Deep hashing is widely used for large-scale image retrieval but remains vulnerable to adversarial examples, raising practical security concerns. Existing targeted adversarial attacks on deep hashing typically rely on lp-norm constrained perturbations, which struggle to balance attack effectiveness and imperceptibility, often requiring perceptible noise and limiting their practicality in real-world retrieval scenarios. We propose UTDHA, the first unrestricted targeted attack for deep hashing models using contrastive-guided latent diffusion. UTDHA generates adversarial examples with a latent diffusion model and performs optimization in the latent space rather than the pixel space, enabling semantic manipulation while preserving image naturalness. Through contrastive guidance, the attack pulls adversarial examples toward the target label while pushing them away from non-target labels. Meanwhile, UTDHA enforces structural and perceptual consistency, producing adversarial examples that are both imperceptible and visually natural. Extensive experiments on three benchmarks demonstrate that UTDHA outperforms existing targeted adversarial attack baselines for deep hashing models in both attack effectiveness and imperceptibility.
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Emerging foundation models have spurred growing interest in task-unspecific time series modeling, which can accommodate data from diverse domains and support various tasks. However, most existing methods still suffer from poor adaptability and generalization across cross-domain time series with varying feature dimensionalities. This study achieves unified time series modeling to transcend task and data boundaries, inspired by the recent advances in visual foundation models. We propose a universal cross-modal Time series modeling method named TimeIG, featuring Tailored Temporal Imaging and Dependent Channel Grouping. Specifically, TimeIG adopts the novel dependent channel grouping method to project heterogeneous time series into hierarchical spaces to identify representative features and learn inter-dependencies across variables. Then, a tailored temporal imaging method is proposed to generate carefully hand-crafted images from time series, facilitating complementary temporal-spatial feature extraction. Finally, a dual-branch module is designed to simultaneously model the sequential and visual data, enabling cross-modal alignment via the proposed Cross-Modal Attention and Dynamic Weighted-Averaging. Extensive experiments on real-world cross-domain time series datasets show that TimeIG consistently outperforms existing SOTA methods. The code of TimeIG is publicly available at https://github.com/missCmj/TimeIG.
Multivariate time series (MTS) classification is foundational to pervasive computing and financial analysis, yet existing multi-scale paradigms are often constrained by suboptimal representation fidelity. We identify two critical bottlenecks: temporal non-causality in standard encoders that induces temporal confounding in non-stationary dynamics, and the absence of explicit channel saliency mechanisms that allows noise to contaminate the latent space. To address these challenges, we propose the Causal Attention and Spatio-temporal Encoder Network (CASE-Net), an architecture designed for structural manifold pre-conditioning. CASE-Net synergizes a Causal Temporal Encoder, which enforces physical arrow-of-time constraints via masked self-attention and causal convolutions, with an Adaptive Channel Recalibration module functioning as an information bottleneck to suppress detrimental noise. Comprehensive evaluations across six heterogeneous domains demonstrate that CASE-Net establishes new state-of-the-art benchmarks on four tasks, achieving a peak accuracy of 98.6% on the AWR dataset and superior robustness in non-stationary regimes.
Supervised infrared-visible image fusion (IVIF) often overfits limited training distributions, creating a critical generalization gap under open-world degradations (rain, haze, low light, noise, blur). To address this issue, we propose AIR-Fusion, a parameter-efficient adaptation of a frozen, restoration-capable latent diffusion backbone for degraded IVIF without full fine-tuning, transferring restoration priors for robust fusion. A Cross-Modal Bridging Adapter (CMBA) aligns infrared cues and textual instructions with the frozen diffusion conditioning space and injects them into multi-scale denoising features to steer instruction-guided restoration-aware fusion. In addition, a Trajectory-Constrained Rectifier (TCR) regularizes stochastic sampling via a pixel-latent closed loop, rectifying intermediate predictions with source-referenced structures and re-encoding them to stabilize the denoising trajectory and recover fine details suppressed by latent compression. Experiments across multiple datasets and degradation settings show consistent improvements in restoration quality and fusion fidelity, with strong generalization under complex and compounded degradations.
While large-scale reasoning models have achieved remarkable performance gains by scaling test-time computation through extended Chain-of-Thought (CoT) sequences, their practical utility is severely constrained by protracted inference lengths and high computational latency. In this paper, we present Differentiable Hierarchical Fusion (DHF), a novel framework that merges reasoning models with efficient base models via differentiable optimization to produce concise, accurate outputs. We introduce a dual-factor adaptive weighting mechanism to capture intra-block (Attention vs. MLP) variance and inter-block (shallow vs. deep layers) importance hierarchies, thereby addressing key limitations of static merging heuristics. Specifically, DHF optimizes fusion coefficients using gradient descent on a loss function that jointly minimizes cross-entropy (for accuracy) and response length (for conciseness). Furthermore, we enhance an outlier-aware initialization strategy to seed coefficients based on activation density and construct multi-calibration datasets to improve the model’s generalization ability. Comprehensive evaluations on Qwen and LLaMA models across six reasoning benchmarks show that DHF reduces the average response length by 55% while boosting accuracy by 2.8%–6% compared to existing state-of-the-art merging methods.
Gradient sparsification, while mitigating communication bottlenecks in Federated Learning (FL), fundamentally alters the geometric landscape of model updates. We reveal that the resultant high-dimensional orthogonality renders traditional Euclidean-based robust aggregation metrics mathematically ambiguous, creating a sparsity-robustness trade-off that adversaries exploit to bypass detection. To resolve this structural dissonance, we propose SafeSparse, a consensus restoration framework that decouples defense into topological and semantic dimensions. Unlike prior arts that treat sparsification and security orthogonally, SafeSparse introduces: (1) a Structure-Aware Calibration mechanism utilizing Jaccard similarity to filter topological outliers induced by index poisoning; and (2) a Directional Semantic Alignment module employing density-based clustering on update signs to neutralize magnitude-invariant attacks. Theoretically, we establish convergence guarantees for SafeSparse. Extensive experiments across multiple datasets and attack scenarios demonstrate that SafeSparse recovers up to 25.7% global accuracy under coordinated poisoning, effectively closing the vulnerability gap in communication-efficient FL.
Accurate estimation of time-varying treatment effects is crucial for optimizing interventions in personalized medicine. However, observational data often contains complex confounding bias and temporal complexities, making counterfactual estimation challenging. We propose Counterfactual Estimation via Temporal-Aware Intervention Networks (TAIN), a novel model that introduces an Intervention-aware Functional Convolution kernel to emphasize the role of treatments and capture complex temporal treatment interactions. TAIN addresses confounding bias from a domain generalization perspective, approximating the unknown target domain using adversarial examples and incorporating Sharpness-Aware Minimization to derive a generalization bound. This approach is more suitable for longitudinal settings compared to existing methods inspired by domain adaptation techniques due to inherent differences between static and longitudinal contexts. Experiments on simulated datasets demonstrate TAIN's superior performance compared to state-of-the-art models for counterfactual estimation over time.
Retrieval-Augmented Generation (RAG) has become a core paradigm for enhancing factual grounding and multi-hop reasoning in Large Language Models (LLMs). Traditional text-based RAG often retrieves logically irrelevant pseudo-evidence, while graph-based RAG is frequently hindered by search-time pruning, which may discard potentially valid reasoning paths. Existing hybrid approaches primarily adopt simple evidence concatenation or unidirectional enhancement, which fails to address the fundamental "Information Island" problem caused by asymmetric reasoning flows between unstructured text and structured graphs. We propose TGS-RAG, a unified framework for Text-Graph Synergistic enhancement. TGS-RAG introduces a bidirectional mechanism: (i) a Graph-to-Text channel that employs a Global Voting strategy from visited graph nodes to re-rank and refine textual evidence, filtering out semantic noise; and (ii) a Text-to-Graph channel that utilizes the Memory-based Orphan Entity Bridging algorithm. This algorithm utilizes textual cues to proactively resurrect valid but previously pruned reasoning paths from the search history without additional database overhead. Experimental results on multiple multi-hop reasoning benchmarks demonstrate that TGS-RAG significantly outperforms state-of-the-art baselines, achieving a superior balance between retrieval precision and computational efficiency.
Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image–text embedding space, yet the semantic organization of this space is rarely inspected. We present a post-hoc framework to explain, verify, and align the semantic hierarchies induced by a VLM over a given set of child classes. First, we extract a binary hierarchy by agglomerative clustering of class centroids and name internal nodes by dictionary-based matching to a concept bank. Second, we quantify plausibility by comparing the extracted tree against human ontologies using efficient tree- and edge-level consistency measures, and we evaluate utility via explainable hierarchical tree-traversal inference with uncertainty-aware early stopping (UAES). Third, we propose an ontology-guided post-hoc alignment method that learns a lightweight embedding-space transformation, using UMAP to generate target neighborhoods from a desired hierarchy. Across 13 pretrained VLMs and 4 image datasets, our method finds systematic modality differences: image encoders are more discriminative, while text encoders induce hierarchies that better match human taxonomies. Overall, the results reveal a persistent trade-off between zero-shot accuracy and ontological plausibility and suggest practical routes to improve semantic alignment in shared embedding spaces.
We study influence maximization in general stochastic models, the observed cascades model, and the independent cascade (IC) model. For general stochastic models with only black-box sample access, we introduce a low-adaptivity optimization framework that improves sample complexity and running time over the prior work and is instrumental to all our results. We further introduce an adaptive algorithm guided by empirical variance, avoiding pessimistic worst-case bounds. Combining our optimization framework with sketching, we obtain the first algorithm with provable guarantees and nearly-linear running time for influence maximization on observed cascades, optimal up to logarithmic factors. For IC, we prove a novel tail bound replacing a factor n with 𝜏 (the number of diffusion steps) in sample complexity, improving over prior work when 𝜏 is small, as is common due to small-world phenomena. Experiments confirm substantial speedups while maintaining solution quality.
Explainable recommendation is crucial for building user trust, yet producing natural-language rationales that faithfully reflect the underlying decision process remains challenging. Most LLM-based explainable recommenders incorporate collaborative signals through shallow prompting or lightweight adapters, which often yields generic explanations that are only loosely grounded in preference evidence, particularly when interactions are sparse. To address this gap, we propose HyperXRec, a novel framework that unifies preference modeling and explanation generation by integrating hyperspherical latent clustering with a cluster-guided mixture-of-experts (MoE) inside an LLM. Specifically, HyperXRec employs a dual-VAE with a von Mises-Fisher mixture prior to learn robust hyperspherical user/item embeddings, resulting in stable and semantically coherent clusters even under sparse interaction settings. These clusters are subsequently leveraged to guide fine-grained expert routing in the LLM, encouraging explanations that are both personalized and explicitly aligned with the learned preference structure. Experiments on multiple benchmarks demonstrate that HyperXRec consistently outperforms strong baselines in explanation fidelity, personalization, and robustness in cold-start scenarios.
Knowledge editing is pivotal for efficiently updating the parametric memory of Large Language Models (LLMs), enabling them to function as evolving agents in dynamic environments. However, mainstream in-parameter knowledge editing approaches suffer from Subject-Dominant Memory Interference: modifying a specific fact inadvertently corrupts the broader structural knowledge associated with the same subject within LLMs. We diagnose the root cause as a shortcut learning pathology, where the optimization objective overfits subject representations while bypassing the essential relational context. To rectify this, we propose Causal Path Alignment (CPA), a principled framework designed to anchor the optimization trajectory to valid causal pathways. CPA enforces parameter updates to route through relation-aware intermediate states, thereby preventing the erasure of contextual dependencies. Experimental results across diverse LLM backbones demonstrate that CPA consistently eliminates the shortcut, significantly improving relation specificity while exhibiting minimal side-effects. Moreover, CPA serves as a model-agnostic plug-in for existing editors, paving the way for reliable and trustworthy in-parameter knowledge editing.
Improving the memory efficiency, throughput, and serving cost of large language models (LLMs) is critical for edge deployment, interactive applications, and sustainable inference. Pruning is a promising approach, but existing methods have limitations: width pruning disrupts the standard transformer architecture and requires custom inference code, while depth pruning causes abrupt accuracy drops. Moreover, many approaches that work well for LLMs fail to preserve performance on small language models (SLMs). We propose COMPACT, which jointly prunes (i) rare vocabulary to shrink embedding layers and (ii) FFN intermediate channels using common-token–weighted activations aligned with the post-pruning token distribution. COMPACT inherits strengths of both depth and width pruning, such as deployment-friendliness, scale-adaptivity, competitive pruning speed, and strong inference performance. Experiments on several LLM families (0.5B–70B) show state-of-the-art downstream performance, with substantial improvements in inference throughput and GPU memory. Project code is at https://github.com/ekwek1/COMPACT.
With the growing complexity of online information, trustworthy fake news detection has become increasingly critical. Although Large Language Models (LLMs) exhibit a strong ability to leverage factual evidence for verification, they remain highly vulnerable to unreliable, noisy, or scarce evidence, undermining robustness in real-world scenarios. Given the generalizability of deceptive patterns in fake news, we consider pattern as a complementary signal under insufficient evidence during factual verification. However, due to LLMs' lack of expertise in deception-specific patterns, realizing such effective collaboration remains challenging. To address these issues, we propose a Router-Guided Fake News Detection Framework with Pattern Augmentation (RGPA). Specifically, we introduce a hierarchical routing mechanism including a case router and an external evidence router. It guides news to appropriate reasoning paths adaptively based on a multi-dimensional quality assessment, prioritizing high-quality evidence while mitigating noise. Furthermore, we design an expert model to capture deceptive features and integrate them into LLMs' reasoning, enabling a synergy of factual verification and pattern awareness under evidence-scarce scenarios. Extensive experiments on two real-world datasets demonstrate that RGPA significantly outperforms existing approaches.
Modern AI-driven systems rely on large populations of autonomous agents that make decentralized routing decisions under uncertainty, where rare but severe tail latency can critically degrade quality of experience, safety, and reliability. To model agents’ aversion to such tail latency, we study nonatomic selfish routing games in which agents minimize the Conditional Value-at-Risk (CVaR) of path latency. CVaR explicitly captures both the likelihood and severity of tail latency, but its inherent nonadditivity across network edges poses a fundamental challenge. We address it by identifying a worst-case dependence structure—tail risk concentration—under which tail latency across network edges is synchronized. We show that CVaR penalizes this dependence structure and becomes additive under worst-case tail dependence, which enables tight inefficiency analysis. To quantify the resulting inefficiency induced by risk-aversion, we adopt the price of risk aversion (PRA), defined as the worst-case ratio between the total system cost at a risk-averse equilibrium and that at a risk-neutral equilibrium. We show that, for arbitrary latency functions and general network topologies, the PRA under CVaR admits a tight upper bound that grows linearly with both the network size and the maximum edge-level upper-tail cost. We further prove that this bound is tight by constructing a family of Braess-type networks that achieve a matching lower bound. These results provide the first tight worst-case inefficiency bound for CVaR-based selfish routing and offer insights into how tail-risk-averse decision making by autonomous agents amplifies congestion externalities in large-scale multi-agent systems.
Inferring latent social ties from large-scale spatiotemporal mobility traces is a foundational AI task with broad applicability. Existing hypergraph-based methods often model higher-order relations by treating hyperedges as static snapshots, thus failing to capture the temporal dynamics and co-evolution of user interactions. Meanwhile, many approaches still struggle to distinguish stable social gatherings from transient noisy co-occurrences. To address these challenges, we propose a novel Dual-Channel Hybrid Graph Neural Network (HyGNN) that jointly models temporal dynamics and high-order structural dependencies. The framework consists of two complementary components: (1) a temporal meeting graph channel that decomposes multi-user interactions into ordered snapshots and aligns trajectory evolution through message propagation, and (2) a gathering hypergraph channel that uses a structure-aware encoder with homogeneity-guided aggregation to filter noise in high-order co-occurrences. A mutual-information-aware fusion module integrates both views while preserving their distinct semantics. Extensive experiments across three real-world datasets show that HyGNN consistently outperforms state-of-the-art baselines in PRAUC and ROCAUC metrics, and robustly captures the nonlinear interplay between mobility and sociality. The code is available at https://github.com/KunLiangChen/HyGNN.
Parameter sharing is a central design choice in cooperative multi-agent reinforcement learning, yet it fundamentally conflicts with the need for role specialization in heterogeneous cooperative environments. Existing role-based methods typically learn monolithic role representations, which often suffer from gradient interference and fail to capture the compositional structure of complex behaviors. Inspired by Trait Theory, we propose DEcompose and COnstruct Roles (DECOR), a framework that models agent roles as dynamic compositions of orthogonal behavioral traits. DECOR introduces an orthogonal Mixture-of-Experts architecture to decompose behaviors into independent traits, mitigating destructive gradient interference under parameter sharing, and a group-consensus guided mechanism to extract team-level tactical intents that guide role composition.Experiments on multiple benchmarks demonstrate that DECOR consistently improves sample efficiency and overall performance over existing related methods.
Joint optimization of pricing, dispatching, and routing is critical for hub-based mobility services but challenging due to complex decision couplings and strict service guarantees, such as Order Response Rate (ORR). Conventional constrained reinforcement learning often struggles in this mixed continuous--combinatorial action space, suffering from oscillatory behavior in Lagrangian dual variables and unstable constraint satisfaction. To address this, we propose PID-SACA, a unified framework that integrates an entropy-regularized actor--critic policy for continuous pricing and dispatching assisted by an embedded routing solver for execution-aware feedback. Crucially, we adapt the PID control mechanism to the Lagrangian dual update process. This approach leverages proportional, integral, and derivative feedback to dampen oscillations caused by stochastic gradient variance, ensuring robust long-term constraint enforcement. We provide theoretical analysis on the boundedness of dual variables, and experiments on publicly available large-scale mobility datasets demonstrate that PID-SACA significantly outperforms baselines, achieving high revenue with stable service compliance. Code: https://github.com/jerry0375/PID-SACA
Multi-view clustering aims to discover shared semantic structures from multiple complementary data views to improve clustering performance. However, most existing methods rely on a single clustering representation for each semantic cluster, which limits the ability to model complex cluster structures and diverse patterns in real-world data. To address this limitation, we propose a Mixture of Clustering Experts with Dual Consistency (MoCE-DC). MoCE-DC introduces a set of learnable clustering experts that characterize the same semantic cluster from multiple perspectives within a unified prototype space. A gating mechanism is employed to adaptively select experts for each sample. MoCE-DC decomposes complex semantic clusters into multiple collaborative sub-structures, significantly enhances the ability to model intricate intra-cluster diversity. To further ensure cross-view semantic consistency, we propose a dual-level alignment mechanism that enforces prediction consistency across views while guiding clustering assignments toward a more discriminative direction. In addition, a gating balance regularization strategy is introduced to mitigate expert collapse and promote balanced expert utilization. Extensive experiments on multiple public multi-view clustering benchmarks demonstrate that MoCE-DC outperforms state-of-the-art methods.
Agents implemented using Large Language Models (LLMs) offer novel solutions for tasks such as document summarization. However, single-agent systems, limited by their own knowledge base, often exhibit information omissions or inconsistencies with the facts, while multi-agent systems lacking effective organization can fall into the trap of social cognitive defects such as flattery and premature consensus. Inspired by the U.S. jury system, we propose JURIS, a judicial-decision-inspired multi-agent collaborative framework. Specifically, the document summarization process simulates a lawsuit scenario: multiple generator agents condense the target text into multiple sentences from different perspectives, much like lawyers on both sides providing different evidence; multiple decision-making agents consider the overall picture based on defense and voting mechanisms to select candidate sentences, much like a jury adopting different evidence; finally, a chief editor agent compiles a summary of all selected sentences, much like a secretary summarizing a plan and a judge giving a final verdict. Extensive experiments on in-domain news and cross-domain benchmarks demonstrate that JURIS consistently and significantly outperforms strong single-agent and multi-agent baselines across automatic evaluation metrics, multi-dimensional quality assessments, and human judgments, validating the effectiveness of decision-driven structured multi-agent collaboration for document summarization.