arXiv:2601.03683v2 Announce Type: replace Abstract: Time series forecasting plays a crucial role in contemporary engineering information systems for supporting decision-making across various industries, where Recurrent Neural Networks (RNNs) have been widely adopted due to their capability in modeling sequential data.…
arXiv:2601.08089v1 Announce Type: new Abstract: Public large language models (LLMs) are typically safety-aligned during pretraining, yet task-specific fine-tuning required for deployment often erodes this alignment and introduces safety risks. Existing defenses either embed safety recovery into fine-tuning or rely on…
arXiv:2601.07760v2 Announce Type: replace Abstract: Kolmogorov-Arnold Networks (KANs) have shown strong potential for efficiently approximating complex nonlinear functions. However, the original KAN formulation relies on B-spline basis functions, which incur substantial computational overhead due to De Boor’s algorithm. To address…
arXiv:2601.08094v1 Announce Type: new Abstract: Subject-independent EEG emotion recognition is challenged by pronounced inter-subject variability and the difficulty of learning robust representations from short, noisy recordings. To address this, we propose a fusion framework that integrates (i) local, channel-wise descriptors…
arXiv:2503.08120v5 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) have emerged as a powerful paradigm in fundamental cross-modality research, demonstrating significant potential in both image understanding and generation. However, existing research in the face domain primarily faces two challenges: $textbf{(1)}$…
arXiv:2512.17875v2 Announce Type: replace-cross Abstract: A key challenge in evaluating VLMs is testing models’ ability to analyze visual content independently from their textual priors. Recent benchmarks such as BLINK probe visual perception through visual prompting, where questions about visual content…
arXiv:2601.08614v1 Announce Type: cross Abstract: Heterogeneity within data distribution poses a challenge in many modern federated learning tasks. We formalize it as an optimization problem involving a computationally heavy composite under data similarity. By employing different sets of assumptions, we…
arXiv:2601.05544v2 Announce Type: replace Abstract: A scoring system is a linear classifier composed of a small number of explanatory variables, each assigned a small integer coefficient. This system is highly interpretable and allows predictions to be made with simple manual…
arXiv:2506.10326v3 Announce Type: replace-cross Abstract: Developing AI agents that can robustly adapt to varying strategic landscapes without retraining is a central challenge in multi-agent learning. Pok’emon Video Game Championships (VGC) is a domain with a vast space of approximately $10^{139}$…
arXiv:2505.22362v3 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have achieved significant success in various learning tasks on graph-structured data. Nevertheless, most GNNs struggle to generalize to heterophilic neighborhoods. Additionally, many GNNs ignore the directional nature of real-world graphs, resulting…
