Zero-shot multimodal anomaly detection is critical for identifying structural defects that are often invisible to traditional RGB images, particularly in scenarios lacking target domain samples. However, existing methodologies face two significant impediments: the prohibitive computational overhead caused by relying on multi-view rendering for depth processing, and the inability of static text prompts to adapt to fine-grained local anomalies. To address these challenges, a unified zero-shot multimodal anomaly detection framework GRASP is proposed. Firstly, a Frequency Domain Enhancement module is introduced to replace costly rendering with spectral transformations, directly synthesizing high-fidelity depth images for efficient utilization. Secondly, a Prompt-Conditioned Variational module is designed to bridge the semantic gap by grounding global textual descriptions into local visual nuances. Finally, a Dual Cross-Injection Alignment module is proposed to enable robust feature fusion for enhancing anomaly classification performance, while a Pyramid Anomaly Map Recalibration module further refines anomaly localization across multiple scales. Extensive experiments on MVTec 3D-AD and Eyecandies demonstrate that GRASP establishes a new state-of-the-art, yielding substantial improvements of 3.0 points in I-AUROC and 2.7 points in AUPRO compared to the SOTA method.
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Graph-level anomaly detection (GLAD) aims to identify graphs that deviate from the majority in a dataset of graphs. Existing methods typically adopt either a global aggregation perspective that summarizes nodes within a graph into a representation vector, or a subgraph-oriented perspective which regards certain local subgraphs as indicators of anomalous properties. However, both paradigms operate from a fixed perspective and may fail to identify anomalous graphs whose discriminative characteristics manifest at multiple levels of structure granularity, where each level features the coarsened graphs at a specific granularity. In this paper, we propose M-GLAD, an unsupervised GLAD method via multi-granular graph structure learning. M-GLAD is grounded in the Prototype-guided Multi-granular Information Bottleneck (PMIB) principle. PMIB aims to measure the mutual information between coarsened graphs at the specific level of structure granularity and the learnable granularity-specific prototypes that summarize normal patterns of graphs at each granularity. The multiple levels of structure granularity of graphs and prototypes are abstracted by a graph structure abstraction module. This formulation enables granularity-aware anomaly scoring that considers anomalous characteristics across different levels of structure granularity. Extensive experiments on eight real-world graph datasets demonstrate that M-GLAD achieves superior performance over competitive baselines.
Multi-objective bandits with hierarchical preferences and safety constraints is central to many real-world decision-making tasks such as healthcare treatment planning and safe autonomous control, where multiple objectives must be optimized according to their priorities while ensuring safety requirements are satisfied. In this paper, we study a multi-objective stochastic linear bandit framework that incorporates hierarchical preferences together with safety constraints, requiring the learner to remain competitive with respect to a known baseline policy. We consider two practically motivated safety models: (i) cumulative constraints, which require the cumulative performance to exceed the baseline, and (ii) stage-wise constraints, which impose this requirement at each time step. We propose two algorithms, LexUCB-C and LexTS-S, designed for the cumulative and stage-wise settings, respectively. We establish regret bounds showing that both algorithms achieve performance comparable to existing single-objective safe linear bandit methods, while simultaneously optimizing multiple objectives. In addition to theoretical guarantees, we develop a carefully designed experimental framework that captures the interaction between hierarchical preferences and safety constraints. Experiments on synthetic and real-world datasets validate our theory and demonstrate the effectiveness of the proposed methods.
Multi-channel data fusion is essential for capturing comprehensive representations in complex systems. While graph convolutional networks have demonstrated remarkable efficacy, existing fusion paradigms primarily rely on discrete architectures governed by first-order propagation. These models are typically confined to discrete message-passing mechanisms, which makes it difficult to characterize the continuous evolution of underlying system dynamics. To address these limitations, we propose a multi-channel Graph Continuous Network (mcGCN), a novel framework for multi-channel data fusion. By formulating the information propagation as a second-order partial differential equation on graphs, mcGCN transitions from discrete layer-wise updates to a continuous dynamical system. Specifically, mcGCN integrates a multi-channel encoding module with continuous feature dynamics to initialize and evolve the latent node representations over static graph topologies. This physical analogy enables more robust information flow and effectively mitigates the performance degradation typically associated with deep graph architectures. Extensive experiments on diverse benchmark datasets demonstrate that our method outperforms state-of-the-art baselines, validating its effectiveness and robustness.
Multi-agent pathfinding (MAPF) under one-shot planning is a core component of warehouse automation, yet classical formulations typically assume four-connected 2D grids with unit-time moves in four directions. To fill reality gaps while still being trackable with discrete combinatorial search, this work proposes a more practical counterpart tailored to differential-drive AGVs. We term this multi-agent warehouse pathfinding (MAWPF), featured with four constraints: (i) agent actions are restricted to straight motion and in-place rotation; (ii) rotations require multi-step costs; (iii) acceleration and deceleration are considered, and; (iv) follower collisions are prohibited to prevent rear-end crashes. To solve MAWPF efficiently, we adapt representative suboptimal MAPF algorithms-PP, LNS2, PIBT, and LaCAM-and conduct comprehensive benchmarking. Our experiments reveal that PP and LNS2 struggle to solve instances with many agents, while PIBT-based approaches achieve preferable scalability with increased solution cost. We believe that these constitute an important step toward adapting classical gridworld MAPF to operational warehouse setups.
Partial order causal link (POCL) planning offers rich structural representations for plan optimization. However, under the classical threat definition, POCL plans form a strict subset of valid partial-order (PO) plans. This gap limits existing POCL optimization techniques to a restricted subspace of PO solutions. We revisit 'white knight' threat semantics, which permit a causal link to persist across a threat provided the deleted condition is re-established by an intermediate producer. We prove that under this definition, the POCL and PO solution spaces become equivalent. By extending a MaxSAT-based optimization framework, we demonstrate that white knights are theoretically necessary for completeness and practically advantageous: they yield plans with significantly fewer ordering constraints in domains with complex causal interference while maintaining computational tractability on standard benchmarks.
Self-attention is central to the success of Transformer architectures; however, learning the query, key, and value projections from random initialization remains challenging and computationally expensive. In this paper, we propose two complementary methods that leverage the Discrete Cosine Transform (DCT) to enhance the efficiency and performance of Vision Transformers. First, we address the initialization problem by introducing a simple yet effective DCT-based initialization strategy for self-attention, where projection weights are initialized using DCT coefficients. This structure-preserving approach consistently improves classification accuracy on the CIFAR-10 and ImageNet-1K benchmarks. Second, we propose a DCT-based attention compression technique that exploits the decorrelation properties of the frequency domain. By observing that high-frequency DCT coefficients typically correspond to noise, we truncate high-frequency components of the input patches, thereby reducing the dimensionality of the query, key, and value projections without sacrificing accuracy. Experiments on Swin Transformer models demonstrate that the proposed compression method achieves a substantial reduction in computational overhead while maintaining comparable performance. Code: https://github.com/NUBagciLab/DCT-Transformer.
We propose a modular logical framework for both reasoning with and measuring inconsistency in propositional knowledge bases. The framework extends propositional logic with markers attached to occurrences of atoms and interpreted as pointers to worlds. This allows different occurrences of the same atom to be evaluated in different contexts unless they share a marker. We define paraconsistent entailment relations via abnormality functions that associate each model with a set of deviations from a preferred behavior. Each entailment relation is then defined with respect to models that are minimal under set inclusion. We show that suitable markings and abnormality functions capture several existing forms of inconsistency-tolerant reasoning, including entailment based on maximal satisfiable subsets and the minimally inconsistent Logic of Paradox. We also show how a range of inconsistency measures can be expressed in the same setting. Thus our framework provides a uniform basis for diverse approaches to inconsistency handling and measurement.
Bounded fitting is an attractive paradigm for learning logical formulas from labeled data examples that offers PAC-style generalization guarantees and can often be implemented leveraging SAT solvers. It has been successfully applied to learning concepts of the description logic ALC. We study bounded fitting for learning concepts in expressive description logics that extend ALC with inverse roles, qualified number restrictions, and feature comparisons. We investigate under which conditions bounded fitting keeps its favorable theoretical properties in this setting, and implement is using a SAT solver. We compare our implementation against state-of-the-art concept learners with encouraging results, demonstrating that it is a practical approach to expressive concept learning.
Chain-of-Thought (CoT) prompting trades inference speed for reasoning accuracy. Existing compressors force a compromise as static gradient techniques treat tokens independently, severing sequential logic, while uncertainty-based pruning ignores the final answer. We introduce Adaptive GoGI-Skip, a framework that resolves this tension by non-linearly coupling Goal-Gradient Importance (GoGI) with Adaptive Dynamic Skipping (ADS). GoGI quantifies each token's functional contribution to answer correctness via gradient sensitivity. ADS leverages runtime entropy to dynamically modulate the GoGI threshold, preserving low-gradient tokens essential for structural coherence at high-uncertainty junctions. Trained on 7,472 MATH traces, our policy transfers zero-shot to AIME, GPQA, and GSM8K, reducing token volume by >45% and accelerating inference up to 2.0x without accuracy loss. These results suggest that thinking-optimal compression demands synergy between teleological goals and epistemic uncertainty.
Many semantics for abstract weighted argumentation assume that each argument is associated with a numerical initial weight. Eliciting these initial weights poses several challenges: (1) accurately providing a specific numerical value is often difficult, and (2) individuals frequently confuse initial weights with acceptability degrees in the presence of other arguments. We therefore propose an elicitation pipeline that allows a user to specify their believed final acceptability degree intervals for each argument. We can determine which portion (if any) of these intervals are rational, refining the intervals, or restoring rationality when the intervals are irrational. This allows us to ultimately identify possible initial weights for each argument.
Real-time clustering of dynamic multi-view data streams is a critical yet challenging task in open-world applications. While several methods have been proposed to address this task, most of them extract features incrementally but fail to output instant clustering results for the current batch. In addition, they neglect semantic consistency, causing the cluster labels of identical concepts to drift unpredictably due to independent processing. To address these limitations, we propose a real-time semantic consistent incremental multi-view clustering framework. Specifically, we constructs a compact historical knowledge base via an adaptive diversity-aware selection mechanism, which guides the clustering of incoming data, enabling immediate inference without accessing the full history. Furthermore, we introduce a semantic alignment strategy based on consensus centers to ensure robust label consistency over time. Extensive experiments demonstrate the effectiveness and efficiency of our proposed method.
Automated radiology report generation aims to create clear and clinically correct diagnostic reports from medical images. Existing retrieval enhancement methods primarily focus on reusing textual knowledge, neglecting the crucial role of local visual pattern memory in clinical diagnosis. Furthermore, cross-modal retrieval lacking explicit clinical semantic constraints can easily introduce irrelevant pathological information, thereby reducing the clinical effectiveness of the generated reports. To address these challenges, we propose ClinAlign—a memory-based retrieval framework aligned with clinical workflow, drawing inspiration from clinical diagnostic workflows. Visually, we construct a disease-aware visual memory bank and enhance local patch representations through proposed Memory‑based Patch Pattern Augmentation (MPPA), thereby improving the perception and discrimination of pathological regions. On the textual side, we construct a disease-aware textual memory bank and introduce Classification-Guided Prompt Augmentation (CGPA), where disease state predictions are converted into structured diagnostic prompts to provide explicit semantic guidance for textual memory retrieval.Extensive experiments on two medical report generation benchmarks, MIMIC-CXR and IU X-Ray, demonstrate the effectiveness and practical value of our proposed method.
Understanding when fair allocation mechanisms lead to stable coalition structures is fundamental for designing robust multi-agent systems. We investigate the compatibility between stable allocations in transferable utility (TU) games and the stability of coalition structures in induced hedonic games, where agents' preferences over coalitions are derived from payoffs assigned by a fixed allocation rule applied to all subgames of the TU game. We analyze FX-FE strong Nash stability (SNS) in induced hedonic games, which captures a strong form of robustness under free-exit and free-entry conditions. We show that any efficient allocation rule ensuring core membership for the grand coalition induces a hedonic game that guarantees the existence of an FX-FE strong Nash stable partition. Examining the Shapley value, we further show that when it lies outside the core, the resulting hedonic game may or may not possess FX-FE strongly Nash stable partitions, highlighting a sensitive interaction between Shapley-based fairness and coalition-level stability. Our framework bridges SHAP methodology from explainable AI with hedonic coalition theory, providing theoretical foundations for understanding when fair allocation mechanisms shape coalition-level incentives in a way that ensures strategic stability in team formation and multi-agent systems.
Recently, Visual State Space Models offer powerful global modeling for Dual-modal Salient Object Detection (SOD). However, they are still constrained by three inherent limitations: first, Mamba's strict reliance on sequential ordering makes it sensitive to cross-modal geometric misalignment, where spatial shifts disrupt token correspondence; second, general indiscriminate scanning treating all tokens equally may lead to signal dilution, where sparse foreground features are overwhelmed by background noise; third, conventional decoders rely on implicit upsampling, causing boundary degradation during resolution recovery. To address these challenges, we propose G-SalAlignMamba, a geometry-aware framework tailored for dual-modal SOD. We introduce Geometry-Aware Encoding with explicit alignment to correct spatial shifts, Semantics-Informed Refinement to prevent signal dilution by prioritizing foregrounds, and Structure-Preserving Decoding that integrates explicit alignment with unsupervised boundary refinement. Extensive experiments show that G-SalAlignMamba achieves state-of-the-art performance on RGB-D and RGB-T benchmarks with favorable efficiency (30.41 FPS, 83.80M parameters). The code is available at https://github.com/PC1-99/G-SalAlignMamb.git.
We study multi-agent contracts, in which a principal delegates a task to multiple agents and incentivizes them to exert effort. Prior research has mostly focused on maximizing the principal’s utility, often resulting in highly disparate payments among agents. Such disparities among agents may be undesirable in practice, for example, in standardized public contracting or worker cooperatives where fairness concerns are essential. Motivated by these considerations, our objective is to quantify the tradeoff between maximizing the principal's utility and equalizing payments among agents, which we call the price of non-discrimination. Our first result is an almost tight bound on the price of non-discrimination, which scales logarithmically with the number of agents. This bound can be improved to a constant by allowing some relaxation of the non-discrimination requirement. We then provide a comprehensive characterization of the tradeoff between the level of non-discrimination and the loss in the optimal utility.
Instance-wise feature selection (IWFS) identifies informative features for each instance, improving generalization by discarding irrelevant information and enhancing interpretability through personalized explanations. Most IWFS methods adopt a selector--predictor architecture, where a selector generates instance-specific masks to guide prediction. This often leads to co-adaptation, in which the selector encodes label information into the mask, resulting in spurious correlations and unfaithful explanations. Existing methods also struggle to capture diverse local patterns, which is critical for IWFS under heterogeneous sparsity. We propose VIBMask, a unified IWFS framework by the variational information bottleneck. VIBMask mitigates co-adaptation by penalizing mutual information between unselected features and the label, and improves expressivity via an ensemble of diverse selectors that capture heterogeneous sparse patterns. We further derive a novel variational lower bound for discrete masks, enabling efficient end-to-end training through reparameterization. Experiments on synthetic and real datasets show that VIBMask consistently outperforms state-of-the-art IWFS methods in both predictive accuracy and informative feature discovery.
Decision-making pipelines increasingly rely on prediction models whose outputs serve as inputs to downstream optimization problems. Decision-Focused Learning (DFL) has emerged as a promising approach to training such models by directly optimizing decision quality rather than predictive accuracy alone. While most existing DFL methods assume a complete-information setting in which the ground-truth optimization parameters are observed, this paper studies Contextual Inverse Optimization (CIO), an incomplete-information setting in which only the resulting solutions are observed. Prior work on CIO has proposed learning algorithms based on optimality conditions for linear programs, as well as methods that repeatedly solve inverse optimization problems to handle integer programs, often incurring a substantial computational burden. In this paper, we propose a learning algorithm for general optimization problems with linear objective functions that eliminates the need to solve inverse optimization problems. The proposed method learns prediction models by solving a Relaxed Inverse Optimization Problem (RIOP), constructed based on feasible solutions randomly sampled from the feasible region, thereby reducing the computational overhead associated with existing CIO methods. Numerical experiments demonstrate that our method achieves competitive performance in terms of regret compared with existing methods, while offering improved computational efficiency for certain classes of downstream optimization problems.
Anomaly detection in dynamic graphs is essential for monitoring evolving systems such as transaction networks and online platforms. Yet existing methods remain limited in realistic edge-stream settings: snapshot-based approaches discretize continuous interactions and miss fine-grained temporal signals, while many continuous-time models rely on scarce node/edge attributes and often fail to explicitly assess whether a destination is compatible with a source’s recent context. Moreover, under extreme class imbalance and the lack of anomaly labels, unsupervised detectors frequently yield ill-defined normality criteria and ambiguous decision boundaries. We propose BAD, an unsupervised framework for anomaly detection in continuous-time dynamic graphs. BAD adopts a minimalistic design that represents nodes with learnable identity embeddings and performs pairwise compatibility modeling via cross-attention between each destination node and the source’s recent neighbors, enabling direct characterization of context-dependent deviations without requiring attributes. To obtain a principled separating boundary, BAD further integrates normalizing flows to model the distribution of normal interactions and derive likelihood-based anomaly scores. Extensive experiments on four real-world datasets demonstrate that BAD consistently performs well, highlighting its robustness under feature-scarce and label-scarce conditions.
Brain effective connectivity (EC) characterizes directional causal interactions among brain regions. However, learning stable and directionally explicit EC networks from multimodal data remains challenging. In practice, functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) differ substantially in spatio-temporal resolution and noise characteristics. Existing methods often rely on manual spatio-temporal alignment and tend to recover only partial causal structures under high noise and Bayesian equivalence classes. To address these challenges, we propose a spatio-temporal constrained Bayesian causal network for multimodal brain effective connectivity learning (STCBN-EC). First, STCBN-EC constructs an anatomically guided EEG–fMRI spatial mapping and derives a unified spatio-temporal representation through slice-level alignment and adaptive modality fusion. Then, a Bayesian causal network is employed to model nonlinear inter-regional dependencies, where uncertainty-driven surrogate scoring is used to evaluate candidate structures. Finally, multimodal representation learning and EC structure estimation are jointly optimized via a gradient-free global optimization strategy. Experiments on simulated and real EEG–fMRI datasets demonstrate that STCBN-EC outperforms state-of-the-art methods and effectively captures state-dependent directional interactions among brain regions.