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arXiv:2601.20257v1 Announce Type: new Abstract: Decision Transformer (DT) shows promise for generative auto-bidding by capturing temporal dependencies, but suffers from two critical limitations: insufficient cross-correlation...
arXiv:2601.20260v1 Announce Type: new Abstract: Multi-modal image fusion aims to consolidate complementary information from diverse source images into a unified representation. The fused image is...
arXiv:2601.20262v1 Announce Type: new Abstract: The growing demand for real-time robotic deployment necessitates fast and on-device inference for vision-language-action (VLA) models. Within the VLA literature,...
arXiv:2601.20267v1 Announce Type: new Abstract: Transformers have become the foundation of numerous state-of-the-art AI models across diverse domains, thanks to their powerful attention mechanism for...
arXiv:2601.20268v1 Announce Type: new Abstract: Recent advances in stochastic differential equations (SDEs) have enabled robust modeling of real-world dynamical processes across diverse domains, such as...
arXiv:2601.20270v1 Announce Type: new Abstract: Phishing continues to be one of the most prevalent attack vectors, making accurate classification of phishing URLs essential. Recently, large...
arXiv:2601.20273v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have gained increasing adoption in high-quality image and video generation. As demand for higher-resolution images and longer...
arXiv:2601.20275v1 Announce Type: new Abstract: Defining psycholinguistic characteristics in written texts is a task gaining increasing attention from researchers. One of the most widely used...
arXiv:2601.20276v1 Announce Type: new Abstract: Long-context LLM agents must access the right evidence from large environments and use it faithfully. However, the popular Needle-in-a-Haystack (NIAH)...
arXiv:2601.20279v1 Announce Type: new Abstract: Recent studies have examined attention dynamics in large vision-language models (LVLMs) to detect hallucinations. However, existing approaches remain limited in...
arXiv:2601.20280v1 Announce Type: new Abstract: Time series forecasting has long been dominated by advances in model architecture, with recent progress driven by deep learning and...
arXiv:2601.20282v1 Announce Type: new Abstract: While explainable artificial intelligence (XAI) for large language models (LLMs) remains an evolving field with many unresolved questions, increasing regulatory...
arXiv:2601.20283v1 Announce Type: new Abstract: Neural ranking models (NRMs) achieve strong retrieval effectiveness, yet prior work has shown they are vulnerable to adversarial perturbations. We...
arXiv:2601.20284v1 Announce Type: new Abstract: Domain adaptation (DA) addresses the challenge of transferring knowledge from a source domain to a target domain where image data...
arXiv:2601.20290v1 Announce Type: new Abstract: This paper studies the multivariate approximation of functions in weighted Korobov spaces using multiple rank-1 lattice rules. It has been...
arXiv:2601.20291v1 Announce Type: new Abstract: Photoacoustic computed tomography (PACT) is a promising imaging modality that combines the advantages of optical contrast with ultrasound detection. Utilizing...
arXiv:2601.20295v1 Announce Type: new Abstract: Bridging the sim2real gap between computationally inexpensive models and complex physical systems remains a central challenge in machine learning applications...
arXiv:2601.20297v1 Announce Type: new Abstract: With the rapid advancement of video generation techniques, evaluating and auditing generated videos has become increasingly crucial. Existing approaches typically...
arXiv:2601.20298v1 Announce Type: new Abstract: We develop a data-driven framework for the synthesis of robust Krasovskii control barrier certificates (RK-CBC) and corresponding robust safety controllers...
arXiv:2601.20299v1 Announce Type: new Abstract: The evaluation and post-training of large language models (LLMs) rely on supervision, but strong supervision for difficult tasks is often...
arXiv:2601.20300v1 Announce Type: new Abstract: Self-supervised learning (SSL) has greatly advanced speech representation learning, but multilingual SSL models remain constrained to languages encountered during pretraining....
arXiv:2601.20301v1 Announce Type: new Abstract: Sharpness-Aware Minimization (SAM) has recently emerged as an effective technique for improving DNN robustness to input variations. However, its interplay...
arXiv:2601.20302v1 Announce Type: new Abstract: Performance degradation due to covariate shift remains a major challenge for deep learning models in medical image segmentation. An open...
arXiv:2601.20303v1 Announce Type: new Abstract: Estimating object mass from visual input is challenging because mass depends jointly on geometric volume and material-dependent density, neither of...