Over-smoothing remains a fundamental challenge in deep Graph Neural Networks (GNNs), where repeated message passing causes node representations to become indistinguishable. While existing solutions, such as residual connections and skip layers, alleviate this issue to some extent, they fail to explicitly model how node representations evolve in a node-specific and progressive manner across layers. Moreover, these methods do not take global information into account, which is also crucial for mitigating the over-smoothing problem. To address the aforementioned issues, in this work, we propose a Dual Mamba-enhanced Graph Convolutional Network (DMbaGCN), which is a novel framework that integrates Mamba into GNNs to address over-smoothing from both local and global perspectives. DMbaGCN consists of two modules: the Local State-Evolution Mamba (LSEMba) for local neighborhood aggregation and utilizing Mamba’s selective state space modeling to capture node-specific representation dynamics across layers, and the Global Context-Aware Mamba (GCAMba) that leverages Mamba’s global attention capabilities to incorporate global context for each node. By combining these components, DMbaGCN enhances node discriminability in deep GNNs, thereby mitigating over-smoothing. Extensive experiments on multiple benchmarks demonstrate the effectiveness and efficiency of our method.

Accurate traffic forecasting plays a vital role in intelligent transportation systems, enabling applications such as congestion control, route planning, and urban mobility optimization. However, traffic forecasting remains challenging due to two key factors: (1) complex spatial dependencies arising from dynamic interactions between road segments and traffic sensors across the network, and (2) the coexistence of multi-scale periodic patterns (e.g., daily and weekly periodic patterns driven by human routines) with irregular fluctuations caused by unpredictable events (e.g., accidents, weather, or construction). To tackle these challenges, we propose HyperD (Hybrid Periodic Decoupling), a novel framework that decouples traffic data into periodic and residual components. The periodic component is handled by the Hybrid Periodic Representation Module, which extracts fine-grained daily and weekly patterns using learnable periodic embeddings and spatial-temporal attention. The residual component, which captures non-periodic, high-frequency fluctuations, is modeled by the Frequency-Aware Residual Representation Module, leveraging complex-valued MLP in frequency domain. To enforce semantic separation between the two components, we further introduce a Dual-View Alignment Loss, which aligns low-frequency information with the periodic branch and high-frequency information with the residual branch. Extensive experiments on four real-world traffic datasets demonstrate that HyperD achieves state-of-the-art prediction accuracy, while offering superior robustness under disturbances and improved computational efficiency compared to existing methods.

Rui Miao, Yixin Liu, Yili Wang, Xu Shen, Yue Tan, Yiwei Dai, Shirui Pan, Xin Wang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Embodied agents in open-ended environments such as Minecraft increasingly adopt planner-controller architectures, with large language models acting as high-level planners.While planning has advanced rapidly, control remains underexplored.Existing systems commonly rely on a monolithic policy to execute subgoals across varying contexts, forcing incompatible behaviors into a shared parameter space and causing interference that scaling only partially mitigates.To address this, we propose MoEC, a Memory-Routed Mixture-of-Experts Controller for Adaptive Minecraft Control.MoEC routes via a subgoal-indexed, nonparametric expert memory and regulates capacity through failure-triggered expert growth and redundancy-aware consolidation.This design enables continual adaptation without full retraining, while maintaining parameter efficiency and with bounded inference cost.We evaluate MoEC on diverse and compositional Minecraft tasks, demonstrating significant gains in adaptability, robustness, and execution consistency over strong baselines, yielding a scalable and efficient alternative for openended control.

The communication topology in large language model-based multi-agent systems fundamentally governs inter-agent collaboration patterns, critically shaping both the efficiency and effectiveness of collective decision-making.While recent studies for communication topology automated design tend to construct sparse structures for efficiency, they often overlook why and when sparse and dense topologies help or hinder collaboration.In this paper, we present a causal framework to analyze how agent outputs, whether correct or erroneous, propagate under topologies with varying sparsity.Our empirical studies reveal that moderately sparse topologies, which effectively suppress error propagation while preserving beneficial information diffusion, typically achieve optimal task performance.Guided by this insight, we propose a novel topology design approach, EIB-LEARNER, that balances error suppression and beneficial information propagation by fusing connectivity patterns from both dense and sparse graphs.Extensive experiments show the superior effectiveness, communication cost, and robustness of EIB-LEARNER.The code is in

Few-shot tabular learning, in which machine learning models are trained with a limited amount of labeled data, provides a cost-effective approach to addressing real-world challenges. The advent of Large Language Models (LLMs) has sparked interest in leveraging their pre-trained knowledge for few-shot tabular learning. Despite promising results, existing approaches either rely on test-time knowledge extraction, which introduces undesirable latency, or text-level knowledge, which leads to unreliable feature engineering. To overcome these limitations, we propose Latte, a training-time knowledge extraction framework that transfers the latent prior knowledge within LLMs to optimize a more generalized downstream model. Latte enables general knowledge-guided downstream tabular learning, facilitating the weighted fusion of information across different feature values while reducing the risk of overfitting to limited labeled data. Furthermore, Latte is compatible with existing unsupervised pre-training paradigms and effectively utilizes available unlabeled samples to overcome the performance limitations imposed by an extremely small labeled dataset. Extensive experiments on various few-shot tabular learning benchmarks demonstrate the superior performance of Latte, establishing it as a state-of-the-art approach in this domain. Our code is available at https://github.com/ruxueshi/Latte.git.

Although pre-trained language models (PLMs) have been widely used in natural language understandings (NLU), they are still exposed to fairness issues.Most existing extrinsic debiasing methods rely on manually curated word lists for each sensitive groups to modify training data or to add regular constraints.However, these word lists are often limited by length and scope, resulting in the degradation performance of extrinsic bias mitigation.To address the aforementioned issues, we propose a Continuous Prompts Adjustment Debiasing method (CPAD), which generates continuous token lists from the entire vocabulary space and uses them to bridge the gap between outputs and targets in fairness learning process.Specifically, CPAD encapsulates fine-tuning objective and debiasing objectives into several independent prompts.To avoid the limitation of manual word lists, in fairness learning phase, we extract outputs from the entire vocabulary space via fine-tuned PLM.Then, we aggregate the outputs from the same sensitive group as continuous token lists to map the outputs into protected attribute labels.Finally, after we learn the debiasing prompts in the perspective of adversarial learning, we improve fairness by adjusting continuous prompts at model inference time.Through extensive experiments on three NLU tasks, we evaluate the debiasing performance from the perspectives of group fairness and fairness through unawareness.The experimental results show that CPAD outperforms all baselines in term of single and two-attributes debiasing performance.

Recent researches have demonstrated pre-training Graph Neural Networks (GNNs) via meta learning can enhance their performance on learning representations from unlabeled data. The main idea behind them is to learn the transferable priors that work across the distribution of tasks. However, existing methods often leverage a uniform task sampling strategy and ignore the relations between their original graphs and them. This may lead the model learning the redundant information in the pre-training process. In this work, we propose Meta Graph Neural Network (MGNN), a graph pre-training method via weighted meta learning. MGNN trys to learn the transferable experiences from diverse distributions without the side-effect of redundant information. First, we break up a task in traditional meta learning into several sub-tasks to construct the minimum evaluation units. Then, to fully utilize the graph information, we use a two-stage optimization with contrastive loss functions to learn the experience priors on node- and graph-levels in meta-training process. Third, to qualify redundant information, we design an evaluation module which calculates the mutual information in a graph view. Finally, we propose a new optimization objective in meta-testing process, which reduce the model’s attention to redundant information to alleviate the negative impact of them. To validate the effectiveness of the proposed method, extensive experiments are conducted on Cora, Citeseer and Pubmed datasets with several GNNs architectures. Experimental results show that our proposed method outperforms existing GNN pre-training algorithms.

Compared with traditional measurement technologies, synthetic aperture radar interferometry (InSAR) has unique advantages in monitoring ground subsidence due to underground mining. However, when the subsidence gradient of the subsidence trough exceeds the maximum measurable gradient of InSAR technology, the interference fringes will be too dense, causing phase aliasing. As a result, it is impossible to obtain correct phase-unwrapping result. The main objectives of this letter are two folded. First is to develop an unwrapping strategy to deal with the unwrapping problem caused by large subsidence gradient at the mine subsidence trough. The main idea of this strategy is to estimate most of the subsidence phase by multiple model inversions based on iterative approach. Then, the model phases from multiple models are combined with the final unwrapped residual phase. Another objective of this letter is to evaluate the feasibility of the three common deformation models, i.e., Mogi, probability integral method (PIM), and Okada, in solving the phase-unwrapping problem. Their advantages and disadvantages are outlined. Both the simulated data and real data are used for this experiment. The result shows that the problem of large subsidence gradient in the differential interferometric synthetic aperture radar (DInSAR) results can be solved by multiple model inversion. Among the three models, the use of Okada model seems to provide slightly more accurate result for solving the large-scale subsidence in the mining area than the other two models with the proposed strategy.