Forecasting 3D human motion is an important embodiment of fine-grained understanding and cognition of human behavior by artificial agents. Current approaches excessively rely on implicit network modeling of spatiotemporal relationships and motion characteristics, falling into the passive learning trap that results in redundant and monotonous 3D coordinate information acquisition while lacking actively guided explicit learning mechanisms. To overcome these issues, we propose an Active Perceptual Strategy (APS) for human motion prediction, leveraging quotient space representations to explicitly encode motion properties while introducing auxiliary learning objectives to strengthen spatio-temporal modeling. Specifically, we first design a data perception module that projects poses into the quotient space, decoupling motion geometry from coordinate redundancy. By jointly encoding tangent vectors and Grassmann projections, this module simultaneously achieves geometric dimension reduction, semantic decoupling, and dynamic constraint enforcement for effective motion pose characterization. Furthermore, we introduce a network perception module that actively learns spatio-temporal dependencies through restorative learning. This module deliberately masks specific joints or injects noise to construct auxiliary supervision signals. A dedicated auxiliary learning network is designed to actively adapt and learn from perturbed information. Notably, APS is model agnostic and can be integrated with different prediction models to enhance active perceptual.The experimental results demonstrate that our method achieves the new state-of-the-art, outperforming existing methods by large margins: 16.3% on H3.6M, 13.9% on CMU Mocap, and 10.1% on 3DPW.

In recent years, rapid advances in Multimodal Large Language Models (MLLMs) have increasingly stimulated research on ancient Chinese scripts.As the evolution of written characters constitutes a fundamental pathway for understanding cultural transformation and historical continuity, how MLLMs can be systematically leveraged to support and advance text evolution analysis remains an open and largely underexplored problem.To bridge this gap, we construct a comprehensive benchmark comprising 11 tasks and over 130,000 instances, specifically designed to evaluate the capability of MLLMs in analyzing the evolution of ancient Chinese scripts.We conduct extensive evaluations across multiple widely used MLLMs and observe that, while existing models demonstrate a limited ability in glyph-level comparison, their performance on core tasks-such as character recognition and evolutionary reasoning-remains substantially constrained.Motivated by these findings, we propose a glyph-driven fine-tuning framework (GEVO) that explicitly encourages models to capture evolutionary consistency in glyph transformations and enhances their understanding of text evolution.Experimental results show that even models at the 2B scale achieve consistent and comprehensive performance improvements across all evaluated tasks.To facilitate future research, we publicly release both the benchmark and the trained models 1 .

Although studies have demonstrated that Large Language Models (LLMs) can perform well on Out-of-Distribution (OOD) tasks, their advantage tends to diminish as the distribution shift becomes more severe. Consequently, researchers aim to retrieve distributionally similar and informative demonstrations from the available source domain to boost the inference capabilities of LLMs. However, in practical scenarios where the target domain is inaccessible, evaluating the unknown distribution is challenging, which indirectly impacts the quality of the selected demonstrations. To address this problem, we propose DOPA, a demonstration search framework that incorporates an OOD proxy to approximate the inaccessible target domain and guide the retrieval process. Building on proxy-based evaluation, DOPA further introduces a Mahalanobis distance-based global diversity constraint to ensure sufficient diversity among the retrieved demonstrations. Experimental results on multiple LLMs and tasks demonstrate that DOPA effectively enhances robustness in OOD settings.

In the field of green product consumption, consumers tend to seek detailed information to assess product efficacy. In recent years, the advent of Generative Artificial Intelligence (GenAI) tools has significantly streamlined consumers’ access to relevant information on green products. However, as emotional factors are decisive in purchase decision-making, existing studies that predominantly focus on rational decision-making frequently overlook this crucial emotional dimension. Addressing this gap, this study adopts the extended emotion heuristic theory to examine the impact of GenAI tools on green product purchase preferences. Using the partial least squares structural equation model, green product consumption data were collected from multiple regions including Chongqing, Guangdong, Hunan, Hubei, Shanghai, Beijing, and others, between January and March 2025. A total of 717 valid responses were analysed using SPSS 28, Amos 28, and Smart PLS 4.0. The results reveal that certain characteristics of GenAI-generated content—specifically, quality (content relevance, content accuracy), communication style (personalisation, anthropomorphism), and serendipity—positively influence purchase intention for green products. Furthermore, emotional connection plays a partial mediating role. These findings extend the application of emotion heuristic theory in the context of artificial intelligence and highlight the significant role of emotional factors in fostering consumption intentions via GenAI tools. The results offer insights for green product marketers and GenAI tool developers to enhance content quality, communication methods, and additional functions, while also informing regulatory policymaking related to GenAI tools.

Graph prompt learning represents a novel paradigm aimed at enhancing the performance of graph learning models on a variety of downstream tasks by providing specific graph prompts. Despite its promise, current graph prompt learning methods are limited by the following limitations. On the one hand, existing methods often rely on manually selected graph information or simple learnable vectors, which can introduce human biases and lack expressiveness. These methods also fall short in guiding models to induce historical prior knowledge and improve generalization. Furthermore, the direct end-to-end tuning strategy of prompts lacks a necessary gentle transition, which impacts model stability and generalization. To overcome these limitations, we introduce the generalizable graph prompt learning framework (GGPL), which incorporates model-level prompt injection and a two-stage prompt tuning strategy. GGPL focuses on encoding subgraph structures and attributes during pre-training and uses SimGRACE to predict subgraph similarities, enhancing the base model's generalization. The model-level prompt injection module, with its prompt embedding backbone and self-prompt generation, seamlessly integrates invariant knowledge. Our two-stage tuning strategy, including transition and task-specific tuning, ensures better guidance and stability. By designing learnable prompt tokens and fine-tuning them with task-specific information, GGPL enables the model to generalize more robustly to downstream tasks. We conduct extensive experiments on six benchmark datasets to verify the model's effectiveness.

Cross-domain text classification aims to transfer models from label-rich source domains to label-poor target domains, giving it a wide range of practical applications. Many approaches promote cross-domain generalization by capturing domaininvariant features. However, these methods rely on unlabeled samples provided by the target domains, which renders the model ineffective when the target domain is agnostic. Furthermore, the models are easily disturbed by shortcut learning in the source domain, which also hinders the improvement of domain generalization ability. To solve the aforementioned issues, this paper proposes TACIT, a target domain agnostic feature disentanglement framework which adaptively decouples robust and unrobust features by Variational Auto-Encoders. Additionally, to encourage the separation of unrobust features from robust features, we design a feature distillation task that compels unrobust features to approximate the output of the teacher. The teacher model is trained with a few easy samples that are easy to carry potential unknown shortcuts. Experimental results verify that our framework achieves comparable results to state-of-the-art baselines while utilizing only source domain data.

Graph neural networks have demonstrated significant power in learning graph representations for homogeneous networks. However, real-world network data can often be denoted by heterogeneous networks with different types of nodes and edges, such as social, traffic, and molecular networks. Network heterogeneity presents significant challenges for network analysis and mining. Motif-based hypergraphs preserve high-order proximity and capture composite semantic interactions. Because not all nodes and edges in the original network always exist in a specific hypergraph, it is essential that multiple motif-based hypergraphs are considered to enhance the network representation. Therefore, we propose a novel framework for exploring M ultiple M o tif-based H ypergraphs for H eterogeneous G raph N eural N etworks to learn network representations, named MoH-HGNN, which leverages hypergraph convolution and attention operations to capture complex connectivity patterns. Specifically, we conducted two levels of attention networks with hierarchical structures, namely hyperedge-level attention to learn the importance among different types of nodes and comprehensive semantic-level attention to capture the importance of different types of motif structures. We extensively experimented on four real-world datasets to verify the effectiveness of our proposed framework.

Deep learning models are often trained on datasets that are limited in size and distribution, which may not fully represent the entire range of data encountered in practice. Thus, making deep learning models generalize to out-of-distribution data has received a significant amount of attention in recent studies due to the critical importance of this ability in real-world applications. Meta learning as an effective knowledge transfer paradigm, which learns a base model with high generalization ability to adapt to new data distributions by minimizing domain shifts across tasks during meta-training. However, most existing meta learning methods assume that the base model can access the labels of different domains, and this assumption is demanding in many real application scenarios. In addition, these methods focus on narrowing data-level domain shifts, while ignoring task-level domain shifts, which may lead to inadequate or even negative transfer. Inspired by human learners who use induction to learn and master new tasks, we propose a novel domain-aware meta learning framework for out-of-distribution generalization, termed SMLG. This framework enables the base model to generalize effectively to unseen domains without relying on domain-specific labels. Specifically, we develop a domain-aware transformation module to obtain meta representation and pseudo domain labels. As a result, the base model can be trained robustly without the need for direct domain label input. Furthermore, to investigate the impact of domain shifts at different levels, we introduce a joint loss function that combines cross-entropy with a domain alignment constraint. Extensive experiments on benchmark datasets demonstrate the efficacy of our framework.

As the representation capability of Pre-trained Language Models (PLMs) improve, there is growing concern that they will inherit social biases from unprocessed corpora.Most previous debiasing techniques used Counterfactual Data Augmentation (CDA) to balance the training corpus.However, CDA slightly modifies the original corpus, limiting the representation distance between different demographic groups to a narrow range.As a result, the debiasing model easily fits the differences between counterfactual pairs, which affects its debiasing performance with limited text resources.In this paper, we propose an adversarial training-inspired two-stage debiasing model using Contrastive learning with Continuous Prompt Augmentation (named CCPA) to mitigate social biases in PLMs' encoding.In the first stage, we propose a data augmentation method based on continuous prompt tuning to push farther the representation distance between sample pairs along different demographic groups.In the second stage, we utilize contrastive learning to pull closer the representation distance between the augmented sample pairs and then fine-tune PLMs' parameters to get debiased encoding.Our approach guides the model to achieve stronger debiasing performance by adding difficulty to the training process.Extensive experiments show that CCPA outperforms baselines in terms of debiasing performance.Meanwhile, experimental results on the GLUE benchmark show that CCPA retains the language modeling capability of PLMs.