarchical modal fusion and Multimodal Large Language Model (MLLM) reasoning for robust and expressive MMKGC. Specifically, we introduce a relation-guided hierarchical modal fusion module, which conducts fine-grained intra-visual fusion and relation-guided cross-modal integration to yield rich entity representations. HFR-MKGC employs a fine-tuned MLLM to perform instruction-based triple reasoning, producing candidate entities for completion. Then, it constructs hard negative samples through textual perturbation by MLLM and visual feature augmentation with rotation and noise. HFR-MKGC optimizes the model via adversarial training. Extensive experiments on three MMKGC benchmarks demonstrate that our method outperforms state-of-the-art methods, validating its effectiveness in MMKGC.
TGCA-LLM: Time-Aware Graph-Text Contrastive Alignment for Enhancing LLMs in Temporal Knowledge Graph Completion
PDF ↗Temporal Knowledge Graph Completion (TKGC) aims to infer missing facts by modeling historical events and latent temporal dependencies in Temporal Knowledge Graphs (TKGs). Recently, TKGC methods that integrate graph embeddings into Large Language Models (LLMs) have shown great promise by leveraging the structural information of TKGs together with the powerful reasoning capabilities of LLMs. However, these embedding-based methods are limited by suboptimal graph representations due to noise and long-tail issues in real-world scenarios, and insufficient cross-modal alignment between graph and language, hindering LLMs' ability to fully capture the temporal and structural information of TKGs. To address these issues, we propose TGCA-LLM, a novel embedding-based framework for TKGC. Specifically, TGCA-LLM first employs time-aware contrastive learning to align fact texts with graph structures in the temporal dimension, generating robust graph embeddings and establishing initial cross-modal alignment. Then, through a two-stage tuning process, it enables LLMs to gradually acquire structural and temporal knowledge from graph embeddings while enhancing their cross-modal reasoning capabilities in TKGC. Extensive experiments on three widely used real-world benchmarks demonstrate that TGCA-LLM outperforms state-of-the-art (SOTA) baselines by at least 8.7% MRR, highlighting its effectiveness.
Monitoring the elemental composition of materials in order to detect abnormal conditions in real-time is essential for applications like manufacturing quality control, environmental monitoring, and space exploration. This is achieved using sensors that analyze the interaction of a material with electromagnetic radiation, producing spectral data streams or a sequence of instances where each represents an ordered set of wavelengths with an associated intensity. While many unsupervised anomaly detection methods exist for tabular streaming data, their applicability to spectral streams remains underexplored. To address this gap, we consider our spectra in a multivariate stream setting and benchmark the performance of state-of-the-art tabular anomaly detection methods on this data. Furthermore, we introduce OnlineBootKNN, a novel unsupervised framework that combines k-nearest neighbors with online bootstrapping and a z-score test to detect anomalies in real-time. We demonstrate the high performance and robustness of our method, as well as the efficacy of the autoencoder-based method, KitNet, on newly simulated real-world spectral datasets. In addition, we compare their efficiency against the other tested techniques. Finally, we highlight the inherent interpretability of OnlineBootKNN, which is crucial for identifying the specific wavelengths, and thus elements, responsible for a detected anomaly.
HiLoMix: Robust High- and Low-Frequency Graph Learning Framework for Mixing Address Association
PDF ↗As mixing services are increasingly being exploited by malicious actors for illicit transactions, mixing address association has emerged as a critical research task. A range of approaches have been explored, with graph-based models standing out for their ability to capture structural patterns in transaction networks. However, these approaches face two main challenges: label noise and label scarcity, leading to suboptimal performance and limited generalization. To address these, we propose HiLoMix, a graph-based learning framework specifically designed for mixing address association. First, we construct the Heterogeneous Attributed Mixing Interaction Graph (HAMIG) to enrich the topological structure. Second, we introduce frequency-aware graph contrastive learning that captures complementary structural signals from high- and low-frequency graph views. Third, we employ weak supervised learning that assigns confidence-based weights to noisy labels. Then, we jointly train high-pass and low-pass GNNs using both unsupervised contrastive signals and confidence-based supervision to learn robust node representations. Finally, we adopt a stacking framework to fuse predictions from multiple heterogeneous models, further improving generalization and robustness. Experimental results demonstrate that HiLoMix outperforms existing methods in mixing address association.
Dynamic recommendation systems aim to provide personalized suggestions by modeling temporal user-item interactions across time-series behavioral data. Recent studies have leveraged pre-trained dynamic graph neural networks (GNNs) to learn user-item representations over temporal snapshot graphs. However, fine-tuning GNNs on these graphs often results in generalization issues due to temporal discrepancies between pre-training and fine-tuning stages, limiting the model’s ability to capture evolving user preferences. To address this, we propose TarDGR, a task-aware retrieval-augmented framework designed to enhance generalization capability by incorporating task-aware model and retrieval-augmentation. Specifically, TarDGR introduces a Task-Aware Evaluation Mechanism to identify semantically relevant historical subgraphs, enabling the construction of task-specific datasets without manual labeling. It also presents a Graph Transformer-based Task-Aware Model that integrates semantic and structural encodings to assess subgraph relevance. During inference, TarDGR retrieves and fuses task-aware subgraphs with the query subgraph, enriching its representation and mitigating temporal generalization issues. Experiments on multiple large-scale dynamic graph datasets demonstrate that TarDGR consistently outperforms state-of-the-art methods, with extensive empirical evidence underscoring its superior accuracy and generalization capabilities.
Reliable zero-shot detection of out-of-distribution (OOD) inputs is critical for deploying vision-language models in open-world settings. However, the lack of labeled negatives in zero-shot OOD detection necessitates proxy signals that remain effective under distribution shift. Existing negative-label methods rely on a fixed set of textual proxies, which (i) sparsely sample the semantic space beyond in-distribution (ID) classes and (ii) remain static while only visual features drift, leading to cross-modal misalignment and unstable predictions. In this paper, we propose CoEvo, a training- and annotation-free test-time framework that performs bidirectional, sample-conditioned adaptation of both textual and visual proxies. Specifically, CoEvo introduces a proxy-aligned co-evolution mechanism to maintain two evolving proxy caches, which dynamically mines contextual textual negatives guided by test images and iteratively refines visual proxies, progressively realigning cross-modal similarities and enlarging local OOD margins. Finally, we dynamically re-weight the contributions of dual-modal proxies to obtain a calibrated OOD score that is robust to distribution shift. Extensive experiments on standard benchmarks demonstrate that CoEvo achieves state-of-the-art performance, improving AUROC by 1.33% and reducing FPR95 by 45.98% on ImageNet-1K compared to strong negative-label baselines.
Traffic forecasting is essential for intelligent transportation systems. Accurate forecasting relies on continuous observations collected by traffic sensors. However, due to high deployment and maintenance costs, not all regions are equipped with such sensors. This paper aims to forecast for regions without traffic sensors, where the lack of historical traffic observations challenges the generalisability of existing models. We propose a model named **GenCast**, the core idea of which is to exploit external knowledge to compensate for the missing observations and to enhance generalisation. We integrate physics-informed neural networks into GenCast, enabling physical principles to regularise the learning process. We introduce an external signal learning module to explore correlations between traffic states and external signals such as weather conditions, further improving model generalisability. Additionally, we design a spatial grouping module to filter localised features that hinder model generalisability. Extensive experiments show that GenCast consistently reduces forecasting errors on multiple real-world datasets.
Potent but Stealthy: Rethink Profile Pollution Against Sequential Recommendation via Bi-Level Constrained Reinforcement Paradigm
PDF ↗Sequential Recommenders, which exploit dynamic user intents through interaction sequences, are vulnerable to adversarial attacks. While existing attacks primarily rely on data poisoning, they require large-scale user access or fake profiles thus lacking practicality. In this paper, we focus on the Profile Pollution Attack (PPA) that subtly contaminates partial user interactions to induce targeted mispredictions. Previous PPA methods suffer from two limitations, i.e., i) over-reliance on sequence horizon impact restricts fine-grained perturbations on item transitions, and ii) holistic modifications cause detectable distribution shifts. To address these challenges, we propose a constrained reinforcement driven attack CREAT that synergizes a bi-level optimization framework with multi-reward reinforcement learning to balance adversarial efficacy and stealthiness. We first develop a Pattern Balanced Rewarding Policy, which integrates pattern inversion rewards to invert critical patterns and distribution consistency rewards to minimize detectable shifts via unbalanced co-optimal transport. Then we employ a Constrained Group Relative Reinforcement Learning paradigm, enabling step-wise perturbations through dynamic barrier constraints and group-shared experience replay, achieving targeted pollution with minimal detectability. Extensive experiments demonstrate the effectiveness of CREAT.
Hard vs. Noise: Resolving Hard-Noisy Sample Confusion in Recommender Systems via Large Language Models
PDF ↗Implicit feedback, employed in training recommender systems, unavoidably confronts noise due to factors such as misclicks and position bias. Previous studies have attempted to identify noisy samples through their diverged data patterns, such as higher loss values, and mitigate their influence through sample dropping or reweighting. However, we observed that noisy samples and hard samples display similar patterns, leading to hard-noisy confusion issue. Such confusion is problematic as hard samples are vital for modeling user preferences. To solve this problem, we propose LLMHNI framework, leveraging two auxiliary user-item relevance signals generated by Large Language Models (LLMs) to differentiate hard and noisy samples. LLMHNI obtains user-item semantic relevance from LLM-encoded embeddings, which is used in negative sampling to select hard negatives while filtering out noisy false negatives. An objective alignment strategy is proposed to project LLM-encoded embeddings, originally for general language tasks, into a representation space optimized for user-item relevance modeling. LLMHNI also exploits LLM-inferred logical relevance within user-item interactions to identify hard and noisy samples. These LLM-inferred interactions are integrated into the interaction graph and guide denoising with cross-graph contrastive alignment. To eliminate the impact of unreliable interactions induced by LLM hallucination, we propose a graph contrastive learning strategy that aligns representations from randomly edge-dropped views to suppress unreliable edges. Empirical results demonstrate that LLMHNI significantly improves denoising and recommendation performance.
Anomaly generation has been widely explored to address the scarcity of anomaly images in real-world data. However, existing methods typically suffer from at least one of the following limitations, hindering their practical deployment: (1) lack of visual realism in generated anomalies; (2) dependence on large amounts of real images; and (3) use of memory-intensive, heavyweight model architectures. To overcome these limitations, we propose AnoStyler, a lightweight yet effective method that frames zero-shot anomaly generation as text-guided style transfer. Given a single normal image along with its category label and expected defect type, an anomaly mask indicating the localized anomaly regions and two-class text prompts representing the normal and anomaly states are generated using generalizable category-agnostic procedures. A lightweight U-Net model trained with CLIP-based loss functions is used to stylize the normal image into a visually realistic anomaly image, where anomalies are localized by the anomaly mask and semantically aligned with the text prompts. Extensive experiments on the MVTec-AD and VisA datasets show that AnoStyler outperforms existing anomaly generation methods in generating high-quality and diverse anomaly images. Furthermore, using these generated anomalies helps enhance anomaly detection performance.
Summarizing event sequences is a key aspect of data mining. Most existing methods neglect conditional dependencies and focus on discovering sequential patterns only. In this paper, we study the problem of discovering both conditional and unconditional dependencies from event sequences. We do so by discovering rules of the form X --> Y where X and Y are sequential patterns. Rules like these are simple to understand and provide a clear description of the relation between the antecedent and the consequent. To discover succinct and non-redundant sets of rules we formalize the problem in terms of the Minimum Description Length principle. As the search space is enormous and does not exhibit helpful structure, we propose the SEQRET method to discover high-quality rule sets in practice. Through extensive empirical evaluation we show that unlike the state of the art, SEQRET ably recovers the ground truth on synthetic datasets and finds useful rules from real datasets.
While Graph Foundation Models (GFMs) have achieved notable progress across diverse tasks recently, their robustness under domain noise, structural perturbations, and adversarial attacks remains largely underexplored. A core limitation lies in inadequate modeling of hierarchical structural semantics, which are intrinsic priors and critical for generalization. In this work, we propose SA^2GFM, a robust GFM framework that enhances domain adaptable representations through Structure Aware Semantic Augmentation. First, to embed hierarchical structural priors, we transform entropy based encoding trees into structure aware textual prompts for feature augmentation. The enriched inputs are processed by a novel self supervised Information Bottleneck mechanism that distills robust and transferable representations through structure guided compression. To mitigate negative transfer in cross domain adaptation, we develop an expert adaptive routing mechanism that integrates a mixture of experts architecture with a null expert design. To enable efficient downstream adaptation, we propose a fine tuning module that optimizes hierarchical structures through joint intra and inter community structure learning. Extensive experiments validate the superiority of SA^2GFM in effectiveness and robustness against random noise and adversarial perturbations on node and graph classification, compared with nine state of the art baselines.
Information-Theoretic Minimal Sufficient Representation for Multi-Domain Knowledge Graph Completion
PDF ↗Multi-domain knowledge graph completion (MKGC) seeks to predict missing triples in a target KG by leveraging triples from multiple KGs in different domains (e.g., languages or sources). Existing studies typically learn and fuse multi-domain KG representations solely with alignments or fusion modules, which can be affected by redundant information within KGs. This issue can conceal task-relevant information in representations, impeding further improvements when scaling to numerous KGs. To this end, we propose IMKGC, an information-theoretic MKGC framework to learn minimal sufficient representations. In particular, IMKGC learns entity representations by explicitly preserving endogenous contextual information within each KG, exogenous complementary information from other KGs, and consistent information of equivalent entities, while suppressing redundant information through variational constraints. Furthermore, we achieve compressed relation representations with a devised relation reasoning decoder that captures relatedness among relations, also improving triple prediction. Extensive experiments on 14 KGs in three benchmark datasets demonstrate that IMKGC significantly outperforms previous state-of-the-art methods, especially in redundant scenarios.
Lifelogging involves the continuous and comprehensive recording of a user’s daily activities, behaviors, and interactions, offering valuable insights for personalized healthcare, event retrieval, and lifestyle analysis. However, extracting meaningful patterns from lifelog data requires models to capture deeper temporal contexts beyond simple retrieval. To address this, we introduce ContextGraph, a lifelog intelligence framework that models lifelogs as a Temporal Knowledge Graph (TKG) to reason about the user’s evolving life patterns over time. ContextGraph computes Day Context Embeddings (DCE) to encode the temporal spread and social scene context of user's daily behavior. Then a novel Lens module extracts semantically meaningful subgraph snapshots around an anchor node in the TKG, representing specific personal contexts in the user’s life. The Lens module also computes an evolution signature for each subgraph, indicating whether it is growing, decaying, or remaining static. By analyzing these evolution signatures, ContextGraph provides actionable insights into the user’s lifelogs such as stable routines, behavioral drifts, or lifestyle changes. Our experiments showcase DCE's versatility, outperforming baselines in graph/node classification and reasoning on the Enzyme and DBLP datasets.
Accurate traffic forecasting plays a vital role in intelligent transportation systems, enabling applications such as congestion control, route planning, and urban mobility optimization. However, traffic forecasting remains challenging due to two key factors: (1) complex spatial dependencies arising from dynamic interactions between road segments and traffic sensors across the network, and (2) the coexistence of multi-scale periodic patterns (e.g., daily and weekly periodic patterns driven by human routines) with irregular fluctuations caused by unpredictable events (e.g., accidents, weather, or construction). To tackle these challenges, we propose HyperD (Hybrid Periodic Decoupling), a novel framework that decouples traffic data into periodic and residual components. The periodic component is handled by the Hybrid Periodic Representation Module, which extracts fine-grained daily and weekly patterns using learnable periodic embeddings and spatial-temporal attention. The residual component, which captures non-periodic, high-frequency fluctuations, is modeled by the Frequency-Aware Residual Representation Module, leveraging complex-valued MLP in frequency domain. To enforce semantic separation between the two components, we further introduce a Dual-View Alignment Loss, which aligns low-frequency information with the periodic branch and high-frequency information with the residual branch. Extensive experiments on four real-world traffic datasets demonstrate that HyperD achieves state-of-the-art prediction accuracy, while offering superior robustness under disturbances and improved computational efficiency compared to existing methods.
Review-based recommendation methods typically integrate multiple behaviors, including interactions, reviews, and ratings, to model user preferences. To effectively extract preference signals from diverse behaviors, some studies train multiple student models to capture distinct behavioral patterns, and leverage online distillation to facilitate collaborative learning among them. However, we argue that these techniques suffer from bias contamination from rating distributions and feature homogenization during cross-behavior knowledge transfer: (1) Rating distribution bias, arising from non-uniform historical ratings, propagates across behaviors through distillation, contaminating the true preference representations of other behaviors. (2) Static distillation strategies often lead to homogenized behavioral features, hindering the learning of behavior-specific preferences. To address these issues, we propose a novel Bidirectional Counterfactual Distillation (BiCoD) framework for review-based recommendation. In BiCoD, we first design an adversarial counterfactual distillation module to suppress the impact of non-uniform rating distributions on distillation, thereby preventing it from contaminating the user's true preference representations across behaviors. Subsequently, we introduce a stage-aware bidirectional distillation strategy to enhance the distinctiveness of behavioral features, facilitating the effective learning of behavior-specific preferences. Extensive experiments on five real-world datasets validate the effectiveness and superiority of the proposed framework.
Enhancing Conversational Recommender Systems with Tree-Structured Knowledge and Pretrained Language Models
PDF ↗Recent advances in pretrained language models (PLMs) have significantly improved conversational recommender systems (CRS), enabling more fluent and context-aware interactions. To further enhance accuracy and mitigate hallucination, many methods integrate PLMs with knowledge graphs (KGs), but face key challenges: failing to fully exploit PLM reasoning over graph relationships, indiscriminately incorporating retrieved knowledge without context filtering, and neglecting collaborative preferences in multi-turn dialogues. To this end, we propose PCRS-TKA, a prompt-based framework employing retrieval-augmented generation to integrate PLMs with KGs. PCRS-TKA constructs dialogue-specific knowledge trees from KGs and serializes them into texts, enabling structure-aware reasoning while capturing rich entity semantics. Our approach selectively filters context-relevant knowledge and explicitly models collaborative preferences using specialized supervision signals. A semantic alignment module harmonizes heterogeneous inputs, reducing noise and enhancing accuracy. Extensive experiments demonstrate that PCRS-TKA consistently outperforms all baselines in both recommendation and conversational quality.