In multivariate time series forecasting, most existing Transformer models follow a fixed modeling paradigm; they either focus on capturing temporal patterns within each variable or on learning the interactions between variables. However, this single approach often fails to adapt to real-world time series, which exhibit complex and diverse characteristics. To this end, we propose Adformer, an adaptive and unified forecasting framework. The framework innovatively integrates a hybrid architecture capable of capturing both intra-variable and inter-variable dependencies. However, we recognize that this hybrid design faces a key challenge: when inter-variable correlations in the data are weak, forcing the model to learn these inter-variable interactions may introduce statistical noise and thus degrade forecasting performance. To enable the model to intelligently circumvent this issue, our hybrid architecture is dynamically guided by a data-driven strategy selection module. This module analyzes the input’s intrinsic correlation structure using unsupervised clustering, based on this analysis, automatically selects the optimal modeling path for the architecture—whether to focus on intra-variable patterns, inter-variable interactions, or a hybrid of both. Additionally, we introduce a frequency-aware loss function, which helps the model focus on meaningful low-frequency components and improves robustness under noisy conditions.Extensive experiments on several public benchmarks demonstrate that our adaptive framework consistently outperforms state-of-the-art methods across various forecasting tasks, showing strong generalization and robustness, and highlighting its potential as a foundation for future time series models.

Domain Adaptation (DA) aims to transfer knowledge from a labeled source domain to an unlabeled or sparsely labeled target domain under domain shifts. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. To tackle this tradeoff, we propose a generalized graph SPectral Alignment framework, SPA++. Its core is briefly condensed as follows: (1) by casting the DA problem to graph primitives, it composes a coarse graph alignment mechanism with a novel spectral regularizer toward aligning the domain graphs in eigenspaces; (2) we further develop a fine-grained neighbor-aware propagation mechanism for enhanced discriminability in the target domain; (3) by incorporating data augmentation and consistency regularization, SPA++ can adapt to complex scenarios including most DA settings and even challenging distribution scenarios. Furthermore, we also provide theoretical analysis to support our method, including the generalization bound of graph-based DA and the role of spectral alignment and smoothing consistency. Extensive experiments on benchmark datasets demonstrate that SPA++ consistently outperforms existing cutting-edge methods, achieving superior robustness and adaptability across various challenging adaptation scenarios.

Applying artificial intelligence to Traditional Chinese Medicine (TCM) treatment has enabled the online intelligent diagnosis of TCM. However, TCM faces two critical challenges in AI-driven prescription systems. The first is limited generalizability. Existing methods merely retrieve similar historical prescriptions, failing to generate novel prescriptions for rare symptoms or patients with unique constitutions. The second is the accuracy degradation. This limitation primarily stems from three critical factors including experiential bias in practitioner-dependent decision patterns, neglect of historical patient context critical for personalization, and geometric distortion induced by Euclidean embeddings of scale-free herb interaction networks. To address these issues, we propose HyperRxGen, a historical-contextualized hyperbolic framework for herb prescription generation. The HyperRxGen paradigm is architected with two core components: a Hyperbolic Multi-Graph Neural Network (HMGNN) and an HMGNN-based Prescription Generator (HM-PG). The HMGNN leverages hyperbolic geometry model to encode TCM knowledge graphs, achieving lower distortion than Euclidean GNNs. The HM-PG injects patients’ historical records into prescription generation process, enhancing personalized treatment consistency through adaptive history weighting. Extensive experiments on real-world datasets demonstrate the superior effectiveness and efficiency of HyperRxGen over various baselines. This work bridges hyperbolic deep learning with clinical decision support, offering a potential paradigm shift for personalized healthcare.