The quantal response (QR) model is widely used in Stackelberg security games (SSGs) to capture boundedly rational adversaries. Existing work on SSGs under QR, however, almost exclusively assumes a homogeneous attacker population, ignoring heterogeneity in attacker preferences and rationality. We study SSG with mixed quantal response attackers, where the follower population consists of multiple discrete attacker types, each following a type-specific QR model. The defender allocates limited resources across targets, while an attacker drawn from this heterogeneous population observes the defender’s strategy and attacks a single target. This results in a highly non-convex equilibrium computation problem. We develop a polynomial-time approximation scheme (PTAS) for this setting when the number of attacker types is bounded, based on an exponential cone programming formulation combined with a carefully designed Branch-and-Bound procedure. Experiments demonstrate that our approach outperforms standard gradient-based methods and that explicitly modeling attacker heterogeneity yields significant gains over traditional SSG models with a single QR attacker.
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Motivated by practical allocation problems, we study integer splittable congestion games with capacitated resources and player-specific costs. In this setting, each player has an integer weight that has to be split in integer units across multiple resources, each with a capacity limiting the total assigned weight, i.e., its congestion. The latency of a resource is equal to a player-specific constant when its congestion is not greater than the capacity and becomes prohibitive, i.e., equal to ∞, once the capacity is exceeded. We analyze the computational complexity of finding an allocation that optimizes utilitarian social welfare under two cost models (total cost and per-unit cost). Furthermore, we investigate the computation of, speed of convergence to, and efficiency of Nash equilibria.
This paper addresses the problem of learning to sparsify stochastic linear bandits, where a decision-maker sequentially selects actions from a high-dimensional space subject to a sparsity constraint on the number of nonzero elements in the action vector. The key challenge lies in minimizing cumulative regret while tackling the potential NP-hardness of finding optimal sparse actions due to the inherent combinatorial structure of the problem. We propose an adaptively phased exploration and exploitation algorithmic framework, utilizing ordinary least squares for parameter learning and specialized subroutines for sparse action selection. When the action set is a Euclidean ball, optimal sparse actions can be efficiently computed, enabling us to establish a Õ(d √T) regret, where d is the dimension of the action vector and T is the time horizon length. For general convex and compact action sets where finding optimal sparse actions is intractable, we employ a greedy subroutine. For general strongly convex action sets, we derive a Õ(d √T) α-regret; for general compact sets lacking strong convexity, we establish a Õ(d T^(2/3)) α-regret, where α pertains to the approximation ratio of the greedy algorithm. Finally, we validate the performance of our algorithms using extensive experiments.
Learning reliable two-view correspondences is essential for geometric computer vision applications. Existing graph-based pruning methods typically aggregate information unidirectionally, capturing only whether a correspondence receives neighborhood support while ignoring its structural contribution during aggregation. This makes it difficult to distinguish locally clustered outliers from globally critical inliers. To overcome this limitation, we propose a Bidirectional Aggregation Graph Network (BAG-Net), which introduces a bidirectional aggregation perspective for correspondence pruning. Specifically, we design Bidirectional Dynamic Graph Convolution (BiDGC) that jointly models forward consistency aggregation and reverse structural dependency, enabling the network to identify key inliers that are widely referenced during feature propagation. To further enhance robustness, we design a Stage-wise Graph Expansion-Contraction (SGEC) architecture that combines multi-scale receptive field evolution with Stage-wise Squeeze-and-Excitation (SSE) and Deep-Guided Modulation (DGM) to stabilize feature propagation across network stages. Extensive experiments on the YFCC100M and SUN3D datasets demonstrate that BAG-Net significantly outperforms existing SOTA methods in both outlier rejection and camera pose estimation, achieving 66.10% and 78.88% mAP5^o% on YFCC100M in known and unknown scenes, respectively. Source code: https://github.com/LeyiWang13/BAG-Net.
Emotion and intent joint understanding in multimodal conversations (MC-EIU) aims to infer emotion and intent information by leveraging the semantic dependencies among multimodal data. However, prior works largely ignore redundancy interference and suffer from insufficient interaction and inter-task noise propagation due to their reliance on shallow mechanisms. To overcome these limitations, we propose a novel framework named Disentangled Hierarchical Interaction and Adaptive Adjustment (DELTA) for MC-EIU. We first design a Disentangled Feature Denoising module based on orthogonal decomposition to effectively filter out noise and redundancy from heterogeneous data. Second, we propose a Dual-Level Adaptive Adjustment mechanism to dynamically optimize learning dynamics from both modality and instance perspectives. Furthermore, we introduce a Hierarchical Task-Bottleneck Interaction(HTBI) module, which employs a set of progressively compressed bottleneck tokens as a communication hub. This design can effectively simulating the multi-round iterative interaction between emotion and intent tasks. Experiments on the benchmark MC-EIU bilingual dataset demonstrate that our framework significantly outperforms state-of-the-art baselines in both emotion and intent tasks.
Multi-modality MRI lesion segmentation leverages complementary contrasts across modalities to better characterize lesion morphology and tissue alterations, thereby improving lesion delineation and reducing misdiagnosis. Existing correlation-driven methods estimate fusion weights by converting cross-modality correlation scores into adaptive fusion weights. However, in multi-center settings, domain shift changes cross-modal correlation patterns, which breaks the learned correlation-to-weight mapping and leads to modality-weight bias that amplifies center-specific shortcuts. To address this issue, we propose an Intervention-based Weight Calibration Network (IWCNet) that models domain shift as a controllable embedding and leverages explicit interventions to mitigate its impact on fusion weight estimation. IWCNet constructs original and intervened branches by performing interventions on the domain embedding, measuring inter-branch prediction effects to calibrate fusion weights. IWCNet further introduces Operator-level Domain Modulation (OLDM) to construct structure-consistent domain-variant feature pairs via operator-level domain intervention, and Effect-Driven Consistency Calibration (EDCC) to jointly leverage intervention effects and cross-branch prediction consistency to calibrate weights. Extensive experiments on multi-center multi-modality MRI datasets demonstrate that IWCNet substantially improves segmentation accuracy and robustness under domain shift.
Single-cell perturbation screens enable systematic discovery of gene regulatory mechanisms, yet the exponential expansion of perturbation space makes comprehensive experimentation impractical. Although in silico predictors have been increasingly proposed to address this challenge, most existing methods either assume static regulatory priors or learn unconstrained data-driven representations. As a result, they often suffer from poor adaptation to state-dependent shifts and weak out-of-distribution generalization. In response, we propose URFPert, an unrolled regulatory flow network tailored for single-cell perturbation prediction. Specifically, URFPert is composed of three fundamental modules: (i) Regulatory Structure Unrolling module, which employs an iterative unrolling optimization algorithm to infer state-dependent gene regulatory networks (GRNs); (ii) Heterogeneous Graph Flow Network, which incorporates the inferred GRNs into a conditional velocity field to yield state-consistent and perturbation-specific trajectories; (iii)Discriminative Optimization Strategy, which combines Mean Squared Error~(MSE) with Maximal Coding Rate Reduction~(MCR²) to promote discriminative perturbation responses and mitigate training confounders. Comprehensive evaluations on five single-cell perturbation datasets demonstrate that URFPert outperforms state-of-the-art methods in unseen perturbation regimes, providing a powerful tool for interpreting regulatory mechanisms. The project code is public via https://github.com/Jiniretttt/URFPert.
Accurate user behavior modeling is fundamental to the prediction of click-through rates (CTR) in industrial recommendation systems and online advertising. Traditional discriminative models, which rely on isolated ID features, struggle to capture the evolving nature of user intents across multiple channels due to the inherent semantic gap. While Large Language Models (LLMs) offer rich semantic understanding to bridge this gap, their direct application faces two main challenges: modality misalignment between continuous semantic representations and discrete item IDs, and prohibitive computational costs that make them unsuitable for real-time inference. To address these issues, we propose the Hierarchical Semantic Interest Evolution Network (HSIEN), a novel generative-discriminative framework. HSIEN leverages an LLM to construct a Markovian state-space model that recursively captures and updates user interests from multi-channel behavior sequences. It then employs a Semantic Mixture-of-Experts (SMoE) strategy to aggregate multi-channel signals into hierarchical intent representations, which are encoded as compact dynamic semantic vectors for integration with downstream discriminative models. Extensive experiments on a public dataset and a large-scale industrial dataset demonstrate that HSIEN significantly alleviates modality misalignment and enhances CTR prediction performance through feature complementarity. This work provides a practical and efficient pathway for leveraging LLMs in large-scale recommendation scenarios.
Estimating the 6D pose of novel objects without CAD models or video sequences remains a challenging problem. Recent works explore text-driven approaches to address this challenge in an open-vocabulary manner. However, these methods typically treat text embeddings as static priors, which lack the flexibility to adapt to specific visual scenes, thereby limiting reliable cross-view correspondences. In this paper, we propose a framework that modulates static textual embeddings into adaptable semantic guidance for RGB-D open-vocabulary 6D pose estimation. Specifically, we introduce a Text Feature Modulation (TFM) module that uses learnable queries to transform fixed text embeddings into adaptive semantic representations. These representations are explicitly calibrated by a Foreground-Grounded Semantic Alignment (FGSA) strategy, which suppresses background distractions via contrastive learning. Furthermore, we design a Semantic-Anchored Cross-view Fusion (SACF) module that aggregates image features from the reference and query views using modulated semantics as an invariant anchor. Finally, we predict object segmentation and establish semantic-aware correspondences, leveraging modulated textual semantics to filter mismatches for robust 6D pose estimation. Extensive experiments on standard benchmarks demonstrate that our method achieves superior performance.
LLM-based role-playing has rapidly improved in fidelity, yet stronger adherence to persona constraints commonly increases vulnerability to jailbreak attacks, especially for risky or negative personas. Most prior work mitigates this issue with training-time solutions (e.g., data curation or alignment-oriented regularization). However, these approaches are costly to maintain as personas and attack strategies evolve, can degrade in-character behavior, and are typically infeasible for frontier closed-weight LLMs. We propose a training-free Dual-Cycle Adversarial Self-Evolution framework with two coupled cycles. A Persona-Targeted Attacker Cycle synthesizes progressively stronger jailbreak prompts, while a Role-Playing Defender Cycle distills observed failures into a hierarchical knowledge base of (i) global safety rules, (ii) persona-grounded constraints, and (iii) safe in-character exemplars. At inference time, the Defender retrieves and composes structured knowledge from this hierarchy to guide generation, producing responses that remain faithful to the target persona while satisfying safety constraints. Extensive experiments across multiple proprietary LLMs show consistent gains over strong baselines on both role fidelity and jailbreak resistance, and robust generalization to unseen personas and attack prompts.
Factored tasks are a classical planning representation that extends SAS+ with limited forms of disjunctive preconditions, conditional effects, and angelic nondeterminism. This added expressiveness allows for a more compact representation of taks than traditional formalisms such as STRIPS or SAS+, and supports a wide range of task transformations. However, existing planning approaches for factored tasks have been limited to heuristic search methods. In this work, we investigate how to encode factored tasks in SAT. We propose several ways to encode the tasks, focusing on different strategies for translating the factored transition relation into propositional logic. We also analyze how to exploit parallelism at various levels in this setting and study the impact of common task transformations on the performance of SAT-based planners.
Deep multi-view clustering draws plentiful attention in various domains, owing to remarkable performance in learning patterns from complementary information of multi-view data. However, previous methods encounter two challenges. They utilize a single pre-defined clustering strategy to perceive diverse structures from data in multiple views subject to heterogeneous distributions, failing to fully capture intricate complementary structures. They leverage either the feature fusion or the result fusion in clustering, which cannot fully integrate complementary information. Therefore, a decoupled multi-agent actor-critic (PRIME) is proposed via defining multi-view clustering as a partially observable Markov game, which establishes a dynamic trial-and-error assignment process of samples in a decentralized perception with centralized learning framework to adaptively learn the optimal clustering strategy. In PRIME, an actor leverages the policy gradient paradigm to independently implement a Markov decision process of data partition in a view, which fully explores data structures to tailor a clustering sub-policy for a view. Meanwhile, a critic utilizes the value function paradigm to centrally guide the Markov game among actors in different views, which constructs both feature and result fusion to progressively enhance complementary knowledge integration for robust clustering results. Extensive experiments on 6 benchmark datasets verify the superiority of PRIME against 10 methods.
Transformer-based architectures have demonstrated strong performance in 3D point cloud understanding, yet many existing methods generate attention weights mainly from semantic feature similarity. In deep networks, feature-centric attention may become less selective as point features are progressively smoothed, a behavior associated with feature homogenization and rank collapse, which can weaken the structural discrimination of local aggregation.We propose PointGP, a geometry-primed framework that uses rectified local geometric topology as the primary cue for attention generation. PointGP introduces a Semantic-Guided Manifold Rectifier to predict feature-conditioned local coordinate offsets, and a Dual-Stream Geometric Kernel to compute attention logits from both raw and rectified geometric cues. By reducing reliance on explicit query-key feature matching while implicitly incorporating semantic guidance through geometric rectification, PointGP provides an effective and efficient mechanism for local point aggregation.Experiments across five benchmarks covering classification, part segmentation, and indoor scene segmentation show that PointGP achieves competitive accuracy with strong parameter and computational efficiency compared with representative strong baselines.
Multi-objective evolutionary algorithms (MOEAs) are among the most widely and successfully applied optimizers for multi-objective problems. However, to store many optimal trade-offs (the Pareto optima) simultaneously, MOEAs are typically run with a large population of solution candidates. This slows down the algorithm and renders the choice of the population size a crucial design decision. In this work, we aim to overcome these difficulties by proposing the dynamic NSGA-II, a variant of the well-known NSGA-II that starts with a small initial population and doubles it after a user-specified number 𝜏 of function evaluations, up to a maximum size of 𝑁ₘₐₓ. We prove that the dynamic NSGA-II with optimal parameters computes the Pareto front of the OneMinMax benchmark of size 𝑛 with high probability in O(𝑛 log² 𝑛) function evaluations, which is considerably faster than the Θ(𝑛² log 𝑛) runtime of the static NSGA-II with optimal parameters. For the OneJumpZeroJump benchmark with gap size 𝑘, we show a runtime of O(𝑛^𝑘 log² 𝑛), improving upon the known runtime of Θ(𝑛^(𝑘 + 1)). We also propose a variant that uses the initial population size for a longer period and achieves slightly better performance. Finally, we show that a simple concurrent-run strategy turns our dynamic NSGA-II variants into parameter-less algorithms that exceed the above runtimes only by a logarithmic factor and hence still outperform the static NSGA-II by a factor of ͠Ω(𝑛).
Accurate long-term forecasting in complex systems is frequently compromised by dataset-level distribution shifts, where diverse underlying behavioral modes and evolving system states drive the dynamic multivariate time-series. While existing methods predominantly focus on local temporal shifts, they fail to explicitly model the global structural challenge where datasets are composites of distinct operational regimes. In this paper, we propose NEST, a specialized framework designed to model and recompose these evolving structures through a two-phase dense MoE architecture. NEST first facilitates structural specialization by partitioning the dataset into distinct operational regimes through unsupervised clustering in a principled moment-entropy space. We introduce a regime-oriented router mechanism that generates initial expert weights based on temporal content, subsequently refined through geometric modulation to regime centroids. Crucially, rather than acting as monolithic predictors, individual experts function as specialized kernels that capture regime-specific dynamics by evolving unique variate-attention patterns. Extensive evaluations on diverse benchmarks, including heterogeneous network traffic and physical phenomena, demonstrate that NEST consistently achieves state-of-the-art performance. Our code and datasets are available at \url{https://github.com/Aaralshin/NEST}.
Multi-Agent Path Finding (MAPF) seeks collision-free paths for teams of agents and has a wide range of practical applications. LaCAM*, an anytime configuration-based solver, currently represents the state-of-the-art. Recent work has explored using guidance paths to steer LaCAM* toward configurations that avoid traffic congestion, thereby improving solution quality. However, existing approaches rely on Frank–Wolfe–style optimisation to repeatedly invoke single-agent search before executing LaCAM*, which creates a large computational overhead in large-scale problems. The guide path is also static, which is only helpful for finding the first solution in LaCAM*. To overcome this problem, we propose a new approach that exploits LaCAM*’s ability to construct a dynamic, lightweight traffic map during LaCAM*'s search. Experiments show that our method achieves higher solution quality than state-of-the-art guidance-path approaches in two variants of MAPF problems.
Detecting hidden confounding is crucial for reliable causal analysis from observational data, directly determining which downstream causal inference method to be deployed. Inspired by the theory of higher-order regression, recent sample-efficient hypothesis testing strategies overcome the restrictive requirement of multiple heterogeneous data environment. Despite their progress on single-environment confounder detection, such methods suffer from intrinsic flaws that the structural functions of the causal models should be specified in prior (linear or specific kernel functions). By contrast, real-world data acquisition exhibits diverse, unknown forms of structural functions, imposing an important but challenging gap between theories of higher-order regressions to practical confounder detection. In this paper, we contribute a Bi-level Kernel Confounder Detection (BiKCD) framework by learning adaptive kernelized structural space of structural functions. Subsequently, our BiKCD constructs hypothesis testing by comparing coefficients from the higher-order regression and the classical ordinary least squares in learned kernelized space. Finally, the hypothesis is calibrated to ensure valid inference under adaptivity. Theoretically, we establish an oracle-type risk bound for the selected structural space over a candidate kernel family, with the Type-I error control for the downstream test. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness of the proposed BiKCD.
While video-to-audio generation has achieved remarkable progress in semantic and temporal alignment, most existing studies focus solely on these aspects, paying limited attention to the spatial perception and immersive quality of the synthesized audio. This limitation stems largely from current models' reliance on mono audio datasets, which lack the binaural spatial information needed to learn visual-to-spatial audio mappings. To address this gap, we introduce two key contributions: we construct BinauralVGGSound, the first large-scale video-binaural audio dataset designed to support spatially aware video-to-audio generation; and we propose an end-to-end spatial audio generation framework guided by visual cues that explicitly models spatial features. Our framework incorporates a visual-guided audio spatialization module that ensures the generated audio exhibits realistic spatial attributes and layered spatial depth while maintaining semantic and temporal alignment. Experiments show that our approach substantially outperforms state-of-the-art models in spatial fidelity and delivers a more immersive auditory experience, without sacrificing temporal or semantic consistency. All datasets, code, and model checkpoints are available at: https://github.com/renlinjie868-web/SpatialV2A.
Motivated by the growing importance of fairness in machine learning, fair k-center clustering has attracted considerable research attention as a fundamental problem. In this problem, a dataset is partitioned into m disjoint groups, and the objective is to select k data points as centers, subject to upper bounds on the number of centers chosen from each group, aiming to minimize the maximum distance between any data point and its assigned center. Focusing on Euclidean spaces, which are ubiquitous in machine learning applications, we first develop a parameterized approximation algorithm for Euclidean fair k-center with an approximation ratio of 2.732. By incorporating this algorithm as a post-processing stage into a one-pass streaming framework for large-scale data, we obtain an approximation ratio of 4.464. These ratios can be further respectively improved to 2.414 and 3.828 with a runtime exponential on k. To ensure polynomial-time complexity, we further design a one-pass streaming algorithm with an approximation ratio of 4.732, which can be further improved to 4.42, outperforming the state-of-the-art ratio. Finally, extensive experiments show that our methods significantly outperform state-of-the-art approaches in terms of clustering accuracy.
Vertical Federated Learning (VFL) involves multiple participants collaborating to train machine learning models on distinct feature sets from the same data samples. This training paradigm with distributed updating focuses on secure and efficient communication. Nevertheless, the trained models exhibit heightened vulnerability to adversarial attacks during inference, which can provoke misclassification. Adversarial Training (AT), which involves exposing models to intentionally crafted misleading examples during training, is widely regarded as the most effective method for enhancing model robustness. However, the significant communication costs entailing such example generation within the VFL context pose an open challenge to developing a Vertical Federated Adversarial Learning (VFAL) framework. To this end, we introduce a Communication-Efficient Vertical Federated Adversarial Learning framework, named CE-VFAL. CE-VFAL framework incorporates the lazy propagation principle, confining most propagations to client models during adversarial updates, thereby minimizing frequent client-server interactions. Moreover, CE-VFAL seamlessly integrates Zeroth Order Optimization (ZOO) into communication, effectively reducing communication load by transmitting the loss difference derived from the raw and perturbed embeddings for multiple point estimation. Furthermore, our theoretical analysis demonstrates the sublinear convergence rate by containing the errors caused by multi-source approximate gradients. Extensive experiments corroborate the robust performance while significantly reducing communication costs.