Accepted Papers

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 S³-TIR (Coupled Spatial-Spectral Splatting), a physics-inspired framework for high-fidelity thermal infrared (TIR) novel view synthesis. Existing 3D Gaussian splatting methods struggle to capture the unique duality of TIR imagery: the coexistence of diffusive smoothness from heat conduction and structural sharpness from material discontinuities. To address this issue, we leverage the intrinsic frequency properties of thermal scenes, where conduction creates smooth fields while discontinuities cause sharp signal jumps. Accordingly, S³-TIR couples adaptive spatial support with internal spectral modulation via a unified generalized-exponential Gabor primitive. This design utilizes spatial support as a physical truncation controller, confining high-frequency textures within sharp boundaries while maintaining smooth diffusion elsewhere. Furthermore, we propose a frequency-aware deferred splatting scheme that enforces multi-view frequency consistency, anchoring frequency distributions to the 3D structure rather than view-dependent appearance. Experiments on diverse benchmarks demonstrate that S³-TIR achieves state-of-the-art performance, notably reducing LPIPS by 42.5% and improving PSNR by 3.2% on the RGBT-Scenes dataset, while yielding structurally stable thermal sequences free from jittering artifacts.

Although Large Vision-Language Models (LVLMs) have demonstrated remarkable reasoning capabilities across various downstream multimodal tasks, they are proven to be vulnerable to carefully designed adversarial examples. Existing LVLM attackers show that exploring external components of adversarial guidance (e.g., forcing adversarial alignment, resembling harmful features) can help improve adversarial effects. However, leveraging the intrinsic patterns of LVLMs to induce adversarial perturbation generation by exploring how LVLMs perceive images has not been deeply studied. Inspired by the cognitive science, in this paper, we make the first attempt to investigate the interference of adversarial perturbation from the perspectives of image phase, and find that LVLMs are sensitive to the phase-aware image structure. Motivated by this, we propose a novel LVLM attack method called BadPhase with further backdoor designs, to implant adversarial phase as triggers into any image inputs via data poisoning so as to control the LVLMs’ predictions. A textual trigger and a backdoor perturbation switcher are also introduced to activate the malicious behavior only when both triggers are present. The whole backdoor optimization is implemented at the test-time to reduce the resource reliance. Experiments on four popular LVLMs and three benchmarks demonstrate the effectiveness of our proposed method.

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

Proactive scaling improves cloud resource efficiency by forecasting system-relevant indicators and dynamically provisioning resources to maximize utilization while satisfying quality requirements. Existing approaches forecast service indicators in isolation, ignore forecasting uncertainty, and scale resource types independently, violating bundled resource constraints and degrading service quality. Therefore, we propose JointScaler, a learning-based framework for multi-indicator distribution forecasting and uncertainty-aware scaling. It captures inter-indicator dependencies via hierarchical attention, models dynamic uncertainties with normalizing flows, and leverages full predictive distributions to optimize bundled resource allocations under quality requirements. Evaluated on 4 real-world datasets, JointScaler improves point and distribution forecasting accuracy by 5.87% and 14.74%, outperforming 12 advanced baselines. In a week-long A/B test on a payment application’s cloud platform, it reduced GPU and CPU usage by 2,400+ and 37,000+ hours with stable service quality, delivering significant economic benefits.

The knowing--doing gap, the mismatch between ideas articulated during model reasoning and the realized creative artifact, remains a fundamental challenge in creative AI and persists in LLM-based artistic creation. This paper responds to this gap by introducing a systematic and interpretable framework for examining how user intent is articulated during model reasoning and selectively realized, or lost, during action in LLMs, using symbolic music composition as an analytical lens. We present a realizational process theory that formalizes creative generation and localizes the knowing--doing gap at the realization stage. We instantiate this theory with Music Atelier (Mutelier), an LLM-as-a-Judge framework that operationalizes idea-level realization analysis and makes the gap observable and analyzable in practice. Across diverse evaluation settings, we show that Mutelier reveals reliable, previously invisible failure modes in which intent-aligned ideas are articulated during reasoning but fail to materialize in the final artifact. By reframing artistic creation as a realizational process, this work provides a principled process-level foundation for understanding how knowing--doing gaps emerge in machine creativity.

Singleton consistency significantly reduces the search space of backtracking search for solving constraint satisfaction problems (CSP). However, it is too expensive to be used throughout the search, and it has not been successfully applied in solving general CSPs. In this paper, we propose a variable-oriented adaptive singleton consistency, namely VOASC, that could be applied in general-purpose constraint solvers. It calculates the filtering efficiency of local consistency enforced by the default propagation engine and the singleton-based consistency for each variable, and then selects an appropriate consistency to propagate the decisions of the variable in subsequent searches. We performed extensive experimentation with the MiniZinc benchmark suite. VOASC demonstrates superior solving performance on both CSP and COP, compared to the two candidate local consistencies and several existing adaptive propagation methods.

Low-Light Image Enhancement (LLIE) remains challenging due to severe noise, color artifacts, and structural degradation in extreme low-light environments. Existing methods primarily rely on photometric cues and often fail to preserve geometric consistency, resulting in blurred edges and distorted textures. To address this limitation, we introduce LAMP, a large-scale dataset of Low-light Aligned Multimodal Pairs for LLIE. LAMP contains over 18k high-quality aligned pairs and consists of two complementary subsets. LAMP-Real is captured using LiDAR-based depth sensors under physically controlled illumination, while LAMP-Synthetic is generated through a physics-calibrated degradation pipeline. Both subsets provide precisely aligned RGB images, depth maps, and pixel-wise semantic annotations. Building upon LAMP, we propose SPACE, a Structure Preservation Aware Cross-modal Enhancement framework that explicitly leverages geometric priors. SPACE introduces a Depth-Adaptive HVI Transformation to decouple luminance and chrominance under depth guidance, effectively suppressing color-space noise. Furthermore, a Depth-Manifold Modulated Attention mechanism constrains feature interactions within a learned depth manifold, ensuring structural coherence during enhancement. Extensive experiments demonstrate that SPACE consistently outperforms state-of-the-art methods in visual quality and structural fidelity. The code and data are available at https://github.com/YueCheong/SPACE.

We study proportional representation in the temporal voting model, where collective decisions are made repeatedly over time over a fixed horizon. Prior work has extensively investigated how proportional representation axioms from multiwinner voting (e.g., justified representation (JR) and its variants) can be adapted, satisfied, and verified in this setting. However, much less is understood about their interaction with social welfare. In this work, we quantify the efficiency cost of enforcing proportionality. We formalize the welfare-proportionality tension via the worst-case ratio between the maximum achievable utilitarian welfare and the maximum welfare attainable subject to a proportionality axiom. We show that imposing proportional representation in the temporal setting can incur a growing, yet sublinear, welfare loss as the number of voters or rounds increases. We further identify a clean separation among axioms: for JR, the welfare loss diminishes as the time horizon grows and vanishes asymptotically, whereas for stronger axioms this conflict persists even with many rounds. Moreover, we prove that welfare maximization under each axiom is NP-complete and APX-hard, even under static preferences and bounded-degree approvals, and provide fixed-parameter algorithms under several natural structural parameters.

Event Causality Identification (ECI) is a crucial task in knowledge discovery that extracts structured causal relationships between annotated event mentions from unstructured text. However, existing approaches typically rely on extensive labeled data, which is scarce for specialized domains and topics. Although Large Language Models (LLMs) show strong promise for few-shot and zero-shot information extraction, they are prone to “causal hallucination,” generating unreliable and spurious causal links. To address these limitations, we propose LLM-SD (Large Language Model Self-Debate), a novel framework that formulates ECI as a structured debate among multiple identical instances of a single LLM. Causality is determined through the integration of adversarial evidence. The framework employs LLMs in distinct roles: an affirmative team argues for the existence of causality, a negative team argues against it, and an adjudication committee evaluates the evidence for determination. An evidence strength grading rule guides the quantification and integration of adversarial evidence. The automatic and LLM-driven verification finally produce a reasoned verdict for event causality. LLM-SD reduces spurious causal links resulting from causal hallucination in LLMs and identifies more long-distance causalities by promoting a balanced evaluation of arguments. Extensive experiments on three benchmark datasets demonstrate that LLM-SD achieves state-of-the-art performance in a zero-shot setting.

Learning to Defer operates by deferring AI-uncertain samples to humans, enabling the system to outperform either alone. Some works extend it by introducing human-AI combination to deferral. However, they remain limited to binary choices. Given that AI, humans, and human-AI combination are each indispensable, we extend the binary deferral paradigm to a ternary one. There are two challenges: i) how to effectively select among the three modes, especially distinguishing humans from combination? ii) how to design an enhanced combination without being degraded by unreliable model predictions? To address the challenges, we propose TriHAI, a tri-mode deferral method that routes samples to suitable modes based on normalized confidence scores obtained through a discriminative gating network, and enhances combination mode by fusing model predictions refined by human-guided Conformal Prediction and human predictions via Bayesian theory. Experiments on three datasets show that TriHAI surpasses other human-AI baselines by up to 9.57% in accuracy.

Few-shot learning aims to recognize novel categories from limited labeled samples, where prototypes estimated from 1--5 supports per class are often unreliable. Semantic-based approaches alleviate this by introducing class-level priors, but they often ignore instance-level cues and rarely optimize queries under the inductive protocol. We propose PMCE, a Probabilistic framework that leverages Multi-granularity semantics with Caption-guided Enhancement for few-shot classification. On base classes, we build a knowledge bank with class-wise visual statistics and class-name embeddings. At test time, the semantic embedding of a novel class retrieves a few similar base classes whose visual priors are aggregated into a class-specific prior and combined with the support-based prototype via MAP estimation. Simultaneously, we train a lightweight enhancer on base classes that fuses frozen BLIP captions with visual features, and apply it to both supports and queries without using query-set statistics or novel labels. A simple caption-consistency regularizer further improves robustness to noisy captions. Experiments on four standard benchmarks with ResNet-12 and Swin-T backbones show that PMCE outperforms state-of-the-art few-shot baselines, achieving up to 7.71% gains over the strongest competitor on MiniImageNet in the challenging 1-shot setting. Our code is available at https://github.com/channa419/PMCE.

4D Gaussian Splatting (4DGS) enables high-quality, real-time rendering of dynamic scenes and is becoming a popular format for sharing 4D assets. This trend calls for robust copyright watermarking that can be verified from rendered videos without access to the original scene or model parameters. However, watermarking 4DGS is challenging: verification relies on short multi-view clips whose frames vary with motion, viewpoint changes, and post-processing. More importantly, decoding from rendered frames is easily dominated by scene content (objects, textures, illumination) rather than subtle watermark traces, leading to unstable evidence across frames and poor generalization across scenes. We propose Guard4D, a decoupled watermarking framework for 4DGS. Guard4D pre-trains a general-purpose message decoder under text supervision and embeds a binary message by optimizing compact spherical-harmonic offsets while keeping geometry and opacity fixed. To improve extraction from short clips and suppress semantic interference, we introduce a temporal modeling and semantic decoupling (TMSD) module that temporally aggregates watermark information and uses clean and noise-perturbed control clips generated from the non-watermarked model to reduce the influence of semantics on watermark decoding. Extensive experiments on dynamic 4DGS scenes demonstrate the effectiveness of Guard4D. Code is available at https://github.com/shisyy/Guard4D.

We present complementary branching, a new branching approach for model counting based on the complement counting paradigm. Instead of branching on individual variables, complementary branching decomposes the counting problem for arbitrary CNF formulas into several subformulas, which naturally augments the classic DPLL branching. We show two novel results in which the main tool is complementary branching. First, we design new #SAT algorithms for sparse CNF formulas running in time O*(2^(alpha n)), for some constant alpha < 1. As a second application, we improve the best known deterministic upper bound for #3-SAT to O*(1.637^n) by using a version of clause learning based on complementary branching. These results demonstrate that complementary branching is a powerful tool for designing faster exact algorithms for propositional model counting.

Recent efforts have successfully scaled plain Vision Transformers (ViTs) to unprecedented sizes, ranging from 6B to 22B parameters. In contrast, their hierarchical counterparts have remained largely constrained to less than 1B parameters. To bridge this gap, we propose EHV, an efficient hierarchical ViT architecture. Our dense model scales from 200M to 5B parameters, and we further extend it into a Sparse Mixture-of-Experts (SMoE) variant, achieving an industry-leading scale of 30B parameters. The unsupervised pretraining process consists of two stages: first, we pretrain on ImageNet-21K using a Masked Autoencoder (MAE); second, the resulting model is distilled from multiple state-of-the-art foundation models on a nearly 27M-image dataset. With only 6.7B active parameters, EHV-5B-MoE demonstrates exceptional transfer learning performance across image classification, fine-grained classification, as well as video and dense prediction tasks, specially achieving a linear evaluation accuracy of 89.0\% on ImageNet-1K. This result surpasses those of comparable models such as EVA-CLIP-18B and DINOv3-7B, indicating that its SMoE architecture learns high-quality, generalizable, and linearly separable feature representations.

Deterministic auctions are attractive in practice due to their transparency, simplicity, and ease of implementation, motivating a sharper understanding of when they can attain the same outcomes as randomized mechanisms.
We study deterministic implementation in single-item auctions under two notions of outcomes: (revenue, welfare) pairs and interim allocations.
For (revenue, welfare) pairs, we show a separation in discrete settings: there exists a pair implementable by a deterministic Bayesian incentive-compatible (BIC) auction but not by any deterministic dominant-strategy incentive-compatible (DSIC) auction.
For continuous atomless priors, we identify conditions under which deterministic DSIC auctions are equivalent to randomized BIC auctions in terms of achievable outcomes.
For interim allocations, under a strict monotonicity condition, we establish a deterministic analogue of Border's theorem for two bidders, providing a necessary and sufficient condition for deterministic DSIC implementability.
Using this characterization, we exhibit an interim allocation implementable by a randomized BIC auction but not by any deterministic DSIC auction.

Gradient boosting remains a strong and widely used method for tabular data learning, but its performance often degrades when training labels are noisy. This behavior is largely related to the way boosting algorithms emphasize samples with large gradients, without explicitly accounting for whether such errors originate from informative hard cases or from unreliable labels. We address this issue by reconsidering how sample reliability is evaluated during boosting. Instead of relying on instantaneous error, we examine the evolution of each sample’s residuals across iterations. Based on this insight, we propose Information-Theoretic Trust Boosting (ITBoost), which uses the Minimum Description Length principle to measure the complexity of residual trajectories. Samples whose residual patterns fluctuate in an irregular manner are treated as less trustworthy and are down-weighted during learning. Theoretically, we derive a tighter generalization bound for ITBoost under label noise. Empirical results on various tabular benchmarks indicate that ITBoost provides improved robustness in noisy environments over leading boosting and deep tabular models, while retaining best average performance on clean data.

We investigate the interaction between quantization and pruning in the compression of open-weight small language models (SLMs), as these techniques are frequently combined in practice without a clear understanding of whether their effects are antagonistic, synergistic or additive. Prior work has reported ordering effects and studied pruning or quantization in isolation, but rarely quantifies their interaction across models and evaluation metrics. We introduce an interaction coefficient that isolates non-additive effects and apply it to Falcon3-1B-Base and LLaMA-3.2-1B, with a limited study on Qwen2.5-1.5B. Our experiments cover four hybrid application orders, multiple pruning sparsities and 4/8-bit quantization. We find non-monotonic interactions: memory savings are dominated by 4-bit quantization, while pruning alone provides limited benefits. Hybrids exhibit the greatest effects at global unstructured pruning sparsity s ∈ {0.2, 0.3} while getting close to additivity at s=0.5. At low sparsity, truthfulness improves, while reasoning losses diminish at higher sparsity. Future work will extend interaction analysis to hardware-aware sparse kernels and larger models.

Accurate trajectory prediction of multiple road users at urban intersections--including motorized and nonmotorized vehicles and pedestrians--is critical for cooperative vehicle-infrastructure systems and intelligent transportation systems. This study focuses on multiple road users' trajectory prediction in urban intersections. Such predictions face two key challenges: modeling interactions among multiple road users in complex scenarios and achieving accurate long-term prediction. Existing solutions inadequately incorporate these interactions and suffer from error accumulation or overall bias when performing long-term prediction. To overcome these challenges, we propose a long-term Trajectory prediction multi-scale AutoRegressive framework (TrajAR), which follows an encoder-decoder structure. The encoder dynamically perceives road users' motion trends, types, signals, and implicit interactions for effective handling of complex scenarios. The decoder employs a multi-scale prediction strategy that progressively refines the predicted trajectory from coarse to fine scales, where coarse-scale predictions establish long-term path backbones and finer scales enhance precision. These designs allow TrajAR to achieve accurate long-term prediction in real-world scenarios, demonstrated through experiments on four real-world urban datasets from different intersections. Our results show an average improvement of 23.8%. We provide the code of TrajAR at https://github.com/LetianGong/TrajAR.

Despite decades of intensive research and optimization, modern Boolean Satisfiability (SAT) solvers have reached a plateau where significant performance gains are increasingly difficult to achieve. While Large Language Models (LLMs) have demonstrated remarkable capabilities in pattern recognition and code generation for combinatorial optimization, their direct application to highly optimized SAT solvers remains a formidable challenge due to the extreme complexity and sensitivity of solver heuristics. In this paper, we introduce AESAT (Auto-Evolving SAT solving), a novel neuro-symbolic framework designed to automatically evolve and optimize the heuristic functions of SAT solvers. AESAT employs a memetic-inspired approach, synergizing LLM-guided individual optimization with evolutionary exploration. By leveraging self-optimized prompting techniques, our framework enables LLMs to iteratively discover and refine sophisticated heuristics that bypass the limitations of human-engineered designs. The efficacy of AESAT is demonstrated by its flagship derivative, AE-Kissat-MAB, which won the main track of the 2025 International SAT Competition by a wide margin. This result represents the first time an LLM-enhanced solver has dominated the world's premier SAT competition, marking a paradigm shift in the automated design of reasoning algorithms.

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.

Batch selection is crucial for improving both training efficiency and predictive performance in deep multi-label classification (MLC). Existing batch selection methods typically rely on a single metric to assess instance importance and use static label weights to distinguish label significance, neglecting the dynamic evolution of metric utility and label significance during training. In addition, the method that explicitly exploits label correlations is largely affected by abundant irrelevant labels and insensitive to local label distributions. To address these issues, we propose D2ACE, a novel multi-label batch selection method guided by Dual Dynamics and Adaptive Correlation Enhancement. D2ACE explicitly captures metric and label-level training dynamics by combining stage-wise Bernoulli mixture sampling, which balances uncertainty and noise-resistant hardness, with dynamic label weighting to recalibrate label priorities at each epoch based on current metric statistics. Furthermore, D2ACE introduces a local context-aware correlation enhancement to focus on relevant labels with instance-adaptive dependencies. Extensive experiments on tabular and image benchmarks demonstrate that D2ACE outperforms existing batch selection approaches across various deep MLC models, achieving stronger predictive performance and more efficient correlation modeling.

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.

Stochastic games have become a prevalent framework for studying long-term multi-agent interactions, especially in the context of multi-agent reinforcement learning.
In this work, we comprehensively investigate the concept of constant-memory strategies in stochastic games.
We first establish some results on best responses and Nash equilibria for behavioral constant-memory strategies, followed by a discussion on the computational hardness of best responding to mixed constant-memory strategies.
Those theoretic insights are later verified on several sequential decision-making testbeds, including the \textit{Iterated Prisoner's Dilemma}, the \textit{Iterated Traveler's Dilemma}, and the \textit{Pursuit} domain.
This work aims to enhance the understanding of theoretical issues in single-agent planning under multi-agent systems, and uncover the connection between decision models in single-agent and multi-agent contexts.
The codebase and the full version of this paper is available at github.com/Fernadoo/Const-Mem.

We introduce the Visual Implicit Geometry Transformer (ViGT), an autonomous driving geometric model that estimates continuous 3D occupancy fields from surround-view camera rigs. ViGT represents a step towards foundational geometric models for autonomous driving, prioritizing scalability, architectural simplicity, and generalization across diverse sensor configurations. Our approach achieves this through a calibration-free architecture, enabling a single model to adapt to different sensor setups.
Unlike general-purpose geometric foundational models that focus on pixel-aligned predictions, ViGT estimates a continuous 3D occupancy field in a bird’s-eye-view (BEV) addressing domain-specific requirements.
ViGT naturally infers geometry from multiple camera views into a single metric coordinate frame, providing a common representation for multiple geometric tasks.
Unlike most existing occupancy models, we adopt a self-supervised training procedure that leverages synchronized image-LiDAR pairs, eliminating the need for costly manual annotations.
We validate the scalability and generalizability of our approach by training our model on a mixture of five large-scale autonomous driving datasets (NuScenes, Waymo, NuPlan, ONCE, and Argoverse) and achieving state-of-the-art performance on the pointmap estimation task, with the best average rank across all evaluated baselines.
We further evaluate ViGT on the Occ3D-nuScenes benchmark, where ViGT achieves comparable performance with supervised methods.

In this work, we introduce XSIM, a sensor simulation framework for autonomous driving. XSIM extends 3DGUT splatting with a generalized rolling-shutter modeling tailored for autonomous driving applications. Our framework provides a unified and flexible formulation for appearance and geometric sensor modeling, enabling rendering of complex sensor distortions in dynamic environments.
We identify spherical cameras, such as LiDARs, as a critical edge case for existing 3DGUT splatting due to cyclic projection and time discontinuities at azimuth boundaries leading to incorrect particle projection.
To address this issue, we propose a phase modeling mechanism that explicitly accounts temporal and shape discontinuities of Gaussians projected by the Unscented Transform at azimuth borders.
In addition, we introduce an extended 3D Gaussian representation that incorporates two distinct opacity parameters to resolve mismatches between geometry and color distributions.
As a result, our framework provides enhanced scene representations with improved geometric consistency and photorealistic appearance. We evaluate our framework extensively on multiple autonomous driving datasets, including Waymo Open Dataset, Argoverse 2, and PandaSet. Our framework consistently outperforms strong recent baselines and achieves state-of-the-art performance across all datasets.

Generalized vehicle trajectory prediction across diverse junctions, including urban intersections and roundabouts, remains a fundamental task in Cooperative Vehicle–Infrastructure Systems (CVIS). This study faces two key challenges: (1) Generalize across junctions with heterogeneous map semantics and traffic behavioral patterns, where the former arises from differences in road topologies and traffic regulations, and the latter reflects diverse behavioral intentions of road users; (2) Scenario-adaptive interaction modeling, where single-modality trajectory learning captures local spatio-temporal correlation, but lacks map constraint and direction-aware interaction contexts. To overcome these challenges, we propose G-VTM, a generalized vision-trajectory model. G-VTM models fine-grained behavioral patterns and relative spatial interaction from trajectory modality. At the vision modality, G-VTM captures global map semantics while modeling scenario- and direction-aware interaction based on intuitive visual perception. Experiments on multiple real-world datasets collected by unmanned aerial vehicles (UAVs) demonstrate that our method achieves strong generalized performance under heterogeneous traffic conditions. The code is provided at https://github.
com/zxyhaclyon/G-VTM.

Cross-task generalization (CTG) enables large language models (LLMs) to handle unseen tasks proficiently, enhancing their adaptability in real-world scenarios. However, existing methods relying on per-token dynamic routing to multiple trained LoRA adapters face high computational and GPU memory costs. Recent Representation Fine-Tuning (ReFT) enhances efficiency for single-task adaptation by editing only prefix and suffix token representations. However, the semantic ambiguity of tokens and absence of a self-guided mechanism for parameter selection in unseen tasks limits their application to CTG. To this end, we propose RaMod, a Representation-Aware Modularity framework to extend the ReFT paradigm to CTG through two novel components: (i) Dual-Modular Representation & Parameter Fine-tuning, which manipulates only a strategically chosen subset of hidden representations with modular interventions to guide the model toward solving unseen tasks; and (ii) Asynchronous Orchestrator, which proactively allocates and releases GPU memory for selected interventions, thereby minimizing storage overhead. Extensive experiments demonstrate that RaMod not only achieves superior CTG performance but also substantially reduces the overhead of the latest CTG baseline, achieving 83%, 100%, and 79% reduction in its additional prefill time, generation delays, and memory consumption relative to original LLMs.

Predicting ligand binding sites on protein surfaces requires capturing complex local geometries and satisfying physical constraints. Existing voxel-based methods suffer from high computational costs and rotation sensitivity, while standard point-cloud GNNs often lack geometric completeness—failing to distinguish chiral structures or subtle topological variations. In this work, we propose UniPocket, a novel E(3)-equivariant surface graph neural network. Inspired by recent advances in efficient geometric completeness, UniPocket constructs a Manifold-Aware Surface Encoder that utilizes local surface normals as virtual reference frames to capture complete geometric invariants without expensive high-order tensor products. Furthermore, we introduce a Ligand-Gated Message Passing mechanism to condition the surface features on the chemical semantics of the target ligand, and a Physics-Aware Vector Rejection Module that enforces steric constraints via orthogonal vector decomposition. Experimental results on standard benchmarks (PDBbind, COACH420, Holo4k) demonstrate that UniPocket achieves state-of-the-art performance, validating that geometric completeness and physical inductive biases are key to precise binding site detection.

In this paper, we revisit multimodal few-shot 3D point cloud semantic segmentation (FS-PCS), identifying a conflict in "Fuse-then-Refine" paradigms: the "Plasticity-Stability Dilemma." In addition, Contrastive Language-Image Pre-training (CLIP)'s inter-class confusion can result in semantic blindness. To address these issues, we present the Decoupled-experts Arbitration Few-Shot SegNet (DA-FSS), a model that effectively distinguishes between semantic and geometric paths and mutually regularizes their gradients to achieve better generalization. DA-FSS employs the same backbone and pre-trained text encoder as the baseline MultiModal Few-Shot SegNet (MM-FSS) to generate text embeddings, which can increase cost-free modalities' utilization rate and better leverage each modality's information space. To achieve this, we propose a Parallel Expert Refinement module to generate each modal correlation. We also propose a Stacked Arbitration Module (SAM) to perform convolutional fusion and arbitrate correlations for each modality pathway. The Parallel Experts decouple two paths: a Geometric Expert maintains plasticity, and a Semantic Expert ensures stability. They are coordinated via a Decoupled Alignment Module (DAM) that transfers knowledge without propagating confusion. Experiments on popular datasets (S3DIS, ScanNet) demonstrate the superiority of DA-FSS over MM-FSS. Meanwhile, geometric boundaries, completeness, and texture differentiation are all superior to the baseline. The code is available at: https://github.com/MoWenQAQ/DA-FSS/.

Remote sensing change detection (RSCD) aims to identify changed regions in bitemporal images. However, conventional one-step modeling suffers from performance degradation caused by imaging temporal differences (e.g., illumination disturbances, seasonal variations). To address this issue, we formulate the problem as an “adversarial attack and defense” paradigm. Then, instead of traditional adversarial training, we propose a brain-inspired deep network based on the drift diffusion model (DDM) decision-making mechanism for RSCD, which is rooted in cognitive neuroscience. By simulating brain decision processes, the network enhances model robustness significantly. Specifically, we design the multi-stage reasoning unit to simulate DDM’s iterative evidence generation, stochastic representation unit with reparameterization sampling to model stochastic diffusion in decision-making, decision aggregation unit to replicate DDM’s evidence accumulation, and loss-guided unit with multi-loss joint constraints to simulate directional drift. Experimental results verify the model’s superior performance on multiple datasets and their perturbed versions.

Spatiotemporal traffic forecasting currently faces dual challenges: capturing long-range periodic dependencies and managing the computational burden of increasingly complex deep neural network architectures. Mainstream models typically contain millions of parameters and struggle to handle long sequence historical data due to memory bottlenecks. To address these challenges, we present MiniST, a minimalist framework that decouples information content from sequence length by exploiting the intrinsic redundancy of traffic data. Instead of modeling dense point-wise dependencies, MiniST projects high-dimensional traffic history into a compact set of Orthogonal Basis Vectors, effectively distilling dominant traffic modes into a disentangled latent space. Furthermore, we introduce a Template-based Update Module (TUM), which models temporal dynamics by anchoring these latent bases to a set of learnable, time-invariant global templates. Extensive experiments on multiple large-scale datasets, including a network with over 50,000 nodes, demonstrate that MiniST achieves state-of-the-art performance in both short-term and long-term traffic forecasting, outperforming 22 benchmark models with improvements of up to 11.58%, while maintaining minimal parameter size and competitive efficiency.The source code is available at MiniST Repository.

Multimodal sentiment analysis relies on textual, acoustic, and visual signals, yet real-world data often suffer from modality missing and quality imbalance. Existing methods generate features for modality missing from available ones, but differences in expression mechanisms and sentiment dynamics across modalities may cause the generated features to deviate from true distributions and mislead prediction. In addition, unreliable modalities may dominate fusion, resulting in representation shift across modality combinations and unstable sentiment representations. To address these challenges, we propose a two-level reference alignment framework. The framework introduces stable references at the feature representation and sentiment decision levels to improve robustness under modality missing. First-level reference alignment leverages complete-modality samples to constrain representations and align different modality combinations into a shared sentiment space. Second-level reference alignment enforces cross-modal consistency at the decision level by suppressing unreliable modalities through prototype retrieval and voting. As a result, the framework maintains stable and reliable sentiment predictions under diverse missing-modality patterns. Experiments on CMU-MOSI and CMU-MOSEI show consistent improvements across various missing-modality settings. Under full-modality input, the proposed method achieves state-of-the-art performance, with ACC of 86.28% and 85.88%, and F1 of 86.24% and 85.86%.

Graphical User Interface (GUI) Agents, powered by large language and vision-language models, hold promise for enabling end-to-end automation in digital environments. However, their progress is fundamentally constrained by the scarcity of scalable, high-quality trajectory data. Existing data collection strategies either rely on costly and inconsistent manual annotations or on synthetic generation methods that trade off between diversity and meaningful task coverage. To bridge this gap, we present GUI-ReWalk: a reasoning-enhanced, multi-stage framework for synthesizing realistic and diverse GUI trajectories. GUI-ReWalk begins with a stochastic exploration phase that emulates human trial-and-error behaviors, and progressively transitions into a reasoning-guided phase where inferred goals drive coherent and purposeful interactions. Moreover, it supports multi-stride task generation, enabling the construction of long-horizon workflows across multiple applications. By combining randomness for diversity with goal-aware reasoning for structure, GUI-ReWalk produces data that better reflects the intent-aware, adaptive nature of human-computer interaction. We further train Qwen2.5-VL-7B on the GUI-ReWalk dataset and evaluate it across multiple benchmarks, including Screenspot-Pro, OSWorld/OSWorld-G, UI-Vision, AndroidControl, and GUI-Odyssey. Results demonstrate that GUI-ReWalk enables superior coverage of diverse interaction flows, higher trajectory entropy, and more realistic user intent. These findings establish GUI-ReWalk as a scalable and data-efficient framework for advancing GUI agent research and enabling robust real-world automation.

Test-time scaling (TTS) has demonstrated remarkable potential in enhancing the reasoning capabilities of Large Language Models (LLMs) and Large Vision-Language Models (LVLMs). However, its application has primarily been limited to domains such as mathematics and programming, owing to their reasoning-intensive nature and the ease of result verification. Its utility in other knowledge-intensive fields, such as medicine and general scientific research, remains underexplored.
To bridge this gap and unlock the potential of TTS in broader domains, we propose Cross-Domain TTS, a novel framework that enables task-tailored scaling. This framework consists of two key components: a conformal prediction-based cold-start strategy and an information-gain-based dynamic reasoning adjustment. The CP-based cold-start strategy guides the model's initialization during test-time scaling based on conformal prediction theory, while the information-gain-based dynamic reasoning adjustment guides the model's reasoning progress through a progress vector according to the information gain of reasoning steps. We conducted experiments using LLMs and LVLMs on cross-domain benchmarks. Our results demonstrate that the proposed framework consistently improves performance across various domain-specific datasets. For instance, in the medical domain, it achieves an improvement of up to 17\% in pass@1 accuracy while reducing inference latency and saving up to 30\% in token consumption. Code is available at https://github.com/Yan0613/Cross-Domain-TTS.

Multimodal reasoning requires a path that retains integrity over a wide range of constraints, from visual grounding to logic consistency. However, the current Process Reward Models (PRMs) focus on heuristically defined rewards that equally weigh these factors, which may lead to the concealment of individual dimension failures (such as visual hallucinations) by the dominating factors, without guaranteeing the validity of the reasoning process in general. Therefore, to overcome the limitation, the paper proposes the concept of Multimodal Multi-Dimensional Scalarization Process Reward Modeling (MMS-PRM), a paradigm specifically developed to enforce the worst dimension’s robustness in multimodal reasoning. Specifically, a hierarchical fine-grained reward space is developed to represent the multimodal risks in the reasoning tasks, and a Chebyshev-based Monte Carlo Tree Search (MCTS) algorithm is introduced, in which the primary focus during the path searching is given to the worst-performing dimension. Moreover, a curriculum-based Direct Preference Optimization (DPO) approach is developed to gradually learn the balanced reasoning skills in the policy. The experimental results show that, without the dimension collapse issue, the MMS-PRM approach significantly improves the reliability of the multimodal reasoning performance and reaches competitive results in various challenging tasks. The code is available at https://github.com/leibniz-Man/MMS-PRM

Empowered by large language models (LLMs), multi-agent systems (MAS) have shown significant potential in code generation by simulating collaborative workflows. However, we identify a collaboration degeneration phenomenon, where one agent dominates while others remain disengaged, occurring in 38.4% of non-routine HumanEval tasks. Surprisingly, introducing competition among LLM agents eliminates this degeneration and sparks innovation in generated code. Inspired by this catfish effect, we posit that competition helps prevent collaboration degeneration. We thus propose C3, a Controllable Competitive Collaboration framework featuring two novel mechanisms: Centralized Auction-Based Competition (CAB) for role allocation via bidding and
Decentralized Communication-Aware Competition (DCC) for solution refinement through opponent-aware adaptation.
Evaluated on 70 complex projects from extended SoftwareDev, C3 reduces collaboration degeneration from 32.4% to 12.8%, while enhancing code innovation and diversity. Human evaluations further confirm C3’s superiority in functionality, maintainability, and robustness over purely collaborative or competitive baselines.

Spiking Neural Networks (SNNs) offer notable advantages in biological plausibility and energy efficiency, making them promising candidates for building low-power Transformers. However, existing Spiking Transformers largely adhere to a passive reactive paradigm, which struggles to focus on task-relevant information and incurs substantial computational overhead when processing redundant visual data. To overcome this fundamental yet underexplored limitation, we propose SAFformer, a novel Spiking Transformer architecture based on an active predictive filtering paradigm. Inspired by the brain’s predictive coding mechanism, SAFformer actively suppresses predictable signals and focuses on salient visual features. Extensive experiments show that SAFformer establishes new state-of-the-art performance on CIFAR-10/100 and CIFAR10-DVS. Remarkably, on ImageNet-1K, it achieves 80.44% Top-1 accuracy with only 26.58M parameters and an energy consumption of 5.88 mJ, demonstrating an exceptional balance between accuracy and efficiency.

Training a unified language model that adapts between intuitive System 1 and deliberative System 2 remains challenging due to interference between their cognitive modes. Recent studies have thus pursued making System 2 models more efficient. However, these approaches focused on output control, limiting what models produce. We argue that this paradigm is misaligned: output length is merely a symptom of the model's cognitive configuration, not the root cause. In this work, we shift the focus to capability control, which modulates how models think rather than what they produce. To realize this, we leverage existing Instruct and Thinking checkpoints through dynamic parameter interpolation, without additional training. Our pilot study establishes that linear interpolation yields a convex, monotonic Pareto frontier, underpinned by representation continuity and structural connectivity. Building on this, we propose DAMI (DynAmic Model Interpolation), a framework that estimates a query-specific Reasoning Intensity lambda(q) to configure cognitive depth. For training-based estimation, we develop a preference learning method encoding accuracy and efficiency criteria. For zero-shot deployment, we introduce a confidence-based method leveraging inter-model cognitive discrepancy. Experiments on five mathematical reasoning benchmarks demonstrate that DAMI achieves higher accuracy than the Thinking model while remaining efficient, effectively combining the efficiency of System 1 with the reasoning depth of System 2.

Time series forecasting is vital in diverse sectors such as energy and transportation, where non-stationary dynamics are deeply intertwined with external events in other modalities such as texts. However, incorporating natural language-based external events to improve non-stationary forecasting remains largely unexplored, as most approaches still rely on a single modality, resulting in limited contextual knowledge and model underperformance. Enabling fine-grained multimodal interactions between temporal and textual data is challenged by two fundamental issues: (1) the gap in modeling interactions among discrete external events and continuous time series in a unified framework; (2) classical uniform diffusion timestep ignores event-induced non-stationary variability, leading to imbalanced denoising difficulty across diffusion stages. In this work, we propose event-aware non-stationary time series forecasting EventTSF, an autoregressive diffusion framework that integrates historical time series and textual events via step-wise diffusion. To mitigate the imbalanced denoising difficulty of uniform timestep sampling, EventTSF uses an event-aware flow-matching timestep conditioned on event semantics. Extensive experiments on 7 synthetic and real-world datasets show that EventTSF outperforms 12 non-stationary time series forecasting baselines, achieving average gains of 41.3% in probabilistic forecasting and 27.5% in deterministic forecasting across all evaluation metrics.

While recent video generative models can synthesize high-fidelity videos, they struggle to portray plausible physical interactions and the resulting state transitions, a critical bottleneck for applications in robotics and VR/AR. To address this, we introduce a framework to generate a scalable synthetic dataset of controllable interactions.
Our pipeline leverages a structured taxonomy and state-of-the-art image editing models to create explicit `start' and `end' state images, which serve as visual anchors for the interaction. To generate a seamless video utilizing these anchors, we propose State-Guided Sampling (SGS), a novel sampling technique that mitigates artifacts common in naive conditional generation. Furthermore, we develop and validate a new automated evaluation system that aligns with human judgments to ensure data quality.
Experiments show that fine-tuning a base model on our dataset significantly enhances its ability to generate plausible interactions. The dataset, code, and evaluation tools will be released.

Diffusion-based image compression has exhibited robust performance. However, most existing methods primarily emphasize spatial domain, causing frequency dependencies to be learned only implicitly. We revisit this problem from a frequency perspective, and observe pronounced heterogeneity between global and local frequency dependencies. Global frequency patterns describe the energy distribution over the entire image, whereas patch level frequency relations govern the coupling of local details. These two forms of dependency differ substantially in both scale and semantic level, yet most methods overlook this distinction. To address this issue, we propose a learning heterogeneous global local frequency dependencies in diffusion-based image compression which uses Fourier and Mamba jointly models both global and local frequency correlations(FMDiff). The core of FMDiff is the dual branch FMBlock. In the frequency branch, features are decomposed into magnitude and phase, then fed into Mamba separately. Its scanning mechanism captures cross frequency coupling within each patch while aggregating long range frequency context along the sequence. Magnitude phase interactive modulation is then used to explicitly restore spectral information corrupted by compression. The spatial branch provides high level semantic constraints and is fused with the frequency branch during diffusion reconstruction. Extensive experiments show that FMDiff consistently improves performance across multiple datasets.

Achieving fairness in machine learning models while maintaining high accuracy is an important but complex task, especially when handling multiple sensitive attributes. Traditional fairness methods often struggle to eliminate bias within subgroups divided by sensitive attributes. Several key challenges have been identified in this context: (1) Multiple sensitive attribute scalability challenge, where methods fail to ensure fairness as the number of sensitive attributes increases, despite scenarios with multiple sensitive attributes being prevalent in real-world applications; (2) Multiple objective optimization conflict challenge, where simultaneously optimizing for accuracy, fairness, and other relevant objectives leads to conflicting gradient updates, causing suboptimal performance. To address these challenges, we propose BAMFair, a Barycenter Aligned Mediation framework for fairness across multiple sensitive attributes. It comprises two core modules: a Global Barycentric Alignment (GBA) module and a Nash Fairness Mediator (NFM) module. Specifically, GBA innovatively introduces a global fair barycenter and minimizes the distances from subgroups divided by sensitive attributes to it, providing a scalable and efficient solution for fairness optimization across multiple sensitive attributes. Subsequently, NFM negotiates an agreement among inconsistent gradient updates between different objectives. Extensive experiments on four real-world datasets validate that BAMFair outperforms state-of-the-art methods in scenarios with multiple sensitive attributes.

Diffusion models show significant potential for low-light image enhancement. However, this task requires satisfying human perceptual preferences and content fidelity transcending simple brightness and color improvement. Existing methods rely on heuristic physical priors or incorporate perceptual metrics directly into timestep-wise training objectives. Such proxy constraints fail to provide reliable trajectory-level guidance for perceptual alignment. Furthermore, they often lead to artifacts or unnatural visual effects in this ill-posed inverse problem. To address these issues, we propose an unsupervised low-light enhancement framework based on Group Relative Policy Optimization (GRPO), which utilizes perceptual preferences to directly optimize the diffusion policy. We introduce a Sliding Window Hybrid ODE-SDE Sampling strategy that confines stochasticity to dynamic sub-intervals, thereby achieving efficient coarse-to-fine exploration and precise advantage attribution. To meet strict fidelity constraints, we construct a Synergistic Perception-Fidelity Reward and introduce an Independent Advantage Estimation strategy to mitigate signal collapse and gradient suppression in multi-objective optimization. Furthermore, we design a Temporal-Aware Dynamic Weighting Mechanism that adaptively adjusts perceptual weights across denoising stages to balance visual enhancement with structural preservation. Extensive experiments on multiple real-world benchmarks demonstrate that our method effectively improves the perceptual performance of existing generative models, yielding results that better align with human aesthetics.

Gradient inversion attacks (GIAs) have shown that shared gradients in federated learning leak private training data. However, current textual GIAs fail in large batch size, as simply increasing batch size can serve as a stable defense against such attacks. In this paper, we propose CDO-GIA, a novel textual gradient inversion attack via continuous-discrete optimization that extends the maximum effective batch size from 4 to 8 for reconstructing text from gradient. In continuous optimization phase, CDO-GIA incorporates a dynamic weight decay mechanism to alleviate embedding homogenization caused by embedding regularization at large batch sizes, thereby enhancing unigrams reconstruction accuracy. Moreover, our method introduces a tabu beam search mechanism guided by the language model prior during discrete optimization. This design facilitates fine-grained exploration of high-dimensional token spaces via precise token order adjustment, thus reconstructing semantically coherent sequences. By alternating optimization between continuous and discrete phases, CDO-GIA effectively doubling practical attack limit of prior textual GIAs. Extensive experiments are conducted on three binary text classification datasets. The experimental results indicate that CDO-GIA surpasses all baseline methods. With a batch size of 8, it achieves performance gains of 25.01%, 155.64%, and 22.72% over the baselines in terms of Rouge-1, Rouge-2 and Rouge-L scores.

Identifying cancer driver genes (CDGs) requires integrating heterogeneous biological networks, each encoding distinct mechanistic insights into tumorigenesis. Existing multi-network methods typically fuse views at the feature level or enforce uniform representations, which obscures network-specific signals and limits interpretability. To address this, we propose LogicFusion, a novel differentiable framework that treats each biological network as an independent probabilistic evidence source and explicitly learns how to combine them using logical reasoning. Specifically, LogicFusion maps the output of each view into probabilistic space as independent evidence, and learns a set of logical rules in a differentiable manner. These rules adaptively select and fuse these view evidences through logical conjunction (AND) and disjunction (OR). The final prediction is then obtained via gene-specific weighting. LogicFusion enables both high-accuracy CDG identification and interpretable attribution of predictions to specific networks. Experiments on pan-cancer and cancer-specific datasets show that LogicFusion consistently outperforms state-of-the-art methods, while providing transparency in multi-view genomic reasoning through learned logical rules.

Multi-view anchor graph clustering has emerged as a high-efficiency paradigm of multi-view learning. However, how to design an effective anchor alignment mechanism within this framework remains an open challenge, which is formally termed the Anchor-Unaligned Problem (AUP). Current research fails to adequately address two pivotal aspects of this challenge: first, the construction of anchor graphs and the alignment of anchors are two independent stages, overlooking their potential synergistic reinforcement; second, selecting anchors from different views as an alignment baseline often renders the clustering performance highly sensitive to the baseline choice. To address these issues, we propose a unified framework termed One-Step Self-Aligned Anchor Learning for Multi-View Clustering (OSAA-MVC). Departing from conventional two-stage strategies, we integrate anchor alignment and anchor graph construction into a joint optimization process, thereby enabling their mutual reinforcement to improve clustering performance. To avoid the baseline selection issue, we introduce a novel mixed anchor strategy, which effectively bypasses the necessity of manual baseline selection while simultaneously capturing both view-specific and cross-view information. This strategy can stabilize clustering performance at a consistently high level. Extensive experiments demonstrate the superior efficiency and effectiveness of our proposed method compared to state-of-the-art competitors. The code is available at https://github.com/Jiamiao2024/OSAA-MVC.

Sleep is a vital physiological process, and its quality directly impacts human health. Although deep learning-based automated sleep staging methods have achieved significant progress, the studies still face limitations in fully mining spatio-temporal graph dependencies. Existing methods face challenges in capturing implicit spatial topology among multi-channel signals and efficiently fusing multi-level features in long-sequence modeling. To address these challenges, this paper proposes a novel hybrid deep learning framework for sleep staging that progressively and explicitly integrates spatial topology modeling, cross-level feature fusion, and global temporal aggregation. By decoupling shallow and deep spatial features and hierarchically organizing feature interactions, the proposed framework effectively captures implicit spatial dependencies while maintaining computational efficiency in long-sequence modeling. Extensive experimental results on multiple sleep staging datasets (83.1% accuracy, 80.1% F1-score on ISRUC-S1, 84.0% accuracy, 83.8% F1-score on ISRUC-S3, and 85.9% accuracy, 83.7% F1-score on Sleep-EDF-153) demonstrate that the proposed method achieves competitive accuracy with significantly reduced computational latency, highlighting its strong potential for mobile and real-time sleep monitoring applications.

Graph clustering is essential in graph analysis for revealing structural patterns and node communities. Despite recent advances in self-supervised contrastive learning that have improved clustering via structural and attribute signals, existing methods still struggle to flexibly capture high-order local structures and often overlook global semantics in complex graphs. These limitations lead to suboptimal node representations, especially in real-world graphs with fragmented structures and ambiguous cluster boundaries. To address these limitations, a contrastive graph clustering framework is proposed to jointly integrate multi-scale local structures with global semantics via attention mechanisms. At the local level, GNN-based topological signals extracted from multiple propagation depths are adaptively fused through attention-based weighting to capture multi-scale neighborhood features. At the global level, semantic prototypes derived from dynamically evolving cluster centers are adaptively aggregated through attention to guide node representations and enhance inter-cluster separability. The model is trained under a dual-view contrastive learning paradigm with a hybrid objective that combines instance-level and structure-aware losses to improve representation robustness and discrimination. Experiments on eight real-world graph datasets demonstrate that our method achieves competitive clustering performance. Code is available at https://github.com/vege12138/w2.

Unlike test-time adaptation (TTA), open-set TTA (OSTTA) aims to robustly adapt to in-distribution (ID) domain shifts while suppressing the detrimental impact of out-of-distribution (OOD) samples encountered at test time. To this end, we propose a novel OSTTA approach named Noise-Immune Self-Purification (NISP). By exploiting the zero-shot priors of pre-trained vision-language models (VLMs), NISP advances from coarse pseudo-labeling to fine-grained, noise-resilient adaptation. Technically, we first introduce a dual-cluster Bayesian Gaussian mixture model to fit VLMs-derived scores, achieving coarse pseudo-ID/OOD separation via posterior-risk thresholding. Subsequently, NISP constructs a noise-immune fine-grained adaptation where the adapter enforces consensus-discrepancy constraints to refine coarse pseudo-labels and suppress noise propagation. We then devise the Jaccard Consistency Score for OOD discrimination. Overall the coarse-to-fine pipeline enables rigorous self-purification and robust online adaptation. Theoretically we show that consensus-discrepancy losses mitigate the deleterious effects of noise. Empirically, NISP achieves state-of-the-art results across multiple OSTTA benchmarks, validating its efficacy. The code is available at https://github.com/njustkmg/IJCAI26-NISP.

Accurate traffic flow prediction is fundamental to Intelligent Transportation Systems (ITS). However, traffic dynamics exhibit inherent multi-scale heterogeneity, where stable global trends are often masked by stochastic local fluctuations. Existing methods struggle to reconcile these conflicting resolutions, leading to sub-optimal forecasting. To address this, we propose the Multi-Scale Spatial-Temporal Diffusion Transformer (MS-STDT). Deviating from previous diffusion approaches that treat generation as a monolithic task, we reframe the forward diffusion process as an intrinsic temporal coarse-graining operation. By leveraging the data's multi-scale hierarchy as a structural anchor, we introduce a coarse-to-fine latent guidance strategy that enables the model to reconstruct stable global trends before refining fine-grained details. This ensures physical consistency and generative stability without requiring external labels. Extensive experiments across six real-world datasets confirm that MS-STDT performs the best, demonstrating significant improvements in predictive accuracy and zero-shot robustness against sensor failures. We provide code and data at https://github.com/ZetaoLiPhD/MS-STDT.

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.

Hand Object Interaction (HOI) generation provides an efficient solution for virtual reality simulation and embodied AI deployment.Recent studies have explored instruction-driven HOI synthesis, yet they overlooked the fine-grained interactive contact and struggled with robust generalization in data-scarce scenarios. In addition, existing datasets are frequently constrained with limited object categories and monotonous motion patterns. To address these limitations, we first integrate a large-scale HOI dataset, namely HOI-X, which contains diverse objects, rich motion patterns, and part-level contact annotations. Along with HOI-X, we propose A3fford-HOI, a fine-grained and generalizable Hand Object Interaction framework with Anatomy-Aligned Affordance Disentanglement. Specifically, we define a Disentangle-2-Interact paradigm in A3fford-HOI. Considering the various interactions of distinct hand anatomical structures,
in the Disentangle stage, we devise an Anatomy-Aligned Affordance Predictor, which leverages the external plausible physical priors in 3D multimodal large language models for affordance disentanglement prediction. Then, the disentangled affordance is employed into the Interact stage, i.e. Affordance Driven Interactor. The interactor integrates textual instructions, object geometric features, and the external knowledge enhanced (MLLMs) affordance via a Diffusion Transformer (DiT) for generalizable HOI generation. Extensive experiments demonstrate that A3fford-HOI maintains superior generation quality and contact plausibility for fine-grained and generalizable hand object interaction.

Knowledge distillation has become a prevalent technique for deploying efficient recommender systems, enabling lightweight student models to approximate the performance of larger teachers. However, we identify a critical issue: distillation systematically amplifies popularity bias, as student models inherit and intensify the popularity-driven shortcuts encoded in teachers trained on interaction data dominated by popular items. To address this limitation, we propose GUIDE (Geodesic aware Unbiased Instructive Distillation with Experts), a collaborative distillation framework that incorporates domain-specific debiasing experts alongside the global teacher. GUIDE tackles two key challenges in this paradigm. First, for expert routing, we introduce Spherical Expert Alignment, which conducts expert-student matching on the spherical manifold with geodesic distance optimization, eliminating magnitude-induced bias and ensuring stable gradient flow. Second, for context fusion, we design a Meta-Debiasing Gate that dynamically arbitrates teacher-expert influence based on real-time user-item context through end-to-end meta-learning. Extensive experiments on multiple real-world datasets demonstrate that GUIDE significantly mitigates popularity bias while preserving recommendation accuracy, with state-of-the-art trade-offs among efficiency, accuracy, and fairness.

Recent generative AI renders multimodal misinformation structurally harder to detect, making reliable detection dependent on semantic verification grounded in verifiable evidence. However, current benchmarks often fail to isolate true semantic checking from superficial shortcut exploitation. We introduce Falsdo, a diagnostic benchmark designed to make robustness and auditability identifiable. Constructed via a heterogeneous web-mining pipeline, Falsdo provides instruction-conditioned grounding and formalizes a counterfactual artifact-control protocol. This stress test reveals a critical failure: when low-level generation traces are equalized, detector performance degrades, indicating reliance on artifacts rather than robust verification. To enable reproducible diagnosis, we propose DREA, a failure-aligned evidence-auditing baseline. DREA specifies evidence acquisition with explicit noise control and implements constrained channel-wise auditing with reliability-aware late fusion. Experiments demonstrate that Falsdo reliably exposes shortcut dependence, while DREA improves instruction-level grounding (Joint-F1) and minimizes degradation under artifact-controlled settings.

Explainable Recommendation (ER) aims to enhance recommendation transparency and prediction accuracy by providing faithful and persuasive explanations. However, Multi-Modal Multi-Interest Explainable Recommendation (MMER) is particularly challenging in two aspects: effectively utilizing diverse multi-modal information to construct reliable semantic evidence and precisely identifying the most relevant user interest to generate faithful explanations. Most previous methods fail to effectively exploit visual information to provide reliable evidence and primarily rely on a single unified representation of user preferences, making it difficult to distinguish diverse user interests and generate faithful explanations. To fill this gap, we propose Joint Multi-Modal Multi-Interest Profiling and Preference-Grounded Reasoning (PRIME) for solving the MMER problem. Specifically, PRIME first organizes items into interest-aware clusters to facilitate interest disentanglement. Based on these clusters, it constructs explicit multi-modal multi-interest user profiles and a comprehensive global item profile to capture fine-grained preferences and distinguish diverse user interests. Furthermore, PRIME performs preference-grounded reasoning by retrieving the most relevant user interest for each item, enabling faithful explanations and accurate predictions. Our experiments on three real-world datasets demonstrate that PRIME outperforms the state-of-the-art methods.

While deep learning has significantly enhanced robotic perception in agriculture, autonomous decision-making in dense and occluded environments remains a persistent challenge. This paper proposes a VR-based expert motion capture framework to bridge this gap by integrating high-fidelity virtual environments with human expertise. By reconstructing a realistic tomato greenhouse in Unity and utilizing 27-point motion capture suits, we captured multi-dimensional expert demonstration data encompassing trajectories, postures, and velocities. In the VR environment, ``hard cases" featuring frequent collisions can be selectively generated at a higher frequency, allowing experts to provide concentrated demonstrations of specialized postures that markedly accelerate algorithmic learning efficiency. Crucially, the virtual framework enables multiple experts to repetitively harvest identical targets to match optimal trajectories—a process precluded in reality by the destructive nature of harvesting. Moreover, it bypasses biological growth and seasonal constraints, facilitating continuous, high-frequency experimentation independent of crop maturation. These expert data were subsequently integrated into a multi-task reinforcement learning model as experience replay and reward templates. Experimental results demonstrate that our expert-driven algorithm significantly outperforms baseline methods in harvesting efficiency, collision avoidance, and stability, establishing VR-guided motion capture as a robust and scalable paradigm for advancing agricultural robotics.

Federated optimization under data heterogeneity presents a significant challenge, often leading to suboptimal model performance. While numerous methods aim to replicate the ideal performance of centralized training, they frequently fall short in highly heterogeneous settings. In this paper, we introduce HaFedHo, an adaptive objective rectification method that harmonizes local training with the ideal data-centralized objective, requiring minimal modifications to the standard federated learning framework. HaFedHo operates by first decoupling the centralized objective and then employing a dynamic Taylor series expansion to accurately estimate the global objective for each client. Our theoretical analysis shows that the estimation error provably converges to zero as training progresses. Furthermore, extensive experiments on real-world datasets demonstrate that HaFedHo surpasses state-of-the-art methods, including SCAFFOLD, MimeLite, and FedDyn, in both test accuracy and communication efficiency. Notably, HaFedHo maintains its superior performance even with a client participation rate as low as $0.2\%$ in severely heterogeneous environments.

Vision-Language-Action (VLA) models bridge multimodal reasoning with physical control, but adapting them to new tasks with scarce demonstrations remains unreliable. While fine-tuned VLA policies often produce semantically plausible trajectories, failures often arise from unresolved geometric ambiguities, where near-miss actions lead to divergent execution outcomes under limited supervision. We study few-shot VLA adaptation from a generation-selection perspective and propose a novel framework, VGAS (Value-Guided Action-chunk Selection). It performs inference-time best-of-N selection to identify action chunks that are both semantically faithful and geometrically precise. Specifically, VGAS employs a finetuned VLA as a high-recall proposal generator and introduces Q-Chunk-Former, a geometrically grounded Transformer critic to resolve fine-grained geometric ambiguities. In addition, we propose Explicit Geometric Regularization (EGR), which shapes a discriminative value landscape to preserve action ranking resolution among near-miss candidates while mitigating value instability under scarce supervision. Experiments and theoretical analysis demonstrate that VGAS consistently improves success rates and robustness under limited demonstrations and distribution shifts. Our code is available at https://github.com/Jyugo-15/VGAS

Conversational Aspect-based Sentiment Quadruple Analysis (DiaASQ) needs to capture the complex interrelationships in multiple rounds of dialogues. Existing methods usually employ simple Graph Convolutional Networks (GCN), which introduce structural noise and fail to consider the temporal sequence of the dialogues, or use standard RoPE, which implicitly captures relative distances in a flat sequence but cannot clearly separate the token-level syntactic order from the utterance-level progression, and may suffer from the Distance Dilution problem. To address these issues, we propose a new framework that combines Thread-Constrained Directed Acyclic Graph (TC-DAG) and Discourse-Aware Rotary Position Embedding (D-RoPE). Specifically, TC-DAG filters out cross-thread noise based on thread constraints, maintains global connectivity through root anchoring, and incorporates the temporal sequence of the dialogues. D-RoPE aligns multi-layer semantics using dual-stream projection and multi-scale frequency signals, captures thread dependencies using tree-like distances, and alleviates the token-level Distance Dilution problem by incorporating utterance-level progressions. Experimental results on two benchmark datasets demonstrate that our framework achieves state-of-the-art performance.

Open-vocabulary 3D understanding aims to align 3D representations with a unified vision-language semantic space. However, existing methods suffer from the challenge of structural asymmetry caused by sparse observations and holistic geometries. Additionally, the inherent semantic chasm between discrete coordinates and abstract natural language hinders cross-modal mapping. This work introduces SpecBridge, a 3D-2D-Text pre-training framework that leverages CLIP priors as a foundational bridge to connect three modalities by synergizing spectral graph theory with transitive semantic learning. The method consists of two main sub-modules. First, Spectral Eigen-Modal Alignment is presented to construct cross-modal correlations within the intrinsic eigen-spectral space via Laplacian eigendecomposition. By aligning low-frequency geometric harmonics with CLIP-guided observation inference, it maintains structural consistency between 3D geometries and 2D projections. Second, a Transitive Spectral-Semantic Alignment method is developed to establish a 3D→2D→Text propagation chain across the CLIP priors bridge. It distills dense CLIP priors into 3D representations through transitive distillation, effectively mitigating the semantic chasm between geometry and language. Extensive experiments confirm that the proposed SpecBridge demonstrates state-of-the-art performance by overcoming challenges triggered by modality discrepancies.

High-fidelity reconstruction of mel-spectrograms from EEG signals remains a formidable challenge, primarily due to the inherent inter-subject variability of neural patterns and the semantic gap between heterogeneous feature representations of these two modalities. To alleviate both issues, this paper proposes a Hierarchical Feature Fusion Network with bi-phase subject IDentifier modulation (HFFN-ID) for reconstructing mel-spectrograms from EEG signals. The primary improvements of the HFFN-ID framework are from two aspects, a Binary-Phase Subject-ID Modulation (BiPSM) mechanism for explicit subject conditioning and a Condition-guided Hierarchical Fusion (CHF) component for dynamic multi-layer feature synthesis, which respectively aim to address the problems of inter-subject variability in neural patterns and heterogeneous cross-modality feature fusion. Moreover, a speech envelope feature-based pre-training strategy is incorporated to initialize the parameter space, inspired by the shared low-level representations across different speech features. On the SparrKULee dataset, HFFN-ID establishes a new benchmark with a pearson correlation coefficient of 0.0723, representing a substantial 44% relative improvement over existing baseline. Importantly, HFFN-ID is highly efficient, achieving a 38.7% parameter reduction and a 2.5× training speedup. These results highlight the effectiveness of subject-conditioned and hierarchically fused architectures for advancing high-fidelity neural speech decoding.

With the rapid growth of digital data, real-world applications increasingly involve hierarchical information that combines static attributes with dynamic records. Modeling such heterogeneous data in a unified and generalizable manner remains challenging. Existing approaches often rely on extensive manual design, are tightly coupled to specific data schemas, and typically process static and dynamic attributes in isolation, thereby overlooking their implicit interactions. We propose UniSAGE, a unified framework for modeling data with both static and dynamic attributes. UniSAGE constructs a global attribute graph that represents hierarchical and temporal relationships in a unified structure. To ensure representational consistency, it introduces two orthogonal parameter subspaces that jointly support static aggregation and dynamic reasoning within a shared semantic space. Building on these unified representations, UniSAGE further enables task-specific interaction between static and dynamic attributes via a lightweight hyper-structure mechanism. UniSAGE is fully automated, robust to evolving data schemas, and capable of capturing complex cross-attribute dependencies. Extensive experiments on multiple public benchmarks and a real-world financial behavior dataset demonstrate that UniSAGE consistently outperforms existing methods, achieving performance improvements of over 10% on several tasks. Our code is available at https://github.com/zjunet/UniSAGE.

Efficient transfer learning methods for large-scale vision–language models (e.g., CLIP) enable strong few-shot transfer, yet existing adaptation methods follow a fixed fine-tuning paradigm that implicitly assumes a uniform importance of the image and text branches, which has not been systematically studied in image classification. Through extensive analysis, we reveal a Branch Bias issue in vision–language image classification: adapting the image encoder does not always improve performance under out-of-distribution settings. Motivated by this observation, we propose A₃B₂, an Adaptive Asymmetric Adapter that alleviates Branch Bias in few-shot learning. A₃B₂ introduces Uncertainty-Aware Adapter Dampening (UAAD), which automatically suppresses image-branch adaptation when prediction uncertainty is high, enabling soft and data-driven control without manual intervention. Architecturally, A₃B₂ adopts a lightweight asymmetric design inspired by mixture-of-experts with Load Balancing Regularization. Extensive experiments on three few-shot image classification tasks across 11 datasets demonstrate that A₃B₂ consistently outperforms 11 competitive prompt- and adapter-based baselines.

In Partially Cooperative Markov Games (PCMG), agents need to learn effective cooperation under individual rewards, with the key challenge lies in leveraging these rewards to enhance team benefits optimally. Model aggregation (MA) is a promising solution due to its simplicity and efficiency, yet it suffers from instability caused by the volatility of individual policies trained with individual rewards and the non-asymptotic nature of aggregation updates. To address these challenges, we propose Two-Stage Model Aggregation (TSMA), a novel multi-policy MA framework for multi-agent reinforcement learning (MARL) that leverages individual policies to enhance team cooperation. Specifically, to enhance the stability and expressiveness of individual policies, each TSMA agent trains two individual policies (trained using the same individual reward) with parameter-level variation and combines them via adaptive weighted aggregation. The aggregated individual policy is then integrated with the team policy (trained using team reward), with integration weights dynamically adjusted over training.
Additionally, L2 regularization is applied during the data-driven training to align their parameter distributions across policies, mitigating instability from non-asymptotic aggregation. As a general framework, TSMA is compatible with standard actor-critic MARL algorithms. We integrate TSMA with MAPPO and MADDPG, and evaluate them on challenging PettingZoo benchmarks, demonstrating significant improvements in PCMG. The code is available at: https://github.com/RL-DLMU/TSMA.

Few-shot medical image segmentation relies on dense, boundary-sensitive prototype matching, yet common pre-training objectives mainly optimize global alignment or reconstruction, creating an objective gap that hurts boundary delineation and increases adaptation cost. This raises the question: how to pre-train representations intrinsically matchable for episodic FSS while requiring minimal adaptation-induced re-organization? We introduce a Drift-Gap diagnostic to quantify intrinsic dense-matching misalignment and adaptation-induced feature drift. Guided by this lens, we propose BOG-PRETRAIN, combining Reliability-Gated Alignment to mitigate noisy report supervision, Semantic-Guided MIM to emphasize boundary-informative regions, and Dual Consistency Regularization to stabilize episodic metric geometry. Across five benchmarks (1/4/16-shot), BOG-PRETRAIN improves mean Dice by +15.5/+15.9/+12.5 points over best priors and reduces mean HD95 by 0.9/4.6/10.1; it achieves the lowest Drift (0.052 vs. 0.155 baseline) and Gap_PT (0.256), with ablations confirming the components' complementarity.

Open‑vocabulary video instance segmentation (OV‑VIS) couples spatial‑temporal reasoning with language grounding, yet its adversarial robustness has remained unexplored.
We present the Dual-Objective Triggers (DOT), the first transferable attack on OV-VIS that simultaneously exploits the vision–language coupling and temporal coherence. DOT deploys a Dual Semantic Perturbation Module that overlays two complementary triggers: a Semantic Suppression Trigger erases the alignment between the true object and the query, while a Plausible Replacement Trigger steers the tracker toward a phantom trajectory that is visually plausible and text‑consistent. To amplify cross‑model transferability without sacrificing perceptual fidelity, we introduce Phase‑Guided Adversarial Training, which injects perturbations primarily in the phase spectrum while blending amplitudes with clean references. Extensive experiments on four state‑of‑the‑art OV‑VIS implementations demonstrate that DOT reduces mAP by up to 69.3\% and raises attack success rate by up to 98\%, outperforming the strongest baselines by a factor of 1.6$\times$ on average, while maintaining a PSNR of 52.49 dB, thus exposing critical security vulnerabilities and laying a foundation for future research on robust and trustworthy vision–language systems.

Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute–object compositions by leveraging shared knowledge from seen primitives. While recent works have begun to incorporate test-time adaptation to mitigate performance degradation caused by label-space distribution shifts, they suffer from primitive-level knowledge underutilization and cross-modal semantic asymmetry, hindering robust understanding of novel compositions. To address these issues, we introduce MLDA, a test-time adaptation framework for CZSL that jointly derives multi-level prototypes and performs dynamic vision–language alignment. Specifically, we construct and progressively update composition- and primitive-level prototype sets to accumulate structured semantic knowledge. By augmenting test inputs with diverse visual perspectives and enriched compositional descriptions, MLDA leverages optimal transport to achieve consistent matching of semantically salient information across modalities. Furthermore, composition activation scores are dynamically updated over the test stream to emphasize informative compositions and suppress irrelevant ones. Extensive experiments are conducted to verify the effectiveness of the proposed method. The code is available at https://github.com/keepgoingjkg/MLDA.

Structured output generation is increasingly critical for real-world AI systems, particularly in on-device settings where small language models (0.5B–8B parameters) must produce machine-executable outputs under strict latency and privacy constraints. Although constrained decoding provides formal guarantees of structural validity without retraining, its effectiveness across different tasks, models, and constraint formalisms remains insufficiently understood. We introduce StructureBench, a comprehensive benchmark for structured generation on edge devices, covering JSON and tool-call generation, code synthesis, mathematics, science, and domain-specific languages, and evaluating over 11 on-device language and vision–language models spanning 0.5B–8B parameters. StructureBench systematically compares prompt-based generation with three widely used constrained decoding frameworks—Outlines, XGrammar, and Guidance—and adopts decoupled metrics to separately assess structural validity and semantic correctness. Our experiments show that constrained decoding consistently enforces syntactic validity, but does not reliably improve semantic accuracy and may even degrade performance for smaller models or complex grammars. These findings reveal clear task- and model-dependent boundaries for effective constrained decoding and highlight the need for scenario-aware adaptation in on-device structured generation. We release StructureBench at https://github.com/Str-Ben/StructureBench.

Driver cognitive distraction is a major cause of road collisions and remains difficult to detect. Unlike manual or visual distraction, cognitive distraction is diverted by thoughts unrelated to driving, even when the driver appears visually attentive and exhibits no explicit physical movements. In this work, we propose EyeCue, a gaze-empowered egocentric video understanding framework, to detect driver cognitive distraction. A key insight is that cognitive distraction manifests in the interaction between eye gaze and visual context. To capture this interaction, EyeCue integrates eye gaze with egocentric video to enable context-aware modeling of the driver's attention over time. Furthermore, to tackle the limited scale and diversity of existing datasets, we introduce CogDrive, a comprehensive multi-scenario dataset that augments four existing driving datasets with cognitive distraction annotations. Through extensive evaluations on CogDrive, we show that EyeCue achieves the highest accuracy of 74.38%, outperforming 11 baselines from 6 model families by over 7%. Notably, EyeCue can achieve an accuracy of over 70% across various driving scenarios (different road types, times of day, and weather conditions) with strong generalizability. These results highlight the importance of modeling gaze-context interactions and the effectiveness of cross-modal interaction modeling for multimodal cognitive distraction detection. Our codes and CogDrive dataset resources are available at https://github.com/langzhang2000/EyeCue.

Federated multi-view clustering (FedMVC) has been widely used to discover latent structures in distributed multi-view data, but most methods assume independent and identically distributed (IID) data. In practice, non-IID distributions with partial and imbalanced categories cause clients to learn biased, local representations, leading to model bias and unstable federated training performance. To address these issues, we propose a FedMVC framework called Bridging Inter-View and Client Heterogeneity: Federated Multi-View Clustering under Non-IID Data (H$^{2}$-FedMVC), which tackles both inter-view and client heterogeneity in mixed-view settings. We propose a leader-guided learning mechanism to address inter-view heterogeneity by enhancing complementary and discriminative feature learning, and an expert adaptive aggregation strategy to handle client heterogeneity by prioritizing well-matched experts in global updates. Theoretical analysis and experiments demonstrate superior performance in heterogeneous mixed-view FedMVC settings, with code provided in the supplementary materials.

Knowledge graphs (KGs) serve as crucial symbolic knowledge sources for many downstream applications but often suffer from incompleteness. Recent efforts have attempted to integrate pre-trained language models and KGs for the knowledge graph completion (KGC) task. However, existing methods introduce substantial model complexity or rely on prompt-based solutions that inject priors at a shallow level, which do not exploit the factual knowledge inherently encoded in model parameters. To address these issues, we propose a novel framework that treats LLMs as parametric knowledge sources for KGC. Our framework performs LLM knowledge elicitation to extract factual knowledge from the model’s internal representations. Then, a cross-granularity representation alignment method transforms sentence-level representations into entity-level representations and aligns them within a unified space. Finally, a dynamic learning schedule balances alignment and expressiveness throughout training. Extensive experiments on multiple benchmark datasets show that our proposed method can be integrated with diverse KGC baselines and consistently improves link prediction in both standard and lifelong settings.

Training with instance-dependent label noise poses a critical challenge in deep learning, severely impeding model generalization. Existing methods often struggle to break the vicious cycle where corrupted labels degrade feature representations, which in turn impairs noise identification. To overcome this, we propose the Meta-Cognitive Resonance Label Correction (MecReC) framework. For feature representation, MecReC structurally decouples representation learning from label correction, leveraging a pre-trained model as a frozen anchor to ensure feature robustness. For label correction, we introduce a novel meta-cognitive resonance mechanism that aggregates multiple complementary signals to comprehensively assess label reliability. These collaborative signals are fed to a lightweight meta-weight network, which automatically learns whether and how to correct each label. Furthermore, our personalized confidence thresholding strategy guarantees a tighter error bound for label purification. Extensive experiments demonstrate that MecReC consistently outperforms SOTA methods, exhibiting superior robustness particularly under extreme noise conditions. Code and additional details are available at https://github.com/JieSu272/MecReC.

Symbolic regression (SR) aims to recover compact and interpretable mathematical expressions from data.
Genetic programming (GP) directly searches over symbolic structures, but its population dynamics can make it difficult to reliably preserve and accumulate useful subexpressions.
In contrast, reinforcement learning (RL)-based SR has shown strong empirical performance, suggesting that learned sampling dynamics may capture useful regularities in symbolic search.
Motivated by this contrast, we analyze expressions sampled during RL training and identify a recurring pattern, termed progressive subexpression reuse, where useful simple subexpressions emerge early, become increasingly frequent, and support the formation of more complex structures.
Based on this observation, we propose Reinforcement Genetic Programming (RGP), a purely GP-based and non-RL framework that explicitly realizes a stage-wise retention--reintroduction loop through a dynamically maintained subexpression pool and pool-guided population initialization.
Experiments on standard SR benchmarks show that RGP matches or outperforms strong baselines without RL policy training, suggesting that progressive subexpression reuse is an effective mechanism for SR search. We release our code at https://github.com/wuxiangdong586-max/RGP.

Despite recent advances in training-free text-guided image editing using pretrained rectified flow models, achieving a balance between text adherence and background preservation remains a challenge. Existing methods typically treat image editing as an open-loop trajectory generation task, adopting either an inversion-based or inversion-free paradigm. However, neither can perfectly achieve this goal. Specifically, inversion-based approaches accumulate inversion errors without feedback, whereas inversion-free methods exhibit unstable trajectories due to the lack of constraints. To address this, we propose EKFEdit, a novel framework that formulates image editing as a sequential state estimation problem in a nonlinear dynamic system, which models inversion-free editing trajectories as the system’s prior dynamics and leverages an Extended Kalman Filter to adaptively fuse observations from external editing algorithms. EKFEdit enables adaptive trajectory correction, effectively combining the complementary strengths of the different paradigms. We instantiate this framework into two concrete approaches (EKFEdit-RF and EKFEdit-DNA) via different observation methods, respectively. Extensive experiments demonstrate that our approaches achieve new state-of-the-art performance compared to previous methods.
Code is available at https://github.com/baiyeweiguang/EKFEdit.

In intraday stock return forecasting, existing Transformer methods face high computational complexity and do not explicitly handle noisy predictors. We propose TransAlpha, a light-weight Transformer variant tailored for cross section data to advance end-to-end forecasting methods with three novel components (Cross-Sectional Denoising for signal purification, Temporal Attention Gating for trend weighting, and Smart Pooling to avoid information loss) along with a multi-component hybrid loss function. We validate on full A-share market stock data and four key segments (CSI 300/500/1000/2000) with 400 proprietary alpha factors of 15-minute frequencies. TransAlpha outperforms state-of-the-art baselines in predictive power and portfolio profitability, indicating tailored Transformer models have significant practical value in quantitative trade.

Similarity-graph-based multi-view clustering is effective in capturing non-Gaussian cluster structures, yet it suffers from an inherent conflict between scalability and geometry-consistent cluster assignment. In particular, cluster labels are typically inferred via secondary clustering rather than being learned directly from the underlying data geometry. Although anchor-based extensions alleviate the computational burden, they introduce the challenge of anchor-number selection.
To address these limitations, we propose Multi-view Clustering via Manifold Decomposition (MvMD), which directly infers cluster labels from multi-view data without explicit similarity graph construction or anchor selection. MvMD reformulates multi-view clustering as a unified multi-view regression problem, where cluster labels are optimized as model variables. To improve robustness against missing small clusters, a global balance regularization is incorporated. Meanwhile, local geometric consistency and the low-rank structure of cluster assignments are jointly enforced through a symmetric matrix factorization scheme, which is efficiently realized via a truncated SVD-based low-rank approximation.
Extensive experiments on benchmark datasets validate the effectiveness and efficiency of the proposed MvMD. The code is available at https://github.com/Vince-Doit/MvMD.

Federated Learning (FL) faces two major challenges when deploying large vision-language models such as contrastive language-image pretraining (CLIP) model, namely high computation/communication overhead and performance degradation due to data heterogeneity. To address these, we propose FedENC, a novel federated adaptive CLIP framework. FedENC keeps the CLIP encoder frozen and introduces a lightweight communicable global expert adaptation module for learning general knowledge across clients and two local experts for client-specific adaptation to reduce computation and communication overhead. To mitigate data heterogeneity, we introduce neural collapse (NC) alignment to encourage the model to learn unified and balanced image feature representations. To enrich the performance, we propose a cosine and Kullback–Leibler (KL) divergence based alignment function to improve the prediction consistency between the global and local experts. Finally, we ensemble their outputs for robust inference. Extensive experiments on six datasets (4 natural + 2 medical) in challenging settings (e.g., domain shift) show that FedENC improves the test accuracy (e.g., by +5.37% on DomainNet compared to LoRA) with reasonable communication load (e.g., 15.2x lower than LoRA, with 3.3GB GPU memory usage). The code is available at https://github.com/AIPMLab/FedENC.

Stochastic gradient methods are central to large-scale learning, yet their generalization theory typically relies on independent sampling assumptions. In many practical applications, data are generated by Markov chains and learning is performed in a decentralized manner, which introduces significant analytical challenges. In this work, we investigate the stability and generalization of decentralized stochastic gradient descent (SGD) and stochastic gradient descent ascent (SGDA) under Markov chain sampling. Leveraging a stability-based framework, we characterize how Markovian dependence and decentralized communication jointly influence generalization behavior. Our analysis captures the effects of network topology, Markov chain mixing properties, and primal–dual dynamics. We establish non-asymptotic generalization bounds for both algorithms, extending existing results on Markov stochastic gradient methods to decentralized and minimax settings.

The impressive performance of large language models (LLMs) arises from their massive scale and heterogeneous module composition. However, this structural heterogeneity poses significant optimization challenges. While adaptive optimizers such as Adam(W) provide per-parameter adaptivity, they do not explicitly account for module-level gradient heterogeneity, often resulting in slower convergence, suboptimal performance, or occasional training instability. Existing approaches typically rely on manually tuned module-specific learning rates or specific optimization strategies, which are computationally costly and difficult to generalize across tasks or models. To establish a more principled approach, we first analyze the noise-damping behavior of Adam in high-noise modules and introduce \textbf{Module-wise Learning Rate Scaling via SNR (MoLS)}. MoLS estimates module-level SNRs to scale Adam updates, allowing automated module-wise learning rate allocation without manual tuning. Empirical results across multiple LLM training benchmarks demonstrate that MoLS improves convergence speed and generalization, achieving performance comparable to carefully tuned module-specific learning rates, while remaining compatible with memory-efficient training algorithms.

In simulation-in-the-loop decision-making systems, reinforcement learning (RL) inference is often constrained by simulator-side execution overhead, where workloads are highly dynamic and sensitive to runtime thread configurations. Existing multithreaded strategies struggle to match thread resources before or during execution, causing resource contention, scheduling overhead, and reduced throughput. Through empirical analysis, we identify the ratio of task execution time to scheduling time as the key factor determining the optimal thread count. Building on this insight, we propose AutoThread, a hybrid adaptive thread-tuning method for mitigating simulation bottlenecks in RL inference. AutoThread employs a Physics-Informed Neural Operator (PINO) as a thread-count predictor and incorporates a finite-source M/M/1 queueing model to constrain and guide prediction, enabling fast and accurate estimation under dynamic workloads. It further performs load-aware online fine-tuning to compensate for prediction errors and refine resource allocation. Experiments show that AutoThread improves average speedup by 18.4\% over static strategies, achieves average throughput of 1.7× and 1.8× that of XGBoost and Reinforcer, respectively, and reduces execution time by up to 83.8\% compared with state-of-the-art methods. Our code and dataset are publicly available at \url{https://github.com/suchenjm/AutoThread}.

Spatio-temporal forecasting is inherently bottlenecked by a phenomenon we term Spatio-Temporal Duality: the paradoxical coexistence of time-varying physical lags and instantaneous semantic synchrony. Constrained by passive coupling, existing architectures often employ indiscriminate spatial aggregation that fails to dynamically arbitrate interaction intensity based on traffic states, forcing models to rely on redundant deep stacking to approximate complex dynamics. We address this issue by introducing Time Arbitrated Spatial GNN (TAS-GNN), an efficient framework that arbitrates spatial interactions through time. Our model leverages deep temporal semantics to dynamically and proactively manage the aggregation of spatial domains, effectively decoupling physical connectivity from semantic relevance. In addition, to ensure model simplicity, we also introduce spectral decoupling via discrete wavelet Transform (DWT) to capture multiscale dependencies with minimal overhead. Experiments on three real-world datasets (PEMS04, 07, 08) show that TAS-GNN not only outperforms the current baseline model in accuracy, but also significantly improves the inference speed while reducing the number of parameters.

Bi-temporal Semantic Change Detection (SCD) is a fundamental analytical framework for capturing state transitions across various domains, with remote sensing being a key application area in this work. However, the prevailing paradigm of comparing two discrete temporal snapshots suffers from inherent limitations due to temporal discontinuity. These limitations lead to pronounced radiometric inconsistencies and pseudo-change noise in Bi-temporal SCD. To address these, we propose the Pseudo-Temporal Feature Fusion Network (PTF-Net), which reconceptualizes bi-temporal SCD as continuous pseudo-temporal evolution modeling. This novel paradigm facilitates the recovery of the missing temporal context between observation points. At its core lies an innovative Non-Linear Style Interpolation Mechanism, which projects discrete bi-temporal observations onto a smooth latent manifold to simulate plausible surface evolution dynamics. The mechanism synthesizes a sequence of semantically coherent intermediate representations, which inherently bridge the temporal gap and disentangles style variations from semantic changes. To fully exploit this synthesized sequence, we design a dedicated Temporal Dynamics Perception Branch. This branch employs efficient temporal interactions to robustly discriminate structural mutations from transient noise, effectively acting as a semantic filter. Extensive experiments on three public datasets reveal that PTF-Net consistently outperforms state-of-the-art methods, achieving comprehensive superiority across mIoU, Fscd, and SeK metrics.

Video captioning models convert frames into visual tokens and generate descriptions with large language models (LLMs). Since encoding all frames is prohibitively expensive, uniform sampling is the default choice, but it enforces equal temporal coverage while ignoring the uneven events distribution. This motivates a Learnable Frame Selector (LFS) that selects temporally diverse and event-relevant frames. LFS explicitly models temporal importance to balance temporal diversity and event relevance, and employs a stratified strategy to ensure temporal coverage while avoiding clustering. Crucially, LFS leverages caption feedback from frozen video-LLMs to learn frame selection that directly optimizes downstream caption quality. Additionally, we identify the gap between existing benchmark and human's cognition. Thus, we introduce ICH-CC built from carefully designed questions by annotators that reflect human-consistent understanding of video. Experiments indicate that LFS consistently improves detailed video captioning across two representative community benchmarks and ICH-CC, achieving up to 2.0% gains on VDC and over 4% gains on ICH-CC. Moreover, we observe that enhanced captions with LFS leads to improved performance on video question answering. Overall, LFS provides an effective and easy-to-integrate solution for detailed video captioning.

Monocular 3D object detection is a cost-efficient alternative to multisensor systems, yet it remains fragile to multi-component adversarial attacks that perturb the image and tamper with camera calibration. Compounded distortions degrade 3D reasoning by disrupting the correspondence between the 3D geometry and 2D image plane. To address this problem, this work proposes MonoPure, a monocular 3D object detection framework that performs multi-component purification via disentangled and projective representations. MonoPure incorporates a disentangled purification and segmentation module that purifies the image data, with a target-region probability map steering diffusion-based purification to focus on task-relevant regions. In addition, MonoPure presents a 3D detection decoder that integrates 2D skeleton keypoints as object-level spatial cues, enabling occlusion-robust 3D detection. Finally, a projective calib-purification module restores compromised intrinsics by iteratively minimizing the reprojection error between projected 3D boxes and calibration-invariant 2D detection boxes. The experiments confirm that MonoPure outperforms prior detectors under multi-component attacks and occlusion.

The rapid progress of identity-feature-based face-swapping technology has raised concerns about impersonation and privacy violations. Although proactive defenses aim to block identity extraction at the source, existing methods suffer from perceptible visual artifacts, poor generalization across diverse deepfake models, and vulnerability to post-processing techniques (e.g., diffusion purification, image compression, and transformations). This work proposes a robust, generalizable proactive face-swapping defense via semantic gradient divergence (SGD-Guard) to address these challenges. It introduces an integrated feature gallery that uses CLIP features and a generalized identity feature, obtained by iteratively refining heterogeneous identity features into a homogeneous representation. This framework facilitates our semantic distortion attack by leveraging consensus weighting to target specific facial attributes within a CLIP-identity joint embedding space, disrupting deepfake generation while preserving visual fidelity. Furthermore, to ensure robustness against purification and post-processing, this method incorporates a module that prioritizes critical transformations by exploiting directional discrepancies. Comprehensive experiments demonstrate that the method effectively defends against diverse face-swapping models with high cross-model transferability.

Cross-modal hashing has been extensively adopted in cross-modal retrieval tasks owing to its high storage efficiency and fast retrieval capability. However, in practical applications, multimodal data often suffer from modality missing issues, which cause semantic incompleteness and thus severely impair both cross-modal alignment and hashing-based retrieval performance. To address these issues, this paper proposes a novel incomplete cross-modal retrieval framework, called retrieval-guided completion hashing with token–patch alignment (RGCH-TPA). Specifically, it first constructs a dynamic memory bank based on the features of complete samples and uses available modalities to retrieve and locate similar regions of incomplete samples. Thus, it achieves intra-modal reconstruction of missing data under the condition of available modalities using retrieved expert signals. Moreover, a token-patch level cross-modal alignment mechanism is introduced to model the interaction between text tokens and image patches, while global representations are jointly integrated to further enhance the discriminability and robustness of the learned cross-modal representations. Extensive evaluations on three benchmark datasets demonstrate that the proposed RGCH-TPA method consistently outperforms other state-of-the-art incomplete cross-modal hashing methods across missing-modality ratios. The source code is available at https://github.com/szq0816/RGCH-TPA.

Partial multi-label learning (PML) addresses weakly-supervised scenarios where each instance is associated with a candidate label set containing both ground-truth and noisy labels. Existing PML methods primarily focus on instance-level features or pairwise label correlations for disambiguation. Building on recent insights that exploiting pairwise label correlations improves disambiguation, we observe that high-order label correlations provide even stronger disambiguation evidence in partial settings because ground-truth labels with high-order correlations frequently co-occur, while noise labels produce inconsistent combinations that rarely repeat. To exploit this property, we propose REHC-PML (Label Confidence REcovery with High-order Label Correlations in Partial Multi-label Learning), which mines frequent high-order co-occurrences from candidate sets to identify global high-order label correlations, selects instance-relevant correlations via Gumbel-Softmax pruning, and propagates their evidence to constituent labels for confidence recovery. Self-training iteratively refines pseudo-labels and trains the classifier. Extensive experiments on both real-world and UCI datasets demonstrate the effectiveness of REHC-PML.

Cognitive diagnosis (CD) aims to infer students' mastery of knowledge concepts from their response behaviors and constitutes a core component of intelligent education and personalized learning. However, existing graph-based CD models struggle to handle the pronounced long-tail distributions in educational data, where most students and concepts interact with only a limited number of exercises, resulting in suboptimal representation learning and poor generalization to low-frequency instances. To address this challenge, we propose HiCD (Hyperbolic insight for Cognitive Diagnosis), a novel hyperbolic model that embeds students, exercises, and concepts into non-Euclidean space. By exploiting the exponential representational capacity of hyperbolic geometry, HiCD naturally captures hierarchical and sparse structures, effectively alleviating long-tail bias while enhancing embedding expressiveness. A key contribution of HiCD is a hyperbolic diagnostic function that operates directly on the manifold, avoiding Euclidean approximations and preserving geometric consistency. Moreover, HiCD decomposes the educational graph into three semantically distinct subgraphs and assigns each a dedicated curvature, enabling adaptive geometric modeling of heterogeneous relations. Extensive experiments on multiple benchmark datasets demonstrate that HiCD consistently improves diagnostic accuracy and robustness, particularly under severe long-tail scenarios. The source code is available at: https://github.com/CyberXie/HiCD.

Large Vision-Language Models (LVLMs) often suffer from hallucinations, generating descriptions that include visual details absent from the input image. Recent preference alignment methods typically rely on supervision distilled from stronger models such as GPT. However, this offline paradigm introduces a Supervision-Perception Mismatch: the student model is forced to align with fine-grained details beyond its perceptual capacity, learning to guess rather than to see. To obtain reliable self-supervision for online learning, we identify a Generative-Discriminative Gap within LVLMs, where models exhibit higher accuracy on discriminative verification than open-ended generation. Leveraging this capability, we propose Online Self-CAlibRation (OSCAR), a framework that integrates Monte Carlo Tree Search with a Dual-Granularity Reward Mechanism to construct preference data and iteratively refines the model via Direct Preference Optimization. Extensive experiments demonstrate that OSCAR achieves state-of-the-art performance on hallucination benchmarks while preserving general multimodal capabilities.

Over-parameterized neural networks, i.e., models with excess
capacity that can fit training data exactly, have demonstrated superior
generalization performance compared to classical models with balanced
capacity. Nevertheless, their deployment in safety-critical domains re-
mains severely constrained by their susceptibility to, e.g., natural per-
turbations and adversarial manipulations. Verification techniques can
solve such problems, but the computational cost of these methods of-
ten scales poorly, specifically when applied to large models. In this work,
we demonstrate that over-parameterization can be exploited not merely
to enhance generalization, but also to mitigate neuron instability, one of
the parameters affecting the efficiency of verification. Our experimental
findings suggest that over-parameterization may serve as a crucial mech-
anism for reconciling the long-standing trade-off between generalization
and verifiability of neural networks.

Cross-view geo-localization (CVGL) aims at localizing a ground-level query by retrieving its corresponding match from a database of geo-tagged satellite images. Existing multi-modal CVGL methods lack a structured design in the fusion stage, limiting their ability to fully exploit the information from multiple modalities. To overcome this limitation, we propose a Structural-Analysis-Based fusion principle that guides the design of network architecture. Following this principle, we present Decoupled Query Fusion (DQF), a novel fusion module that decouples feature interactions through role-specific learnable queries. These learnable queries aggregate features into distinct slots. Specifically, modality-specific queries capture unique information from each modality, while cross-modal queries extract redundant and synergistic information between different modalities. To better discriminate positive samples at varying geographic distances, we further propose Geo-aware Circle Loss, which adaptively weights supervision signal based on the geographical distance between samples and anchors. Extensive experiments on large-scale benchmarks demonstrate that our method significantly outperforms state-of-the-art approaches. Comprehensive ablation studies further validate the effectiveness of each component. Our code and models will be made publicly available.

Spatially resolved transcriptomics integrates gene expression with spatial coordinates to decode tissue microenvironments. Existing methods predominantly utilize graph structures to model relationships between spots. However, their performance is bottlenecked by the reliability of gene feature graph, facing the following hurdles: (1) ubiquitous housekeeping genes cause high-expression spots to densely connect with heterogeneous spots, leading to a skewed graph structure; (2) information reduction during Highly Variable Gene (HVG) selection results in the loss of intrinsic local structures. To address these challenges, we propose a Spot-Adaptive Structural Rectification method, called SASR. Specifically, SASR employs a hyperspherical expansion constraint that projects gene expression profiles onto a unit hypersphere to maximize angular distances, effectively separating spots falsely clustered by high total counts. Simultaneously, a topological consistency constraint repairs structural fractures caused by HVG selection via aligning latent embeddings with the local structures of the raw full-gene space. The complementary synergy balances angular discriminability with topological fidelity for accurate clustering. Experiments demonstrate that SASR effectively corrects structural biases and surpasses state-of-the-art methods in spatial clustering.

Existing multimodal recommendation models using complex fusion mechanisms (e.g., attention) or multi-stage processes (e.g., early or late fusion) integrate different modalities. However, attention-based adaptive fusion is prone to shortcut learning, where dominant collaborative signals (ID) can overshadow other modalities. This dominance affects the entire fusion process: early fusion often amplifies biases driven by identity, while late fusion struggles to extract preference-relevant signals from misaligned modalities, even with alignment regularization. To address these issues, we propose Multi-Teacher Single-Student Online Distillation for Multimodal Recommendation (MTS2-4MM), which reframes the multimodal recommendation task from direct fusion to controllable knowledge transfer. Specifically, we construct multiple teachers to specialize in complementary perspectives, and a unified student distills their guidance via objectives at both the ranking and representation levels. This design explicitly controls modality contributions, improves robustness to modality noise and misalignment. Furthermore, we design a Modality-specific Preference Extractor to explicitly extract user preferences across different modalities equally. Extensive experiments across five real-world datasets demonstrate that MTS2-4MM consistently outperforms state-of-the-art baselines, achieving improvements of up to 7.22%.

Large language models (LLMs) are increasingly used for hardware and firmware code generation, but existing studies primarily evaluate functional correctness while largely overlooking security. However, LLM-generated code that appears functionally sound may embed security flaws which could induce catastrophic damages after deployment. This critical research gap motivates us to design a benchmark for assessing security awareness under realistic specifications. In this work, we introduce HardSecBench, a benchmark with 924 tasks spanning Verilog Register Transfer Level (RTL) and firmware-level C, covering 76 hardware-relevant Common Weakness Enumeration (CWE) entries. Each task includes a structured specification, a secure reference implementation, and executable tests. To automate artifact synthesis, we propose a multi-agent pipeline that decouples synthesis from verification and grounds evaluation in execution evidence, enabling reliable evaluation. We evaluate diverse LLMs and find that they often satisfy functional requirements while leaving security risks. We also find that security results vary with prompting. These findings highlight pressing challenges and offer actionable insights for future advancements in LLM-assisted hardware design. Our data and code are available at https://github.com/chenqirui2002/HardSecBench.

Combinatorial optimization (CO) problems have widespread applications in science and engineering, but they present significant computational challenges. Recent advancements in generative models, particularly diffusion models, have shown promise in bypassing traditional optimization solvers by directly generating near-optimal solutions.
However, existing diffusion solvers are highly sensitive to the quality of the training dataset, particularly the number of problem instances for training and the optimality of their labels. Notably, we observe an exponential scaling law between the performance of diffusion-based solvers and the number of near-optimally labeled instances needed. When training instances are scarce or sub-optimally labeled, diffusion-based solvers suffer significant performance degradation.
To enhance the robustness of diffusion solvers to dataset quality, we propose a robust diffusion solver for combinatorial optimization capable of learning from sub-optimally labeled instances follows a two-stage generate-then-decode framework, integrating an objective-guided diffusion model, further reinforced by classifier-free guidance, to produce solutions that surpass the optimality of the training dataset.
Experiments demonstrate the improved robustness in \myalg compared to the diffusion-based solver baseline, in a range of combinatorial optimization benchmark tasks such as TSP (Traveling Salesman Problem) and MIS (Maximum Independent Set).

We study repeated allocation of shared resources among agents with time-varying demands and capped linear utilities. In this setting, independently maximizing the minimum utility in each round satisfies sharing incentives (agents weakly prefer participating in the mechanism to not participating), strategyproofness (agents have no incentive to misreport their demands), and Pareto efficiency. However, this max-min mechanism can lead to large disparities in the total resources received by agents, even when they have the same average demand. We introduce credit fairness, a property that, together with Pareto efficiency, strengthens sharing incentives by ensuring that agents who lend resources in early rounds are able to recoup them in later rounds. Credit fairness can be achieved in conjunction with either Pareto efficiency or strategyproofness individually, but we show that, under anonymity, it cannot be achieved together with both. We propose a mechanism that is credit fair and Pareto efficient, and evaluate it in a computational resource-sharing setting.

Given a social network 𝒢 with n nodes and m edges, the opinion maximization (OM) problem aims at identifying k (k ≪ n) opinion leaders to maximize their opinion propagation in 𝒢. Despite its significance and prevalence, existing OM methods often struggle to strike a satisfactory balance between theoretical performance and practical efficiency. This paper provides an inverse optimization perspective for OM by reformulating it as an opinion minimization (OMin) problem, whose objective function holds succinct expression and desired properties. Then, we introduce a bounded estimation scheme (BES) that can estimate the objective function of OMin with bounded error in Õ(kn+m) expected time, thereby making the optimization for OMin more efficient. To further enhance the optimization efficiency, a decomposition-based decremental estimation algorithm (DDEA) with a near (1-1/e) approximation ratio in Õ(kmn) expected time is specifically designed for OMin. Extensive experiments on real-world social networks of varying scales demonstrate the effectiveness of BES and the superiority of DDEA.

Standard unsupervised multi-view feature selection (UMFS) methods for large datasets exhibit limitations in modeling the competition between cross-view alignment and intra-view diversity, resulting in suboptimal solutions and expensive computational costs. This challenge is exacerbated by the diverse signals inherent in complex samples, which tend to mask shared patterns, thus complicating the pursuit of an optimal trade-off. In this work, we propose a Progressive Adversarial Feature Selection (ProAd-FS) framework for large-scale multi-view learning, which formulates this static trade-off objective as a dynamic competitive process. To be specific, ProAd-FS develops an adversarial decoupled architecture that assigns these competing objectives to distinct inter-view and intra-view games, guided by a robust gated curriculum to prioritize the learning of underlying structures. The entire framework exhibits linear computational complexity in both sample size and feature dimension, and embeds structural sparse regularization for end-to-end optimization. Extensive experimental results show that ProAd-FS achieves state-of-the-art performance while being highly scalable as well, providing an effective solution for large-scale UMFS tasks.

Graph-based multi-view clustering, with its ability to mine potential associations between samples, has attracted extensive attention. To capture high-order correlations, tensor-based frameworks have been introduced to model multiple graphs jointly. Although these methods have achieved promising performance, existing methods mainly focus on stacking consistency graphs with low-rank constraints, while overlooking high-order specificity within diversity graphs, and the exploration of fine-grained diversity information among different samples across views remains insufficient. To address these issues, we propose High-Rank Tensor Specificity Induced Cooperative Multi-ViewGraph Learning (HTS-CMGL). Specifically, we first obtain the consistency graphs and diversity graphs from multi-view data, which are further reconstructed into the consistency tensor and the diversity tensor, respectively. Subsequently, a novel Enhanced Tensor Rank (ETR) is imposed on the consistency tensor, which is a tighter approximation of the tensor rank and is more noisy-robust to explore the high-order consistency. Meanwhile, we design a new Tensor High-Rank Logarithmic Norm (THLN) on the diversity tensor. By leveraging a unique high-rank constraint mechanism, THLN not only actively preserves high-rank and informative features, but also simultaneously captures view-level and sample-level diversity information. Extensive experiments on multiple datasets demonstrate the effectiveness of our proposed method on clustering multi-view data.

Immune Checkpoint Inhibitors (ICIs) represent a cornerstone of modern cancer immunotherapy. However, their clinical application is frequently accompanied by immune-related Adverse Events (irAEs) of diverse severity. Predicting Gene-Disease Associations (GDAs) is crucial for identifying the related genes that cause irAEs but remains experimentally expensive in the context of cancer immunotherapy. While existing graph neural network-based methods provide solutions to this problem, they often overlook disease-disease and disease-gene associations that may exist but have not yet been identified, leading to contaminated representations. To address these limitations, we propose Gene-Disease Associations based on Optimal Transport (GDAs-OT), a novel framework for GDA prediction. GDAs-OT constructs gene-gene, disease-disease and gene-disease relationships as graphs. Optimal transport was utilized to identify potential disease-disease associations to expand the disease-disease graph structure, and select high-confidence negative pairs in the gene-disease graph. By mapping nodes into a refined embedding space, the processed gene-disease graph guides node representation learning, providing robust node representations. Comprehensive experiments demonstrate that GDAs-OT outperforms state-of-the-art methods across most evaluation metrics. In addition, GDAs-OT successfully identifies potential risk genes for irAEs, providing a computational foundation for understanding the mechanisms of immunotherapy toxicity. Code is available at
https://github.com/RuhaoLiu/GDAs-OT.

Molecular representation learning aims to learn vector embeddings that capture molecular structure and geometry, thereby enabling property prediction and downstream scientific applications. In many AI for science tasks, labeled data are expensive to obtain and therefore limited in availability. Under the few-shot setting, models trained with scarce supervision often learn brittle structure–property relationships, resulting in substantially higher prediction errors and reduced generalization to unseen molecules. To address this limitation, we propose PCEvo, a path-consistent representation method that learns from virtual paths through dynamic structural evolution. PCEvo enumerates multiple chemically feasible edit paths between retrieved similar molecular pairs under topological dependency constraints. It transforms the labels of the two molecules into stepwise supervision along each virtual evolutionary path. It introduces a path-consistency objective that enforces prediction invariance across alternative paths connecting the same two molecules. Comprehensive experiments on the QM9 and MoleculeNet datasets demonstrate that PCEvo substantially improves the few-shot generalization performance of baseline methods. The code is available at https://github.com/DrugD/PCEvo.

Low-Rank Adaptation (LoRA) is a popular approach that enables large language models (LLMs) to quickly adapt to domain-specific tasks by adding lightweight trainable adapters. Existing multi-LoRA serving systems typically exploit parameter sharing to serve hundreds of LoRA models with a single base model. However, most systems rely on first-come-first-serve (FCFS) scheduling, which can incur severe queuing delay and lead to excessive adapter memory usage, squeezing KV cache space and reducing concurrency and throughput. To address these challenges, we propose M-LoRA, a memory-aware multi-LoRA serving system that reduces queuing delay and improves throughput through efficient request scheduling guided by fine-grained memory modeling. M-LoRA consists of three components: (1) Multi-LoRA Length Predictor, which estimates the output length and KV-cache demand of each request; (2) Memory-aware Speculative Scheduler, which dispatches requests based on predicted KV-cache and adapter memory requirements to maximize throughput under a fixed GPU memory budget; and (3) Recovery Mechanism, which corrects mispredictions while preserving generation quality. Compared to state-of-the-art LoRA serving systems, M-LoRA reduces average per-token latency by 87% and improves throughput by up to 1.7x.

Designing functional protein sequences that satisfy multiple desired properties is a core research focus of protein engineering. Prior methods struggle with inability or inefficiency when dealing with numerous, often conflicting, properties. We propose Multi-Property Protein Diffusion, (MP2D), a unified framework for multi-objective protein sequence optimization that integrates conditional discrete diffusion with constrained MCTS and global iterative refinement. MP2D formulates diffusion denoising as a constrained sequential decision-making process and employs MCTS to explore diverse denoising trajectories guided by Pareto-based rewards. A global iterative refinement strategy further enables repeated remasking and re-optimization of candidate sequences, while a dynamic Pareto constraint prevents candidate bloat and maintains balanced trade-offs across objectives. We evaluate MP2D on two challenging multi-objective protein design tasks: antimicrobial peptide and protein binder optimization, involving four to five conflicting properties. Experimental results demonstrate that MP2D consistently outperforms existing multi-objective baselines, achieving robust and balanced improvements across all objectives without retraining generative models. These results highlight MP2D as a practical and scalable solution for multi-objective functional protein design.

FlashAttention improves efficiency through tiling, but its online softmax still relies on floating-point arithmetic for numerical stability, making full quantization difficult.
We identify three main obstacles to integer-only FlashAttention: (1) scale explosion during tile-wise accumulation, (2) inefficient shift-based exponential operations on GPUs, and (3) quantization granularity constraints requiring uniform scales for integer comparison.
To address these challenges, we propose QFlash, an end-to-end integer FlashAttention design that performs softmax entirely in the integer domain and runs as a single Triton kernel.
On seven attention workloads from ViT, DeiT, and Swin models, QFlash achieves up to 6.73x speedup over I-ViT and up to 8.69x speedup on Swin, while reducing energy consumption by 18.8% compared to FP16 FlashAttention, without sacrificing Top-1 accuracy on ViT/DeiT and remaining competitive on Swin under per-tensor quantization.
Our code is publicly available at https://github.com/EfficientCompLab/qflash.

Gaze target estimation aims to infer the position of a person's gaze within a scene. Within mainstream design logic, multi-branch methods require extra supervision and annotations, while streamlined designs prioritize low-level visual saliency over true gaze intent. The former leads to a high annotation burden and hinders domain transfer, whereas the latter causes misalignment between predicted attention and actual gaze targets. To address this issue, we propose TextGaze, a unified cross-modal architecture that leverages a Large Vision-Language Model (LVLM) as scalable semantic guidance to balance the two design paradigms. The model extracts visual features from a frozen encoder and utilizes an LVLM to obtain gaze-aligned textual cues. We design a transformer-based fusion module with hierarchical text supervision to preserve task semantics. Lightweight decoding heads enable the joint prediction of gaze heatmaps and in-/out-of-frame status. We evaluate our method on four mainstream datasets, and the results show competitive performance across key metrics with robust cross-dataset generalisation without extra fine-tuning. Overall, we provide a streamlined alternative to traditional designs and highlight the potential of LVLMs as accessible auxiliary guidance for gaze estimation. All contents are available at: https://github.com/idremo/TextGaze-IJCAI2026.

Reliable forecasting of cross-regional terrorism fatalities is essential for early warning and security planning. However, real-world terrorism data are characterized by extreme sparsity, abrupt volatility, and weak, rapidly shifting non-Euclidean spatial relationships, posing substantial challenges for existing spatiotemporal forecasting models in learning stable multi-scale dependencies. To address these challenges, we propose a robust spatiotemporal forecasting framework, termed WBMCF. In the temporal dimension, a single-level Discrete Wavelet Transform (DWT) is introduced to stabilize temporal structures, and a Bidirectional Gated Mamba module is employed to capture stage-dependent long-range temporal dependencies; additionally, in the spatial dimension, a topology-agnostic Convolutional Feed-Forwar+d Network (ConvFFN) is incorporated to model cross-regional dependencies via implicit feature coupling, thereby eliminating reliance on fixed spatial topologies. Extensive experiments on five regional subsets of the Global Terrorism Database (GTD) demonstrate that WBMCF achieves stable and competitive forecasting performance under sparse and noisy conditions. The source code is available at https://github.com/weibaozhong/WBMCF.

Fair multi-view clustering aims to exploit complementary information across views to improve clustering quality, while ensuring unbiased outcomes with respect to sensitive groups. Existing methods typically separate multi-view clustering from fairness optimization into distinct stages. However, such a decoupled design provides limited synergy between the two objectives, which often makes progress on one objective detrimental to the other. To address this challenge, we propose Mutually Reinforced Fair Multi-View Clustering (MR-FMVC), which optimizes fairness alignment and clustering iteratively to enable coordinated optimization. Specifically, we first disentangle view representations into common and private components to mitigate fairness-related discrepancies across views. Subsequently, the optimization proceeds by interleaving cross-group matching in the common space with updating cluster centroids in the joint representation space, ultimately assigning matched samples to consistent clusters. Extensive experiments on four fairness datasets demonstrate that MR-FMVC achieves a superior trade-off between clustering performance and fairness.

Federated learning (FL) enables privacy-preserving video human activity recognition without centralizing data. Despite its potential, FL remains fundamentally constrained by the high communication overhead incurred during training.
Existing methods, such as model quantization, gradient sparsification and low-rank approximation, aim to mitigate this overhead but often at the cost of model expressivity, leading to reduced classification accuracy.
In this work, we take a representation-centric perspective and show that for video-base activity recognition using pretrained embeddings, class-discriminative information lies in a very low-dimensional subspace, that preserves linear seperability between activity classes. Motivated by this observation, we propose Federated discriminative subspace discovery, a framework that collaboratively identify and operate within such a subspace.
Instead of communicating full classifier updates, each client first projects its local video embeddings using a shared random projection matrix. It then computes the covariance of the projected embeddings and transmits this statistic to the server for aggregation. The server reconstructs a global covariance matrix, performs singular value decomposition, and selects the top-k eigenvectors that define the global class-discriminative subspace. Subsequent communication occurs only in this compact subspace. Extensive experiments on UCF101, HMDB51, and Toyota-SmartHome demonstrate that, learning and sharing the classifier in the discovered subspace, reduces communication costs by up to 61%, with a moderate drop in activity recognition accuracy compared to the full model.

Answer Set Programming (ASP) is a popular knowledge representation and reasoning framework with countless applications for modeling and solving combinatorial problems. With increasingly large applications, identifying crucial details and collapsing irrelevant or indistinguishable information becomes crucial for a human in the loop. This idea led to the investigation of different notions of abstraction, which relate to forgetting and projection. Very recently, clustering formalisms have been designed that also preserve program dependencies. However, crucial computational properties, such as finding abstractions, remained entirely unexplored to date. In this work, we examine the computational complexity of existing abstraction techniques based on clustering (faithful and uniform abstractions). Besides checking, we tackle the crucial question of constructing abstractions, by also proposing a novel syntactic operator to achieve uniform abstractions, when possible. Moreover, we investigate whether symmetry detection can be used to determine abstractable parts in a program, and give insights on the difference of interchangeability and indistinguishability, by introducing a relaxation of the uniform abstraction condition to capture semantic indistinguishability. We observe that this notion enables us to obtain intuitive faithful abstractions, while symmetry does not. We explore properties needed to reach abstractions from syntactic symmetry.

Open-world test-time adaptation (OWTTA) is increasingly studied for its ability to adapt models at inference time in the presence of both domain discrepancy and semantic variance. Existing methods typically rely on either discriminative models or vision-language models (VLMs) alone, leaving their complementarity in OWTTA underexplored. In this paper, we propose a general framework that explicitly leverages the complementary strengths of discriminative models and VLMs to enable robust open-world test-time adaptation. We first define well-structured logits for OWTTA and provide a theoretical analysis that reveals the complementary properties of logits from discriminative models and VLMs. Building on a unified formulation, we then decompose OWTTA into two coupled modules: confidence-calibrated filtering (CCF), which provides an estimate of in-distribution membership, and semantic complementarity adaptation (SCA), which gives the refined predictions through complementary logit fusion. Extensive experiments across multiple benchmarks empirically confirm the logit complementarity between discriminative models and VLMs, and show that our method effectively leverages it to consistently improve performance.

The huge parameters and extensive training corpora of Large Language Models (LLMs) empower them to tackle complex tasks. Yet, their massive size incurs substantial inference costs, hindering deployment on resource‑constrained edge devices. Low‑bit quantization provides a way to compress models and improve their practicality for edge-side deployment. Nonetheless, many edge applications do not require the full generalization abilities of LLMs and instead only depend on their knowledge in specific domains. This paper introduces Gradient Enhancement Task Aware Post-training Quantization, i.e., GTAQ, to address the generalization issue. Concretely, GTAQ locates task‑critical weights via gradient‑based validation and retrieval, and then amplifies these salient weights. For that purpose, GTAQ preserves and strengthens weights related to the target tasks, enabling task‑aware uniform‑bit quantization. We extensively evaluate the LLaMA family of language models on WikiText, C4, and MMLU. Our experiments show that GTAQ consistently delivers notable performance improvements across tasks, surpassing both general‑purpose and task‑aware quantization baselines. At the same time, GTAQ yields more than 3.5× speedup in model inference and substantially cuts storage requirements. https://github.com/YihuaJerry/GTAQ.git.

Reinforcement learning (RL) agents often struggle to generalize to new tasks and contexts without updating their parameters, mainly because their learned representations and policies are overfit to the specifics of their training environments. To boost agents' in-context RL (ICRL) ability, this work formulates ICRL as a two-agent emergent communication problem and introduces CORAL (Communicative Representation for Adaptive RL), a framework that learns a transferable communicative context by functionally separating latent representation learning from control. In CORAL, an Information Agent (IA) is pre-trained as a world model on a diverse distribution of tasks. Its objective is not direct return maximization, but world modeling and distilling its understanding into concise messages. The emergent communication protocol is shaped by a novel Causal Influence Loss, which measures the effect that the message has on the next action. During deployment, the previously trained IA serves as a fixed contextualizer for a new Control Agent (CA), which learns to solve tasks by interpreting the provided communicative context. Our experiments demonstrate that this approach enables the CA to achieve significant gains in sample efficiency and successfully perform zero-shot adaptation with the help of pre-trained IA in diverse online and offline environments, validating the efficacy of learning a transferable communicative representation.

Many practical decision-making problems involve tasks whose success depends on the entire system history, rather than on achieving a state with desired properties. Markovian Reinforcement Learning (RL) can be inadequate for such tasks, while modeling them as non-Markovian reward decision processes (NMRDPs) enables agents to handle temporal dependencies.
Existing approaches offer limited formal guarantees on both (near-)optimality and sample efficiency. We address both issues with QR-max, a novel model-based algorithm for NMRDPs with discrete actions that factorizes Markovian transition learning from non-Markovian reward handling via reward machines. To our knowledge, this is the first model-based RL algorithm for discrete-action NMRDPs that leverages this factorization to obtain PAC convergence to ε-optimal policies with polynomial sample complexity.
We then extend QR-max to continuous state spaces with Bucket-QR-max, a SimHash-based discretizer that preserves the same factorized structure and achieves fast and stable learning without manual gridding or function approximation. We experimentally compare our method with state-of-the-art model-based RL approaches on environments of increasing complexity, showing substantially improved sample efficiency and greater robustness in finding optimal policies.

Generative models continue to advance rapidly in both fidelity and diversity, posing increasing challenges for reliably distinguishing generated images from real ones. Existing reconstruction-based detection methods often rely on assumptions tied to specific generative models, which leads to limited cross-model generalization and substantial computational overhead. In this paper, we propose General Feature Reconstruction Error (GFRE), a fast and generalizable detection paradigm that leverages reconstruction behavior in a general-purpose representation space. Our key insight is that real and generated images exhibit consistently different reconstruction stability when projected into universal visual representations. Instead of tracing generator-specific artifacts, GFRE employs a lightweight autoencoder to model the reconstructability of image representations, producing a reconstruction signal that is inherently generator-agnostic and transferable across diverse generative processes. Extensive experiments on images synthesized by 18 different generative models demonstrate that GFRE consistently outperforms existing state-of-the-art methods, achieving a 4.70% improvement in detection accuracy and an 8.20% gain in cross-model generalization. Moreover, by avoiding costly generator inversion and diffusion-based reconstruction, GFRE reduces reconstruction error extraction time by up to 150x, enabling efficient and scalable deployment.

Learning from label-limited streams presents significant challenges, particularly when coupled with concept drift and dynamic class imbalance. Existing works often struggle to maintain a discriminative feature space under these constraints, biasing decision boundaries toward majority classes or outdated concepts. To address this, we propose a novel framework named Neural collapse Inspired Label-limited Evolving stream learning (NILE). Instead of utilizing learnable classifiers, NILE exploits Neural Collapse (NC) geometry to explicitly construct a Simplex Equiangular Tight Frame (ETF) as a fixed classifier, ensuring maximal inter-class separability to guide feature discriminability. To maintain this discrimination with limited labels, NILE employs hybrid active learning to prioritize uncertain and minority samples, and an NC-adapted semi-supervised mechanism to enhance representation learning. NILE further updates the classifier by dynamically adjusting the ETF structure, enabling continual adaptation to drifting concepts. Extensive experiments show that NILE can effectively guide features toward the NC state, significantly outperforming state-of-the-art baselines in nonstationary environments.

Generating high-quality synthetic tabular data, regarding fidelity, diversity, and utility, is crucial for many practical purposes. Recent tabular data synthesis methods, including two-stage generative modeling approaches, have achieved this with nearly perfect fidelity. However, we observe that there remains room for improving diversity, and we show that this limitation arises from an additional constraint imposed on the latent support.
Our main contribution is that we effectively eliminate this redundant constraint by directly deriving the objective function from the KL-divergence between the ground-truth density and the generative model used for synthetic sample generation. We empirically demonstrate that our model, which relies on a single prior distribution, significantly improves the quality of the synthetic data, especially in terms of diversity. Our implementation code is available at https://anonymous.4open.science/r/SPT-1EE2/.

Continual unsupervised domain adaptation (UDA) for multi-domain cardiac image segmentation is crucial for clinical deployment under strict privacy constraints, where data from different centers cannot be stored or revisited. However, existing methods still suffer from catastrophic forgetting and training instability caused by noisy pseudo-labels and representation drift. In this study, we propose a novel continual UDA framework that enforces dual-level alignment at the input and feature levels. At the input level, the Domain-Style Adapting Generative Replay (DSA-GR) module employs trajectory-consistent diffusion distillation and style adaptation to synthesize high-fidelity, domain-consistent replay images without storing raw data. At the feature level, the Cross-domain Prototype-guided Feature Consolidation (CPFC) mechanism mitigates representation drift by maintaining a cross-domain prototype memory bank that aggregates features from the current and replay streams. These prototypes act as robust anchors for prototype-guided contrastive learning, rectifying the feature space and suppressing pseudo-label drift. Extensive experiments on two public datasets demonstrate that our framework consistently outperforms state-of-the-art methods. Code will be released at https://github.com/hlyf-xs/CUDA_Seg.

The primary challenge in incremental learning for deep neural networks (DNNs) lies in balancing memory stability with learning plasticity as they incrementally acquire new knowledge. Existing incremental learning methods typically require storing portions of old datasets or relying on specialized network architectures to achieve learning without forgetting. Recent neuroscience findings reveal that hippocampal sharp-wave ripples play critical roles in consolidating neocortex memory by internally triggering the reactivation of spontaneous neural circuits without external stimulus. Inspired by this biological mechanism, we propose an incremental learning method termed Sharp-Wave Ripples Learning (SWRL), a novel incremental learning framework composed of two functionally complementary modules: a neocortex-like network for long-term memory and a hippocampus-like model for rapid new-knowledge encoding. SWRL continuously integrates newly acquired knowledge from the hippocampus-like module into the neocortex-like network via a meta-weighted memory integration mechanism guided by internally generated SWR-like signals. Our framework is task-agnostic, architecture-agnostic, and exemplar-free. Experimental results on multiple public benchmarks demonstrate that SWRL achieves superior incremental learning performance under various scenarios across diverse DNN architectures, requiring no access to previous data or modifications to the base architecture. The code and appendix are both released at https://github.com/SYVAE/SWRL.

Adversarial training is effective on balanced datasets, but its robustness degrades under long-tailed class distributions, where tail classes suffer high robust error and unstable decision boundaries. We propose \emph{Manifold-Constrained Adversarial Training (MCAT)}, a unified framework that enforces the semantic validity of adversarial examples by penalizing deviations from class-conditional manifolds in feature space, while promoting balanced geometric separation across classes via an ETF-inspired regularization. We provide theoretical results that link geometric separation to lower bounds on adversarially robust margins, and show that manifold-constrained adversarial risk upper-bounds robust risk on high-density semantic regions. Extensive experiments on standard long-tailed benchmarks demonstrate consistent improvements in overall, balanced, and tail-class adversarial robustness. The codes and appendix are available on https://github.com/yneversky/MCAT.

Online EEG decoding is pivotal for real-world Brain-Computer Interfaces (BCIs) but confronts significant challenges arising from continuous distribution shifts, including both inter- and intra-subject variations. Existing Unsupervised Continual Domain Adaptation (UCDA) methods typically rely on rigid hard boundaries such as subject switch labels or fixed batch sizes to trigger adaptation. Meanwhile, some online EEG decoding methods that utilize detected shifts as soft boundaries are ill-suited for unsupervised scenarios and lack effective distribution alignment strategies. To address these issues, we propose a novel Prototype-driven online EEG Decoding framework (PRED). PRED incorporates a Subject Shift Detector (SSD) based on policy stability to reliably identify latent domain shifts unsupervisedly, thereby constructing adaptive soft boundaries. Furthermore, we design a Prototype-guided Shift Correction (PSC) mechanism that leverages a multi-granular memory structure to guide distribution alignment while preserving semantic stability. Experiments on three public datasets confirm that PRED achieves superior plasticity and stability, and demonstrates significant futurity, i.e., the capability of forward transfer to a subject's future samples.

Inductive Logic Programming (ILP) learns interpretable logical rules from data. Existing methods are transductive: their learned parameters are bound to specific predicates and require retraining for each new task. We introduce Neural Rule Inducer (NRI), a pretrained model for zero-shot rule induction. Rather than encoding literal identities, NRI represents literals using domain-agnostic statistical properties such as class-conditional rates, entropy, and co-occurrence, which generalize across variable identities and counts without retraining. The model consists of a statistical encoder and a parallel slot-based decoder. Parallel decoding preserves the permutation invariance of logical disjunction; an autoregressive decoder would instead impose an arbitrary clause order. Product T-norm relaxation makes rule execution differentiable, allowing end-to-end training on prediction accuracy alone. We evaluate NRI on rule recovery, robustness to label noise and spurious correlations, and zero-shot transfer to real-world benchmarks, and we believe this work opens up the possibility of foundation models for symbolic reasoning. Code and the reference checkpoint are available at https://github.com/phuayj/neural-rule-inducer. An extended version with full appendices is available at https://arxiv.org/abs/2605.04916.

Referring Multi-Object Tracking (RMOT) faces a fundamental structural contradiction between the high-discriminability demand and the sparse semantic supervision. This mismatch is particularly acute in highly homogeneous scenarios that require fine-grained discrimination over complex compositional semantics. However, under sparse supervision, models overfit to salient yet insufficient cues, leading to shortcut learning and degraded compositional discrimination. To resolve this, we propose COAL (Counterfactual and Observation-enhanced Alignment Learning), a framework that advances RMOT beyond isolated structural optimization through knowledge regularization. First, we introduce Explicit Semantic Injection (ESI) via a VLM to densify the observation space and enhance instance discriminability. Second, leveraging LLM reasoning, we propose Counterfactual Learning (CFL) to augment supervision, enforcing strict attribute verification for robust compositional recognition. These strategies are unified within a Hierarchical Multi-Stream Integration (HMSI) architecture, which distills external knowledge into domain-specific discriminative representations. Experiments on Refer-KITTI and Refer-KITTI-V2 benchmarks validate COAL's efficacy. Notably, it surpasses the state-of-the-art by 7.28% HOTA on the highly challenging Refer-KITTI-V2. These results demonstrate the effectiveness of knowledge regularization for resolving the sparsity–discriminability paradox in RMOT.

As Large Language Models (LLMs) scale to handle massive context windows, achieving surgical feature-level interpretation is essential for high-stakes tasks like legal auditing and code debugging. However, existing local model-agnostic explanation methods face a critical dilemma in these scenarios: feature-based methods suffer from attribution dilution due to high feature dimensionality, which prevents them from providing faithful explanations. In this paper, we propose Focus-LIME, a coarse-to-fine framework designed to restore the tractability of surgical interpretation. Focus-LIME utilizes a proxy model to curate the perturbation neighborhood, allowing the target model to perform fine-grained attribution exclusively within the optimized context. Empirical evaluations on long-context benchmarks demonstrate that our method makes surgical explanations practical and provides faithful explanations to users.

Existing multi-modal automatic modulation recognition (AMR) methods primarily focus on exploiting multi-view representations of raw signal data to improve performance, but still struggle to effectively model and exploit high-level human prior knowledge. Although recent studies attempt to introduce large language models (LLMs) to integrate the textual modality, most of them merely treat LLMs as feature extractors, without further exploiting the LLM's potential. To this end, we propose a knowledge-enhanced multi-modal automatic modulation recognition framework based on large language model (AMR-LLM). Specifically, we first specially design an AMR instruction construction mechanism based on knowledge to activate the LLM's potential for signal perception, which designates the LLM as a signal domain expert, introduces human prior knowledge as a supplement, and exploits the unique physical information of each data sample as connection guidance. Then, to enable the LLM to directly perceive digital signals, we introduce a progressive multi-modal fusion strategy mapping signal features into the space of LLMs. Experimental results on multiple benchmark datasets demonstrate that AMR-LLM achieves approximately 5% higher accuracy than current state-of-the-art methods. Moreover, it achieves the current domain performance with only 30% of the data.

Temporal Knowledge Graph Reasoning (TKGR) aims at inferring missing (especially future) events from historical data. Current evaluation in TKGR uniformly weights all events, ignoring that most are trivial repetitions, which overestimate the true reasoning ability. Therefore, the rare outstanding events, whose prediction demands deeper reasoning, should be distinguished and emphasized. To this end, we propose a strikingness-aware evaluation framework, which introduces a rule-based strikingness measuring framework (RSMF) to quantify event strikingness by comparing its expected occurrence with peer events derived from temporal rules. Strikingness is then integrated as a weighting factor into metrics like weighted MRR and Hits@k. Experiments on four TKG benchmarks reveal: 1) All representative models perform worse as event strikingness increases, 2) Path-based methods excel on low-strikingness events and representation-based ones on high-strikingness events, 3) We design an ensemble method whose gains stem from fitting trivial events rather than reasoning improvement. Our framework provides a more rigorous evaluation, refocusing the field on predicting outstanding events.

Graph Neural Networks (GNNs) are widely used in high-stakes decision-making, where explanation robustness is crucial. Yet, even minor structural perturbations can drastically change explanations without changing predictions. Existing methods primarily characterize structural perturbations by their edit distance or budget, and enforce robustness through uniform constraints or averaging mechanisms over this perturbation set, which fail to capture the inherently direction-dependent effects of structural changes.
In this paper, we propose GeoXGNN (Local Geometry Improves Explanation Robustness for Graph Neural Networks), a robust explanation framework that explicitly models the directional sensitivity of structural perturbations from a geometric perspective. GeoXGNN introduces local, direction-aware metrics in the node representation space to characterize how perturbations along different directions affect node embeddings and explanation outcomes. Based on the learned local metrics, GeoXGNN further enforces directional constraints, encouraging explanations to remain robust along high-sensitivity directions while preserving explanatory fidelity.
Extensive experiments on multiple node-level and graph-level benchmark datasets demonstrate that GeoXGNN significantly improves explanation robustness over existing methods.

Multi-view learning aims to enhance performance by integrating information from multiple sources. While different views offer complementary perspectives, extracting consistent and discriminative representations remains a significant challenge due to discrepancies in representation and presence of noise. Existing methods typically separate the denoising and alignment processes, mapping the denoised heterogeneous views to a shared subspace for alignment. This means that noise may propagate or even amplify during the alignment stage, ultimately leading to suboptimal solutions. To address this issue, we propose center-guided spectral diffusion, which replaces traditional alignment with generative modeling. This method avoids noise propagation in multi-view alignment process and prevents multi-view features be aligned to the noise subspace during fusion process. Specifically, we first performs unconditional diffusion in both the feature space and a low-rank spectral space to learn a stable consensus centre anchor. This anchor is then used to condition a guided generative diffusion process, enabling the model to generate more consistent and realistic sample representations. By combining conditional and unconditional diffusion, the proposed method alleviates the noise amplification problem commonly found in traditional alignment methods. Experimental results show that the proposed method outperforms state-of-the-art methods on several datasets.

Fourier neural operators (FNOs) are effective and efficient surrogates for approximating solutions of PDEs and generalize across discretizations. However, owing to the reliance on frequency truncation to maintain learning efficiency of FNOs, empirical studies suggest that FNOs exhibit spectral bias toward low-frequency information, which may hinder the learning capability especially for certain PDEs with strong high-frequency oscillations. To address this limitation, we propose SirenFNO, a novel framework that leverages sinusoidal representation networks (SIRENs) to learn implicit neural representations and performs mode-wise kernel parameterization. Our SIREN parameterization learns a full-grid spectrum with a constant and discretization-independent parameter count, thereby eliminating the need for frequency truncation. We further extend SirenFNO with functional tensor decompositions to enhance parameter and learning efficiency. Empirical results show that our SirenFNO consistently outperforms FNO with approximately 4 to 15 times parameter reductions with preserved discretization invariance, and our functional decomposition variants obtain performance improvements with a maximum of 73 times fewer parameters across multiple PDE benchmarks.

Multi-modal point cloud completion aims to recover complete 3D geometric structures from partial observations by integrating auxiliary data. Although image-guided techniques are well-established, the potential of natural language as a source of high-level semantic cues remains under-explored. Therefore, effectively introducing natural language and synergizing it with the image modality to assist 3D structure recovery is key to breaking current performance bottlenecks. To address this, we propose Query-Aware Gating Gramian Alignment (QGGA), a framework for multi-modal completion. QGGA employs a novel query-dependent bidirectional attention mechanism to dynamically filter information flows between modalities and adaptively regulate fusion intensity. To facilitate alignment among the three heterogeneous modalities, we utilize the volume of the parallelotope spanned by multi-modal local features as a consistency metric, constraining geometric alignment by minimizing this volume. Finally, the fused and aligned features are fed into a coarse-to-fine decoder to first predict a coarse point cloud and then progressively recover the complete shape through stepwise upsampling and refinement. Furthermore, we construct ShapeNet-ViPC-Desc, a benchmark dataset enriched with detailed text descriptions. Experimental results demonstrate that QGGA achieves new state-of-the-art (SOTA) performance on this benchmark. The source code is freely accessible at https://github.com/whysq/QGGA.

Attribute-aware sequential recommendation entails predicting the next item a user will interact with based on a chronologically ordered history of past interactions, enriched with item attributes. Existing methods typically leverage self-attention mechanisms to aggregate the entire sequence into a unified representation used for next-item prediction. While effective, these models often suffer from high computational complexity and memory consumption, limiting their ability to process long user histories. This constraint restricts the model's capacity to fully capture long-term user preferences. In some scenarios, modeling item interactions purely through attention may also not be the most effective approach to extract sequential patterns. In this work, we propose ConvRec, an alternative method with linear computational and memory complexity that employs convolutional layers in a hierarchical, down-scaled fashion to generate compact, yet expressive sequence representations. To further enhance the model's ability to capture diverse sequential patterns, each layer aggregates the neighboring items gradually to reach a comprehensive sequence representation. Extensive experiments on four real-world datasets demonstrate that our approach outperforms state-of-the-art sequential recommendation models, highlighting the potential of convolution-based architectures for efficient and effective sequence modeling in recommendation systems. Our implementation code and datasets are available at https://github.com/ismll-research/ConvRec.

Virtual Knowledge Graphs (VKGs) provide an effective solution for data integration by mapping heterogeneous data sources to a unified ontology.
However, existing VKG construction frameworks primarily focus on one-shot construction, which often results in partial data coverage and support for only initial information needs.
After the VKG has been deployed, new information needs will inevitably arise over time.
Therefore, enriching VKGs to support evolving information needs remains an expert-intensive iterative task.
In this work, we formulate the task of Information-Needs-Guided VKG Enrichment (IN-VKGE), and propose an iterative framework that leverages large language models to assess whether information needs can be supported using SPARQL execution feedback and generate ontology and mapping enrichment proposals.
Experiments on two real-world VKGs show that our approach outperforms existing paradigms, and produces enrichment proposals that receive high expert ratings for effectively resolving the identified information needs.

Clustering is fundamental to scRNA-seq analysis, serving as a cornerstone for identifying cell populations and resolving tissue heterogeneity. However, existing methods focus on mining numerical statistical patterns, suffering from semantic agnosticism by neglecting the intrinsic biological functions encoded by genes. While Large Language Models (LLMs) offer promising semantic capabilities, their direct adaptation to cell clustering is hindered by the structural mismatch between generative pre-training objectives and discriminative downstream tasks. To bridge this gap, we propose scLLM-DSC, a novel LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering framework. Diverging from data-driven paradigms, scLLM-DSC establishes a semantically-grounded representation by synergizing two views: a Knowledge-Driven Semantic View derived from NCBI gene priors and contextualized Cell2Sentence embeddings, and a Structure-Aware Topological View extracted via a graph-guided encoder. Crucially, we introduce a cross-modal contrastive alignment mechanism to enforce consistency between biological semantics and transcriptomic features within a unified latent space. Extensive benchmarks demonstrate that scLLM-DSC significantly outperforms eleven state-of-the-art baselines in clustering accuracy.

Accurate prediction of prerequisite relations among concepts is important for course planning and intelligent tutoring systems. Existing text-based methods are frequently contaminated by noise such as redundant phrasing, ambiguous sentences, and domain-specific colloquialisms. Moreover, previous methods based on graph structures may have failed to capture the complex interactions between concepts and documents, an information gap exists between the two approaches. Therefore, we propose an LLM Enhancement and Concept-Document Interactive Modeling for Prerequisite Relation Prediction (LECDPR) model. Concretely, LECDPR elicits an LLM via two-stage prompting to distill concept descriptions and contextual evidence from documents, resulting in semantic space embeddings of concepts. Then, the complex interactive relations between concepts and documents are modeled through three propagation mechanisms to produce relational space embeddings, aligns the two embeddings through random masking and contrastive learning, and adaptively fuses the resulting embeddings. Finally, the fused concept representations are used for more accurate prerequisite relation prediction. Extensive experiments on the public UCD, Lecture Bank, and MOOC datasets show that LECDPR outperforms ten baselines on ACC, AUC, and F1. Our code is available at https://github.com/lecdpr/LECDPR.

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.

The Spherical Sliced Wasserstein (SSW) distance has emerged as a fundamental tool for comparing probability measures on the hypersphere, with broad applications spanning geology, medical imaging, computer vision, and representation learning. However, its reliance on uniformly sampling a large number of projection directions from the unit hypersphere can lead to suboptimal performance, as many directions are weakly informative and fail to capture salient differences between distributions. We address this limitation through Fusion Stereographic Spherical Sliced Wasserstein (FS3W), a data-adaptive divergence that incorporates distribution-dependent information into projection selection. FS3W uses a neural slicing fusion mechanism to refine prior directions, steering them toward projections aligned with discriminative geometric structures in the data. This adaptive approach produces more expressive and accurate characterizations of distributional discrepancies on the hypersphere. We evaluate FS3W across five diverse tasks, including gradient flows, Earth density estimation, and self-supervised representation learning. Empirical results consistently demonstrate that FS3W outperforms existing spherical optimal transport–based methods.

Designing effective reward functions remains a major challenge in reinforcement learning (RL), particularly in open-ended environments where task goals are abstract and difficult to quantify. In this work, we present VLM-AR3L, a framework that leverages Vision-Language Models (VLMs) to provide both absolute and relative rewards for RL. VLM-AR3L interprets an agent’s visual observations in the context of a natural language task goal, and learns both absolute and relative rewards from VLM-generated preference labels. The absolute reward model predicts scalar evaluations for individual states, while the relative reward model compares consecutive observations to infer progress or regression toward the task goal. Their integration combines the stability of state-based evaluation with the robustness of comparative supervision. We evaluate VLM-AR3L across benchmarks spanning classic control, manipulation, and open-world embodied tasks, with a particular focus on Minecraft given its visual complexity and long-horizon decision-making requirements. Experimental results show that VLM-AR3L consistently outperforms prior VLM-based reward learning methods. Videos and code are available on the project website: https://vlm-ar3l.github.io/.

Graph self-supervised learning (SSL) typically relies on large-scale unlabeled datasets, heavily inflating computational costs. However, empirical evidence suggests that these datasets contain substantial redundancy—our analysis reveals that uniformly subsampling 50% of graphs retains over 96% of downstream performance. To exploit this redundancy, we introduce GraphSculptor for pre-training coreset construction. Unlike methods dependent on additional training-time signals or limited solely to topological statistics, GraphSculptor provides a label-free solution that constructs coresets via two complementary perspectives: intrinsic structure and contextual semantics. Concretely, structural diversity is quantified using intrinsic graph statistics, yielding a structural feature vector for each graph, while semantic diversity is captured by utilizing a pre-trained language model to encode descriptions generated via graph-to-text. GraphSculptor integrates these signals into a unified metric space and performs cluster-aware selection to preserve joint structural--semantic diversity. We further derive a theoretical bound on the loss gap between coreset and full-data pre-training, offering theoretical motivation for our selection formulation. Extensive experiments demonstrate that GraphSculptor effectively "sculpts" the dataset: a 10% coreset achieves 99.6% of full-data performance while reducing pre-training time by nearly 90%, offering a scalable solution for data-efficient graph pre-training.

Generative models have emerged as a powerful paradigm in sequential recommendation due to their superior distribution modeling. However, long-tail data distributions inevitably induce popularity bias, as iterative generation trajectories gravitate toward dense clusters of popular items. Current debiasing methods struggle to enforce consistent step-wise constraints, rendering them ineffective for the multi-step process of generative recommendation. To address this challenge, we propose a method called Popularity-Debiased Flow Matching for sequential recommendation (PDFlow). Specifically, grounded in the flow matching framework, PDFlow integrates a residual popularity vector field beyond the main flow to model the popularity debiasing flow at each step. This enables real-time trajectory intervention to adjust the generative path, preventing the model from overemphasizing popular items. Subsequently, we incorporate cross-popularity alignment loss, using co-occurrence patterns within the same sequence to align features of both popular and tail items, thereby mitigating popularity-driven distribution separation. Jointly, trajectory correction prevents popularity overfitting while cross-popularity alignment ensures latent fairness, effectively mitigating popularity bias. Extensive experiments on four real-world datasets demonstrate the effectiveness of PDFlow in improving long-tail coverage and overall recommendation performance. All codes and datasets are available at https://github.com/eqmll/PDFlow.

Conventional threat detection methodologies encounter substantial limitations in real-world complex network settings, owing to their dependence on single-dimensional analysis, vulnerability to adversarial perturbations, and propensity for overfitting. This study introduces an adversarial training optimization framework that incorporates hierarchical label encoding and prompt learning, designed to enhance model robustness and generalization in threat detection. The framework first establishes a hierarchical structure of attack scenarios and types, leveraging a graph attention network to encode semantic and structural dependencies among labels. Subsequently, the classification task is reformulated as a masked language modeling problem through prompt learning, enabling effective semantic alignment. Furthermore, a novel adversarial training mechanism is proposed, which utilizes local hierarchical information as a potent regularization signal. Within a game-theoretic architecture comprising a generator, an encoder, and a discriminator, the encoder is steered to integrate authentic hierarchical priors and produce high-fidelity oracle representations. This process encourages the generator to implicitly assimilate sample-specific hierarchical knowledge during adversarial learning, thereby improving the model's resilience to noisy inputs and its capacity to detect infrequent attacks. Evaluation results show that this method achieves an accuracy of 0.9972–1.0000 in complex mixed scenarios for scenario detection and 0.9987–0.9999 for attack category detection, while exhibiting strong robustness against interference.

Network Traffic Anomaly Detection (NTAD), particularly under zero-positive settings, is a critical task in cybersecurity. Existing zero-positive NTAD approaches primarily rely on reconstruction-based pipelines. Nevertheless, these methods are susceptible to an identical shortcut issue, where models indiscriminately reconstruct both normal and anomalous inputs, leading to anomaly overgeneralization. To address this limitation, we propose BiPred, the first prediction-based detection paradigm for NTAD. BiPred symmetrically divides each network traffic sample into two segments and reformulates the NTAD task as a bidirectional prediction across these two segments. Unlike reconstruction-based methods, our BiPred not only eliminates shortcut learning by design but also establishes explicit bidirectional contextual dependencies, making it more sensitive to anomalous traffic. Moreover, we develop a novel Residual Scanning Mamba block that encodes multi-view contextual information to support bidirectional prediction. A residual fusion mechanism is proposed for the multi-view scanning Mamba to suppress the accumulation of inter-view redundancy. This design prevents representation degradation and provides multi-view contextual details for prediction. Extensive experiments demonstrate the superiority of our BiPred paradigm, highlighting a new research direction for NTAD. Code is available at https://github.com/ikun0124/BiPred.

Most of the existing decentralized multi-agent systems facilitate coordination by achieving consensus. However, reaching global consensus among a large number of agents in complex settings is challenging because resolving a local disagreement often triggers cascading adjustments across the system. This problem is further exacerbated under intermittent communication. In the field of transportation, human drivers usually rely on intentions (e.g., turn signals) in conjunction with traffic rules (such as "yielding to through traffic") to achieve efficient cooperation, instead of establishing consensus with all surrounding vehicles. Inspired by this observation, we propose the Intention \otimes Latent Rule paradigm to facilitate coordination beyond consensus, which is instantiated by DIRECT (Decentralized Intention and Latent Rule Emergence Cooperation). Within this framework, agents forecast their intentions based on current actions, and the latent rule inference module derives latent-rule representations from available information to regulate competing intentions, enabling cooperative action selection even when communication is disrupted. Extensive experiments on challenging StarCraft II micromanagement and Multi-Agent Particle Environments demonstrate that DIRECT consistently outperforms state-of-the-art methods, achieving more robust coordination across a range of communication-degraded scenarios. Our code is available at https://github.com/AAI-ZZU/DIRECT-master.

We present an extension of the Angluin-style learning algorithm for tree automata that incorporates deductive inference.
The learning algorithm is provided with a term rewriting system that specifies properties of the target tree language (e.g., the order of subtrees under a symbol f is irrelevant).
This term rewriting system is used to infer answers to some queries, which reduces the query complexity of the learning algorithm.
We present examples of rewrite systems that express natural properties of tree-structured data, which yield a significant reduction in the number of queries.

Hyper-relational Knowledge Graphs (HKGs) extend traditional knowledge graphs by introducing high-order dependencies, where auxiliary qualifiers provide detailed information. However, modeling such intricate topologies (e.g., mixtures of hierarchies, cycles, and chains) is challenging. Existing methods suffer from structural distortion due to reliance on a single geometric space and overlook dynamic semantics across entity roles. To address these issues, we propose AdaGeM (Adaptive Geometric Learning on Manifolds), an innovative space-adaptive framework that integrates geometric learning with a role-aware Transformer. First, we propose an adaptive geometric hypergraph encoder that projects entities into a product manifold and design a topology-aware gating mechanism to dynamically select optimal geometric spaces for each entity. Second, we develop a role-aware Transformer equipped with role-specific projections and micro-structural bias injection to refine embeddings by distinguishing entity semantics across different roles and focusing on valid n-ary interactions, respectively. Extensive experiments on benchmark datasets demonstrate that AdaGeM outperforms state-of-the-art baselines in entity/relation prediction tasks, with ablation studies validating the necessity of multi-manifold modeling for heterogeneous HKG structures.

In this work, we investigate the effect of lookahead branching strategies in neural network verification. We present a general recipe to integrate lookahead into any branch-and-bound verifier and demonstrate how one of the current state-of-the-art branching heuristics, FSB, can be viewed as a special instantiation of the lookahead branching strategy. We also describe how, in addition to improving the quality of branching decisions, lookahead can generate additional lemmas that accelerate verification. We instantiate the method in two representative branch-and-bound-based verifiers (Marabou and α-β-CROWN), and demonstrate that lookahead leads to consistent speedups in verification time and up to 57% more solved instances. Code is available at https://github.com/ai-ar-research/lookahead-branching.

Training critique models to provide useful feedback for actor models is an effective approach in scalable oversight, especially for complex tasks like math reasoning. However, current research lacks suitable datasets for effectively training critique models and integrating them in a principled way at both test time and training time. To bridge this gap, we first propose AutoMathCritique, an automated and scalable framework for collecting critique data. Using it, we create MathCritique-76k, a dataset of $76,321$ instances paired with step-level feedback, which is then used to train critique models for generating natural-language feedback. We show that critique models consistently improve the actor’s performance—particularly on difficult queries at test time—with larger gains as inference compute scales. Building on the findings, we propose a critique-in-the-loop self-improvement method that incorporates critique-based supervision into the actor’s self-training process. Extensive experiments demonstrate that this method improves the actor’s exploration efficiency and solution diversity, especially on challenging queries, leading to a stronger actor model. Our code and datasets are at https://mathcritique.github.io/.

Continual test-time adaptation (CTTA) adapts a pre-trained medical segmentation model online to an unlabeled target stream whose distribution changes over time. However, most existing CTTA methods rely on pseudo-labeling and self-supervised objectives, which inevitably yield noisy supervision under domain shifts. To mitigate this limitation, we introduce off-the-shelf Vision Foundation Models (VFMs) as external knowledge sources. Zero-shot VFMs are insufficient for medical segmentation because they lack medical semantics, yet they contain rich and heterogeneous generic knowledge. To exploit such external knowledge, we propose Reciprocal Distillation with a Frozen Foundation Model (ReDi-FM), a novel framework with two core components: using structure-aware prompts from the source model to guide the frozen VFM to generate target-adapted supervision, and distilling the resulting knowledge back into the adapting model. For robust distillation, we introduce two complementary objectives: uncertainty-driven hard distillation for precise guidance in ambiguous regions, and hard class-balanced soft distillation for richer supervision of under-represented and challenging structures. A consensus-aware gate further stabilizes adaptation when the two teachers disagree. Extensive experiments on multi-domain medical segmentation benchmarks demonstrate that ReDi-FM outperforms state-of-the-art CTTA methods. Code is available at https://github.com/M4cheal/ReDi-FM.

Online goal recognition in continuous domains poses two central challenges: efficiently encoding large trajectories and effectively comparing them.
Recent work addresses these challenges by using custom state-space representations and metrics to compare observations against hypotheses.
However, these approaches often overlook well-established encoding techniques used in other domains that offer substantial advantages.
This paper introduces a novel method for online goal recognition that leverages path signatures, a compact, expressive representation of rough path theory that captures key semantic features of trajectories in an efficient way, enabling more meaningful comparisons between them.
Experiments show that our method consistently outperforms the state of the art in predictive accuracy and online planning efficiency, while remaining competitive offline.

Multi-label data often contain high-dimensional features, outlier instances, and noisy labels, all of which can lead to the curse of dimensionality and decreased performance in downstream tasks. Although numerous data reduction methods have been developed, existing approaches face two major limitations: 1) existing methods typically select features, instances, or labels independently, without considering how noise or redundancy in one dimension may negatively influence the selection of others; 2) there are very few feature and instance co-selection methods that commonly assume label annotations are free of noise, which is seldom true in practice. To address these issues, we propose Evidential Multi-Label Multi-Dimensional Selection (EMMS), which jointly performs feature, instance, and label selection on multi-label data. EMMS introduces a dual projection mechanism with sparsity constraints that transforms high-dimensional data first into a latent space and then into the label space. Simultaneously, projection residuals are explicitly modeled to facilitate the identification of representative instances, enabling unified selection across features, instances, and labels. Moreover, EMMS employs evidence theory to fuse instance-level and label-level evidence, thereby enhancing the reliability of the learned labels and reducing the influence of noisy labels, which in turn promotes multi-dimensional selection. Extensive experiments demonstrate that EMMS consistently outperforms state-of-the-art methods.

Current Parameter-Efficient Fine-Tuning (PEFT) methods typically operate under an implicit assumption: once a target module is selected, every token passing through it contributes equally to the downstream task and requires a parameter update. In this paper, we challenge this convention by revealing a pervasive token-level redundancy in the fine-tuning of large models (LMs). We propose TS-PEFT, a theoretical framework utilizing proximal optimization that acts as a dynamic probe to identify token-level redundancy during the fine-tuning process. Extensive experiments demonstrate that indiscriminately updating all tokens is not only computationally superfluous but often introduces optimization noise. Surprisingly, by discarding 30%-70% of token updates, TS-PEFT consistently matches or exceeds the performance of dense baselines such as LoRA, DoRA. Our in-depth analysis shows that the learned token-level sparsity is a superior indicator of module importance compared to traditional weight criteria, providing a novel data-driven perspective on the intrinsic adaptation mechanism of LMs.

Continual knowledge graph embedding (CKGE) has gained popularity for managing dynamic knowledge graphs. Unlike general graph continual-learning approaches, CKGE focuses on retaining triple-level knowledge, thereby overcoming the inability of static models to accommodate continuously arriving facts. However, as new fact triples come in, real-world knowledge graphs often experience structural distribution shifts: the underlying topology and the way facts interconnect evolve over time. This presents a challenge that the structural distribution of historical data may diverge from that of emerging data, creating a distributional mismatch that complicates the adaptation to new trends. To address this, we propose Pattern-Anchor Alignment (PAAL), which introduces relation-level structural anchors to explicitly model these structural shifts. By quantifying the alignment between the structural distribution of emerging triples and historical anchors, PAAL implements an alignment-modulated optimization strategy. It utilizes a calibrated gating mechanism to precisely regulate the intensity of historical constraints based on structural stability. Experimental results indicate the effectiveness of PAAL, showing average H@1 improvements of 11.77% and 9.29% on pattern-shift and standard CKGE benchmarks, respectively. Our code is available at https://github.com/cangjie553/PAAL.

With the explosive growth of text generated by large language models (LLMs) on the internet, technologies for detecting LLM-generated text are increasingly important. However, existing technologies often require frequent access to LLMs during detection, significantly restricting their application in scenarios where only the text is accessible. To tackle this challenge, we identify that human-written texts exhibit higher entropy than LLM-generated texts. Motivated by this, we propose a novel method named IED, which leverages the Information Entropy for generative text Detection. Specifically, IED first transforms the given text into a vector of which each dimension represents the information entropy of each word, and then adopts a classifier to conduct the detection. Further, we propose IEGD which adopts the information gain to construct the vector by enhancing the differentiation between human-written and LLM-generated texts. To evaluate the effective of the proposed method, we construct a new large-scale dataset comprised of generated texts using various LLMs (LLaMA-7B, GPT-NeoX, etc.). Extensive experiments show that the proposed methods achieve improvement over state-of-the-art methods by up to 10.81%.

Deepfake detection faces dual challenges in real-world deployment: cross-domain generalization and demographic fairness. Existing approaches often struggle with a trade-off between these goals. Generalization-oriented detectors can over-rely on demographic shortcuts, while fairness constraints tend to steer optimization away from the most discriminative decision boundary. To address this, we propose Orthogonal Semantic Decoupling (OSD), a framework that decouples demographic semantics from forgery cues. Specifically, we perform Singular Value Decomposition on the pretrained weights of a vision-language model, freezing the principal semantic subspace while learning parameter-efficient low-rank experts in the residual subspace. The experts comprise (1) Demographic Semantic Experts, a set of experts specialized via hard sampling and routed based on the similarities between image embeddings and text embeddings of predefined descriptions; and (2) a Universal Forgery Expert, which captures forgery features transferable across domains and demographics. Extensive experiments across multiple benchmarks demonstrate that our approach outperforms state-of-the-art methods in both generalization and fairness, breaking the trade-off. The code is available at https://github.com/sonder-lin/osd-deepfake-detection.

In human-aware vision-and-language navigation (HA-VLN), agents need to follow instructions while navigating safely among people in crowded environments. Existing methods often struggle to effectively transform forward-looking risk prediction into decision-making basis, resulting in intelligent agents being prone to collisions in crowded scenarios and taking overly cautious actions, thereby reducing navigation efficiency. Moreover, it is difficult for high-level decisions based on social norms to be stably implemented in dynamic crowd scenarios, which can easily lead to behavioral jitter and planning deviation. In this work, we propose the AHead-VLN, a navigation framework that incorporates forward-looking risk information in the reasoning process. Specifically, we designed the path risk association (PRA) module, which compiles future risks into structured risk events aligned with each candidate path, making risk information decision-ready for the large language model (LLM) reasoning. To balance executability and decision stability, we have further designed the hierarchical LLM planner (HLP) with two specialized components. The ``Brain'' LLM makes route choices based on instructions and risks, while the ``Cerebellum'' LLM prepares executable action scripts for candidate routes and retrieves the appropriate script for execution. Experiments on the HA-VLN benchmark show that AHead-VLN improves success while enhancing safety compared to strong baselines.

The diffusion auction is an emerging auction model which leverages social networks to recruit more buyers, thereby improving auction revenue and allocation efficiency. Previous studies on diffusion auctions have primarily focused on the single-parameter domain, where each buyer's valuation function is represented by a single private value. However, due to the complex interdependencies between allocations and network structures, few studies have examined the combinatorial auction domain, in which buyers' valuation functions exhibit multidimensionality. In this paper, we fully characterize implementable diffusion mechanisms within the combinatorial auction domain for the first time. Based on the characterization, we identify a class of allocation policies for each of which the optimal payment, namely the one maximizing the seller's revenue, can be derived in closed form. Furthermore, we apply the established theory to design a practical diffusion combinatorial auction, named the Exhausted-Reference Pricing Mechanism, which is incentive-compatible, individually rational, and weakly budget balanced.

Recent deep learning-based ISP methods are primarily constrained by mimicking fixed camera pipelines, consequently struggling to achieve expert-level aesthetic quality. To this end, we propose AesISP, the first expert-level aesthetic ISP framework formulated as a reward flow model. Addressing the ill-posed nature of ISP, AesISP establishes a Privileged Prior Distillation paradigm. By leveraging expert-image-guided latent proxies, we decompose the intractable task into a tractable, proxy-driven learning process. Subsequently, we propose MeanFlow++, which reformulates the flow matching objective via target-prediction parameterization and a Progressive Temporal Curriculum. By evolving from learning instantaneous to long-range average velocities, this mechanism rectifies transport trajectories and uses deterministic mapping to impose explicit constraints, anchoring the generation trajectory to the expert manifold. Finally, we introduce a multi-dimensional reward-driven reinforcement learning approach.Leveraging Group Relative Policy Optimization (GRPO) to balance trade-offs across dimensions such as color and lighting, it steers the model to converge precisely on expert-level aesthetic standards. Experiments demonstrate that AesISP outperforms state-of-the-art methods in both quantitative metrics and aesthetic quality.

In many large language model (LLM) applications, generating structurally diverse candidate outputs is crucial for downstream decision-making and reasoning. However, conventional beam search allocates search budget at the token-prefix level, so multiple beam slots may be occupied by lexically different but structurally similar hypotheses. To address these limitations, we propose Grammar-State Aware Beam Search (GSA-Beam). Our method first organizes search at the constraint state level rather than individual prefix level, enabling explicit control over structural diversity. It further incorporates a dynamic state-level beam regulation mechanism that adaptively adjusts beam allocation based on the branching of constraint states, efficiently exploring the feasible structured solution space. Experiments on JSON-schema constrained generation show that GSA-Beam improves explicit state coverage and final structural diversity while preserving schema validity; across several LLM backbones, it reduces reference latency by 22.5% on average compared with standard constrained beam search. Code is available at https://github.com/Paradozile/GSABeam.

Multimodal recommender systems exploit visual and textual signals to alleviate data sparsity, but this also makes them more vulnerable to evasion-based promotion attacks. Existing defenses are largely limited to single-modal settings and mainly focus on poisoning-based threats, leaving evasion-based threats underexplored. In this work, we first identify a cross-modal gradient mismatch under the multi-user promotion setting, where visual and textual perturbations are optimized in inconsistent directions due to the dominance of distinct user groups. This phenomenon dilutes the attack effectiveness and leads robust training to underestimate worst-case risks. To address this issue, we propose Untargeted Adversarial Training with Multimodal Coordination (UAT-MC). UAT-MC tackles the challenge of unknown targeted items in evasion-based attacks (as opposed to poisoning-based attacks) by treating all items as potential targets, and introduces a gradient alignment mechanism to explicitly correct this mismatch. This design ensures synchronized perturbations across modalities, thereby maximizing adversarial strength for robust training. Extensive experiments demonstrate that UAT-MC significantly improves robustness against promotion attacks while maintaining acceptable recommendation performance under the defense–accuracy trade-off. Code is available at https://github.com/
gmXian/UAT-MC.

Time series forecasting (TSF) refers to a fundamental task of predicting future sequential data based on historical observations. One representative category of TSF methods is transformer-based approaches, which translate time series into token sequences (i.e., as raw texts) before applying well-established transformers. In this paper, we conducted extensive preliminary experiments to evaluate their stability against various noises, and we empirically observed an interesting phenomenon: applying noises with appropriate levels to training time series can promote the predictive accuracy of transformer-based TSF methods. We analyze this phenomenon from the perspective of token attention and find one basic reason is that applying noises with appropriate levels can lead to token attention weights wavy, which is more consistent to the basic characteristic of time series data. Motivated by this phenomenon and analysis, we apply perturbations and propose a sub-objective wrt, perturbations constraining token attention weights to be wavy. Accordingly, we propose a novel TSF method, namely Wave-Attention-aware TransformER (WATER). We empirically evaluate \baby across the commonly used benchmark datasets, and experimental results indicate that it consistently outperforms the existing TSF methods.

Probabilistic time series forecasting seeks to quantify the uncertainty of future observations. While recent works introduce latent variables to alleviate the spurious dependencies caused by hidden confounders, thereby reducing overly wide confidence intervals, simply incorporating latent factors is not sufficient. When the underlying latent dynamics evolve at multiple temporal scales, existing methods may entangle temporally coarse and fine latent dynamics, which introduces spurious latent transitions and in turn amplifies predictive uncertainty. Therefore, disentangling temporally coarse and fine latent dynamics is essential for achieving sharper and more reliable probabilistic forecasting results. Building on this insight, we propose COFE (COarse And FinE latent dynamics disentanglement), a variational autoencoder–based framework that models the temporal distribution by disentangling and modeling the rapidly changing and slowly varying latent dynamics simultaneously. In particular, the proposed COFE harnesses the independence of estimated noises between adjacent latent states as well as a sparsity constraint on estimated noise to disentangle latent dynamics across different scales effectively. More specifically, we show that the multi-scale latent dynamics are disentangled with rigorous theoretical guarantees. Extensive experiments on 15 benchmark datasets, compared against 12 state-of-the-art baselines, demonstrate that COFE achieves superior uncertainty quantification, validating its effectiveness in a wide range of real-world scenarios. Code is available at https://github.com/polars8948/COFE

Exploiting unlabeled data through semi-supervised learning (SSL) or leveraging pre-trained models via fine-tuning are two prevailing paradigms for addressing label-scarce scenarios. Recently, growing attention has been given to combining fine-tuning of pre-trained vision-language models (VLMs) with SSL, forming the emerging paradigm of semi-supervised fine-tuning. However, existing methods often suffer from model bias and hyperparameter sensitivity, due to reliance on prediction consistency or pre-defined confidence thresholds. To address these limitations, we propose a simple yet effective plug-and-play methodology named Bi-Consistency-Guided Self-Training (Bi-CoG), which assigns high-quality and low-bias pseudo-labels, by simultaneously exploiting inter-model and intra-model consistency, along with an error-aware dynamic pseudo-label assignment strategy. Both theoretical analysis and extensive experiments over 14 datasets demonstrate the effectiveness of Bi-CoG, which consistently and significantly improves the performance of existing methods.

Reachability games are two-player games played on a graph, where the objective of REACH player is to reach the target set whereas the objective of SAFE player is to stay away from the target set. Reachability games have important applications in artificial intelligence and reactive synthesis, and many of these applications give rise to infinite-state reachability games. In this paper, we study turn-based reachability games on infinite-state graphs defined over valuations of a finite set of real variables. We consider the problem of determining the existence of and computing a winning strategy for REACH player. Our contributions are twofold. First, we propose ranking certificates for reachability games, a sound and complete proof rule for proving that REACH player has a winning strategy from the specified initial state. Second, we consider polynomial reachability games, where transitions and objectives are described by polynomial constraints over real variables, and propose a fully automated algorithm for computing a winning strategy for REACH player together with a formal correctness witness in the form of a ranking certificate. The algorithm is sound, semi-complete, and runs in sub-exponential time. Our experiments demonstrate the ability of our method to solve challenging examples from the literature that were out of the reach of existing methods. Specifically, for the classical Cinderella-Stepmother game, we are able to compute an optimal winning strategy for an arbitrary precision parameter for the first time.

We present two new algorithms for the problem of lexicographic inference from conditional knowledge bases. These algorithms are based on SAT and MaxSAT encodings of the underlying problem of lexicographic comparisons of classical interpretations and require only a polynomial number of SAT solver calls. In our experimental evaluation we show that our new algorithms signficantly outperform the state of the art.

Compositional Zero-Shot Learning (CZSL) aims to combine known attributes and objects as primitives for recognizing previously unseen attribute-object pairs. Prior works either predict attributes and objects independently, missing their strong contextual dependency, or use unidirectional conditional modeling (e.g., object-guided attribute prediction), which is prone to error propagation. We propose PRPC, a Progressive Reasoning framework with Primitive Correction, which explicitly models the bidirectional dependency between attributes and objects via step-wise inference. PRPC performs mutual correction of primitives to suppress prediction errors in earlier steps. Specifically, we formulate CZSL as structured, Q&A-style Chain-of-Thought reasoning process and constrain the MLLM to follow predefined semantic steps to generate intermediate decisions. To further enhance the reliability and logical consistency of intermediate reasoning, we introduce reinforcement learning post-training with a GRPO-based objective, providing step-level rewards aligned with the progressive inference procedure. Extensive experiments on three CZSL benchmarks demonstrate that PRPC achieves state-of-the-art performance, validating the effectiveness of progressive reasoning and bidirectional correction for robust compositional generalization.

We study reinforcement learning (RL) in multi-dimensional continuous state and action spaces with one-sided feedback, where the agent receives partial observations of the state and obtains reward information for only a subset of the state-action space at each time step. This setting introduces substantial challenges in both learning efficiency and privacy preservation. To address these challenges, we propose POOL, a novel privacy-preserving RL algorithm. We conduct a comprehensive theoretical analysis of POOL, deriving a sample complexity bound of O~((1+E_rho) H^3 alpha^-2), which matches the known lower bounds for non-private RL. Here, E_rho denotes the privacy parameter, H is the time horizon, and alpha is optimality-gap parameter. Our findings show that it is possible to enforce strong privacy guarantees while maintaining high learning efficiency, marking a significant step toward practical, privacy-aware RL in multi-dimensional environments with one-sided feedback.

Cross-Domain Graph Anomaly Detection supports the transfer of knowledge to unknown targets. Nevertheless, current approaches frequently struggle in the Cross-Broad-Domain paradigm which is characterized by two significant discrepancies: feature heterogeneity and structural disparity. Such diverse geometric topologies result in substantial manifold mismatch, as the traditional dependence on linear residuals in flat Euclidean space induces considerable geometric distortion when accommodating various topological patterns. We propose a novel framework termed Geometry-aware Riemannian Anomaly DEtector (GRADE) to tackle these difficulties. To address the semantic gap, we develop a domain-based contrastive learning technique combined with target-adaptive prompt tuning, which dynamically adjusts source representations to align with the target distribution while maintaining local contexts. To tackle the manifold mismatch, we present the Riemannian Residual Displacement (RRD), a curvature-adaptive metric that maps node representations onto multi-curvature Riemannian manifolds. By quantifying geodesic deviations in curved spaces, RRD corrects geometric distortions and identifies anomaly patterns that remain invariant to topological alterations. Additionally, a geometry-aware prototype alignment approach secures these residuals to data-independent priors for resilient inference. Comprehensive experiments on 11 benchmark datasets across 4 distinct domains demonstrate that GRADE significantly outperforms state-of-the-art baselines in both zero-shot and few-shot settings.

We study multi-task reinforcement learning (RL), a setting in which an agent learns a single, universal policy capable of generalising to arbitrary, possibly unseen tasks. We consider tasks specified as linear temporal logic (LTL) formulae, which are commonly used in formal methods to specify properties of systems, and have recently been successfully adopted in RL. In this setting, we present a novel task embedding technique leveraging a new generation of semantic LTL-to-automata translations, originally developed for temporal synthesis. The resulting semantically labelled automata contain rich, structured information in each state that allow us to (i) compute the automaton efficiently on-the-fly, (ii) extract expressive task embeddings used to condition the policy, and (iii) naturally support full LTL. Experimental results in a variety of domains demonstrate that our approach achieves state-of-the-art performance and is able to scale to complex specifications where existing methods fail.

Transformer models are rapidly becoming a cornerstone of modern Internet of Things (IoT) applications, yet their computational and memory demands far exceed the capabilities of a single typical ultra-low-power IoT device. We present CATS, a framework for distributed transformer inference on ultra-low-power wireless devices, enabling multiple devices to collaboratively execute models far larger than what a single device can sustain. At its core, CATS is a communication-aware distributed transformer inference scheme co-designed across transformer partitioning, wireless communication and training. It employs SomeGather, a new pruned communication primitive that selectively broadcasts activation columns to reduce communication bandwidth and RAM usage without sacrificing model accuracy. Building on SomeGather, we design a partitioning method that exploits this primitive for efficient model parallelism. To cope with unreliable wireless communication, CATS employs message-dropout during training, which mimics packet losses and yields models that are robust to message loss during inference. In real-world experiments, we show that CATS brings distributed transformer inference to ultra-low-power wireless devices for the first time, with deployments on up to 16 devices that collaboratively execute transformer models up to 14 times larger than what a single device can run.

Optimizing survival outcomes, such as patient survival or customer retention, is a critical objective in data-driven decision-making. Off-Policy Evaluation (OPE) provides a powerful framework for assessing such decision-making policies using logged data alone, without the need for costly or risky online experiments in high-stakes applications. However, typical estimators are not designed to handle right-censored survival outcomes, as they ignore unobserved survival times beyond the censoring time, leading to systematic underestimation of the true policy performance. To address this issue, we propose a novel framework for OPE and Off-Policy Learning (OPL) tailored for survival outcomes under censoring. Specifically, we introduce IPCW-IPS and IPCW-DR, which employ the Inverse Probability of Censoring Weighting technique to explicitly deal with censoring bias. We theoretically establish that our estimators are unbiased and that IPCW-DR achieves double robustness, ensuring consistency if either the propensity score or the outcome model is correct. Furthermore, we extend this framework to constrained OPL to optimize policy value under budget constraints. We demonstrate the effectiveness of our proposed methods through simulation studies and illustrate their practical impacts using public real-world data for both evaluation and learning tasks.

Large visual-language models demonstrate exceptional transferability in biomedical image analysis, yet their robustness remains challenging in generalization scenarios such as few-shot learning. This limitation stems from an overly entangled prompt space, causing instability in the conditional distribution, coupled with saliency bias that drives attention to dominate key regions, thereby restricting the coverage of visual representations. Furthermore, cross-modal fusion often struggles to extract critical information signals from redundant alignments. To address these structural bottlenecks, we propose TriCLIP, a decomposition-based framework designed to reconstruct the entire information flow across prompt conditioning, visual evidence extraction, and cross-modal fusion. Prompt Distribution Regularization enhances robustness to linguistic variations by optimizing class-conditional random context perturbations for continuous prompts. Feedback-driven Counterfactual Masking constructs a feedback-driven reverse saliency view that suppresses dominant evidence to promote complementary cue learning and mitigate attention bias. Finally, Selective Regulation Fusion regulates fusion selectivity through paired reinforcement-suppression principles, amplifying information correspondence while suppressing redundant channels. Extensive experiments on diverse biomedical benchmarks indicate that the proposed TriCLIP consistently outperforms existing methods.

Deep learning has achieved remarkable success in automated electrocardiogram (ECG) diagnosis. However, the long-tailed distribution of real-world ECG data remains a critical challenge. Existing methods mainly operate at the signal feature level and fail to exploit semantic relationships among diagnostic labels, which limits their ability to effectively recognize rare categories. To address this issue, we propose a Semantics-Guided Representation Learning (SGRL) framework, designed to leverage semantic priors to guide feature learning and thereby correct feature bias under long-tailed distributions. Specifically, we propose a Label Semantic Prompt Adaptation (LSPA) strategy, which introduces ECG-calibrated learnable parameters into label prompts to generate adaptive label semantic embeddings under a semantic-consistency constraint, thereby overcoming the limitations of coarse-grained label prompts in characterizing fine-grained intra-class morphological variations. Subsequently, we propose Semantic-Guided Representation Decorrelation (SGRD) that fuses ECG embeddings with adaptive label semantics and suppresses inter-class semantic correlations to obtain discriminative class semantic anchors, thereby improving tail-class separability. Extensive experiments on multiple public datasets demonstrate the significant superiority of SGRL in long-tailed ECG classification tasks. Our code is available on https://github.com/gfywudi/SGRL.

Retinal angiography provides critical vascular information, yet optical coherence tomography angiography (OCTA) acquisition remains slower and less accessible than conventional structural OCT. A key open question is: how angiographic signals can be recovered? To address the issues of artifacts and structural blurring in existing generative methods, we propose a wavelet flow matching-based model, FlowOCT. This method operates directly on OCT wavelet coefficients, learning frequency-specific velocity fields to map them to the OCTA wavelet distribution. To ensure anatomical accuracy of the vasculature, we introduce constraints such as depth-wise contrastive alignment and two-dimensional projection consistency. Given the sparse nature of vascular signals in OCTA data, a structure-focused loss function and corresponding evaluation metrics are designed. Experiment results show that FlowOCT outperforms existing methods in terms of both image quality and perceptual similarity. Downstream diagnostic tasks further validate its superior authenticity and generalization capability. Code is available at https://github.com/cnu-medilab/FlowOCT.

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.

The learnware paradigm aims to construct a dock system that maintains a collection of learnwares, each consisting of a well-established model coupled with a specification, thereby enabling users to directly reuse existing models to solve their tasks without training models from scratch. As a core component of the paradigm, the specification sketches the model’s properties and establishes reusability between the learnware and user tasks. Existing methods have demonstrated promising performance by designing specifications that characterize the task distributions mastered by well-established models. However, in complex real-world scenarios, task distributions often overlap, thereby weakening the uniqueness of specifications and obscuring the reusability relationships between learnwares and tasks. In this paper, we design an Explicitly Distinctive Specification (EDS) that enforces specification uniqueness, improving the discriminative capability of the learnware paradigm and avoiding ambiguity caused by overlapping task distributions. This specification method explicitly leverages distributional discrepancies between specifications to strengthen the uniqueness of the model properties sketched during the submitting stage and subsequently exploits the enhanced distributional discriminability in the deploying stage to improve learnware reusability. Extensive experiments demonstrate the effectiveness and strong performance of the proposed EDS within the learnware paradigm under complex task scenarios.

Virtual Knowledge Graphs (VKGs) provide unified access to legacy relational data sources through a high-level ontology modeling a domain of interest. The content of the ontology elements (classes and properties) is virtually mapped to underlying data sources through declarative mappings. The standard approach to interacting with a VKG system is to use SPARQL as the query language. However, the complexity of SPARQL creates a high entry barrier for normal users. In this paper, we study the VKG-QA task, which enables users to interact with the VKGs through a natural language (NL) interface by translating their questions into SPARQL queries. One critical challenge in such translation is silent failures, where a syntactically correct SPARQL query returns empty results because it involves ontology elements that lack mappings to the underlying data. To address this, we propose NaVQA (Navigation-based VKG Question Answering), a framework leveraging Large Language Models that (1) identifies and indexes the active ontology elements based on the mapping specifications, and (2) iteratively constructs a query graph from the NL question using the active ontology elements and their relations to synthesize SPARQL. Experiments on three real-world VKGs show that NaVQA significantly mitigates silent failures and outperforms existing baselines.

Model counting of Disjunctive Normal Form (DNF) formulas is a critical problem in applications such as probabilistic inference and network reliability.
For example, it is often used for query evaluation in probabilistic databases. Due to the computational intractability of exact DNF counting, there has been a
line of research into a variety of approximation algorithms.

We develop a new Monte Carlo approach with an adaptive stopping rule and short-circuit formula evaluation. We prove it achieves Probably Approximately Correct (PAC) learning bounds and has
asymptotically improved time and randomness complexity compared to previous methods. We also show experimentally that it out-performs prior algorithms by at least three orders of magnitude in running time and scalability.

With the rising demand for trustworthy AI in clinical practice, strong interpretability is now a critical requirement as well as accuracy. However, the modality gap for medical visual question answering is quite severe when continuous visual signals are forcibly projected into discrete text space for reasoning, and the loss of necessary diagnostic information leads to low precision and black-box opacity. To address this problem, we propose MedVCoT, which incorporates latent visual reasoning into the medical visual question answering(VQA) domain. Rather than merely integrating modules, MedVCoT utilizes the specialized expertise of MedSAM to train a large vision-language model so that it can autonomously generate consistent and continuous latent visual tokens within Visual Chain-of-Thought. This mechanism forces the model to explicitly "see" the lesion in the latent space before formulating a textual diagnosis, ensuring answers are causally rooted in verifiable visual evidence rather than statistical hallucination. We achieve this through a progressive 3-stage training procedure: medical feature alignment, visual reasoning learning by utilizing latent tokens generated, and instruction tuning for complex clinical scenarios. Extensive experiments show that MedVCoT can achieve state-of-the-art performance on multiple benchmarks, outperforming other methods by large margins. Meanwhile, it provides pixel-level segmentation masks to validate its diagnostic reasoning. Our demo is available at https://zhuqh19.github.io/MedVCoT.

Remote Sensing Image-Text Retrieval (RSITR) aims to achieve precise retrieval between remote sensing images and textual descriptions. However, existing methods neglect the multi-dimensional cognitive attributes inherent in remote sensing data and struggle to handle them simultaneously, leading to suboptimal retrieval performance. In this paper, we propose a novel Progressive Decoupling-Aggregation-Refinement framework for RSITR (PDAR-RSITR) to comprehensively capture multi-dimensional cognitive attributes. Specifically, we adapt Multi-dimensional Cognitive Decoupling to learn features across cognitive attributes of different dimensions via multi-process clustering. Subsequently, we utilize Salient Cognitive Aggregation to select and aggregate the most salient attributes via dynamic routing. Furthermore, we propose Expert Collaborative Refinement to enhance critical cross-modal relationships via three complementary expert perspectives. Extensive experimental results demonstrate that PDAR-RSITR significantly outperforms existing state-of-the-art methods across multiple metrics.

Code-switching (CS) speech translation (ST) aims to translate speech that alternates between multiple languages into a target language text, posing significant challenges due to the complexity of semantic modeling and the scarcity of CS data. Previous studies mainly rely on the models themselves to implicitly learn semantic representations and resort to costly manual annotations. To mitigate these limitations, we propose enhancing Large Language Models (LLMs) with a Mixture-of-Experts (MoE) speech projector composed of language expert groups, where each group specializes in the semantic space of a specific language for fine-grained speech feature modeling. A language-specific loss and an intra-group load balancing loss are jointly introduced to guide efficient token routing across and within expert groups. Furthermore, we introduce a multi-stage training paradigm that utilizes readily available automatic speech recognition (ASR) and monolingual ST data, facilitating speech-text alignment and improving translation performance. To bridge the data gap for smooth domain transfer, a transition loss is employed to improve adaptation to CS scenarios. Extensive experiments on widely used datasets demonstrate the effectiveness and generality of our approach, achieving average improvements of 0.86 BLEU and 0.93 COMET over SeamlessM4T, with maximum improvements of 1.49 BLEU and 1.41 COMET across different test sets. Our code and supplementary appendices are available at https://github.com/XMUDeepLIT/CSST-SSA.

Optimization in industrial systems often involves calibrating from a semi-optimized state, where global exploration methods like Reinforcement Learning (RL) or Bayesian Optimization (BO) are inefficient or unsafe. We propose Causal Newton Optimization (CNO), an online algorithm that iteratively calibrates inputs under a known causal graph but unknown structural equations. CNO estimates local linear causal effects via additive interventions and employs a log-linear variance regression to robustly guide Newton-based updates. Evaluations on synthetic systems and a chemical plant simulator demonstrate that CNO achieves the best balance between objective improvement and robustness. While traditional PID control suits standard dynamical systems, CNO significantly outperforms RL and BO in complex structural causal models, providing the robust stability vital for safety-critical real-world applications.

Responsibility allocation---determining the extent to which agents are accountable for outcomes---is a fundamental challenge in the design and analysis of multi-agent systems. In this work, we model such systems as concurrent stochastic multi-player games and introduce a notion of retrospective (backward) counterfactual responsibility, which quantifies an agent's accountability for outcomes resulting from a given strategy profile. To allocate responsibility among agents, we utilise the Shapley value and formally show that this method satisfies key desirable properties, including fairness and consistency. Building on this foundation, we propose a formal framework that supports both verification and strategic reasoning in responsibility-aware multi-agent systems. Furthermore, by adopting Nash equilibrium as the solution concept, we demonstrate how to compute stable strategy profiles in which agents trade off responsibility against expected reward.

Argumentation dynamics provides techniques for revising argumentation theories in real-world domains (e.g., an autonomous medical diagnostic agent). Within this context, enforcement in abstract argumentation has become a prominent topic. Enforcement aims to modify an argumentation framework to satisfy given acceptability conditions while minimizing change from the original framework. Motivated by the need to address both syntactic and semantic notions of change, we propose formula-based enforcement, a generic framework that strictly generalizes existing approaches, by additionally covering cases they cannot handle, including semantic change. We analyze its complexity under central argumentation semantics, obtaining results from NP-completeness to completeness for the third level of the polynomial hierarchy. For second-level complete variants, we present an exact procedure based on MaxSAT solving and counterexample-guided abstraction refinement (CEGAR), and evaluate it empirically.

Dynamic graph neural networks (DyGNNs) are widely used to model evolving interactions, but may fail under data distribution shift. Due to limited and unreliable interventions and insufficient disentanglement, the existing dynamic graph domain generalization approaches lead to suboptimal results. We formalize a message sufficiency causal view: a node representation is fully mediated by its received message multiset. Building on this perspective, we propose Latent environment Extrapolation and Message Disentanglement (LEMD), a novel robust representation learning framework for dynamic graph domain generalization. A message extrapolation mechanism under soft uncertainty constraints is proposed to obtain the diverse counterfactual message distributions. Causal information is disentangled fully from the messages to suppress shortcuts via a recoverable evolving disentanglement module. We further provide rigorous theoretical analysis and proofs to ensure the effectiveness of LEMD. Across all six datasets and two tasks, LEMD consistently improves over state-of-the-art dynamic graph generalization baselines under distribution shift, and achieves the best performance increase of 7.7% relative compared to the suboptimal baseline. The code of LEMD for reviewer is available at https://github.com/W-WuJi/LEMD.

Multi-view clustering aims to utilize information from multiple feature representations to uncover underlying data structures. Most existing methods emphasize learning a consensus representation by enforcing consistency across views. However, those structures that cannot be directly incorporated into the clustering space receive little attention, and are often discarded as noise in practice. In this paper, we argue that such information can be informative signals for the clustering process and should be explicitly modeled rather than suppressed or ignored. Specifically, we propose salient-residual decoupled multi-view learning for clustering, SRDMVC, introducing a novel decomposition-fusion iterative optimization, which separates the feature space into a salient space and a residual subspace effectively and fuses them using a novel attention mechanism. The residual subspace can capture the deep-level structure of view information, making positive contributions to the clustering process, under proper constraints. Without assuming stronger view alignment or complementarity, SRDMVC enhances cluster discrimination, avoids spurious consensus, and alleviates representation degradation. Extensive experiments on benchmark datasets demonstrate the superior performance of the method we propose.

We introduce and study the multi-agent stochastic shortest path (MSSP) problem, in which k agents strive to reach a target state, aiming to minimize the expected time to reach the target by any agent. We analyze the computational and strategy-complexity of the problem in both autonomous and coordinated settings, and we design efficient strategy-synthesis algorithms. The algorithms are experimentally evaluated on instances of increasing size against natural baselines.

We investigate the complexity of stable (or perturbation-resilient) instances of k-Means and k-Median clustering problems in metrics with small doubling dimension. While these problems have been extensively studied under multiplicative perturbation resilience in low-dimensional Euclidean spaces, we adopt a more general notion of stability, termed "almost stable", known from the literature as (alpha,epsilon)-perturbation resilience. Additionally, we extend our results to k-Means/k-Median with penalties, where each data point is either assigned to a cluster centre or incurs a penalty.
We show that certain special cases of almost stable k-Means/k-Median (with penalties) are solvable in polynomial time. To complement this, we also examine the hardness of almost stable instances and (1 + 1/poly(n))-stable instances of k-Means/k-Median (with penalties), proving super-polynomial lower bounds on the runtime of any exact algorithm under the widely believed Exponential Time Hypothesis (ETH).

Continual adversarial defense (CAD) aims to defend target models against continuously emerging attacks. However, existing CAD methods typically rely on maintaining large amounts of replay data or multiple expert modules to mitigate catastrophic forgetting, or suffer from reduced robustness against adversarial examples or degraded performance on clean images. As a result, they often fail to provide clear advantages over standalone robust models. To address these limitations, we formulate four fundamental principles for CAD: (1) continual adaptation to new attacks without catastrophic forgetting, (2) few-shot adaptation, (3) memory-efficient adaptation, and (4) high classification accuracy on both clean and adversarial data. Guided by these principles, we explore and integrate cutting-edge techniques from continual learning, few-shot learning, and ensemble learning, and propose Universal Continual Adversarial Defense (UCAD), a universal framework that enables both standard and robust models to perform effective defense under the CAD setting. Extensive experiments validate the effectiveness of UCAD against multi-stage adversarial attacks and demonstrate significant improvements over a wide range of baseline methods. Moreover, we observe that as the number of encountered attacks increases, UCAD becomes increasingly robust, consistently enhancing the defense capability of existing robust models until saturation.

Spiking neural networks (SNNs), which are brain-inspired and spike-driven, achieve high energy efficiency. However, a performance gap between SNNs and artificial neural networks (ANNs) still remains. Knowledge distillation (KD) is commonly adopted to improve SNN performance, but existing methods typically enforce uniform alignment across all timesteps, either from a teacher network or through inter-temporal self-distillation, implicitly assuming that per-timestep predictions should be treated equally. In practice, SNN predictions vary and evolve over time, and intermediate timesteps need not all be individually correct even when the final aggregated output is correct. Under such conditions, effective distillation should not force every timestep toward the same supervision target, but instead provide corrective guidance to erroneous timesteps while preserving useful temporal dynamics. To address this issue, we propose Selective Alignment Knowledge Distillation (SeAl-KD), which selectively aligns class-level and temporal knowledge by equalizing competing logits at erroneous timesteps and reweighting temporal alignment based on confidence and inter-timestep similarity. Extensive experiments on static image and neuromorphic event-based datasets demonstrate consistent improvements over existing distillation methods. The code is available at https://github.com/KaiSUN1/SeAl.

Partial Multi-Label Feature Selection (PMLFS) integrates partial multi-label learning and Multi-Label Feature Selection (MLFS) within a unified optimization framework. Owing to the presence of noisy labels, it is more practical and challenging compared to traditional MLFS. However, existing methods often focus solely on label disambiguation while neglecting to distinguish the contributions of features to ground-truth labels and noisy labels. To address this, we propose a novel MLFS method based on information decomposition and orthogonality constraints, named PML-IDOC. The method partitions partial label information into ground-truth label information and noisy label information, imposing a local orthogonality constraint between ground-truth and noisy labels to prevent the latter from interfering with the former. Unlike previous approaches that constrain the low-rank property and sparsity of noisy labels, our method constructs separate mapping relationships from instances to ground-truth labels and noisy labels. By enforcing global orthogonality between the two coefficient matrices, it ensures low correlation between them, thus achieving the decoupling of feature contributions to ground-truth and noisy labels. Extensive experimental results demonstrate the effectiveness of PML-IDOC. The code is available at https://github.com/yunbao520/PML-IDOC

Spatial transcriptomics has significantly advanced tissue biology and makes it possible to study the spatial interactions of cells in the microenvironment of complex tissues. However, accurately inferring intercellular communication from these data remains challenging due to the need to effectively integrate spatial topology with high-dimensional gene expression information. To address this challenge, we propose DAHGT-CCI, a novel method for inferring intercellular communication from spatial transcriptomics data. By fusing multimodal heterogeneous features to construct a unified node representation and introducing a meta-relation-aware dynamic attention mechanism, DAHGT-CCI can adaptively learn the weights of edges in the graph, thereby achieving superior precision and fine-grained inference of intercellular interactions. We evaluated the performance of DAHGT-CCI on six different spatial transcriptomics datasets. In these benchmarks, DAHGT-CCI outperformed existing graph-based learning methods and cell-cell communication inference models in terms of accuracy, AUROC, recall, and F1 score. These results demonstrate that DAHGT-CCI can more accurately reconstruct cell communication networks in complex tissue microenvironments, offering an indispensable computational tool for studying developmental processes, disease mechanisms, and potential therapeutic targets from a spatially resolved perspective.

Tool learning methods have enhanced the ability of large language models (LLMs) to interact with real-world applications.
Many existing works fine-tune LLMs or design prompts to enable LLMs to select appropriate tools and correctly invoke them to meet user requirements. However, it is observed in previous works that the performance of tool learning varies from tasks, datasets, training settings, and algorithms. Without understanding the impact of these factors, it can lead to inconsistent results, inefficient model deployment, and suboptimal tool utilization, ultimately hindering the practical integration and scalability of LLMs in real-world scenarios. Therefore, in this paper, we explore the impact of both internal and external factors on the performance of tool learning frameworks. Through extensive experiments on two benchmark datasets, we find several insightful conclusions for future work, including the observation that LLMs can benefit significantly from increased trial and exploration. We believe our empirical study provides a new perspective for future tool learning research.

We study a robust scheduling problem on identical machines in which machines may fail after the initial assignment. The goal is to compute an initial schedule together with a recovery strategy that minimizes the post-failure makespan, under the constraint that jobs on available machines cannot be moved.

We consider two failure models: a strong adversary, which selects the failed machines, and a weak adversary, which selects only the number of failures. For up to k failures, we give algorithms with robustness ratios 1.618 for the strong adversary and 1.5 for the weak adversary. For the single-failure case, k = 1, we obtain best possible ratios 1.387 and 1.281, respectively, matching our lower bounds.

Problems defined on binary decision spaces have been intensively studied in the theory of multi-objective evolutionary algorithms (MOEAs). In contrast, no mathematical runtime analyses exist so far for MOEAs dealing with decision variables that take a finite number \(r>2\) of values, despite the prevalence of such problems in practice. In this work, we begin to fill this research gap. We analyze how the classic SEMO algorithm with unit-strength local mutation computes the Pareto front of an \(r\)-valued counterpart of the classic \oneminmax benchmark. For the expected number of function evaluations until the Pareto front is covered by the population of this MOEA, we prove an upper bound of \(O(n^2 r^2 \log n)\) and a near-tight lower bound of \(\Omega(n^2 r (r + \log n))\). We can close the small remaining gap between these two bounds by considering a variant of the algorithm that accepts only strictly better solutions; for this variant, we show an upper bound of \(O(n^2 r (r + \log n))\), matching our lower bound (which also holds for this variant).
Our results suggest that classic MOEAs encounter no significant additional difficulties when dealing with multi-valued decision variables. However, significantly more advanced tools may be required to obtain tight bounds for algorithms with more complex population dynamics.

Infrared and Visible Image Fusion (IVIF) has shown promise in visual tasks under challenging environments, but fusion under unregistered conditions faces inherent misalignments. Current studies to solve them either predict the deformation parameters coarse-to-fine (i.e., coarse registration and fine registration) or estimate the deformation fields in multi-scales for registration. Though straightforward, they overlook the cumulative errors in registration, which contaminate the fusion stage and severely deteriorate the resulting images. We introduce the Spatial-Frequency Registration and Fusion (SFRF) framework, which incorporates uncertainty estimation and infrared thermal radiation distribution consistency into a unified pipeline to handle the error accumulation for robust registration and fusion across both spatial and frequency domains. Specifically, SFRF constructs a Multi-scale Iterative Registration (MIR) framework that iteratively refines the deformation field across scales, leveraging uncertainty estimation at each stage to mitigate error accumulation and enhance alignment accuracy dynamically. To ensure the accurate alignment of infrared thermal distributions during registration, thermal radiation distribution consistency is employed as a frequency-domain supervisory signal, promoting global consistency in the frequency domain. Based on the spatial-frequency alignment, SFRF further adopts a Dual-branch Spatial-Frequency Fusion (DSFF) module, which incorporates spatial geometric features and frequency distribution information to reconstruct visually appealing images. SRFR achieves impressive performance across diverse datasets. Code is available at github.com/xhhaoyan/SFRF.

Many multiagent systems depend on collective decisions made by self-interested agents, which raises deep questions about coalition formation and stability. We study social choice with endogenous, outcome-contingent transfers, where agents voluntarily form contracts that redistribute utility depending on the collective decision, allowing for fully strategic coalition formation. We show that under consensus rules, individually rational strong Nash equilibria (IR–SNE) always exist, implementing welfare-maximizing outcomes with feasible transfers, and provide a simple, efficient algorithm to construct them. For more general anonymous, monotonic, and resolute rules, we identify necessary conditions for viable deviations, significantly limiting the possibility of destabilizing coalitions. By bridging cooperative and noncooperative perspectives, our approach shows that transferable utility can achieve core-like stability, restoring efficiency and budget balance even where classical impossibility results generally apply. Overall, this framework offers a practical and robust way to coordinate large-scale strategic multiagent systems.

Time-series clustering remains challenging due to the inherent trade-off between clustering effectiveness and computational efficiency.
Similarity-based methods often suffer from quadratic complexity caused by pairwise distance computations, while deep learning–based approaches typically rely on costly iterative training and a large number of trainable parameters. In this paper, we propose MSRGC-Net, an efficient time-series
clustering framework that integrates multiscale reservoir computing, granular-ball-based anchoring graph construction, and consensus learning.
MSRGC-Net adopts a training-free reservoir computing paradigm to extract multiscale temporal representations from raw time series without
backpropagation, significantly reducing computational overhead. To capture the intrinsic structure of the resulting representations, granular-ball
computing is employed to adaptively model data distributions via density-consistent regions, yielding compact and robust anchor graph representations. Furthermore, a consensus-based anchoring graph optimization strategy is introduced to effectively align multiscale reservoir representations and integrate complementary information across temporal scales. Extensive experiments on widely used univariate and multivariate benchmark datasets demonstrate that MSRGC-Net consistently outperforms state-of-the-art methods in clustering performance while maintaining superior computational efficiency.

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 Neural Networks (GNNs) are vulnerable to backdoor attacks, where models behave normally on clean data but exhibit targeted misclassifications once specific triggers are activated. Existing backdoor attacks on GNNs mainly focus on enhancing trigger stealthiness or diversifying attack paradigms. However, these methods overlook a fundamental property of GNNs: trigger-induced malicious signals inevitably propagate through graph neighborhoods, causing unintended mispredictions on clean nodes, i.e., Collateral Damage. To address this issue, we propose the Collateral Damage Constrained Graph Backdoor Attack (CDCA), a novel framework that explicitly controls malicious diffusion. Specifically, the proposed method combines neighborhood-aware target node selection with a self-constrained trigger generation strategy to suppress trigger-induced propagation by enforcing prediction consistency on clean K-hop neighboring nodes. Extensive experiments on real-world datasets demonstrate that the proposed method remains effective while significantly reducing collateral damage.

Inspired by natural language processing prompt learning, recent heterogeneous graph prompt-tuning methods have been developed to better align pre-trained models with downstream tasks. However, existing heterogeneous prompt methods primarily focus on holistic framework design, causing prompts to heavily depend on specific pre-trained models and thus limiting generalization. To address this, we focus on the common encoder module shared across pre-trained models and the multi-relational edge structure unique to heterogeneous graphs, proposing a novel heterogeneous graph prompt-tuning method named HGMRP. It simultaneously resolves the issue of inconsistent objectives during prompt optimization when messages propagate across different relational edges. By assigning learnable prompt vectors to different types of edges, HGMRP integrates multi-relational prompts directly into the message-passing process. Furthermore, we extend single-relation prompts into a composite structure that includes both relation-specific and shared components, thereby enhancing expressive capability. Extensive experiments conducted on four widely used heterogeneous graph datasets show that HGMRP can adapt to various types of pre-trained models and significantly outperforms existing heterogeneous prompt methods, validating its effectiveness and superiority.

Short video harmful content detection aims to automatically identify diverse anomalies from user-generated media. This task presents unique challenges due to frequent editing cuts and highly variable anomaly densities, limiting the effectiveness of traditional surveillance-based approaches. Moreover, existing Vision-Language Model-based approaches typically rely on rigid instance selection mechanisms that fail to adapt to the unpredictable duration of anomalies in such unconstrained videos. To address these issues, we propose SVLA, a Shot-conditioned Vision-Language Adaptation framework, for effectively detecting harmful contents from online short videos. Our approach introduces a novel π-adaptive strategy to dynamically estimate shot-level anomaly density, replacing rigid selection with calibrated supervision. Furthermore, we employ a shot-conditioned temporal encoder to respect video hierarchy and adopt a dual-path contextual adapter to resolve semantic ambiguity. To benchmark this task, we construct a new dataset (SVA) covering more genuine online short videos that involve seven anomaly categories. Experiments on the SVA dataset demonstrate that SVLA can achieve the state-of-the-art performance and outperform its competitors across diverse scenarios. Codes and datasets are available at: https://github.com/xushuai7/IJCAI-SVLA.

Many real-world applications require solving families of expensive multi-objective optimization problems~(EMOPs) under varying operational conditions. This can be formulated as parametric expensive multi-objective optimization problems (P-EMOPs) where each task parameter defines a distinct optimization instance. Current multi-objective Bayesian optimization methods have been widely used for finding finite sets of Pareto optimal solutions for each task. However, P-EMOPs present a fundamental challenge: the continuous task parameter space can contain infinite distinct problems, each requiring separate expensive evaluations. To address this, we propose learning an inverse model to amortize the multi-objective optimization cost across the continuous task-preference space, enabling direct solution prediction for any query without the need for expensive re-evaluation. This paper introduces a novel parametric multi-objective Bayesian optimizer that learns this inverse model by alternating between (1) generative solution sampling via conditional generative models and (2) acquisition-driven search leveraging inter-task synergies. This approach enables effective optimization across multiple tasks and finally achieves direct solution prediction for unseen parameterized EMOPs without re-evaluations. We theoretically justify the faster convergence by leveraging inter-task synergies through task-aware Gaussian processes. Based on that, empirical studies in synthetic and real-world benchmarks further verify the effectiveness of the proposed parametric optimizer.

Continual Visual Question Answering (VQA) requires learning from non-stationary streams of visual inputs and questions while preserving past knowledge. Most prior methods adapt by updating a largely shared parameter set. This often leads to cross-level task interference, hindering accurate adaptation to the current task and object. To address this limitation, we propose HyLoVQA. It maintains a drift-resilient memory bank of anchors. The bank stores the content of visual objects and textual tasks, and they are updated using current input features. Conditioned on retrieved anchors, a hypernetwork generates lightweight Low-Rank Adaptation (LoRA) adapters. This ensures parameter efficiency, allowing the model to adapt to each task and object dynamically. Additionally, we formulate an alignment loss that aligns semantic discrepancies in the feature space with functional changes in the parameter space, thereby constraining LoRA adapters to remain focused on the current task and object. Extensive experiments on VQA v2 and NExT-QA under both standard and compositional settings demonstrate the superiority of HyLoVQA over prior state-of-the-art methods. The code is available at https://github.com/HubuKG/HyLoVQA.

Chemical Language Models (CLMs) pre-trained on large-scale molecular data are widely used for molecular property prediction. However, the common belief that increasing training resources, such as model size, dataset size, and training compute, improves both pre-training loss and downstream task performance has not been systematically validated in the chemical domain. In this work, we evaluate this assumption by pre-training CLMs while scaling training resources and measuring transfer performance across diverse molecular property prediction (MPP) tasks. We find that while pre-training loss consistently decreases with increased training resources, downstream task performance shows limited improvement. Moreover, alternative metrics based on the Hessian or loss landscape also fail to estimate downstream performance in CLMs. We further identify conditions under which downstream performance saturates or degrades despite continued improvements in pre-training metrics, and analyze the underlying task-dependent failure modes through parameter space visualizations. These results expose a gap between pre-training-based evaluation and downstream performance, and emphasize the need for model selection and evaluation strategies that explicitly account for downstream task characteristics. The code is available at https://github.com/sagawatatsuya/MolScaleTransfer.

3D Gaussian Splatting enables efficient, high-fidelity novel view synthesis with explicit Gaussians and differentiable rendering. However, adverse weather introduces rain streaks and droplets as well as volumetric scattering, producing view-dependent, spatially varying degradations that break the clean multi-view consistency assumption and lead to geometric drift and unstable appearance. Existing approaches are largely confined to the 2D image domain and seldom model the 3D degradation formation process, limiting controllable weather synthesis and consistent restoration in 3DGS. To our knowledge, we are the first to incorporate real meteorological observations as an external prior and propose a 3D-level unified rendering–restoration framework for joint deraining and dehazing within a single 3DGS pipeline. Precipitation is mapped to interpretable parameters controlling the density and morphology of rain Gaussians, while an atmospheric-scattering extinction strength drives 3D fog generation for reproducible weather modeling. We adopt a closed-loop dual-branch optimization where the rendering branch fits degraded observations to capture weather degradation patterns, and the restoration branch regularizes scene Gaussians toward a clean domain with multi-scale perceptual consistency, suppressing artifacts and improving cross-view detail fidelity. Experiments across diverse scenes and weather conditions consistently outperform strong baselines on all three metrics, with a particularly notable PSNR improvement of 1.78 dB on average.

Federated Learning (FL) enables distributed learning across multiple clients without sharing raw data. When statistical heterogeneity across clients is severe, Clustered Federated Learning (CFL) can improve performance by grouping similar clients and training cluster-wise models. However, most CFL approaches rely on multiple communication rounds for cluster estimation and model updates, which limits their practicality under tight constraints on communication rounds. We propose Data Collaboration-based Clustered Federated Learning (DC-CFL), a single-round framework that completes both client clustering and cluster-wise learning, using only the information shared in DC analysis. DC-CFL quantifies inter-client similarity via total variation distance between label distributions, estimates clusters using hierarchical clustering, and performs cluster-wise learning via DC analysis. Experiments on multiple open datasets under representative non-IID conditions show that DC-CFL achieves accuracy comparable to multi-round baselines while requiring only one communication round. These results indicate that DC-CFL is a practical alternative for collaborative AI model development when multiple communication rounds are impractical. Our source code is publicly available at https://github.com/soutasuga/DC-CFL.

Knowledge Tracing (KT) is fundamental to intelligent education systems, yet relies on educational logs that are selectively observed. The non-random nature of exercise recommendations and student choices inevitably induces severe selection bias. Most existing KT methods neglect this issue, training on observed logs using standard empirical risk, which yields biased mastery estimates and accumulates errors in subsequent recommendations. To address this, we introduce a doubly robust (DR) formulation for KT that integrates a propensity model with an error imputation model, theoretically guaranteeing unbiasedness if either model is accurate. Beyond unbiasedness, in the sequential setting of KT, we identify that the estimator's performance is compromised by variance-dependent stochastic deviations that accumulate over time, thereby causing training instability and limiting performance. To mitigate this, we derive a generalization bound that explicitly characterizes the impact of estimator variance and identifies temporal smoothness as a key factor in controlling it. Building on these theoretical insights, we propose the Temporal Smoothness Doubly Robust (TSDR) framework. TSDR jointly optimizes the KT predictor and the imputation model with a smoothness regularizer, effectively reducing variance while preserving the unbiasedness guarantee of DR. Experiments on multiple real-world benchmarks demonstrate that TSDR consistently enhances various state-of-the-art KT backbones, underscoring the vital role of principled bias correction in KT.

Personalized review generation is vital for e-commerce engagement, yet integrating user-provided images while maintaining distinct user personas remains a critical challenge. Existing methods often struggle to bridge the semantic gap between objective visual signals and subjective linguistic patterns, frequently resulting in hallucinations or homogenized, impersonal content. To address these limitations, we propose Snap2Review, a novel framework that harmonizes cross-modal retrieval with fine-grained preference learning. First, we optimize a cross-modal retriever that maps visual features directly to semantically related historical reviews, using these interactions as precise anchors to strictly ground the generation. Second, we introduce Tri-DPO, which employs a stratified negative sampling strategy with varying difficulty levels, ranging from obvious errors to subtle stylistic mismatches, to force the model to discern fine-grained user preferences. By distinguishing these fine-grained nuances, our model moves beyond generic praise to accurately mimic the user's authentic writing style. Extensive experiments on real-world datasets demonstrate that Snap2Review significantly outperforms strong baselines in both relevance and personalization metrics.

Image quality is critical for accurate medical diagnosis. However, MRI, CT, and ultrasound images are often of low resolution and quality due to cost constraints, complicating the visualization of key anatomical structures and lesions. While such limitations are common in practice, traditional methods treat image enhancement as a separate preprocessing step, failing to fully leverage its potential synergy with image segmentation. To address this, we propose DiSIINet (Diffusion-based Symbiotic Information Interaction Network), which is built on the principle that enhancement and segmentation should mutually reinforce each other in a unified model. Based on Denoising Diffusion Implicit Models (DDIM), DiSIINet integrates an enhancement branch and a segmentation branch. These branches interact through a novel Symbiotic Information Interaction (SII) module, which facilitates dynamic, feature-level information exchange via cross-attention during the reverse diffusion process. This design enables both tasks to iteratively improve each other. The DDIM backbone ensures high-quality output and efficient inference through deterministic sampling. Experiments on multi-modal medical datasets (MRI, CT, ultrasound) show that DiSIINet achieves significant performance improvements compared to sequential or independent enhancement and segmentation approaches. The code is available at: https://github.com/Reconsider80/DiSIINet.

As a prominent paradigm for large-scale unsupervised learning, anchor-based multi-view clustering aims to reveal the latent structures across heterogeneous data representations with high efficiency. Despite achieving some progress, existing methods typically suffer from the following two limitations. Firstly, they rely on pre-constructed anchors or rigid constraints (e.g., orthogonality), whereas the intrinsic topological correlations among anchors are completely discarded. Secondly, most existing methods either perform clustering directly on the bipartite graph or treat different views equally, thereby failing to capture the global structural information. To this end, the Dual-tOpology learning with adapTive Anchors (DOTA) is proposed, which not only learns the sample-anchor relationship (i.e., bipartite graph) but also preserves the topology structure among anchors, significantly enhancing the discriminability of learned representation while preserving the underlying data manifold. By integrating an adaptive view-weighting strategy to balance view contributions, DOTA derives discriminative global sample embeddings by propagating spectral information through the bipartite graph. Extensive experiments on benchmark datasets demonstrate the superiority of DOTA.

Cooperative perception allows agents to extend perceptual capabilities through inter-agent communication. However, most existing methods still adopt a frame-wise paradigm, which limits the exploitation of spatio-temporal consistency in dynamic scenes. Therefore, when observations are partially occluded or degraded, these methods are unable to leverage historical context for compensation, leading to unstable perception and reduced detection accuracy. To address these challenges, we propose World4V2X, the first world model framework tailored for V2X cooperative perception. The proposed method first introduces a spatial observability modeling module that defines spatial consistency boundaries to distinguish reliable regions from uncertain ones, thereby enabling spatial consistency modeling over multi-agent heterogeneous observations. Building upon this, we construct a consistency-guided world modeling module. Specifically, a temporal consistency evolution mechanism leverages features from historical and current frames to model the state evolution of the scene, capturing environmental dynamics and producing temporal consistency beliefs. Meanwhile, a consistency-guided deterministic reconstruction mechanism exploits spatial boundaries and temporal beliefs to perform diffusion-based refinement for robust cooperative perception. Extensive experiments on the OPV2V and V2XSet datasets demonstrate that World4V2X achieves state-of-the-art perception performance across diverse V2X scenarios.

Multivariate Time Series Classification (MTSC) demands models that can effectively capture complex temporal patterns across multiple scales while remaining computationally efficient. However, existing approaches generally struggle to reconcile fine-grained representation learning, especially under class imbalance and real-world constraints. In this paper, we present FreSH, a Frequency-Segmented Hierarchical Multi-Expert Framework designed to address these challenges. FreSH introduces a new perspective for MTSC by enabling adaptive, multi-scale analysis of temporal signals, allowing different aspects of the data to be modeled in a complementary and coordinated manner. By combining localized specialization with holistic context modeling, FreSH achieves strong representational capacity without incurring excessive computational overhead. An adaptive fusion strategy further enhances flexibility, enabling the model to dynamically emphasize the most informative components of the input. In addition, we incorporate a more robust optimization objective that improves learning stability across varying sample difficulties and class distributions. Extensive evaluations on 30 UEA benchmark datasets and real-world vibration data demonstrate that FreSH consistently outperforms state-of-the-art methods in classification accuracy, while substantially reducing model size and efficiency. The implementation code is publicly available at https://github.com/Wangmy2120/FreSH00.

Artificial intelligence models typically perform well on large-scale datasets, yet their effectiveness tends to degrade in real-world scenarios with scarce data, such as medical diagnostics. In contrast, humans can learn and reason effectively from few examples. Even when novel objects differ significantly from prior observations, humans can quickly infer category distributions accurately. Inspired by this, we explore how to leverage human-inspired cognitive mechanisms to improve the distribution calibration in few-shot learning models. Based on the ``fast-slow thinking'' dual-process theory, we propose a novel cognition-inspired few-shot learning framework. It mimics the human cognition when handling novel information: it first rapidly screens relevant knowledge through ``fast thinking'' and then infers inter-class relationships and calibrates distributions via ``slow thinking''. Specifically, the fast-thinking stage employs a gating mechanism to quickly match input samples with known base-class prototypes, activating relevant candidate knowledge. In the slow-thinking stage, the model aggregates inter-class edge embeddings into a summary relation graph and then applies divergent Gaussian sampling to generate multiple relation graphs representing different association strengths, thereby achieving distribution calibration for few-shot classes. Experiments on public few-shot benchmarks and medical image datasets show competitive performance. Visualization analyses further reveal that our framework exhibits meaningful interpretability.

Multi-view clustering has garnered significant attention for its ability to integrate heterogeneous data and uncover underlying categorical structures. However, prevailing autoencoder-based methods often yield indiscriminative embeddings, rendering them prone to trivial solutions. While diffusion models have shown promise in modeling complex data distributions, their generative processes remain inherently unstable. More critically, the resulting latent representations often lack sufficient intra-class compactness and inter-class separability, leading to suboptimal clustering performance. In this paper, we propose SCD-MVC, a Stable Conditional Diffusion-driven framework for Multi-View Clustering. Specifically, it integrates conditional diffusion generation into the latent encoder to generate a target view conditioned on the remaining views, thereby facilitating robust joint distribution modeling. To align the generative process with clustering objectives, we introduce a clustering-guided semantic alignment module. This module regulates the diffusion path to enforce the learning of representations that are both view-consistent and discriminative. Furthermore, we introduce a temporal consistency regularization that enforces alignment between representations of adjacent diffusion steps. This stabilizes training and guides the optimization toward clustering-friendly representations. Extensive experiments on nine benchmark datasets demonstrate that SCD-MVC consistently outperforms eight state-of-the-art methods, achieving performance gains ranging from 6.68% to 13.09%. The source code is available at https://github.com/Knighttt0011/SCD-MVC.

Online time series forecasting relies on continual updates to cope with concept drift. In multi-step forecasting, however, the ground truth for an H-step prediction arrives only after H steps, so a delayed residual entangles persistent drifts with transient shocks and seasonal fluctuations. Existing methods typically apply a uniform update rule to all delayed errors, which can overreact to noise and under-adapt to real drift. We view delayed residuals as compressed observations of latent drift over the horizon, and propose PADRE, a drift evidence driven framework that converts each delayed feedback event into a context-sensitive adaptation decision. PADRE builds a Tri-View drift representation from (i) the current input context, (ii) recent delayed-residual trajectories, and (iii) retrieved typical types of drift, and quantifies their agreement via a geometric consistency measure. To exploit recurring drift structure, PADRE mines an offline Pattern Bank of regime-conditioned residual prototypes and retrieves a soft pattern prompt online. A lightweight prompt guided policy outputs a continuous update gate that scales the backbone’s gradient step, enabling decisive updates under coherent evidence and conservative updates otherwise. Experiments on five real-world benchmarks and three backbones demonstrate consistent gains under delayed feedback, reducing MSE by 6%–15% relative to recent baselines and improving update stability across horizons.

Microscopic road-network weights represent fine-grained, time-varying traffic conditions obtained from individual vehicles. An example is travel speeds associated with road segments as vehicles traverse them. These weights support tasks including traffic microsimulation and vehicle routing with reliability guarantees.
We study the problem of time-varying microscopic weight completion. During a time slot, the available weights typically cover only some road segments. Weight completion recovers distributions for the weights of every road segment at the current time slot.
This problem involves two challenges: (i) contending with two layers of sparsity, where weights are missing at both the network layer (many road segments lack weights) and the segment layer (a segment may have insufficient weights to enable accurate distribution estimation); and (ii) achieving a weight distribution representation that is closed-form and can capture complex conditions flexibly, including heavy tails and multiple clusters.

To address these challenges, we propose DiSGMM that combines sparsity-aware embeddings with spatiotemporal modeling to leverage sparse known weights alongside learned segment properties and long-range correlations for distribution estimation. DiSGMM represents distributions of microscopic weights as learnable Gaussian mixture models, providing closed-form distributions capable of capturing complex conditions flexibly. Experiments on two real-world datasets show that DiSGMM can outperform state-of-the-art methods.

Adapting large, adversarially pre-trained models to specialized domains via transfer learning is a promising path toward building secure AI systems. However, a critical challenge arises when fine-tuning on limited downstream data: models often suffer from catastrophic forgetting of robustness, where the generalizable robust features from pre-training are erased, leading to severe robust overfitting. To address this, we first establish the underlying principle that adversarial vulnerability is not diffuse, but is concentrated in a low-dimensional subspace defined by the principal components of the model's weight matrices. We then introduce Principal component-based Implicit regularization with Low-rank Optimization (PILO), a novel parameter-efficient framework that operationalizes this insight. PILO employs a principled decoupling of learning objectives: a spectrally-guided adversarial branch, initialized via Singular Value Decomposition (SVD), performs a surgical update on the identified vulnerable subspace to enhance robustness. This is complemented by a transient, standard low-rank branch that gently adapts the model to natural data, preserving clean accuracy. This dual-branch design acts as a form of implicit spectral regularization, anchoring the model to its robust pre-trained state. Extensive experiments show that PILO significantly outperforms state-of-the-art full-parameter and parameter-efficient methods in robust accuracy across multiple benchmarks, all while reducing trainable parameters by up to 71%. Our work thus establishes a new, more effective paradigm for robust transfer learning through principled and targeted parameter optimization.

Source-Fully-Free Domain Adaptation (SFF-DA) has emerged as a strategic paradigm to adapt Vision-Language Models (VLMs) without any access to source data or task-specific source models. However, we identify a critical "Dual Semantic Drift" that hinders this process: static drift caused by the stagnation of frozen hand-crafted templates, and dynamic drift resulting from noisy, instance-level generated captions. These issues lead to a severe stability-plasticity dilemma, manifesting as semantic misalignment and class collapse.
To address this, we propose DSSG (Dual-Stream Semantic Guidance), an end-to-end framework that reconciles fine-grained plasticity with global stability. Our core contribution is the Dual Semantic Guidance (DSG) module, which integrates a captioning stream for detailed domain nuances with a template stream to anchor global categorical consistency. Furthermore, a Dynamic Cross-Modal Knowledge Distillation (CMKD) module is introduced to adaptively optimize teacher-student alignment across modalities. Extensive experiments demonstrate that DSSG significantly outperforms current state-of-the-art methods across multiple benchmarks, providing a robust solution for the unsupervised transfer of foundation models. The code is available on https://github.com/mrmenand/DSSG.

Finding the maximum common induced subgraph (MCIS) between two graphs is a well-known NP-hard problem. While sequential MCIS algorithms have been extensively studied, parallel computing has emerged as an important direction for further performance enhancement with the advancement of computing resources. In this paper, we propose a parallel MCIS framework integrating a dynamic task decomposition method guided by search information and a novel pruning strategy based on shared information. The experimental results demonstrate that the algorithm enhanced by our framework achieves superior performance over both state-of-the-art sequential and existing parallel algorithms. Extensive results further demonstrate the wide scalability and generality of our framework, and the effectiveness of our strategies.

Coverage-based prime implicant explanations are formal explanations offering a number of valuable assets, especially in terms of faithfulness and generality. Unfortunately, deriving a coverage-based prime implicant explanation for an instance is computationally hard in the general case (the problem of identifying such an explanation being at the second level of the polynomial hierarchy).
In this paper, we focus on the computation of a coverage-based prime implicant explanation for an instance given a tree-based model. We show that the specific nature of the domain theory linking the Boolean conditions in such models makes the problem computationally easier. We present a greedy algorithm to derive coverage-based prime implicant explanations when dealing with a tree-based model. We also present an empirical evaluation showing that this algorithm is efficient enough to be used in many practical cases.

Hybrid tables, also referred to as `smart' in the literature, represent a valuable modeling technique within Constraint Programming (CP). These tables allow us to handle disjunctive cases (constraining expressions) in a compact and structured way, by authorizing tuples (table entries) to contain simple unary and binary arithmetic restrictions (similar to internal constraints).
In this paper, we show the practical interest of using hybrid tables for planning-like combinatorial puzzles, when the transition from one state to the next can be encoded by a single hybrid table.
Experimental results show that these hybrid models exhibit greater compactness and efficiency in solving compared to their conventional counterparts.

Large language models (LLMs) present a trade-off between performance and cost, where more powerful models incur greater expense. LLM routing aims to mitigate expenses while maintaining performance by sending queries to the most suitable model. However, existing methods cannot perform well for different user cost-performance preferences. To address this gap, we introduce a novel perceptive LLM routing paradigm for personalized and user-centric cost-performance optimization, which efficiently learns users' implicit preferences through little interaction. To handle the challenge of heterogeneous user needs, we formulate preference profiles as a set of distinct tasks in contextual bandit and propose MetaRouter, a meta-learning framework designed for preference-aware LLM routing. Experimental results show that MetaRouter outperforms strong baselines on both in-distribution and out-of-distribution tasks. Furthermore, it exhibits high efficiency in learning user preferences, robustness to changes in the routable LLMs, and scalability to multi-model routing.

Supervised proxy-based deep cross-modal hashing has become the dominant paradigm for large-scale retrieval. However, prevalent methods model class proxies as deterministic points in the embedding space. This rigid assumption causes severe gradient conflicts in multi-label scenarios, where gradient conflicts arising from label co-occurrence lead to severe gradient contention and optimization collapse.To resolve this, we propose Kent-based Distributional Proxy Hashing (KDPH), a novel framework that shifts proxy representation from static points to flexible anisotropic Kent distributions on the hypersphere. Unlike point proxies that must shift their positions to accommodate conflicting gradients, KDPH absorbs these conflicts by dynamically adjusting its directional variance. This allows the proxy to maintain a stable semantic mean direction while stretching to cover diverse label correlations. Furthermore, to ensure stable training of these geometric parameters, we derive a tailored loss function incorporating the Cayley transform to enforce strict orthogonality. To the best of our knowledge, KDPH is the first framework to successfully introduce the Kent distributions into cross-modal hashing.Experiments on three benchmark datasets demonstrate that KDPH mitigates proxy collapse and chaotic oscillation, significantly outperforms state-of-the-art methods. Code is available at https://github.com/Senmo996/KDPH-official-code.

Label Distribution Learning (LDL) represents each instance with a label distribution, but these distributions are often incomplete in practice due to high annotation costs and annotators’ cognitive burden. Recent Incomplete Label Distribution Learning (InLDL) methods leverage global and local correlations to impute missing values, imputation errors remain systematic and non-uniform across labels. However, in the more challenging scenario of imbalanced label distributions, such non-uniform errors disproportionately harm low-degree labels, skewing learning toward dominant labels. To address the more complex challenge, we propose Label Distribution Imputation and Bias-Corrective Representations (LDIBR) for incomplete and imbalanced label distribution learning. LDIBR performs instance-adaptive imputation conditioned on instance features and the binary observation mask. It learns a prior, a reliability-gated correction, and entry-wise fusion weights to produce a normalized imputed distribution. Additionally, LDIBR decomposes the imputed distributions into a shared low-rank core and an instance-specific residual. The core captures stable global degree patterns, while the residual isolates sample-specific deviations and imputation noise. Comprehensive experiments across multiple benchmark datasets with a 50% missing ratio validate both the effectiveness and robustness of the LDIBR approach. The code is available at https://github.com/wenhuihji/LDCBR.

Incomplete propagation data significantly hinders robust fake news detection. Recent approaches leverage large language models to simulate missing user interactions via role-playing, thereby enriching propagation with synthetic signals. However, such propagation data is intrinsically unreliable, and directly fusing it can lead to biased representations, leading to limited detection performance. In this paper, we alleviate the unreliability of synthetic propagation from the mutual information perspective and propose a novel information-theoretic propagation denoising and fusion (InfoPDF) framework to learn effective representations from both real and synthetic propagation. Specifically, we first generate attribute-specific synthetic propagation using large language models. Then we model each synthetic propagation graph as a probabilistic latent distribution to guide reliability-aware adaptive fusion with real propagation. During training, we design a mutual information-based objective to learn compressed and task-sufficient propagation representations. It jointly suppresses noisy signals across attribute-specific synthetic propagation, maintains consistency between real and synthetic propagation representations, and ensures task sufficiency for fake news detection and attribute prediction. Experiments on three real-world datasets show that InfoPDF consistently achieves superior performance across various fake news detection tasks. Further analysis demonstrates that InfoPDF can estimate attribute-level reliabilities and learn more discriminative propagation representations.

Backdoor attacks pose a serious threat to deep neural networks, especially when training relies on third-party data, allowing adversaries to inject malicious behaviors through data poisoning. In this work, we reveal that backdoor behaviors tend to be absorbed by a simpler parallel branch when jointly trained with the main network. Motivated by this insight, we propose Trapping and Removing (TR), a simple yet effective training-time defense that introduces a lightweight shortcut branch as a “honeypot” to trap backdoor knowledge. After training, backdoors can be removed by discarding the shortcut, without requiring any additional data. To further enhance backdoor isolation while maintaining benign performance, we design a knowledge decoupling strategy with entropy-based weight assignment, encouraging poisoned samples to flow through the honeypot while guiding the main network to focus on benign learning. In addition, we introduce an automatic shortcut generation strategy to improve generalization across model architectures. Extensive experiments on four benchmark datasets and five model architectures demonstrate that our approach effectively mitigates a wide range of backdoor attacks while preserving performance on benign data. Code: github.com/Zixuan-Zhu/TR.

Combinatorial and optimization problems are fundamental to many industrial AI applications. Solving large-scale real-world instances of such problems typically requires careful problem formalization, specialized solvers, and expert-designed heuristics.
Thus, experts need to specify not only *what* solutions are, but also *how* they are derived.

By introducing the tool CheckMate, we show that algorithm generation via code evolution represents a paradigm shift by eliminating the need to formulate the *how*. CheckMate solely relies on the *what*. Specifically, a formal specification ensures solutions' correctness and enables systematic performance evaluation of the generated programs, while a natural language description guides the evolutionary process.

The effectiveness of our method is demonstrated on selected problems from two industrial domains: configuration and scheduling. In all cases, the evolved algorithms consistently outperform state-of-the-art solvers. This underscores the potential of formal methods in guiding code evolution for automatically solving complex real-world problems.

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.

Concept erasure aims to suppress sensitive content in diffusion models, but recent studies show that erased concepts can still be reawakened, revealing vulnerabilities in erasure methods. Existing reawakening methods mainly rely on prompt-level optimization to manipulate sampling trajectories, neglecting other generative factors, which limits a comprehensive understanding of the underlying dynamics. In this paper, we model the generation process as an implicit function to enable a comprehensive theoretical analysis of multiple factors, including textual conditions, model parameters, and latent states. We theoretically show that perturbing each factor can reawaken erased concepts. Building on this insight, we propose a novel concept reawakening method: Latent space Unblocking for concept REawakening (LURE), which reawakens erased concepts by reconstructing the latent space and guiding the sampling trajectory. Specifically, our semantic re-binding mechanism reconstructs the latent space by aligning denoising predictions with target distributions to reestablish severed text-visual associations. However, in multi-concept scenarios, naive reconstruction can cause gradient conflicts and feature entanglement. To address this, we introduce Gradient Field Orthogonalization, which enforces feature orthogonality to prevent mutual interference. Additionally, our Latent Semantic Identification-Guided Sampling (LSIS) ensures stability of the reawakening process via posterior density verification. Extensive experiments demonstrate that LURE enables simultaneous, high-fidelity reawakening of multiple erased concepts across diverse erasure tasks and methods.

The rapid advancement of AIGC video generation calls for evaluation frameworks that move beyond technical fidelity and incorporate human-centered aesthetic assessment. Existing benchmarks often overlook fine-grained perceptual qualities such as visual aesthetics, artistic style, and human preference. To address this limitation, we introduce VGA-BenchV2, an extended human-aligned benchmark and optimization framework for jointly evaluating and improving video generation quality and aesthetic value.

Built upon VGA-Bench, VGA-BenchV2 preserves the original fine-grained taxonomy with two primary dimensions—Aesthetic and Generation—and 52 sub-dimensions. Guided by this taxonomy, we curate 1,016 diverse prompts and collect over 60,000 videos generated by 12 mainstream video generation models. More importantly, VGA-BenchV2 substantially expands human-labeled supervision by adding 36,000 task-level annotations, including 16,200 for aesthetic quality, 13,200 for aesthetic tagging, and 6,600 for generation quality, corresponding to 13.46×, 11.15×, and 1.55× scale-ups over VGA-Bench, respectively.

Leveraging this enlarged annotation corpus, we develop a hybrid evaluator architecture consisting of VAQA-Net for continuous aesthetic scoring and two Qwen-based Large Vision-Language Model evaluators, VTag-Net and VGQA-Net, for aesthetic tagging and generation quality assessment. Extensive experiments demonstrate strong alignment with human judgments across diverse generation models. Beyond evaluation, VGA-BenchV2 further introduces an evaluation-to-optimization pipeline, where the learned aesthetic evaluator serves as a reward model for reinforcement learning-based generator fine-tuning. This closes the loop from benchmark construction and human supervision to automated evaluation and model optimization, enabling video generators to improve not only in realism but also in aesthetic quality and human preference alignment. Resources are available at https://huggingface.co/datasets/BestiVictoryLab/VGA-Bench.

Time series forecasting (TSF) plays a vital role across various domains such as finance, energy, healthcare, and meteorology. Currently, most deep learning based TSF methods typically operate with a fixed lookback window. This approach comes from the high compute and memory costs of long contexts, as well as the standard practice of using sliding windows. This creates a trade-off. Making the window larger reduces the number of training samples, which can harm stability and generalization. However, keeping the window small prevents the model from using long history during inference. We propose an inference-only streaming autoregressive framework that replaces repeated full-context recomputation with a one-time context warmup and incremental decoding, enabling efficient long-history forecasting without retraining. While straightforward caching attentions is brittle for time series due to distribution shifts and noisy or redundant histories, we address these issues with cache-consistent normalization and selective memory under a fixed cache budget. Across diverse benchmarks, our approach substantially reduces inference latency with no or marginal accuracy loss, and often improves performance when longer lookbacks are beneficial.

Point cloud completion seeks to recover geometrically consistent shapes from partial or sparse 3D observations. Although recent methods have achieved reasonable global shape reconstruction, they often rely on Euclidean proximity and overlook the intrinsic nonlinear geometric structure of point clouds, resulting in suboptimal geometric consistency and semantic ambiguity. In this paper, we present a manifold-aware point cloud completion framework that explicitly incorporates nonlinear geometry information throughout the feature learning pipeline. Our approach introduces two key modules: a Geodesic Distance Approximator (GDA), which estimates geodesic distances between points to capture the latent manifold topology, and a Manifold-Aware Feature Extractor (MAFE), which utilizes geodesic-based k-NN groupings and a geodesic-relational attention mechanism to guide the hierarchical feature extraction process. By integrating geodesic-aware relational attention, our method promotes semantic coherence and structural fidelity in the reconstructed point clouds. Extensive experiments on benchmark datasets demonstrate that our approach consistently outperforms state-of-the-art methods in reconstruction quality. The code has been available online https://anonymous.4open.science/r/Manifold-Aware-Point-Cloud-Completion--F380/README.md.

For minimally rigid graphs, the same edge-length data can admit multiple realizations (up to translations and rotations). Finding graphs with exceptionally many realizations is an extremal problem in rigidity theory, but exhaustive search quickly becomes infeasible due to the super-exponential growth of the number of candidate graphs and the high cost of realization-count evaluation. We propose a reinforcement-learning approach that constructs minimally rigid graphs via 0- and 1-extensions, also known as Henneberg moves. We optimize realization-count invariants using the Deep Cross-Entropy Method with a policy parameterized by a Graph Isomorphism Network encoder and a permutation-equivariant extension-level action head. Empirically, our method matches the known optima for planar realization counts and improves the best known bounds for spherical realization counts, yielding new record graphs.

A common criticism of liquid democracy within the relevant academic literature is that delegation cycles can occur, seemingly resulting in unused voting power. Yet, practitioners argue that delegation cycles are not only unproblematic but are even formed intentionally by participants. To bring theory closer to reality, we introduce a model that captures this strategic behavior under uncertainty. We study the existence, structure and quality of Nash equilibria, revealing that delegation cycles naturally emerge. To complement these findings, we perform computational experiments using best-response dynamics.

C-RASP is a simple programming language that was recently shown to capture
concepts expressible by transformers.
In this paper, we develop new algorithmic techniques for automatically
verifying C-RASPs.
To this end, we establish a connection to the
verification of synchronous dataflow programs in Lustre, which enables us to
exploit state-of-the-art model checkers utilizing highly optimized
SMT-solvers. Our second contribution addresses learning a
C-RASP program in the first place. To this end, we provide a new algorithm
for learning a C-RASP from examples using local search.
We demonstrate efficacy of our implementation for benchmarks of C-RASPs in the
literature, in particular in connection to the following applications:
(1) transformer program optimization, and (2)
constrained learning of transformer programs (based on a partial specification).

We examine how causal beliefs affect an agent's choices and how feedback on those choices leads to updated causal beliefs. Building on the structural-equations framework for modeling causality, we first examine the general problem of updating causal beliefs in the face of novel (and possibly inexplicable) data. We model an agent who is uncertain of the true causal model, and therefore entertains a probabilistic belief over the set of possible models. We then consider how causal beliefs influence choices by building a model of agency and utility on top of the usual structural-equations framework. Using these two components, we propose a notion of steady state, where the feedback received from an agent's optimal action, given her current beliefs about the true causal model, can be rationalized by those beliefs.

Autonomous agents are increasingly expected to support scientific research, and recent benchmarks report progress in code repair and autonomous experimentation. However, these evaluations typically assume a pre-configured execution environment, which requires resolving complex software dependencies, aligning hardware and framework versions, and configuring distributed execution, yet this capability remains largely unbenchmarked. We introduce ResearchEnvBench, a benchmark for environment synthesis in research code execution. Given a research repository, documentation, and a target execution setting, agents must construct an environment that successfully executes at runtime. Evaluations on diverse research repositories reveal a substantial gap in current SOTA agents, with failures dominated by incomplete dependency resolution and brittle version coupling. ResearchEnvBench provides a realistic testbed for advancing autonomous agents toward reproducible scientific research.

In approval-based budget division, the task is to allocate a divisible resource to the candidates based on the voters' approval preferences over the candidates. For this setting, Brandl et al. (2021) have shown that no distribution rule can be strategyproof, efficient, and fair at the same time. In this paper, we aim to circumvent this impossibility theorem by focusing on approximate strategyproofness. To this end, we analyze the incentive ratio of distribution rules, which quantifies the maximum multiplicative utility gain of a voter by manipulating. While it turns out that several classical rules have a large incentive ratio, we prove that the Nash product rule (NASH) has an incentive ratio of 2, thereby demonstrating that we can bypass the impossibility of Brandl et al. by relaxing strategyproofness. Moreover, we show that an incentive ratio of 2 is optimal within three natural classes of rules and that the positive result for the Nash product rule even holds when voters may report arbitrary concave utility functions. Finally, we complement our results with an experimental analysis.

Despite extensive research, computational creativity still lacks a conceptually grounded, practically relevant, and empirically applicable formal definition across contexts such as art, scientific research, and games. Existing approaches to creativity often focus on proposing informal conceptual definitions without providing formal computational concepts. Alternatively, they propose formal definitions without carefully examining their underlying assumptions or providing validation of these definitions by testing their performance in specific application settings. To address these problems, this paper proposes several formal postulates of creativity grounded in prior psychological work on creativity, especially as related to Boden’s three core criteria of creativity: newness, value, and surprise. Furthermore, we propose a conceptual mathematical definition of creativity that aligns with these postulates. We also validate our proposed creativity formula in two applications: determining the creativity of particular chess moves, and assessing the creativity of paintings by some artists, such as Picasso, Renoir, and other painters. Experimental results demonstrate that our formula of creativity successfully captures creative patterns across both the chess and the art domains. This suggests that creativity can be effectively characterized through a unified mathematical structure based on three fundamental criteria, namely newness, value, and surprise, whose specific measurements are adapted to the particular domains.

Existing AI-generated image (AIGI) detectors perform well in-domain but degrade severely under distribution shift. We observe that this failure is mainly caused by content shortcuts, where detectors spuriously couple forgery artifacts with semantic content, such as object categories or demographic attributes, learning content–label correlations instead of generalizable forgery patterns. To address this issue, we propose PURE (Purging Unrelated Representations for Content-Agnostic Forgery Detection), which achieves content-agnostic detection through two complementary components: a Causal Semantic Generative (CSG) mechanism that disentangles semantic representations from forgery-irrelevant nuisance factors, and a Gaussian Mixture Model (GMM)-based prototype alignment module that suppresses category-specific content bias. Extensive experiments on CIFAKE, GenImage, and AlFace show that PURE achieves superior generalization under spurious correlation reversal. The code is available at https://github.com/wuxinyu519/PURE.

Semi-supervised clustering (SSC) enables personalized clustering under limited user supervision. Given the sparsity of initial user intents, constraint propagation has been proposed as a powerful approach to explore and generate new performance-enhancing constraints. However, existing methods struggle to handle a large number of ``gray samples'' that deviate from initial supervision signals and exhibit ambiguous semantics with indistinct boundaries. Compared with ``distinct samples'' that closely match the supervision, gray samples typically contain richer latent semantics, and accurately identifying their relational types can significantly improve clustering performance. To address this challenge, we propose Adversarially Enhanced Propagation-driven Intent-aware Clustering (AEPIC). Specifically, we design an Adversarially Enhanced Constraint Propagation (AECP) mechanism that leverages global adversarial learning over dual relational links to identify gray samples and expand them into meaningful pseudo-user intents. In addition, an intent-aware regularization strategy integrates these pseudo-user intents into representation learning and clustering optimization, further improving clustering performance. Experiments on 5 benchmark datasets demonstrate that, under sparse supervision, AEPIC consistently outperforms state-of-the-art semi-supervised clustering methods.

Medical image segmentation is still challenging, especially in semi-supervised scenarios where limited annotations are expected to support both accurate boundary delineation and coherent anatomical structures. We propose LR-GCF, a Local-Rotation-Driven Global Consistency Framework that couples strong local geometric perturbations with global consistency regularization in a dual-view architecture. Given an image, LR-GCF transforms it to different variants through patch-wise jigsaw-rotated perturbations. Such variants are decoded using parallel Swin Transformer decoders and perform consistency regularization by virtue of cross pseudo supervision. A rotation prediction head is designed to predict the rotation of each local patch. This makes locally discriminative representations robust to rotations and helps the model recognize global anatomical structure under strong perturbations. To further stabilize global modeling, we introduce a rotation-aligned attention consistency loss that encourages the inter-patch correlation across different variants to be as similar as possible. Moreover, we design a Position Encoding Duplicating scheme to propagate positional encodings from the low-resolution token map to high-resolution token maps, which helps alleviate the misalignment problem in rotation-aligned attention consistency. Extensive experiments on multiple medical image segmentation benchmarks demonstrate that LR-GCF consistently achieves superior performance under limited supervision, by jointly optimizing local detail extraction and stable global anatomical modeling. Source code is available at https://github.com/shuaiaihang/LR-GCF.

Reasoning chains in Large Language Models (LLMs) often exhibit heavy-tailed length distributions, yet existing efficiency methods rely on suboptimal linear penalties that suppress complex reasoning, limiting both accuracy and generalization. To address this, we first empirically observe that reasoning lengths are well approximated by a log-normal distribution, and provide an intuitive explanation for this phenomenon. Based on this insight, we propose the Powered Length Penalty (PLP), an adaptive regularizer that penalizes redundancy in short sequences while gradually reducing penalties for longer sequences, preserving deep reasoning. Trained solely on the elementary GSM8K dataset, PLP significantly improves reasoning efficiency by reducing inference costs on GSM8K and MATH500, while simultaneously enhancing accuracy on the challenging AIME2024 benchmark. Furthermore, PLP transfers effectively to diverse domains, including MMLU and GPQA. These results suggest that modeling reasoning length distributions and adapting penalties accordingly can mitigate the typical trade-off between efficiency and performance, enabling more reliable and cost-effective reasoning across tasks.

Graph self-supervised learning aims to mine intrinsic signals from graph data itself to train models. It enables the acquisition of high-quality representations without manual annotations, making it suitable for various label-scarce scenarios and thus garnering substantial interest. Existing graph self-supervised methods typically assume that each node possesses a single semantic meaning. However, many real-world entities exhibit multiple semantics, where a single node may simultaneously belong to multiple categories. Since existing approaches are mainly designed for single-semantic settings, they often struggle to accurately capture the multiple semantics within nodes. Meanwhile, existing multi-label graph learning methods mainly depend on extensive manual annotations, which are costly and of limited applicability. To address this, we make a bold attempt to extend graph self-supervised learning to multi-label graphs. It is particularly challenging, as lacking labels makes it difficult to identify multiple semantics, let alone determine the number of underlying categories for nodes. To this end, we propose a Multi-semantic Aware Self-Supervised pretraining method (MASS) for multi-label graphs. Specifically, we propose a multi-pseudo-label decomposition mechanism, enabling adaptive learning of multiple semantics within nodes without annotations. Extensive results validates the effectiveness of MASS, and it achieved competitive performances with supervised baselines.

Coalition formation studies how to partition a set of agents into disjoint coalitions based on their preferences. In this paper,
we consider the class of additively separable hedonic games and study the setting in which agents' valuations of each other evolve over time. Since rearranging coalitions can be costly, our goal is to find a stable partition close to the current one, where distance is measured by the number of agents that must change coalition.
We study this problem for four stability notions based on single-agent deviations: Nash, individual, contractual Nash, and contractual individual stability. For all four, deciding whether a close stable partition exists is NP-complete, even under severe restrictions on the valuations and even when only a single valuation changes. On the positive side, we give polynomial-time algorithms for the two contractual notions under restricted symmetric valuations, and show that over long sequences of updates, these algorithms maintain stability with constant average reconfiguration cost.

Online planning in continuous state, action, and observation spaces remains challenging for autonomous systems. While Monte Carlo Tree Search (MCTS) scales effectively via sampling, most continuous (PO)MDP solvers do not exploit gradient-based action optimization. We propose Action-Gradient MCTS (AGMCTS), a framework that combines global tree search with local gradient-based action refinement, while maintaining consistent value estimates. We provide three key theoretical contributions: (1) an action score gradient theorem for particle belief states; (2) the Multiple Importance Sampling (MIS) Tree that supports frequent action-branch updates by reusing prior samples without introducing estimator drift; and (3) tractable action score gradients for smooth generative models using the Area Formula. Empirical results demonstrate that AGMCTS outperforms state-of-the-art sample-based solvers in multiple challenging continuous MDP and POMDP benchmarks.

Reward learning via human feedback is a crucial capability for beneficial AI. Current methods are built on decision-making theories that assume a matched dynamics model between the learning agent and the feedback provider. However, humans often form imperfect internal dynamics models, and their feedback reflects these misconceptions. While this relationship has long been hypothesised, its manifestation in sequential decision-making remains largely an assumption. Our work provides the first comprehensive empirical investigation of this relationship through a randomized controlled trial (N=211). We followed a two-stage design where we first initialized the participants' understanding of the dynamics in a grid-world navigation domain and then manipulated it using text-based instructions. Causal mediation analysis revealed that humans' internal models play a mediating role in feedback behaviour. We show that this relationship is invariant across visual contexts and is robust to three common feedback types: pairwise preferences, trajectory corrections, and off-switch interventions. These findings confirm a critical limitation of current reward learning methods and establish the missing psychological foundation for approaches that incorporate dynamics understanding.

Adaptive Learning Systems (ALS) play a key role in promoting the equity of education, which can provide personalized teaching on a large scale. However, the existing question recommendation methods face the problem of data sparsity at the scenario of cold-start, and lack the ability of long-term teaching reasoning. To address these challenges, we propose QuesRecAgent, a multi-agent framework which involves Large Language Models (LLMs) and the Relexion mechanism for adaptive question recommendation. Different from the traditional black-box methods, the QuesRecAgent applies a framework Dual-Loop State Decoupling architecture, which achieves a rapid updating of beliefs through simulated interaction in the inner loop and do the authoritative diagnosis and meta-strategy reflection to optimize the teaching strategy in the outer loop. Furthermore, with the support of a Topological Knowledge Graph (TKG) and the theory of Zone of Proximal Development (ZPD), the QuesRecAgent ensures a high-quality learning guidance even facing students with limited interaction records. Our experiments on three real-world datasets show that the QuesRecAgent outperforms state-of-the-art baselines in total knowledge gain and other metrics, providing a robust and interpretable solution for promoting high-quality personalized education.

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.

Unified remote sensing multimodal models exhibit a pronounced spatial reversal curse: although they can accurately recognize and describe object locations in images, they often fail to faithfully execute the same spatial relations during text-to-image generation, where such relations constitute core semantic information in remote sensing. Motivated by this observation, we propose Uni-RS, the first unified multimodal model tailored for remote sensing, to explicitly address the spatial asymmetry between understanding and generation. Specifically, we first introduce explicit Spatial-Layout Planning to transform textual instructions into spatial layout plans, decoupling geometric planning from visual synthesis. We then impose Spatial-Aware Query Supervision to bias learnable queries toward spatial relations explicitly specified in the instruction. Finally, we develop Image–Caption Spatial Layout Variation to expose the model to systematic geometry-consistent spatial transformations. Extensive experiments across multiple benchmarks show that our approach substantially improves spatial faithfulness in text-to-image generation, while maintaining strong performance on multimodal understanding tasks like image captioning, visual grounding, and VQA tasks.

Designing enzymes with substrate-binding pockets is a critical challenge in protein engineering, as catalytic activity depends on the precise interaction between pockets and substrates. Currently, generative models dominate functional protein design but cannot model pocket-substrate interactions, which limits enzyme generation with precise catalytic environments. To address this issue, we propose EnzyPGM, a unified framework that jointly generates enzymes and substrate-binding pockets conditioned on functional priors and substrates, with a particular focus on learning accurate pocket–substrate interactions. At its core, EnzyPGM includes two main modules: a Residue-atom Bi-scale Attention (RBA) that jointly models intra-residue dependencies and fine-grained interactions between pocket residues and substrate atoms, and a Residue Function Fusion (RFF) that incorporates enzyme function priors into residue representations. Also, we curate EnzyPock, an enzyme–pocket dataset comprising 84,336 enzyme–substrate pairs across 1,036 four-level enzyme families. Extensive experiments demonstrate that EnzyPGM achieves state-of-the-art performance on EnzyPock. Notably, EnzyPGM reduces the average binding energy by 0.47 kcal/mol over EnzyGen, showing its superior performance on substrate-specific enzyme design. The code is available at https://github.com/John-Lin98/EnzyPGM.

IC3, also known as property-directed reachability (PDR), is a commonly-used algorithm for hardware safety model checking. It checks if a state transition system complies with a given safety property. IC3 either returns UNSAFE (indicating property violation) with a counterexample trace, or SAFE with a checkable inductive invariant as the proof to safety. In practice, the performance of IC3 is dominated by a large web of interacting heuristics and implementation choices, making manual tuning costly, brittle, and hard to reproduce.
This paper presents IC3-Evolve, an automated offline code-evolution framework that utilizes an LLM to propose small, slot-restricted and auditable patches to an IC3 implementation. Crucially, every candidate patch is admitted only through proof-/witness-gated validation: SAFE runs must emit a certificate that is independently checked, and UNSAFE runs must emit a replayable counterexample trace, preventing unsound edits from being deployed. Since the LLM is used only offline, the deployed artifact is a standalone evolved checker with zero ML/LLM inference overhead and no runtime model dependency. We evolve on the public hardware model checking competition (HWMCC) benchmark and evaluate the generalizability on unseen public and industrial model checking benchmarks, showing that IC3-Evolve can reliably discover practical heuristic improvements under strict correctness gates.

Spatiotemporal fusion (STF) bridges the gap between temporal and spatial resolutions in satellite imagery, enabling effective monitoring of Earth's surface dynamics. However, existing methods rely on cloud-free reference images, a constraint that fails in realistic, cloud-prone scenarios. To overcome this, we propose the Cloud-Aware Wavelet Generative Adversarial Network (CLAW-GAN), a novel framework for high-fidelity reconstruction under cloud-contaminated conditions. CLAW-GAN introduces Regression Wavelet Analysis (RWA) to decouple spectral backgrounds from structural details. While a change-aware gated mechanism accounts for land-cover changes, the Frequency-Separated Fusion (FSF) module then independently integrates these components. To ensure visual realism, a multi-scale discriminator operates in the wavelet domain, enforcing consistency across high-frequency subbands to minimize artifacts. Evaluated on the newly introduced Global Cloud-shrouded Agricultural Regions (GCAR) benchmark and the simulated Daxing dataset, CLAW-GAN achieves state-of-the-art performance and demonstrates superior robustness across varying cloud coverage.

Zinc-based alloys are indispensable emerging absorbable metallic biomaterials, and their macroscopic performance is governed by microstructural characteristics. Intermediate phases—key microstructural constituents—are pivotal in regulating mechanical and functional properties. However, intermediate phase segmentation in zinc alloy microstructures faces formidable challenges: scarce annotated datasets, low contrast, difficulty detecting small targets, and heterogeneous morphologies. To this end, we construct IPSM-Bench, the largest high-quality dataset for zinc-alloy intermediate phase segmentation. Furthermore, we propose SCoP-SAM, a new Spatial Context Prior-guided SAM method that leverages the gradient structure and grayscale properties of intermediate phases to capture spatial context priors and incorporates them into the entire SAM encoding-decoding process, improving segmentation performance. Based on the proposed IPSM-Bench, we establish a new benchmark for intermediate phase segmentation to systematically evaluate state-of-the-art (SOTA) methods and advance research on zinc alloy microstructure analysis. Extensive experiments on IPSM-Bench and additional public alloy benchmarks demonstrate that our SCoP-SAM not only achieves SOTA performance for zinc-alloy intermediate phase segmentation but also generalizes remarkably well to other alloy scenarios.

While 3D Gaussian Splatting (3DGS) has excelled in dynamic scene reconstruction, it struggles with multi-view object motion blur, where view-dependent non-uniform blur violates fundamental multi-view geometric constraints. Existing methods fail to balance complex motion fitting with physical consistency across different views. To address the challenge, we propose TaylorMoDe-GS, the first 3DGS framework tailored for multi-view dynamic object deblurring. Specifically, we shift the modeling paradigm from displacement fitting to velocity driven modeling. This is achieved by analytically deriving instantaneous 3D velocities via Taylor series. To map 3D physical motion onto the 2D image plane, we propose a velocity splatting technique. Building upon this, we introduce a neural Peano remainder network to compensate for high frequency non-linear dynamics, effectively resolving the conflict between physical priors and fitting flexibility. Combined with a learnable physical blur synthesis mechanism, our framework ensures rigorous spatiotemporal and view consistency. Extensive experimental results validate that our method achieves notable advancements in restoring high-fidelity dynamic scenes and physically consistent motion fields, effectively addressing the challenges of multi-view dynamic object deblurring. The code is released at https://github.com/Chiffin-0816/TaylorMoDe-GS.

Large Vision-Language Models (LVLMs) demonstrate powerful generative capabilities yet remain prone to object hallucinations. Most existing methods mitigate this issue through training or decoding strategies, but provide limited exploration of how hallucinations arise from internal representations during generation.
In this work, we study hallucination from the perspective of dynamic representation shift during generation and propose Representation Intervention based on Visual Grounding Shift (RIVS). Specifically, we first design an adaptive threshold-based method to identify visual attention drop points within the generation, finding that hallucinated tokens usually occur with decay of visual attention. Based on this observation, we construct a hallucination-related subspace from representation differences around these points on a small calibration set, without constructing contrast samples or extra supervision.
During inference, we leverage the resulting hallucination-related subspace to perform an online projection-based intervention on intermediate hidden states to suppress the hallucination-related directions, mitigating hallucinations while preserving language quality.
Our RIVS is training-free and computationally efficient. Experiments on four hallucination and two reasoning datasets demonstrate that RIVS consistently reduces hallucinations in both long and short sequence generation tasks.

Vision Transformers (ViTs) exhibit notable susceptibility to adversarial attacks, presenting a significant challenge for their deployment in security-sensitive applications. Despite their considerable empirical successes, a rigorous theoretical foundation for ViT's adversarial generalization behavior has not been adequately established. To address this limitation, we leverage empirical Rademacher complexity to analyze the mechanism of perturbation accumulation through deep ViTs layers. We establish a high-probability generalization bound for ViTs in classification tasks under adversarial settings. Our theoretical framework elucidates the roles of several factors in mitigating perturbation effects, norm regularization of weight matrices (in both MLP and attention modules) and depth-wise propagation constraints on layer-wise norms. Extensive experiments on benchmark datasets corroborate our theoretical insights, bridging the gap between ViTs architecture design and adversarial robustness.

Predicting drug–target affinity (DTA) is central to drug discovery, yet most deep learning models rely on a single static protein structure, neglecting the conformational heterogeneity that underlies many binding mechanisms. We propose MultiGeo, a DTA prediction framework that explicitly leverages multiple protein conformations rather than a single snapshot. For each target, MultiGeo starts from an ensemble of structure predictions and uses confidence scores to adaptively select a small set of high-quality, diverse conformers. A hierarchical structural encoder then extracts multi-scale geometric features from these conformers, which are aggregated by a GRU to obtain an ensemble-level representation. To avoid indiscriminately mixing noisy or redundant views, we introduce a disagreement-aware gating mechanism that adaptively fuses this ensemble representation with the dominant structure only when the additional conformers provide complementary information. Finally, ligand–target interactions are modeled via a block-wise cross-attention module that captures multi-perspective dependencies between ligand and protein features. Extensive experiments on multiple DTA benchmarks demonstrate that MultiGeo consistently outperforms state-of-the-art baselines, showing that explicitly encoding conformational diversity yields more accurate and robust affinity prediction.

Target speaker extraction (TSE) aims to extract the speech of a target speaker from mixtures containing multiple competing speakers. Conventional TSE systems predominantly rely on speaker cues, such as pre-enrolled speech, to identify and isolate the target speaker. However, in many practical scenarios, clean enrollment utterances are unavailable, limiting the applicability of existing approaches. In this work, we propose DAE-TSE, a keyword-guided TSE framework that specifies the target speaker through distinct keywords they utter. By leveraging keywords (i.e., partial transcriptions) as cues, our approach provides a flexible and practical alternative to enrollment-based TSE. DAE-TSE follows the Detect-Attend-Extract (DAE) paradigm: it first detects the presence of the given keywords, then attends to the corresponding speaker based on the keyword content, and finally extracts the target speech. Experimental results demonstrate that DAE-TSE outperforms standard TSE systems that rely on clean enrollment speech. To the best of our knowledge, this is the first study to utilize partial transcription as a cue for specifying the target speaker in TSE, offering a flexible and practical solution for real-world scenarios. Our code (https://github.com/GnafiY/DAE-TSE) and demo page (https://gnafiy.github.io/DAE-TSE_demo) are now publicly available.

The Dynamic Flexible Job Shop Scheduling Problem (DFJSP) necessitates a trade-off between instant reaction to stochastic disturbances and global optimization of production goals. Conventional priority rules are insufficiently flexible to handle complex disruptions, whereas learning-based approaches often compromise interpretability or fail to generalize across problem scales. Although Large Language Models (LLMs) offer advanced reasoning capabilities to bridge this gap, their substantial inference latency is incompatible with the millisecond-level decision cycles of industrial control systems. To resolve this conflict, we introduce RACE-Sched, an asynchronous agent-based framework that decouples policy execution from logical reasoning via a dual-stream architecture. The Reactive Stream executes low-latency symbolic heuristics to enable real-time dispatching, while the parallel Deliberative Stream leverages an LLM to synthesize, validate, and evolve these rules. Candidate rules undergo rigorous testing in a sandbox and are deployed via atomic updates, ensuring safety without blocking the control loop. Additionally, a semantic rule repository indexes validated heuristics for retrieval-based initialization which enhances transferability across problem scales. Extensive evaluations on GEN-Bench, MK-Bench, and JMS-Bench demonstrate that RACE-Sched outperforms leading Deep Reinforcement Learning and other LLM-based baselines. This approach harmonizes real-time constraints with long-horizon reasoning to achieve superior solution quality and robust adaptation to dynamic events.

Label Distribution Learning (LDL) effectively addresses label ambiguity by modeling the degree to which each label describes an instance. A key challenge in LDL is Label Enhancement (LE): recovering label distributions from logical labels. Existing LE methods typically treat logical labels as supervisory signals and learn a direct mapping from features to label distributions. However, they fail to fully exploit the rich information encoded in logical labels, limiting their performance. We propose CVMG (Cross-View Fusion and Mixed Graph-based Label Enhancement), a novel approach that addresses this limitation through two key innovations. First, we employ a cross-attention mechanism to integrate logical labels and features, leveraging their complementary information to generate enriched feature representations. Second, we construct a mixed dependency graph that captures both instance-level relationships from enhanced features and category-level dependencies from logical labels. Label distributions are then recovered through propagation over this graph. Extensive experiments on 13 real-world datasets demonstrate that CVMG significantly outperforms state-of-the-art methods, validating the effectiveness of our approach.

Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, but their performance degrades significantly in long-horizon tasks due to cumulative error propagation. This limitation largely arises from static feature fusion mechanisms that rely on fixed weights to combine visual, language, and action representations, preventing the model from adapting to different phases of task execution. To address this limitation, we propose S²-VLA, a framework that introduces a State-Space Guided Adaptive Attention (SSGAA) mechanism. SSGAA maintains a belief state that tracks task progression and generates dynamic gating weights to adaptively fuse information from three complementary sources visual features for spatial perception, task intents for high-level task planning, and temporal action sequences for execution consistency. This adaptive fusion allows the model to shift its focus throughout task execution, aligning with the evolving requirements of different task stages. Despite its compact 2B parameter size, S²-VLA consistently outperforms larger 7B-scale models and achieves state-of-the-art performance on long-horizon manipulation benchmarks, including LIBERO and SimplerEnv. highlighting the importance of adaptive feature fusion for long-horizon robotic manipulation.

Large Language Models (LLMs) powerful generative capabilities also pose significant risks, underscoring the need for effective detoxification methods to ensure safer deployment. Due to the polysemantic nature of LLM neurons, recent neuron intervention methods inevitably entangle unrelated concepts, compromising generation quality and interpretability. Sparse Autoencoders (SAEs) have opened new horizons for decomposing model activations into monosemantic features, offering interpretability and targeted feature-level steering. Empirical findings reveal that, despite capturing interpretable features, indiscriminate interventions on toxicity-related features expose the fragility of LLMs, achieving toxicity mitigation at the cost of degraded fluency. Building upon this finding, we propose DeLFE, a lightweight controlled detoxification approach that identifies specific toxic features across model layers and performs targeted interventions on them. DeLFE learns toxicity subspaces from label-guided SAE feature subsets to characterize toxic v.s. non-toxic activation patterns. When auto-completing a response token-by-token, DeLFE tracks the toxicity-triggering risks and steers toxic features away from the subspace via a flow-matching feature transformation. We further design three feature-level strategies that adjust intervention timing and strength to reconstruct the target model’s original activations. Extensive experiments demonstrate that our method achieves strong detoxification effectiveness while maintaining high generation quality across models of varying sizes and diverse base LLMs.

Multi-step retrosynthetic planning aims to decompose target molecules into available starting materials by iteratively invoking single-step prediction models within a search algorithm. The success of retrosynthetic planning depends on the joint guidance of single-step reasoning and global search across steps. However, most existing frameworks make step-wise decisions based only on the current molecular state, without explicitly modeling synthesizability signals that reflect long-range reachability. In this work, we propose GuideRetro, a synthesizability-aware framework for multi-step retrosynthetic planning that integrates global synthesizability knowledge into step-wise retrosynthetic prediction. GuideRetro learns transferable knowledge from large-scale reaction networks by modeling the evolution of synthetic complexity along reaction pathways. During planning, a route-aware synthesis state modeling module combines the evolving retrosynthetic route with retrieved global signals to guide reactant generation at each step. Experiments on benchmark datasets show that GuideRetro achieves state-of-the-art performance. The integration of global synthesizability knowledge and route-aware modeling improves planning accuracy and search efficiency under realistic retrosynthetic settings. The code is available at: https://github.com/L-AJ/GuideRetro.

Reconstructing PDE-governed fields from sparse and irregular measurements is challenging due to their ill-posed nature. Deterministic surrogates are trained on dense fields that struggle with limited measurements and uncertainty quantification. Generative models, by learning distributions over spatiotemporal fields, can better handle sparsity and uncertainty. However, existing generative approaches enforce data consistency and PDE constraints simultaneously via sampling-time gradient guidance, resulting in slow and unstable inference. To this end, we propose PerFlow, a Physics-embedded rectified Flow for efficient sparse reconstruction and uncertainty quantification of spatiotemporal dynamics. PerFlow decouples observation conditioning from physics enforcement, performing guidance-free conditioning by feeding observations into rectified-flow dynamics while embedding hard physics via a constraint-preserving projection (e.g., incompressibility or conservation). Theoretically, we establish invariance guarantees to ensure that trajectories remain on the physics-consistent manifold throughout sampling. Experiments on various PDE systems demonstrate competitive reconstruction accuracy with sound physics consistency, while enabling efficient conditional sampling (e.g., 50 steps) and up to 320x faster inference than 2000-step guided diffusion baselines.

Network traffic analysis is crucial for maintaining the security of networks. Yet deploying accurate models on edge nodes remains challenging due to protocol diversity, complex traffic behaviors, and stringent resource constraints. Although recent deep learning models achieve strong performance, their dense architectures incur high inference latency, making them unsuitable for edge deployments.
Mixture-of-Experts offers a promising solution through conditional computation, yet existing methods overlook latent inter-task relations and suffer from severe expert load imbalance, leading to expert collapse and suboptimal efficiency.

To address these limitations, we propose TAMoE, a novel Task-Aware Mixture-of-Experts framework for multi-task network traffic analysis.
TAMoE integrates task semantics into both routing and representation learning through a novel Task-Aware Routing, enabling the router to dynamically select experts conditioned on both traffic features and task information.
Besides, to train stably usable and scalable multi-task models, we further design a collaborative multi-task training strategy that encompasses both a dataset mixed construction and a joint training process.
Experiments on six public benchmarks show that TAMoE achieves state-of-the-art results. Deployment on an NVIDIA® Jetson AGX Orin edge node demonstrates that TAMoE reduces inference latency by 60.6% compared to dense models and lowers the Gini Coefficient from 0.391 to 0.275 relative to Multi-Router MoE, achieving more balanced expert utilization.

Open cross-domain federated graph learning facilitates collaborative learning among clients from distinct graph domains while preserving privacy. However, severe structure and feature heterogeneity in open scenarios exacerbates the multiplicative amplification of structural and feature noises within graph neural network encoders, leading to significant negative transfer. Existing methods addressing partial heterogeneity are insufficient. We theoretically reveal this amplification mechanism and propose a novel learning method FedFINFO, which leverages full-informativeness shared knowledge. Specifically, we construct an aligned and general feature space integrating node semantic and structural roles. We proposed a counterfactual multi-view framework to explicitly learn a structure masker for extracting consensus subgraphs. These jointly suppress the mutual amplification of noises at the source. We also propose a history-aware adaptive multi-channel aggregation mechanism to enable dynamic and personalized sharing driven by channel similarity while preventing collaborative oscillation. FedFINFO effectively reconciles common knowledge sharing with personalized knowledge retention, significantly mitigating negative transfer. Extensive experiments under cross-dataset and cross-domain settings demonstrate the superiority of our method.

We address the problem of selecting k representative nodes from a network, aiming to simultaneously achieve two objectives: identifying the most influential nodes and ensuring that the selection proportionally reflects the diversity within the network. We propose a general approach to accomplish this by combining ideas from network science and computational social choice. Notably, our algorithms depend only on the connections between nodes and do not utilize any additional information that would explicitly identify groups of nodes. We analyze them theoretically, and demonstrate their effectiveness through a series of experiments.

Fully automatic camera movement directly affects the art quality of dance expressiveness, especially in terms of visual expression, as well as choreography and music. Current studies mainly focus on synthesizing camera movements conditioned on dance and music, but they overlook the camera movement style, which is essential factor for artistic and visual coherence. In this paper, we introduce DanceStyleCam, a unified framework that incorporates the style-consistent characteristic into dance camera movement synthesis with diverse stylistic characteristics. Specifically, a style-aware feature learning module is proposed to map dance style information into compact embeddings, facilitating stable and discriminative style learning. To further guarantee that the generated camera movements remain faithful to the target style, we propose a style-consistent adversarial training scheme, leading and optimizing the model to learn better style-consistent representations. In addition, we also enrich the DCM dataset with diverse camera movement style annotations. Extensive experiments demonstrate that DanceStyleCam outperforms state-of-the-art methods in both generation quality and style consistency. The project page is: https://anonymous.4open.science/r/DanceStyleCam.

Predicting cross-sectional stock returns is challenging due to low signal-to-noise ratios and evolving market regimes. Classical factor models offer interpretability but limited flexibility, while deep learning models achieve strong performance yet often underutilize financial priors. We address this gap with PRISM-VQ (PRior-Informed Stock Model with Vector Quantization), a dynamic factor framework that integrates expert prior factors, vector-quantized discrete latent factors learned from cross-sectional structure, and a structure-conditioned Mixture-of-Experts to generate time-varying factor loadings. Vector quantization acts as an information bottleneck that suppresses noise while capturing robust market structure, with discrete codes serving both as latent factors and as routing signals for temporal expert specialization. Experiments on CSI 300 and S&P 500 show consistent improvements in cross-sectional return prediction and portfolio performance over strong baselines while preserving interpretability. Our code is available at https://github.com/finxlab/PRISM-VQ.

Deep incomplete multi-view clustering methods can mine patterns of incomplete multi-view data without labels, gaining great attention in various domains. However, current methods are obsessed with aligning view-specific representations from available samples with complete views to learn view-invariant information for missing data recovery, ignoring fruitful view-complementary information beneficial to data imputations. Meanwhile, such the view-invariant learning often leads to the problem of dominant view dependency that models tend to over-rely on views with clear clustering structures and neglect weaker ones, causing a performance bottleneck. Therefore, a content-style disentanglement guided representation learning is proposed for the incomplete multi-view clustering (CSMVC). Specifically, CSMVC designs a view-specific dual-representation learning architecture that extracts view-invariant content representations and view-specific style representations from self-supervised reconstructions of each view with the guidance of the Hilbert schmidt independence criterion. Then, it performs a content-centered style-modulated representation imputation mechanism to infer missing data via fully utilizing inter-view complementary and consistent information. Meanwhile, it devises a cross-view consensus structure mining strategy to capture the clustering structure with intra-cluster compactness and inter-cluster separability from attention-based fusion representations that adaptively aggregate content representations and style representations of each view. Finally, comprehensive experiments show that CSMVC outperforms state-of-the-art methods.

The increasing availability of trajectory data is often hampered by sparsity and noise. Existing trajectory recovery methods are further limited by either information loss from coordinate discretization into location IDs, or the inefficiency of conventional diffusion models that require a lengthy denoising process from pure noise. To address these challenges, we propose DiffVec, a novel and efficient diffusion framework for free-space trajectory recovery that operates directly on continuous coordinate data. The backbone of our framework is MVformer, a Transformer-based architecture that models motion vectors—the differences between consecutive coordinates—to better capture the local motion patterns of movement. This model is trained within our ResTraj diffusion paradigm, which commences the denoising process from a structured, interpolated prior to significantly reduce sampling steps. Extensive experiments on the Geolife and Porto datasets demonstrate that DiffVec significantly outperforms state-of-the-art baselines across MAE, MSE, and NDTW.

Current self-evolution methods mostly chase recall, they learn from any trajectory that ends with the right answer, even when the path relies on a "lucky guess". Such reasoning appears valid in-distribution but often fails the moment the task shifts slightly. We argue that robust evolution implies Structural Invariance: a reasoning path is valid only if its core dependency graph remains isomorphic under counterfactual perturbations. I-EDI enforces this with Verifiable Counterfactual Simulation (VCS). Instead of treating data generation as mere "sample and filter," we treat it as an intervention. Unlike standard rejection sampling, VCS acts as a structural stress test: it accepts a reasoning trace only if it remains valid across generated counterfactuals (e.g., numerical perturbations, context shifts). While this strict filtering reduces the volume of training data (trading recall for precision), our experiments on MATH and GSM8K show it effectively prevents hallucination accumulation, achieving superior out-of-distribution generalization compared to baselines.

Physical-layer device identification serves as a critical security mechanism for mitigating cyberattacks and enhancing network resilience. However, its deployment in modern full-duplex Ethernet networks faces two major challenges: (1) the presence of Out-of-Distribution (OOD) data caused by temporal distribution shift and diverse local transmitters, and (2) the difficulty of environment-agnostic device identification, where the unique features of target devices are often obscured in mixed signals. To address these challenges, we propose GenID, which consists of two core components. First, GenID constructs prototypes to emulate the full communication environment through a two stage process: offline and online. Based on the prototype context, it then introduces an invariant feature extraction method to disentangle mixed signals and improve generalization under OOD conditions.Extensive experiments demonstrate that GenID significantly outperforms state-of-the-art baselines, achieving an increase of at least 7.94\% in Macro-F1 score under OOD environments. Our code is available on https://github.com/zyw2004/GenID_IJCAI2026.

Subgame solving is a technique for scaling algorithms to large games by locally refining a precomputed blueprint strategy during gameplay. While straightforward in perfect-information games where search starts from the current state, subgame solving in imperfect-information games must account for hidden states and uncertainty about the opponent's past strategy. Gadget games were developed to ensure that the improved subgame strategy is robust against any possible opponent's strategy in a zero-sum game. Gadget games typically contain infinitely many Nash equilibria. We demonstrate that while these equilibria are equivalent in the gadget game, they yield vastly different performance in the full game, even when facing a rational opponent. We propose gadget game sequential equilibria as the preferred solution concept. We introduce modifications to the sequence-form linear program and counterfactual regret minimization that converge to these refined solutions with only mild additional computational cost. Additionally, we provide several new insights into the surprising superiority of the resolving gadget game over the max-margin gadget game. Our experiments compare different Nash equilibria of gadget games in several standard benchmark games, showing that our refined equilibria consistently outperform unrefined Nash equilibria, and can reduce the exploitability of the overall strategy by more than 50%.

Social recommendation leverages social relations to enhance user preference modeling. However, real-world social networks are often noisy and unreliable, where misleading social relationships introduce anisotropic perturbations into user representations. Existing denoising approaches, including heuristic filtering and generative reconstruction, struggle to produce social embeddings that are well aligned with user preferences, limiting their effectiveness in downstream recommendation tasks. To address this challenge, we propose Manifold-Aligned Rectification Flow (MARF), a flow matching based framework that explicitly rectifies noisy social representations toward preference-aligned embeddings. MARF jointly integrates social relations and user-item interactions to construct a preference-aware manifold as the target distribution, guiding the learning of a continuous vector field that captures preference-oriented transformations in the social domain. Through this learned transport process, MARF effectively bridges the gap between the social and preference domains, yielding robust and discriminative user representations. Extensive experiments demonstrate that our proposed model consistently outperforms state-of-the-art social recommendation methods, particularly under sparse and noisy conditions, validating its effectiveness and robustness.

Reconstruction-based frameworks are widely adopted in Time Series Anomaly Detection (TSAD), assuming that models reconstruct normal behavior well but yield larger errors on anomalies. However, in unsupervised TSAD, minimizing reconstruction loss alone often breaks this assumption. Models tend to overfit local patterns for trivial reconstruction and fail to capture the global dependencies that characterize normal behavior. Consequently, anomalies that violate global dependencies can also be reconstructed well, leading to a misalignment between reconstruction and detection. To address this challenge, we propose AnoMamba, a novel TSAD framework that aligns reconstruction with anomaly detection by enhancing global dependency modeling. Specifically, AnoMamba employs patch embedding to reduce local redundancy and introduces a Mamba variant with global step-size reweighting to select meaningful global dependencies. The reweighting process is guided by multi-scale long-tail priors, which adaptively balance global and local dependencies to mitigate overfitting in the unsupervised setting. Extensive experiments on both univariate and multivariate benchmarks demonstrate that AnoMamba consistently outperforms state-of-the-art methods in both accuracy and efficiency, while offering interpretability through its hidden attention map.

We initiate the study of online contracts, which integrate the game-theoretic considerations of economic contract theory, with the algorithmic and informational challenges of online algorithm design. Our starting point is the classic online setting with preemption, in which a hiring principal faces a sequence of adversarial agent arrivals. Upon arrival, the principal must decide whether to tentatively accept the agent to their team, and whether to dismiss previous tentative choices. Dismissal is irrevocable, giving the setting its online decision-making flavor. In our setting, the agents are rational players: once the team is finalized, a game is played where the principal offers contracts (performance-based payment schemes), and each agent decides whether or not to work. Working agents reward the principal, and the goal is to choose a team that maximizes the principal's utility. Our main positive result is a 1/2-competitive algorithm when agent rewards are additive, which matches the best-possible competitive ratio. Our algorithm is randomized and this is necessary, as we show that no deterministic algorithm can attain a bounded competitive ratio. Moreover, if agent rewards are allowed to exhibit combinatorial structure known as XOS, even randomized algorithms might fail. En route to our competitive algorithm, we develop the technique of balance points, which can be useful for further exploration of online contracts in the adversarial model.

Systematic safety evaluation of large language models must uncover diverse policy violations under tight query budgets. However, most red-teaming methods optimize attack success rate and repeatedly probe a narrow set of vulnerabilities, yielding redundant failures and leaving rarer yet critical violation categories unexplored. Under fixed budgets, such inefficient exploration delays the first discovery and limits category coverage. To address these limitations, we propose the Budget-Efficient Adaptive Cognitive Offense Navigator (BEACON), a budget-aware safety testing framework that uses Cognitive-Guided Monte Carlo Tree Search to navigate the violation search space under fixed budgets. BEACON innovatively approaches safety testing as a budget-constrained failure discovery process, aiming to identify diverse safety violations as early as possible within a fixed query budget. It also provides an efficiency-oriented evaluation perspective that measures early discovery and harm category coverage under budget constraints. Experiments on standard benchmarks and frontier LLMs show that BEACON discovers failures earlier and achieves higher coverage across policy violation categories. These results underscore the value of evaluating safety testing through discovery efficiency rather than attack success rate alone. Warning: This paper contains examples of harmful language and images, and reader discretion is recommended.

Tool-augmented methods aim to enhance the reasoning capabilities of large language models (LLMs) by invoking external tools, which can be broadly categorized into training-free and training-based methods. Training-free methods can directly instruct LLMs to invoke external tools, but they exhibit limited generalization in dynamic environments. In contrast, training-based methods use a teacher model to generate tool-use trajectories and fine-tune a student model to distill the trajectories that can generalize across tasks. However, they still face two major challenges: (1) Knowledge boundary mismatch: teacher trajectories may contain steps that the student cannot execute without tools due to their mismatch of knowledge boundaries. (2) Tool overuse propagation: the student inherits the misaligned tool-use strategy from teacher model. To address these challenges, we propose RADAR, an automated framework for knowledge boundary discovery and tool overuse mitigation. First, RADAR performs semantic anchoring to select representative seeds. Second, simulated annealing probing is employed to actively explore the student model’s tool-use boundary and acquire sparse, verified student-aware labels. Third, it globally propagates these labels and rewrites conflicts, yielding deployment-aligned decisions with fewer unnecessary tool calls. We conduct experiments on multiple benchmarks, compared to SOTA method, RADAR reduces average tool-use by 52.7% while improving accuracy by 3.2% across three models.

Recently, transformer-based trackers have become the leading approach, surpassing traditional CNN-based trackers in accuracy. However, their high computational demands hinder their deployment on edge platforms, necessitating efficient solutions. To address this, we propose a novel Neural Architecture Search (NAS) framework designed for transformer-based trackers, named NASTrack. This framework incorporates token pruning to optimize both transformer block structures and the layer-wise token keeping ratio, striking a balance between performance and efficiency. To handle the larger search space introduced by the keeping ratio, we propose a blacklist strategy and a matching-based distillation driven by the Token Overlap Ratio (TOR). Our method discovers hundreds of high-performing trackers, with FLOPs ranging from 1G to 18G. The searched trackers consistently outperform existing efficient state-of-the-art trackers such as CompressTracker and LiteTrack under comparable computational budgets.The code and models are available at https://github.com/Cyhoon84/NASTrack.git.

Treewidth is a fundamental graph invariant that quantifies how tree-like
a given graph is. It is extensively used with dynamic programming to
design fixed-parameter tractable algorithms for many NP-hard graph
combinatorial optimization problems. However, despite broad theoretical
applicability, treewidth dynamic programming (TDP) does not scale in
practice beyond graphs with very small treewidth. Rather than applying
TDP as a standalone technique, we demonstrate that TDP can serve as a
broadly applicable enhancer for a wide range of graph combinatorial
optimization algorithms. Our framework leverages the concept of
treewidth modulators, which refer to vertex sets whose removal
significantly reduces the treewidth. We further propose an empirically
efficient procedure for generating such treewidth modulators. To enhance
an algorithm 𝒜, we use 𝒜 to heuristically make decisions on the
modulators vertices, after which the remaining decisions outside the
treewidth modulators become scalable for TDP.

To demonstrate the general applicability of our proposed framework. We
experimented with three classic graph combinatorial optimization models:
Maximum Independent Set, Minimum Vertex Cover, and Max Cut. We apply TDP
to enhance algorithms across diverse paradigms, including evolutionary
search, greedy heuristics, and graph-neural-network-based heuristics.
For all combinations of optimization models and base algorithms, TDP
significantly improves performance over the original methods. In many
settings, TDP-enhanced greedy heuristics are competitive with, and
sometimes clearly outperform, state-of-the-art commercial solvers.

Multimodal semantic communication systems face a critical challenge in extracting and aligning semantic features across heterogeneous modalities within a unified representation space, particularly under noisy transmission conditions. To address this, we propose Bridge, a cross-modal learning framework that integrates video, audio, and text into a unified semantic space through feature disentanglement and contrastive alignment. Bridge separates modality-specific and modality-invariant representations, enhancing both intra-modal precision and inter-modal generalization. During decoding, it leverages heterogeneous foundation models to preserve semantic fidelity under channel noise. Extensive experiments on multiple multimodal benchmarks under varying SNR levels demonstrate that Bridge maintains high semantic consistency and reconstruction quality, improving image reconstruction by approximately 16.3% in low-SNR regimes. Our work provides a practical and theoretically grounded framework for next-generation multimodal semantic communication systems.

Social bots threaten online platforms by spreading disinformation and manipulating public discourse. Graph neural networks have emerged as effective tools for bot detection by modeling user interactions, yet two fundamental challenges limit their practical deployment: severe class imbalance where bots constitute a small minority of users, and camouflaged edges where bots forge deceptive connections to humans to evade detection. Class imbalance causes decision boundaries to shift toward the majority class, while camouflaged edges corrupt neighborhood aggregation through spurious message passing. We present BotVA, a unified framework addressing both challenges through variational feature augmentation and adversarial graph learning. Our approach makes three key contributions: (i) a conditional variational autoencoder that models minority class distributions and synthesizes semantically coherent features, effectively expanding minority support in representation space; (ii) an adversarial training paradigm where a generator simulates camouflage by injecting deceptive edges while a graph transformer discriminator with semantic attention learns to identify and downweight such perturbations; and (iii) a two-stage training strategy that pretrains the variational module before alternating generator-discriminator optimization to ensure stable convergence. Experiments on three benchmarks demonstrate that BotVA achieves state-of-the-art accuracy and F1-score, exhibits strong robustness under camouflage perturbations, and maintains competitive performance with limited supervision.

Effective multi-agent coordination requires aligning incentives while adhering to complex requirements. However, real-world systems often impose situational constraints, context-dependent requirements triggered only under specific conditions, which challenge standard Correlated Equilibria (CE) solutions. We propose Situational-Constrained Density-Based Correlated Equilibria (SC-DBCE), a novel concept in Markov Games that formalizes situational constraints as logic implications. To solve this, we introduce Situational-Constrained Correlated Policy Iteration (SC-CPI), a reinforcement learning algorithm employing a smooth Log-Sum-Exp mechanism for constraint optimization. Evaluations on multi-agent games, smart grids, and warehouse robotics demonstrate that SC-CPI consistently outperforms baselines in both equilibrium quality and constraint adherence. To our knowledge, this is the first method learning CE under situational constraints.

Detecting text generated by large language models~(LLMs) is crucial but challenging. Existing detectors depend on impractical assumptions, such as white-box settings, or solely rely on text-level features, leading to imprecise detection ability. In this paper, we propose a simple but effective and practical LLM-generated text detection method, VaryBalance. The core of VaryBalance is that, compared to LLM-generated texts, there is a greater difference between human texts and their rewritten version via LLMs. Leveraging this observation, VaryBalance quantifies this through mean standard deviation and distinguishes human texts and LLM-generated texts. Comprehensive experiments demonstrated that VaryBalance outperforms the state-of-the-art detectors, i.e., Binoculars, by up to 34.3% in terms of AUROC, and maintains robustness against multiple generating models and languages.

Class-Incremental Learning (CIL) aims to enable models to sequentially learn new tasks while retaining knowledge from previous ones. Recently, merging-based pre-trained CIL methods have gained significant attention due to their competitive performance and high inference efficiency. However, most existing approaches decouple training from merging, neglecting the compatibility among task-specific adapters. This incompatibility introduces severe conflicts during integration, resulting in catastrophic forgetting and notable performance degradation. To address this limitation, we propose MOBO, a Merging-Oriented Bi-Level Optimization framework that synergizes the optimization of the current task model with the performance of the final merged model. At the upper level, we introduce a global loss that anticipates the merged model's behavior, guiding the current task parameters toward a solution space that facilitates effective merging. At the lower level, we employ a dynamic weighted merging strategy to optimize merging coefficients and update the merged model. By alternately optimizing the task-specific and merged models, MOBO effectively mitigates the performance loss caused by incompatible adapter integration. Comprehensive experiments on CIFAR-100, CUB-200, ImageNet-R, ImageNet-A, and VTAB demonstrate the superiority of our approach, highlighting its robustness and scalability, particularly on long task sequences.

The Maximum Weight Clique Problem (MWCP) is NP-hard and is typically solved exactly within a Branch-and-Bound (BnB) framework, where the quality of upper bounds critically determines the pruning efficiency. Recent solvers enhance independent-set-based bounds using MaxSAT reasoning. However, their effectiveness is limited by two inherent limitations: (i) independent sets in conflicts are discarded after a single use, preventing their reuse in subsequent reasoning; and (ii) conflicts are detected in a fixed order regardless of their potential contribution to bound tightening. To address these issues, we propose two complementary techniques. The UnLocking Mechanism (ULM) preserves conflict cores and enables the reuse of independent sets in subsequent reasoning, while the Benefit-Enhanced Strategy (BES) steers the MaxSAT reasoning process toward conflicts and independent sets with higher pruning potential. By integrating ULM and BES, we develop a new solver, ULE-MWC (UnLocking Enhanced MWC). Experimental results on standard benchmarks show that ULE-MWC consistently yields tighter upper bounds and outperforms state-of-the-art exact solvers in both runtime and search efficiency.

Diffusion policy has shown impressive performance in robotic manipulation tasks while struggling with out-of-distribution shifts and limited demonstrations. Recent advances primarily focus on improving geometric or perceptual representations for diffusion policy. However, these approaches rely heavily on instantaneous observations, making them vulnerable when visual inputs deviate from the training distribution. Our key insight is that robust generalization in diffusion policy requires both structured reasoning over skill compositions and an explicit memory mechanism for accumulating and reusing learned policy knowledge. To this end, we propose "raise one and infer three" diffusion policy (ROITDP), a novel approach that introduces two complementary mechanisms. First, we propose a reasoning mechanism built upon the Chain-of-Skill Noise Watermark, which encodes skill-level reasoning graph representations into the initial noise space, thereby enabling temporally coherent multi-step reasoning throughout the diffusion process under distribution shifts. We further introduce a memory mechanism in the form of a Self-evolving Policy Knowledge Bank that discretizes spatio-temporally refined trajectories into reusable skill primitives via a VQ-VAE architecture, providing adaptive memory to guide and refine action generation. Extensive experimental results on both simulated and real-world environments demonstrate the superiority and robustness of our method.

Self-supervised heterogeneous graph learning has achieved promising results in diverse applications but still faces two issues: (i) existing methods focus on either feature similarity or meta-path to capture homophily, neglecting their inherent complementarity; (ii) existing methods rely on meta-paths to capture interactions among same-type nodes, which may introduce noise and inherently exclude long-range cross-type interactions. To address these issues, we first propose a self-expressive solver that captures the complementary homophily between meta-paths and node features to obtain homophilous representations. Meanwhile, we design separate path encoders to model diverse interactions, thus explicitly including cross-type interactions while mitigating noise via adaptive fusion. Theoretical analysis verifies that homophilous representations exhibit a high-order grouping effect to capture complementary homophily, while path encoders possess adaptive smoothness capabilities to filter noise. Extensive experiments on diverse datasets, including a large-scale dataset, demonstrate the superiority of the proposed method.

Camouflaged Object Detection (COD) aims to segment objects that are hidden within complex backgrounds. Due to the low visual contrast of camouflaged objects, annotations are costly, motivating unsupervised COD (UCOD) to eliminate labeling expenses. Most UCOD methods follow the “MLLMs + other foundation models + SAM” paradigm, which relies on spatial interactions that are unreliable in camouflaged scenarios, leading to fundamental performance bottlenecks. In this paper, we propose a novel UCOD framework that leverages SAM3 through category-level interaction with MLLMs, bypassing unreliable spatial prompts. To address SAM3’s sensitivity to category granularity, we introduce Fine-grained Category Query, guiding MLLMs to generate full-granularity category chains for robust category prompting. To mitigate suboptimal segmentation caused by high camouflage and background confusion, we propose Semantic–Geometric Dual Confirmation, which jointly validates segmentation masks from semantic and spatial perspectives. Furthermore, we introduce Semantic–Geometric Reasoning Injection, which injects critical semantic and geometric cues into MLLMs to refine category reasoning and progressively correct segmentation errors under extreme camouflage or MLLM hallucinations. Extensive experiments show that our method significantly outperforms existing UCOD approaches and achieves performance comparable to weakly supervised COD.

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.

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 \textbf{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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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: \url{https://github.com/NUBagciLab/DCT-Transformer}.

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.

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.

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-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.

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.

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.

Federated unlearning (FU) is critical for complying with legal mandates like the right to be forgotten in decentralized systems, yet current methods face a persistent dilemma between non-target knowledge loss and high request latency. To resolve these issues, we propose FedUP, a one-shot federated unlearning framework utilizing lightweight pluggable filters that act as a "knowledge funnel" to screen out target data while preserving original model performance. By freezing original model parameters and training filters at the server side using differentially private (DP)-protected class centroid samples, FedUP bypasses the need for multi-round client-server communication and complex retraining, reducing unlearning latency from minutes to mere seconds. Additionally, the framework's pluggable architecture ensures inherent reversibility, enabling the seamless restoration of forgotten knowledge by simply removing the filters. Extensive experiments on diverse image and text tasks demonstrate that FedUP effectively reduces non-target knowledge loss and achieves superior unlearning precision and efficiency across various scenarios. Code is available at: https://github.com/suows/FedUP-code.

Inverse reinforcement learning (IRL) has made significant progress in recovering reward functions from expert demonstrations. However, a key challenge remains: how to extract reward functions that generalize across related but distinct tasks. In this paper, we address this by focusing on transferable IRL, learning intrinsic rewards that can drive effective behavior in unseen but structurally aligned environments. Our method leverages a variational autoencoder to learn an abstract representation of the state space shared across multiple source tasks. This abstracted space captures high-level features that are invariant across tasks, enabling the learning of a unified abstract reward function. The learned reward is then used to train policies in a separate, previously unseen target task without requiring new demonstrations in the target task. We evaluate our approach on multiple environments from Gymnasium and AssistiveGym, demonstrating that the learned abstract rewards consistently support successful policy learning in novel task settings.

Trajectory generation is a pivotal technique for mitigating data sparsity, but existing methods struggle to simultaneously achieve strict road network alignment and capture realistic movement characteristics. To bridge this gap, we propose RNTrajGen, a two-stage fine-grained trajectory generation framework constrained by road networks. Specifically, we first develop a Road Network Knowledge-Enhanced Encoder (RNKEE) to provide semantically rich representations for trajectory generation. By leveraging graph attention networks, RNKEE encodes static prior knowledge of the road network while integrating self-supervised learning to extract latent movement patterns from real-world trajectories. Subsequently, RNTrajGen follows a two-stage generation strategy: it first generates a sequence of road segments to ensure strict topological alignment, and then infers the moving ratios of trajectory points along road segments to capture fine-grained movement characteristics. Extensive experiments on two real-world datasets demonstrate that RNTrajGen significantly outperforms state-of-the-art baselines, and the generated trajectories exhibit high utility in downstream prediction tasks. Our code is available at https://github.com/jkzh986/RNTrajGen.

Image clustering aims to partition unlabeled image datasets into distinct groups. A core aspect of this task is constructing and leveraging prior knowledge to guide the clustering process. Recent approaches introduce semantic descriptions as prior information, most of which typically relying on matching-based techniques with predefined vocabularies. However, the limited matching space restricts their adaptability to downstream clustering tasks. Moreover, these methods primarily focus on reducing bias to improve performance, frequently overlooking the importance of variance reduction. To address these limitations, we propose GSEC (Image Clustering based on Generative Semantic Guidance and Bi-Layer Ensemble), a framework designed to reduce bias through generative semantic guidance and mitigate variance via ensemble learning. Our method employs Multimodal Large Language Models to generate semantic descriptions and derive image embeddings via weighted averaging. Additionally, a bi-layer ensemble strategy integrates cross-modal information through BatchEnsemble in the inner layer and aligns outputs via an alignment mechanism in the outer layer. Comparative experiments demonstrate that GSEC outperforms 20 state-of-the-art methods across six benchmark datasets, while further analysis confirms its effectiveness in simultaneously reducing both bias and variance.

Cognitive diagnosis aims to infer students’ concept-level mastery from exercise response logs and exercise-concept associations. Fully leveraging heterogeneous relations and modeling large mastery-difficulty variations remain challenging, especially with a single predictor. To address these challenges, we propose RMCD, a unified cognitive diagnosis model that integrates relation-aware graph learning with Mixture-of-Experts (MoE) prediction. RMCD constructs a heterogeneous relational graph over students, exercises, and concepts with multiple relation types, and learns node and edge representations simultaneously. It derives relation-strength vectors from student-concept and exercise-concept edges to distinguish relation effects and refine node representations. RMCD further introduces an MoE-based prediction head that adaptively combines multiple expert predictors to capture diverse mastery-difficulty discrepancies. Experiments on benchmark datasets demonstrate that RMCD consistently outperforms state-of-the-art cognitive diagnosis methods. Our algorithm is available at https://github.com/swu-qjw-lab/code/tree/main/RMCD.

Many state‑of‑the‑art anomaly detection models operate as black boxes, limiting interpretability and hindering reliable deployment. While recent advances in explainable artificial intelligence have focused on explaining why individual instances are detected as anomalous, comparatively little attention has been paid to how such explanations can be systematically exploited to improve the detectors themselves. To address this gap, we propose EAD‑CE (Enhancing Anomaly Detection with Counterfactual Explanations), a model‑agnostic, closed‑loop framework that tightly integrates detection, explanation, and improvement. Specifically, given a trained anomaly detector and its detected anomalies, EAD‑CE generates minimal and semantically meaningful counterfactual explanations that reveal how targeted feature perturbations influence anomaly scores or decisions. Feature importance inferred from these counterfactual explanations is then used to guide a dynamic feature-weight optimization process, enabling detector refinement without modifying its underlying architecture. Extensive experiments on nine real‑world datasets and three anomaly detection models demonstrate that EAD‑CE accurately identifies anomaly‑driving features, substantially enhances interpretability, and consistently improves detection performance (average AUC increases by 6.9%, up to 23%). Implementation details are provided to support reproducibility.

Low-rank gradient projection (LoRP) has recently emerged as a memory-efficient alternative to low-rank adapters (LoRA) for finetuning large language models. Existing LoRP methods, however, implicitly fix the projection unit to a single gradient row, leaving the effect of grouping multiple rows (or subdividing a row) largely unexplored. In this work, we systematically investigate the impact of the projection unit on LoRP methods. Specifically, we extend existing LoRP approaches by introducing an additional degree of freedom, projection granularity, beyond the traditional rank hyperparameter. This enables a framework capable of performing Various-grained Low-Rank Projection of gradients, which we term VLoRP. Using VLoRP, we observe that, under an identical memory budget, fine-grained projections consistently deliver superior performance. Moreover, VLoRP requires no extra computation and minimal code changes, effectively providing a no-cost accuracy boost to LoRP. Finally, we provide convergence analysis on VLoRP with either SGD or an Adam-based memory-efficient optimizer, and extensive experiments are conducted to validate our findings, covering tasks such as Commonsense Reasoning, MMLU, and GSM8K

Redundant manipulators are broadly used in safety tasks that require precise execution. However, some actual factors, such as assembly defects and mechanical wear, inevitably introduce uncertainties in their physical parameters. Due to the insufficient excitation, existing methods fail to achieve high-accuracy physical parameter identification, which seriously affects the accurate operation of multiple safety tasks. Considering that quadratic programming (QP) can integrate desired behaviors with constraints into a unified optimization framework and can be solved online in real time, this paper formulates the collision-free trajectory tracking of redundant manipulators with uncertain structure as a QP problem, which describes the trajectory tracking, obstacle avoidance and joint motion limits simultaneously. To tackle the above issues, we first develop an online physical parameters identification method called regularization and zeroing neural dynamics (RZND) by using the state information of the manipulator, which realizes highly accurate physical parameters identification and real-time Jacobian matrix updates. On the basis of this, a multi-task quadruple projection neural network (MT-QPNN) solver is proposed by applying projection operators to handle the joint constraints, and further the trajectory tracking and obstacle avoidance tasks of the redundant manipulator are well achieved. Relative experiments verify that the presented RZND method and MT-QPNN solver can accurately identify physical parameters and enable collision-free tracking with superior performance.

Domain Generalization (DG) for medical image segmentation is both highly challenging and critically important. However, existing medical DG methods largely overlook the issue of Catastrophic Forgetting (CF): Models often sacrifice their ability to retain source-domain knowledge while pursuing cross-domain robustness. This can directly threaten diagnostic safety in already-deployed clinical scenarios. To address this, we investigate data augmentation strategies and catastrophic forgetting for medical image DG segmentation. First, we propose a structure-guided style diffusion augmentation method. Constrained by anatomical structure consistency in the frequency domain, this method performs cross-domain diffusion on the amplitude spectrum, generating samples with more diverse and broader style coverage to better support domain generalization. Then, we design a collaborative learning network with a dual-branch interactive architecture (CoDG-Net), together with a novel learning bias-guided strategy that adaptively regulates knowledge transfer at both the layer level and the task level, thereby effectively mitigating catastrophic forgetting on the source domain. Experiments and ablation studies on single-source and multi-source medical DG benchmark datasets demonstrate that CoDG-Net not only outperforms existing state-of-the-art methods in target-domain segmentation performance, but also achieves a lower forgetting rate on the source-domain data. The code is available at: https://github.com/wangprocess/CoDG-Net.

Graph Neural Networks are susceptible to label noise, in which message-passing mechanisms serve as conduits for propagating erroneous supervision. Current mitigation techniques typically recover clean labels via heuristics that lack theoretical grounding, which often leads to ineffective denoising. To tackle the issue, we propose NAEM, a latent label estimation framework that models clean labels as latent variables by integrating neighborhood agreement into the Expectation-Maximization (EM) paradigm. To overcome the posterior collapse problem in standard EM under severe noise, we design a structure-aware E-step that leverages neighborhood agreement as a structural prior. This mechanism acts as a dynamic confidence gate based on local consensus to prevent the model from overfitting to noise. Simultaneously, by explicitly modeling the noise transition matrix in the M-step, NAEM decouples noise dynamics from semantic representation learning. Extensive experiments on five benchmark datasets demonstrate that NAEM consistently outperforms SOTA methods under varying noise conditions, validating the effectiveness of our framework.

Mainstream industrial information retrieval systems (e.g., search and recommendation) are usually built upon Multi-Stage Cascade Architectures (MCAs), which balance effectiveness and efficiency through a coarse-to-fine "retrieval-ranking" pipeline. However, the optimization objectives across different stages are substantially inconsistent, propagating or even amplifying the early-stage errors that ultimately degrade the quality of final results. While emerging end-to-end generative models offer a potential solution by unifying the pipeline, their online serving performance is severely hindered by the auto-regressive process inherited from the standard decoder-only structure.

To bridge this gap, we introduce DaV-Gen, a novel unified solution designed to fundamentally refactor the paradigm for both search and recommendation via a "Draft-and-Verify" mechanism. Inspired by the process used by speculative decoding, our framework redesigns the generation task into two synergistic operations within a single model. During training, the model is concurrently optimized for both candidate drafting and fine-grained verification. This is achieved by a composite loss function that jointly trains the model on two distinct but related objectives: 1) a contrastive loss that structures the embedding space for efficient drafting, and 2) a fusion loss that combines generative likelihood with vector similarity to produce a superior verification score. This integrated training strategy equips the model with dual capabilities. At inference time, it first performs highly efficient vector-based drafting to generate a candidate set, and then verifies these candidates using the more powerful fused scoring function, thereby achieving both the speed of sparse drafting and the precision of advanced generative models within a unified, end-to-end architecture.

Recent advances in text-to-audio (TTA) and audio-to-audio (ATA) generation models have enabled the creation of highly realistic environmental sounds, raising growing concerns about malicious audio manipulation in real-world scenarios. To address this emerging threat, the ESDD 2026 Challenge was introduced as the first large-scale benchmark for Environmental Sound Deepfake Detection (ESDD), featuring two tracks that evaluate generalization to unseen generators and robustness under black-box, low-resource conditions. In this paper, we present BEAT2AASIST, an enhanced deepfake detection framework built upon the BEATs-AASIST baseline. Motivated by the observation that token-based audio representations may weaken explicit preservation of structured acoustic cues, the proposed method introduces a dual-branch AASIST architecture that explicitly splits BEATs-derived representations along frequency or channel dimensions. This design enables specialized modeling of complementary spoofing artifacts that may be attenuated in unified representations. To further enrich acoustic features, we incorporate multi-layer fusion strategies that aggregate information from multiple transformer layers using concatenation, CNN-gated, and SE-gated mechanisms. In addition, vocoder-based data augmentation with multiple high-fidelity neural vocoders is employed to enhance robustness against unseen and black-box spoofing attacks. Experimental results on the EnvSDD dataset demonstrate that BEAT2AASIST achieves strong and consistent performance across both challenge tracks. In particular, the proposed approach attains 3rd place in Track 2 and 4th place in Track 1 in the ESDD 2026 Challenge, despite using fewer ensemble components than top-ranked systems. These results suggest that explicit modeling of heterogeneous acoustic subspaces, combined with targeted representation fusion and data augmentation, provides an effective and efficient design strategy for real-world environmental sound deepfake detection. The code is available at https://github.com/ikwak2/BEAT2AASIST.

Effective document-level machine translation (DocMT) requires capturing long-range discourse dependencies. Recent work has explored retrieval-based and discourse-aware context selection. However, these approaches often lack an explicit mechanism for modeling structured discourse dependencies between distant paragraphs in a document.
In this paper, we propose G²C-MT (Graph-Guided Context for Machine Translation),
which views DocMT context selection as a structured path discovery problem on a lightweight discourse graph,
rather than retrieving unstructured context sets or relying on expensive LLM-based discourse modeling.
In detail, we represent each paragraph as a node and model the relationship between each pair of nodes, considering their semantic similarity, adjacency, and keyword overlap.
Furthermore, we propose a depth-biased random walk over the graph to sample a backward context path for each target paragraph. The context path will be used to prompt a large language model (LLM) for translation.
This framework naturally supports multi-path context sampling, which can improve robustness by aggregating diverse translation candidates for discourse-ambiguous inputs.
Experiments conducted across various domains show that G²C-MT outperforms strong baselines on multiple LLMs, including DeepSeek-V3, Gemini-2.5-Flash-lite, and the Qwen-2.5/3 series.

Discovering shapelets -- i.e., discriminative temporal patterns within time series -- has been widely studied to address the inherent complexity of time-series classification (TSC) and to make model decision-making processes more transparent.
However, existing methods primarily focus on population-level shapelets optimized across the entire dataset, which leads to two fundamental limitations: (i) population-level patterns often misalign with instance-specific features, resulting in suboptimal performance and potentially misleading interpretations, and (ii) most methods treat shapelets as independent entities, overlooking important temporal dependencies and interactions among multiple patterns.
To address these limitations, we propose INSHAPE, an interpretable TSC framework that discovers variable-length, discriminative temporal patterns specific to each time series.
INSHAPE identifies these patterns as non-overlapping segments and models their temporal dependencies, thereby providing clear instance-level interpretations while achieving strong predictive performance.
Furthermore, INSHAPE bridges local and global interpretability through a bottom-up approach, aggregating instance-level shapelets into prototypical (population-level) shapelets.
Extensive experiments on 128 UCR and 30 UEA benchmark datasets show that INSHAPE consistently outperforms state-of-the-art shapelet-based methods while providing more intuitive and interpretable insights.

Traffic forecasting is fundamentally challenging due to the complex and dynamic spatiotemporal dependencies inherent in road networks. Although existing prediction models are able to achieve certain results on this task, existing Transformer-based models usually rely on simple embedding strategies and do not fully utilize the prior knowledge embedded in traffic patterns and network topology. To address these limitations, we propose ASTPKEformer, a prior knowledge-guided Transformer framework for traffic prediction. Based on the pure Transformer spatiotemporal self-attention mechanism, this model first uses a multi-scale temporal channel alignment module to generate discriminative feature embeddings. Then, it incorporates temporal prior embeddings and spatial graph structure prior embeddings to provide learning guidance for the model in both temporal and spatial dimensions. To further enhance the representation capability, an embedding cross-fusion mechanism is introduced to strengthen the interaction between the previous embeddings and the adaptive spatiotemporal embedding. Extensive experiments on six real-world traffic datasets demonstrate that ASTPKEformer consistently outperforms state-of-the-art (SOTA) baselines, validating its effectiveness and strong generalization ability.

Unauthorized personalization based on diffusion models pose a severe and growing threat to digital privacy by enabling the unauthorized replication and exploitation of individual identities. Existing disrupting-based defenses primarily add invisible perturbations arbitrarily across the entire image space to disrupt the generation process. However, we reveal that these methods fundamentally overlook the spatio-temporal dynamics of the personalization process, resulting in inefficient optimization that fails to sufficiently disrupt the core identity encoding mechanism. To mitigate these limitations, we propose IdentityMask, a robust protection framework that shifts the paradigm from arbitrary confusion to precise, targeted feature corruption. By anchoring the perturbation on subject-specific semantics and prioritizing the most critical diffusion timesteps, our framework ensures the disruption is maximized precisely where the identity is encoded. Additionally, a novel manifold projection strategy is introduced to embed the adversarial signals into the intrinsic structure of the image, rendering the protection resilient against state-of-the-art purification. Extensive experiments across diverse datasets, personalization techniques, and defense settings demonstrate that IdentityMask consistently outperforms prior state-of-the-art approaches in both protection efficacy and robustness.

Optimization problems such as Viterbi decoding and V-optimal histogram construction seek a path of exact length L through a state space that minimizes a cost function. These problems are traditionally solved using dynamic programming (DP). A best-first-search (BFS) solution is also applicable, yet requires maintaining a priority queue. In all cases, memory usage grows linearly with both state space size and path length. In this paper, we propose CompactBFS, a framework that limits the growth of the BFS priority queue to space-efficiently determine the exact optimal cost for a fixed-length path and then constructs such a path by a divide-and-conquer strategy that eliminates the memory overhead. We apply CompactBFS to Viterbi decoding, which remains relevant to speech recognition, and V-optimal histogram construction. Our experimental results demonstrate significant gains over state-of-the-art solutions in runtime and memory consumption.

Heterogeneous Graph Prompt Learning (HGPL) has emerged as a promising paradigm for bridging the gap between the objectives of pre-training foundation models and their downstream applications in heterogeneous graph settings. However, existing HGPL methods are primarily designed for in-domain scenarios, whereas real-world deployments often span multiple domains, and the data used for pre-training and downstream tasks may originate from different distributions. Consequently, the applicability of current HGPL approaches is limited in in-domain settings, and their performance typically degrades when application domains shift. To address this serious limitation, we develop CHoE, a cross-domain HGPL method built upon an expert network. During pre-training, we introduce and train structure-conditioned experts, and during prompt tuning, we propose a structure-aware expert routing and load balancing mechanism to select structurally compatible experts for each meta-path view. In addition, we design a prompt-based semantic fusion module to integrate representations across views for downstream prediction. Extensive experiments show that CHoE consistently improves performance in few-shot cross-domain applications, outperforming all baseline approaches.

Unsupervised 3D semantic segmentation is vital for label-free open-world perception. However, current methods typically struggle with two core limitations: fixed category assumptions that restrict adaptability in complex scenes, and geometric incompleteness caused by irregular point sampling, which degrade the integrity of local geometric descriptors for fine-grained or long-tail objects. To address these, we propose DyG-Seg, a Dynamic Geometric-Semantic Alignment framework. First, targeting the challenge of incomplete geometry, the Geometric Manifold Rectification (GMR) leverages a diffusion model to reconstruct integral object geometry and recover the underlying manifold topology, thereby enhancing feature discriminability. Utilizing this geometric prior, the Dynamic Prototype Contrastive Clustering (DPCC) applies non-parametric Bayesian inference to automatically estimate the optimal number of clusters and generate pseudo-labels. Finally, our Iterative Optimization with Dynamic Constraints integrates Dynamic Class Balancing (DCB) to mitigate long-tail bias. Capitalizing on the recovered manifold topology, we enforce Spatial Geometric Consistency to rectify structural incompleteness and ensure spatially coherent semantic predictions. Extensive experiments on ScanNet and S3DIS demonstrate that DyG-Seg discovers latent semantic structures and significantly outperforms state-of-the-art unsupervised methods.

Most text-to-motion models adopt a fixed-length sampling paradigm, treating time as a hyperparameter rather than a decision variable inferred from semantics. This leads to a systematic temporal mismatch between text and generated motion: short prompts suffer from duration trailing, while long prompts exhibit stage-wise semantic collapse.

To address this issue, we propose TPPMG (Temporal Planning-driven Progressive Motion Generation), a temporally planned progressive framework that follows the pipeline semantics, temporal structure, and motion realization. TPPMG has two key components: (1) a Temporal Planner, which uses an LLM with a duration calibrator to infer stage boundaries and durations from text, yielding an explicit semantics-to-temporal structure mapping; and (2) a Progressive Diffusion Generator, which introduces heterogeneous noise scheduling within fixed windows and a sliding-window mechanism at inference to resolve the mismatch between fixed-length diffusion training and variable-length sampling, thereby generating variable-length motion sequences that match the planned temporal structure.

We further build three benchmarks: HumanML3D-Short for short texts, and two multi-stage benchmarks HumanML3D-Concat and BABEL-Concat, along with a comprehensive evaluation suite. Experiments show that TPPMG effectively suppresses duration trailing for short texts and simultaneously improves stage-structure accuracy, segment-level semantic consistency, and boundary continuity for long texts.

Backdoor attacks pose a serious threat to deep reinforcement learning (DRL). Current defenses typically rely on reward anomalies to reverse-engineer triggers and model finetuning to remove backdoors. However, complex trigger patterns undermine their robustness, and fine-tuning entails high costs, limiting practical utility. To this end, we shift defense concerns to trigger-agnostic backdoor output behaviors and propose BehaviorGuard, an online behavior-based backdoor detection and mitigation framework for DRL. Specifically, we find that regardless of attacks, backdoored policies induce consistent shifts in action distributions to ensure reliable activation, leaving detectable traces in high-quantile regions and distribution tails, even in the absence of triggers. Based on this, we design a novel metric that captures behavioral drift in action distributions to identify and suppress backdoor actions at runtime. To our knowledge, this is the first online backdoor defense that counters attacks both in single- and multi-agent DRL. Evaluated across diverse benchmarks with different backdoor attacks, BehaviorGuard consistently surpasses prior methods in both efficacy and efficiency.

Mobile Manipulation (MM) involves long-horizon decision-making over multi-stage compositions of heterogeneous skills, such as navigation and picking up objects. Despite recent progress, existing MM methods still face two key limitations: (i) low sample efficiency, due to ineffective use of redundant data generated during long-term MM interactions; and (ii) poor spatial generalization, as policies trained on specific tasks struggle to transfer to new spatial layouts without additional training. In this paper, we address these challenges through Adaptive Experience Selection (AES) and model-based dynamic imagination. In particular, AES makes MM agents pay more attention to critical experience fragments in long trajectories that affect task success, improving skill chain learning and mitigating skill forgetting. Based on AES, a Recurrent State-Space Model (RSSM) is introduced for Model-Predictive Forward Planning (MPFP) by capturing the coupled dynamics between the mobile base and the manipulator and imagining the dynamics of future manipulations. RSSM-based MPFP can reinforce MM skill learning on the current task while enabling effective generalization to new spatial layouts. Comparative studies across different experimental configurations demonstrate that our method significantly outperforms existing MM policies. Real-world experiments further validate the feasibility and practicality of our method. The source code is available at https://csu-hero-lab.github.io/SG-MM Web.

Recent advances in time series forecasting (TSF) leverage large language models (LLMs) to provide semantic priors, enabling more robust forecasting under limited training data. However, directly fusing semantic signals into temporal features often induces modality entanglement and obscures local temporal structures when cross-modal correlations are weak, noisy, or inconsistent. To address these issues, we propose Attention as Selection (AAS), a dual-branch framework that decouples temporal and semantic representations. Specifically, we define cross-modal attention as a selection process, where semantic prompts are employed to induce a sparse temporal attribution distribution over temporal positions. This guides the model to focus on critical time steps without interfering with the construction of temporal representations. Furthermore, low-entropy regularization is employed alongside global cross-modal consistency constraints to regulate the selection behavior, ensuring that semantic guidance remains sparse, stable, and aligned with temporal dynamics. Extensive experiments on six real-world datasets demonstrate that AAS outperforms existing methods across various forecasting scenarios. Code is available at https://github.com/VIMLab-hfut/Attention-as-Selection.

Existing degradation-resistant infrared-visible image fusion methods struggle to effectively handle composite degradations, where multiple degradation types exhibit intricate coupling and mutual interference. To address this challenge, we propose SpeciFuse, an infrared-visible image fusion network that learns degradation-specific representations. By explicitly modeling the specificity of individual degradations through a siamese architecture trained on single-degradation data, our method effectively addresses the conflicts arising from multiple degradations, thereby achieving robust fusion performance that generalizes to diverse composite degradation scenarios. In SpeciFuse, a degradation-type specificity decoupling module is designed to disentangle the feature representations by attenuating their redundant correlations in a latent space. It minimizes off-diagonal elements to enforce independence among the encodings of distinct degradation types, while preserving dominant diagonal elements to maintain shared content information. Furthermore, a degradation-aware gated fusion module leverages these decoupled features to dynamically modulate cross-modal fusion across both channel and spatial dimensions. This facilitates a degradation-conditional fusion process that adaptively suppresses degradation artifacts while preserving beneficial information across varying conditions. In extensive comparisons with state-of-the-art methods, SpeciFuse demonstrates more robust performance under diverse composite degradations. The code is available at https://github.com/xbsj-cool/SpeciFuse.

Federated learning (FL) has emerged as a promising paradigm for adapting large language models (LLMs) to distributed data. To mitigate the high communication and memory overhead, parameter efficient techniques such as Low Rank Adaptation (LoRA) are widely adopted. However, practical deployments often exhibit rank and data heterogeneity, making direct aggregation of LoRA updates biased and unstable. Existing approaches enforce a unified rank or align heterogeneous updates into a single shared subspace, which undermines personalization and accuracy. Moreover, under differential privacy (DP), adding noise to such mixed updates could perturb client specific directions that should remain local, resulting in utility loss. To address these issues, we propose Selective Decoupled Federated LoRA (SDFLoRA), a structure aware LoRA framework that decouples each client update into a shared component for updating and a private component that preserves client specific semantics. Subspace alignment and aggregation are applied selectively to the shared module, while private modules remain local. We further design low rank re-compression to maintain a fixed rank budget for the shared module. This structure supports privacy aware optimization by injecting DP noise exclusively into the shared module. Experiments on multiple benchmarks demonstrate that SDFLoRA beats federated LoRA baselines and exhibits strong robustness under privacy constraints.

Unsupervised learning of neural operators is constrained by numerical instability, causing predictions to diverge in long-horizon rollouts. To address this, we present a physics-constrained unsupervised neural operator for long-horizon PDE learning on generalized geometries (UNOP). This framework replaces differential constraints with integral consistency for stable, label-free learning. Unlike prior works, UNOP is built upon Latent Integral Physics Embedding (LIPE), which enforces physical consistency through integral constraints. To extend integral formulations to generalized geometries, the Geometry-Agnostic Latent Adapter (GALA) projects them onto a unified latent grid of PDE inputs, providing a regularized domain for spatial integral evaluation. Based on this shared embedding, the Gated Spectral Evolution Operator (GSEO) performs stable temporal integration while retaining spatial regions with sharp gradients and fine-scale structures, with the evolution constrained by the LIPE objective. Experiments on 1D, 2D, and 3D benchmarks show UNOP outperforms state of the art methods, reducing error accumulation by up to 60% in
20-step rollouts. Code is available at https://github.com/chengxinrui/UNOP.

High-resolution sinogram completion is critical for computed tomography reconstruction, as missing projections can introduce severe artifacts. While diffusion models provide strong generative priors for this task, their inference cost grows prohibitively with resolution. We propose HRSino, a training-free and efficient diffusion inference approach for high-resolution sinogram completion. By explicitly accounting for spatial heterogeneity in signal characteristics, such as spectral sparsity and local complexity, HRSino allocates inference effort adaptively across spatial regions and resolutions, rather than applying uniform high-resolution diffusion steps. This enables global consistency to be captured at coarse scales while refining local details only where necessary. Experimental results show that HRSino reduces peak memory usage by up to 30.81% and inference time by up to 17.58% compared to the state-of-the-art framework, and maintains completion accuracy across datasets and resolutions.

Generating high-quality adversarial texts with low query budgets remains a challenging problem in the hard-label scenario. Most existing approaches rely on greedy algorithms, where one position in the text is selected for substitution, followed by the substitutions of other positions. This local search approach may fail to discover high-quality adversarial examples and often leads to excessive query costs. Ideally, an optimal adversarial sample would consider all possible position combinations in the text, but exhaustive search is computationally impractical. To address this challenge, we propose a sampling-based method called LBA, which constructs an approximate distribution of high-quality adversarial examples by integrating both prior and posterior knowledge, and utilizes this distribution for sampling. As sampling progresses, posterior knowledge updates the approximate distribution, which in turn guides more effective sampling. Extensive experiments on six language models, ranging from small-scale to large-scale architectures across four datasets, demonstrate that LBA significantly outperforms state-of-the-art baselines on all evaluation metrics. Additionally, LLM-based assessment indicates that LBA generates more semantically preserved and comprehensible adversarial texts.

Post-training quantization (PTQ) is widely used to compress large language models without retraining. However, many existing weight-only methods rely on heuristic objectives and greedy rounding, thus leading to noticeable degradation under low-bit quantization In this work, we introduce OJBKQ (Objective-Joint Babai-Klein Quantization with K-Best Sampling), a layer-wise PTQ method that formulates weight quantization as a joint optimization problem over activations and weights. This formulation results in
a multiple-right-hand-side box-constrained integer least squares (BILS) problem in each layer, which is NP-hard.
For each column of the weight matrix, we apply an extended Babai nearest-plane algorithm and an extended version of Klein’s randomized Babai algorithm to find the minimum-residual Babai–Klein point, a sub-optimal solution to the BILS problem.
Experimental results on large language models show that OJBKQ achieves lower perplexity at 3–4 bits compared to existing PTQ approaches, while maintaining comparable computational cost.

Predicting responses to genetic perturbation is pivotal for elucidating gene regulatory machinery. However, existing methods often rely on statistical perspectives to model differential expression, overlooking the constraints of the underlying molecular interactome, which renders predictions susceptible to spurious correlations. Based on the fact that a genetic perturbation is a molecular stimulus acting through functional connectivity to reconfigure cellular expression profiles, we propose PertDCR, a framework for context-aware Perturbation response prediction via Distance-Constrained Refinement. Specifically, PertDCR addresses the heterogeneity of perturbation effects by contextualizing the perturbation source with relevant gene programs inferred from the protein-protein interaction network as well as cell representation. To activate biologically valid responses, PertDCR predicts the distance between the perturbation source and highly responsive genes within the PPI network, and dynamically modulates the response intensity based on distance, thereby facilitating accurate perturbation prediction. Extensive experiments demonstrate that PertDCR achieves state-of-the-art performance across diverse unseen genetic perturbations, and the predictions are consistent with underlying biological mechanisms.

Coarse-grained time series (CGTS) are critical for business and macroeconomic analysis. However, CGTS are typically updated infrequently and contain few observations, so model-centric training on raw data is prone to overfitting and degraded forecast accuracy. To address this, we propose SI-CVAE, a scale-invariant conditional generative model built around variable-length subsequences, and adopt a data-centric "train on synthetic, test on real" paradigm to enhance downstream forecasting. Specifically, we first introduce a variable-length subsequence clustering-and-matching algorithm to capture cross-scale recurring patterns within a series. We then design a frequency-domain conditional VAE with a frequency-domain linear decoder to enable controllable, arbitrary-length sequence synthesis while enforcing scale-invariance constraints. Finally, we develop a time-domain reconstruction strategy with subsequence-conditioned fusion to ensure temporal continuity and spectral consistency across concatenated segments. We conduct extensive experiments on six ship-sales datasets and four U.S. macroeconomic indicators. Results show that replacing or augmenting the original training set with SI-CVAE–generated data yields consistent accuracy gains across multiple forecasting baselines, and that SI-CVAE attains higher synthetic-data quality than state-of-the-art generators on standard metrics.

Graph Transformers (GTs) have emerged as a powerful paradigm for graph representation learning, leveraging attention mechanisms to enable flexible information exchange. Recent GTs often adopt local attention to restrict computation to neighborhoods, thereby reducing costs and enhancing scalability. However, local attention computation poses a serious issue that significantly reduces the efficacy of the overall approaches – because computation is restricted to local neighborhoods, node pairs that share topological patterns receive similar attention scores, thereby reducing attention’s discriminative power. Consequently, GT-based models typically overemphasize common structural motifs but fail to capture node-specific properties. To address this problem, we propose DNFormer, a novel GT grounded in differential modeling. We introduce a differential local attention mechanism in DNFormer that computes attention scores as differences across multiple topology-aware similarity measures, rather than relying on a single score. This mechanism suppresses shared topological patterns among neighbors and emphasizes relative node-to-node distinctions. As a result, DNFormer mitigates structural dominance in attention aggregation, better capturing node-specific relational patterns while preserving local structural properties. Extensive experiments confirm DNFormer's superior performance over representative GT baselines.