While neural networks are increasingly prevalent in solving partial differential equations (PDEs), the high cost of generating precise training data through numerical simulations poses a significant challenge to model efficiency and accuracy. To address this, we propose LiPS, a data augmentation framework based on Lie point symmetries. By leveraging the inherent mathematical symmetries of PDEs, LiPS generates physically consistent training samples that enrich the data distribution without violating underlying physical laws. We further introduce an innovative architecture that integrates contrastive learning with causal reasoning. During self-supervised pre-training, the model generates causal attention maps to identify regions critical for PDE evolution, which the causal reasoning engine then uses to refine latent space representations. Extensive experiments on multiple benchmarks, including Navier-Stokes and Spherical Shallow Water equations, validate the superiority of LiPS over state-of-the-art baselines. Notably, our approach achieves up to a 38% reduction in Mean Squared Error (MSE) in out-of-distribution generalization scenarios, demonstrating exceptional robustness to unseen physical environments.

Recent advances in multimodal learning have largely relied on pairwise contrastive objectives to align different modalities, such as text, video, and audio, in a shared embedding space. While effective in bi-modal setups, these approaches struggle to generalize across multiple modalities and often lack semantic structure in high-dimensional spaces. In this paper, we propose MOVER, a novel framework that combines optimal transport-based soft alignment with volume-based geometric regularization to build semantically aligned and structured multimodal representations. By integrating a transport-guided matching mechanism with a geometric volume minimization objective (GAVE), MOVER encourages consistent alignment across all modalities in a modality-agnostic manner. Experiments on text-video-audio retrieval tasks demonstrate that MOVER significantly outperforms prior state-of-the-art methods in both zero-shot and finetuned settings. Additional analysis shows improved generalization to unseen modality combinations and stronger structural consistency in the learned embedding space.

The survival of numerous endangered wildlife species is increasingly jeopardized by drastic climate changes, ecological disturbances, and human activities, leading to rapid population declines. Identifying endangered and rare species using computer vision is an important task that can aid in biodiversity conservation. However, many rare species are recognizable only by specialized biologists, making the labeling process of images both challenging and costly. Moreover, even with labeled data, training models with imbalanced datasets, i.e., datasets with under-represented categories, often results in inherent learning biases. To address high labeling costs and challenges associated with sparse label learning, we propose a novel semi-supervised learning framework, 4S-Classifier, which comprises two key modules: Rare Species Bank (RSBank) and Attention-based Rare Species Embedding (RSEmbed). The RSBank module stores embeddings of rare species across multiple training epochs, using clustering, kernel density estimation, and confidence scores to enhance the learning of under-represented categories. The RSEmbed module acts as a fusion-based augmentation approach that employs embeddings to improve model performance on these sparse and rare species data. By integrating both modules, our framework achieves a classification accuracy of 88.37% on an endangered species dataset (iNaturalist) and 91.56% on a wildlife dataset (Wildlife Insights) with only 25% of the labeled data, demonstrating its outstanding performance. The code is available at https://github.com/SIPLab24/4S-Classifier

Although generative AI has been applied to protect wildlife in various scenarios, studies have identified the lack of an integrated application toolkit to curb illegal wildlife trade. We therefore introduce DRCO1, the first application framework that is integrated with LLM by proposing useful policies. In the Decision-Making Module, a black-box LLM, combined with a refined ReAct-like prompting template, selects policy promoters in one region. In the Restrictive-Partial-Legalization Module, our innovative Dynamic Iterative Constraint Method is employed to calculate the controlled volume of wildlife trade under the influence of policy, which may provide an idea for Explainable AI research. The Curve-fitting module fits current and future data into a curve with an 86.27% fit to the original Product Life Cycle Curve. The Optimal-Algorithm Module proposes a Dynamic WP-CUCB Algorithm with Policy Consideration to optimize the allocation of wildlife patrol resources in the region. Experiments indicate that policies generated by DRCO can lead to significant control and promote "AI for social good". Our code and more details will be open-sourced online.

Gradient clipping is widely used to stabilize deep network training, but its formulation as a hard, fixed threshold limits flexibility and ignores gradient distribution dynamics. We propose SPAMP (Statistical Per-layer Adaptive Modulation and Projection), a unified framework that generalizes clipping into smooth, per-layer gradient shaping. SPAMP tracks local gradient statistics, dynamically estimates thresholds, and applies power-based transformations to modulate update magnitudes in a differentiable manner. This perspective recasts clipping and warmup as dual mechanisms for controlling the effective update scale ηt‖gt‖, offering a principled alternative to rigid heuristics. Extensive experiments across image and language tasks demonstrate that SPAMP improves stability, convergence, and robustness over existing methods.

While Transformer architectures have demonstrated impressive scalability across domains, they continue to face challenges in long-context reasoning, computational efficiency, and structural generalization - largely due to rigid layer stacking, dense attention, and reliance on positional encodings. We present ReSSFormer, a Recursive Sparse Structured Transformer that integrates three complementary innovations: Recurrent Reasoning & Memory Unit (R2MU) for iterative reasoning with bounded depth, Adaptive Sparse Attention Module (ASAM) for efficient and focused context selection, and Self-Organizing Encoder Structure (SOES) for position-free structure induction. ReSSFormer replaces conventional depth stacking with recurrent inference, substitutes full attention with token- and expert-level sparsity, and models latent token topology directly from content. Across language modeling, multi-hop QA, and structure-sensitive tasks, ReSSFormer consistently outperforms strong baselines under comparable FLOPs and parameter budgets, highlighting its scalability, efficiency, and structural flexibility.

We introduce TRiCo, a novel triadic game-theoretic co-training framework that rethinks the structure of semi-supervised learning by incorporating a teacher, two students, and an adversarial generator into a unified training paradigm. Unlike existing co-training or teacher-student approaches, TRiCo formulates SSL as a structured interaction among three roles: (i) two student classifiers trained on frozen, complementary representations, (ii) a meta-learned teacher that adaptively regulates pseudo-label selection and loss balancing via validation-based feedback, and (iii) a non-parametric generator that perturbs embeddings to uncover decision boundary weaknesses. Pseudo-labels are selected based on mutual information rather than confidence, providing a more robust measure of epistemic uncertainty. This triadic interaction is formalized as a Stackelberg game, where the teacher leads strategy optimization and students follow under adversarial perturbations. By addressing key limitations in existing SSL frameworks—such as static view interactions, unreliable pseudo-labels, and lack of hard sample modeling—TRiCo provides a principled and generalizable solution. Extensive experiments on CIFAR-10, SVHN, STL-10, and ImageNet demonstrate that TRiCo consistently achieves state-of-the-art performance in low-label regimes, while remaining architecture-agnostic and compatible with frozen vision backbones.