AAAI 2026 Papers

Discovering subgroups with the maximum average treatment effect is crucial for targeted decision making in domains such as precision medicine, public policy, and education. While most prior work is formulated in the potential‑outcome framework, the corresponding structural causal model (SCM) for this task has been largely overlooked. In practice, two approaches dominate. The first estimates pointwise conditional treatment effects and then fits a tree on those estimates, effectively turning subgroup estimation into the harder problem of accurate pointwise estimation. The second constructs decision trees or rule sets with ad‑hoc 'causal' heuristics, typically without rigorous justification for why a given heuristic may be used or whether such heuristics are necessary at all. We address these issues by studying the problem directly under the SCM framework. Under the assumption of a partition-based model, we show that optimal subgroup discovery reduces to recovering the data-generating models and hence a standard supervised learning problem (regression or classification). This allows us to adopt any partition-based methods to learn the subgroup from data. We instantiate the approach with CART, arguably one of the most widely used tree-based method, to learn the subgroup with maximum treatment effect. Finally, on a large collection of synthetic and semi‑synthetic datasets, we compare our method against a wide range of baselines and find that our approach, which avoids such causal heuristics, more accurately identifies subgroups with maximum treatment effect.

Aiming to overcome distribution shift and label sparsity that hinder cross-domain generalization of Graph Neural Networks (GNNs), Unsupervised Graph Domain Adaptation (UGDA) transfers knowledge from a label-rich source to an unlabeled target graph. Yet in practice, strict privacy protocols often withhold the source graph, reducing UGDA to the more constrained Source-Free UGDA (SFUGDA) where only a pre-trained source GNN remains. In this setting, the source GNN serves as a simple, task-specific graph foundation model. Despite recent progress, existing source-free UGDA methods remain hampered by source-knowledge absence: deprived of source graphs, they lose the reference distribution needed to gauge domain shift and must lean on noisy target cues, incurring biased adaptation and catastrophic forgetting. To overcome this drawback, this paper devises Source-Free Graph foundation model Adaptation via pseudo-source Reconstruction (SFGAR), a two-stage SFUGDA framework that first generates pseudo-source graphs to recover the source distribution encoded in a frozen pre-trained GNN, then adversarially aligns these synthetic graphs with the unlabeled target. Theoretical analysis shows that this proxy alignment tightly bounds the target-domain generalization error. Extensive experiments on public benchmarks validate the state-of-the-art performance of SFGAR.

With the deep integration of artificial intelligence and interactive technology, Graphical User Interface (GUI) Agent, as the carrier connecting goal-oriented natural language and real-world devices, has received widespread attention from the community. Contemporary benchmarks aim to evaluate the comprehensive capabilities of GUI agents in GUI operation tasks, generally determining task completion solely by inspecting the final screen state. However, GUI operation tasks consist of multiple chained steps while not all critical information is presented in the final few pages. Although a few research has begun to incorporate intermediate steps into evaluation, accurately and automatically capturing this process information still remains an open challenge. To address this weakness, we introduce ProBench, a comprehensive mobile benchmark with over 200 challenging GUI tasks covering widely-used scenarios. Remaining the traditional State-related Task evaluation, we extend our dataset to include Process-related Task and design a specialized evaluation method. A newly introduced Process Provider automatically supplies accurate process information, enabling presice assessment of agent's performance. Our evaluation of advanced GUI agents reveals significant limitations for real-world GUI scenarios. These shortcomings are prevalent across diverse models, including both large-scale generalist models and smaller, GUI-specific models. A detailed error analysis further exposes several universal problems, outlining concrete directions for future improvements.

Modern multi-view clustering (MVC) is dominated by two paradigms: multi-view fusion and pseudo-label-guided learning. Pseudo-labeling methods can suffer from confirmation bias; their reliance on a fixed-granularity supervision from an initial clustering can cause learned embeddings to drift from the data's true structure and lose discriminative power. Conversely, fusion methods excel at integrating information but often struggle to robustly differentiate between high-quality and noisy views, which can obscure final cluster boundaries and degrade performance. To address these complementary challenges, we propose GAPS (Granularity-Aware Pseudo Supervision), a novel MVC framework. GAPS introduces a granularity-aware supervision mechanism that generates a full hierarchy of pseudo-labels, enabling the selection of a supervision level that best aligns with the data's intrinsic multi-scale structure. Furthermore, to ensure a high-quality supervisory signal, it incorporates a reliability-aware view selection strategy using a novel Separation-Compactness Index (SCI) to identify and leverage the most informative view for pseudo-label generation. This dual approach ensures the supervisory signal is both structurally adaptive and derived from the most reliable source, leading to highly effective final representations. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness and superiority of GAPS over other competitors.

With the rapid advancement of deep learning, drug target interaction (DTI) prediction has seen substantial performance enhancements. However, existing methodologies face a critical, yet unaddressed challenge, i.e., the Modality Reliability Gap. Such a gap arises from the unpredictable variance in the informativeness and reliability of 1D sequence versus 3D structural data across different drug-target pairs, critically limiting model robustness and domain generalization capabilities. To overcome it, we introduce DrugCMF, a novel Drug-Target interaction prediction method via Confidence-aware Multimodal Fusion framework designed specifically to bridge the Modality Reliability Gap. Specifically, the DrugCMF employs a four-stage approach: (1) it extracts rich features by utilizing four pre-trained models to obtain token-level embeddings from both 1D sequences and 3D structures. (2) it preserves modality informativeness by independently learning interaction patterns within each modality through a Token-level Interaction module. (3) it explicitly quantifies the reliability gap by employing a novel confidence estimation mechanism to dynamically learn weights for each modality. (4) it bridges the gap by using these confidence scores to guide a learnable cross-modal fusion module, adaptively fusing information from the most trustworthy source. By methodically addressing the Modality Reliability Gap, DrugCMF significantly outperforms SOTA methods.

Orthognathic surgery is a crucial intervention for correcting dentofacial skeletal deformities to enhance occlusal functionality and facial aesthetics. Accurate postoperative facial appearance prediction remains challenging due to the complex nonlinear interactions between skeletal movements and facial soft tissue. Existing biomechanical, parametric models and deep-learning approaches either lack computational efficiency or fail to fully capture these intricate interactions. To address these limitations, we propose Neural Implicit Craniofacial Model (NICE) which employs implicit neural representations for accurate anatomical reconstruction and surgical outcome prediction. NICE comprises a shape module, which employs region-specific implicit Signed Distance Function (SDF) decoders to reconstruct the facial surface, maxilla, and mandible, and a surgery module, which employs region-specific deformation decoders. These deformation decoders are driven by a shared surgical latent code to effectively model the complex, nonlinear biomechanical response of the facial surface to skeletal movements, incorporating anatomical prior knowledge. The deformation decoders output point-wise displacement fields, enabling precise modeling of surgical outcomes. Extensive experiments demonstrate that NICE outperforms current state-of-the-art methods, notably improving prediction accuracy in critical facial regions such as lips and chin, while robustly preserving anatomical integrity. This work provides a clinically viable tool for enhanced surgical planning and patient consultation in orthognathic procedures.

Vision foundation models (e.g., SAM2, CLIP) show strong generalization in natural image analysis but degrade significantly in specialized domains like medical imaging. This is critical for tasks such as brain tumor segmentation, where errors directly affect surgical planning and patient outcomes. In such contexts, segmentation must be highly reliable and structurally precise, underscoring the need for adaptable methods with low error tolerance. While fine-tuning is the dominant strategy, it is computationally expensive and prone to forgetting. To address this, we propose CausalBridgeNet, a causality-guided correction framework for medical image segmentation. Inspired by predictive coding theories of the Bayesian brain, our method introduces a Predictive Causal Reasoning Unit (PCRU) that estimates structured error maps and delivers targeted feedback to iteratively refine predictions. This forms a closed-loop, error-aware correction mechanism without modifying the foundation model. By keeping the backbone frozen, CausalBridgeNet preserves general visual priors while enhancing task-specific accuracy. On the BraTS 2025 benchmark, it achieves an average Dice score of 84.48 and HD95 of 5.48 across tumor subregions, demonstrating its effectiveness for high-precision medical segmentation.

The multi-view clustering methods based on tensor regression can make full use of the potential structural information between views and achieve data-level fusion. However, existing tensor regression-based approaches for anchor graph often overlook the probabilistic nature of anchor graph, focusing solely on sample labels while ignoring the influence of anchor labels on clustering results. To overcome these limitations, we introduce Tensorized Label Learning via Balanced Tensor Regression (TLL-BTR). Our key idea is to exploit the probabilistic nature of the anchor graph by regarding the sample labels as a projection tensor that maps the anchor graph into the label space, thereby producing anchor labels. By enforcing constraints on these anchor labels, we guide the concurrent learning of sample labels and achieve co-label learning between anchors and samples. To prevent trivial solutions, we maximize the nuclear norm to promote an even distribution of samples across clusters. Extensive experiments on benchmark datasets demonstrate that TLL-BTR consistently outperforms state-of-the-art methods.

Accurate and efficient simulations of physical phenomena governed by partial differential equations (PDEs) are important for scientific and engineering progress. While traditional numerical solvers are powerful, they are often computationally expensive. Recently, data-driven methods have emerged as alternatives, but they frequently suffer from error accumulation and limited physical consistency, especially in multiphysics and complex geometries. To address these challenges, we propose PEGNet, a Physics-Embedded Graph Network that incorporates PDE-guided message passing to redesign the graph neural network architecture. By embedding key PDE dynamics like convection, viscosity, and diffusion into distinct message functions, the model naturally integrates physical constraints into its forward propagation, producing more stable and physically consistent solutions. Additionally, a hierarchical architecture is employed to capture multi-scale features, and physical regularization is integrated into the loss function to further enforce adherence to governing physics. We evaluated PEGNet on benchmarks, including custom datasets for respiratory airflow and drug delivery, showing significant improvements in long-term prediction accuracy and physical consistency over existing methods.

The rapid and reliable assembly of defect-free atom arrays poses a fundamental challenge for neutral atom quantum computing. While parallel rearrangement methods using spatial light modulators show promise, they suffer from significant overhead in two sub-tasks: atom-site matching and hologram generation. We propose a framework to address these bottlenecks and enhance the efficiency and fidelity of the assembly process. It features a new optimization objective for atom-site matching that minimizes the longest movement path, and a Fourier U-Net model that integrates Fourier operators with image-to-image translation to enable real-time hologram generation. The model is trained in a fully self-supervised paradigm, leveraging the physical properties of holography to remove the need for costly ground-truth labels. Experimental results show our framework not only significantly outperforms the state-of-the-art supervised CNN-based model but also achieves an inference speed orders of magnitude faster than traditional iterative algorithms, enabling real-time, dynamic atom rearrangement.

Real-world heterogeneous data is commonly modeled as heterogeneous information networks (HINs). Building upon advancements in graph neural networks (GNNs), existing research has significantly progressed in semi-supervised and self-supervised paradigms for heterogeneous GNNs (HGNNs). However, these methods overlook inherent structural deficiencies in raw heterogeneous graphs. We identifies unique structural noise in HINs: missing potential critical edges and multi-relational semantically redundant edges, which force existing HGNNs to learn suboptimal representations on fixed topologies. Crucially, prior limited studies address only partial noise while remaining architecturally entrenched and tightly coupled with specific models. To break this bottleneck, we propose a plug-and-play Heterogeneous graph Structure ADaPter (HSADP) that simultaneously resolves task/model decoupling challenges while accounting for HIN-specific structural properties with with two core components: a dynamic homogeneous subgraph enhancer recovering latent topology across semantic views and a learnable heterogeneous edge discriminator dynamically suppressing redundant edges while collaboratively optimizing semantic graphs. Extensive experiments across multi-domain datasets demonstrate our method’s effectiveness and compatibility. The adapter significantly boosts node classification accuracy for multiple SOTA approaches and surpasses specially designed heterogeneous graph structure learning models.

Multivariate Time-Series (MTS) analysis is crucial across various domains. Considering the spatial and temporal consistency of MTS, existing methods leverage graph structures with temporal augmentation and contrastive learning to achieve robust learning of spatial dependencies and temporal patterns. Given the inherent high-order correlations in MTS, hypergraphs present a promising approach. However, two key challenges limit their further development: 1) Feature-based perspectives capture limited spatial information, while structural perspectives encode richer spatial consistency and evolution dependency; 2) Various semantic patterns (e.g., synergy, inhibition) entangle in sensor correlations, leading to semantic ambiguity. The underlying reason is that conventional hypergraph structures cannot distinguish specific semantic roles within or across hyperedges. Thus, we propose Role Hypergraph Contrastive Learning for MTS analysis. Specifically, we introduce the concept of role to generalize hypergraphs to Role Hypergraphs, enabling precise modeling of sensor correlations by assigning each vertex-hyperedge pair with a semantic role. Building on this structure, we design a role hypergraph contrastive learning paradigm to comprehensively capture the spatial and temporal dependencies: From a structural perspective, role hypergraph structural contrasting captures spatial short-term consistency and long-term evolution; from a feature perspective, alignment of complementary role information ensures sensor-level temporal consistency. Experiments on classification and forecasting tasks demonstrate the effectiveness and interpretability of our method.

In medical image classification, data privacy constraints and the high cost of expert annotations pose significant challenges to building generalizable models. Federated semi-supervised learning (FSSL), which combines the privacy-preserving nature of federated learning with the label efficiency of semi-supervised learning, offers a promising direction. However, in real-world deployments, client data often exhibits highly non-independent and identically distributed (Non-IID) characteristics. This distributional heterogeneity undermines the reliability of pseudo-labels generated by global models, ultimately limiting model generalization. A key limitation of existing FSSL approaches lies in their reliance on a static labeled set fixed prior to training. Such strategies lack the ability to adaptively correct pseudo-label noise or address class imbalance throughout training, particularly under Non-IID settings. To address this, we propose FSSAL, a novel framework that introduces an active learning component into the FSSL pipeline. By continuously identifying informative and representative samples during training, our method adaptively refines the labeled set and enhances the model’s robustness to distribution shifts. FSSAL employs client-private models for pseudo-label generation to reduce global bias, applies a class-aware dynamic thresholding mechanism to ensure more reliable and balanced label selection, and incorporates a sample selection strategy guided by both feature diversity and model uncertainty. Extensive experiments on four public medical image classification datasets demonstrate that FSSAL consistently outperforms competitive FSSL methods in accuracy and F1-score, especially under highly Non-IID conditions, highlighting its robustness and practical potential.

Although multimodal fusion has made significant progress, its advancement is severely hindered by the lack of adequate evaluation benchmarks. Current fusion methods are typically evaluated on a small selection of public datasets, a limited scope that inadequately represents the complexity and diversity of real-world scenarios, potentially leading to biased evaluations. This issue presents a twofold challenge. On one hand, models may overfit to the biases of specific datasets, hindering their generalization to broader practical applications. On the other hand, the absence of a unified evaluation standard makes fair and objective comparisons between different fusion methods difficult. Consequently, a truly universal and high-performance fusion model has yet to emerge. To address these challenges, we have developed a large-scale, domain-adaptive benchmark for multimodal evaluation. This benchmark integrates over 30 datasets, encompassing 15 modalities and 20 predictive tasks across key application domains. To complement this, we have also developed an open-source, unified, and automated evaluation pipeline that includes standardized implementations of state-of-the-art models and diverse fusion paradigms. Leveraging this platform, we have conducted large-scale experiments, successfully establishing new performance baselines across multiple tasks. This work provides the academic community with a crucial platform for rigorous and reproducible assessment of multimodal models, aiming to propel the field of multimodal artificial intelligence to new heights.

Human-defined creativity is highly abstract, posing a challenge for multimodal large language models (MLLMs) to comprehend and assess creativity that aligns with human judgments. The absence of an existing benchmark further exacerbates this dilemma. To this end, we propose CreBench, which consists of two key components: 1) an evaluation benchmark covering the multiple dimensions from creative idea to process to products; 2) CreMIT (Creativity Multimodal Instruction Tuning dataset), a multimodal creativity evaluation dataset, consisting of 2.2K diverse-sourced multimodal data, 79.2K human feedbacks and 4.7M multityped instructions. Specifically, to ensure MLLMs can handle diverse creativity-related queries, we prompt GPT to refine the human feedback to activate stronger creativity assessment capabilities. CreBench serves as a foundation for building MLLMs that understand human-aligned creativity. Based on the CreBench, we fine-tune open-source general MLLMs, resulting in CreExpert, a multimodal creativity evaluation expert model. Extensive experiments demonstrate that the proposed CreExpert models achieve significantly better alignment with human creativity evaluation compared to state-ofthe-art MLLMs, including the most advanced GPT-4V and Gemini-Pro-Vision.

Aggregated time series are widely used in business and economics, where top-level sequences (e.g., category sales) aggregated from underlying sequences (e.g., individual items) often exhibit clearer trends and are therefore typically the primary focus of forecasting tasks. However, treating top-level sequences as ordinary multivariate time series is inappropriate in the presence of coupled aggregation constraints. The core challenge arises in coupled aggregation structures, where a single underlying sequence contributes to multiple top-level sequences, as simple nonnegativity constraints of underlying sequences induce highly complex constraints among top-level sequences. Existing methods fail to achieve high accuracy while satisfying these constraints. To address this, we propose ProCAST, a projection-based framework that adjusts forecasts from any multivariate base model to satisfy coupled aggregation constraints. By introducing virtual underlying sequences and leveraging orthogonal and oblique projection, our method ensures that the top-level forecasts are feasible without explicitly deriving complex constraints. Theoretically, we prove that the proposed method guarantees improved accuracy under distance-based loss functions. Experiments on real-world datasets show that our method completely eliminates constraint violations while achieving higher accuracy than current state-of-the-art approaches.

Sparse neural systems are gaining traction for efficient continual learning due to their modularity and low interference. Architectures like Sparse Distributed Memory Multi-Layer Perceptrons (SDMLP) construct task-specific subnetworks via Top-K activation and have shown resilience against catastrophic forgetting. However, their rigid modularity poses two fundamental challenges: (1) the isolation of sparse subnetworks severely limits cross-task knowledge reuse; and (2) increased sparsity reduces interference but often degrades performance due to constrained feature sharing.We propose Selective Subnetwork Distillation (SSD), a structurally guided continual learning framework that treats distillation not as a regularizer, but as a topology-aligned information conduit. By identifying neurons with high activation frequency, SSD selectively distills knowledge within previous Top-K subnetworks and output logits—without requiring replay or task labels—preserving both sparsity and functional specialization.Unlike conventional distillation, SSD operates under hard modular constraints and enables structural realignment without altering the sparse architecture.While our method is validated on SDMLP, its structure-aligned mechanism has the potential to generalize to other sparse networks as a plug-in module for promoting representation sharing.Comprehensive experiments on Split CIFAR-10, CIFAR-100, and MNIST demonstrate that SSD improves accuracy, retention, and manifold coverage, offering a structurally grounded solution to sparse continual learning.

In multi-objective decision-making with hierarchical preferences, lexicographic bandits provide a natural framework for optimizing multiple objectives in a prioritized order. In this setting, a learner repeatedly selects arms and observes reward vectors, aiming to maximize the reward for the highest-priority objective, then the next, and so on. While previous studies have primarily focused on regret minimization, this work bridges the gap between regret minimization and best arm identification under lexicographic preferences. We propose two elimination-based algorithms to address this joint objective. The first algorithm eliminates suboptimal arms sequentially, layer by layer, in accordance with the objective priorities, and achieves sample complexity and regret bounds comparable to those of the best single-objective algorithms. The second algorithm simultaneously leverages reward information from all objectives in each round, effectively exploiting cross-objective dependencies. Remarkably, it outperforms the known lower bound for the single-objective bandit problem, highlighting the benefit of cross-objective information sharing in the multi-objective setting. Empirical results further validate their superior performance over baselines.

In this paper, we develop a novel local graph pooling method, namely the Separated Subgraph-based Hierarchical Pooling (SSHPool), for graph classification. We commence by assigning the nodes of a sample graph into different clusters, resulting in a family of separated subgraphs. We individually employ the local graph convolution units as the local structure to further compress each subgraph into a coarsened node, transforming the original graph into a coarsened graph. Since these subgraphs are separated by different clusters and the structural information cannot be propagated between them, the local convolution operation can significantly avoid the over-smoothing problem caused by message passing through edges in most existing Graph Neural Networks (GNNs). By hierarchically performing the proposed procedures on the resulting coarsened graph, the proposed SSHPool can effectively extract the hierarchical global features of the original graph structure, encapsulating rich intrinsic structural characteristics. Furthermore, we develop an end-to-end GNN framework associated with the SSHPool module for graph classification. Experimental results demonstrate the superior performance of the proposed model on real-world datasets.