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arXiv:2602.15257v1 Announce Type: cross Abstract: We present the first comprehensive, large-scale study of training long-context vision language models up to 344K context, targeting long-document visual …
arXiv:2602.15273v1 Announce Type: cross Abstract: Information ecosystems increasingly shape how people internalize exposure to adverse digital experiences, raising concerns about the long-term consequences for information …
arXiv:2602.15293v1 Announce Type: cross Abstract: This paper concerns the question of how AI systems encode semantic structure into the geometric structure of their representation spaces. …
arXiv:2602.15327v1 Announce Type: cross Abstract: For deploying foundation models, practitioners increasingly need prescriptive scaling laws: given a pre training compute budget, what downstream accuracy is …
arXiv:2602.15338v1 Announce Type: cross Abstract: Large language model (LLM) alignment relies on complex reward signals that often obscure the specific behaviors being incentivized, creating critical …
arXiv:2602.15707v1 Announce Type: cross Abstract: Real-time conversational assistants for procedural tasks often depend on video input, which can be computationally expensive and compromise user privacy. …
arXiv:2602.15076v1 Announce Type: new Abstract: Safety is a fundamental challenge in reinforcement learning (RL), particularly in real-world applications such as autonomous driving, robotics, and healthcare. …
arXiv:2602.15089v1 Announce Type: new Abstract: In predictive maintenance of equipment, deep learning-based time series anomaly detection has garnered significant attention; however, pure deep learning approaches …
arXiv:2602.15155v1 Announce Type: new Abstract: Implicit Neural Representations (INRs) have emerged as promising surrogates for large 3D scientific simulations due to their ability to continuously …
arXiv:2602.15159v1 Announce Type: new Abstract: Learning from electronic health records (EHRs) time series is challenging due to irregular sam- pling, heterogeneous missingness, and the resulting …
arXiv:2602.15184v1 Announce Type: new Abstract: Recent advances in scientific machine learning (SciML) have enabled neural operators (NOs) to serve as powerful surrogates for modeling the …
arXiv:2602.15200v1 Announce Type: new Abstract: Post-training compression of Transformer models commonly relies on truncated singular value decomposition (SVD). However, enforcing a single shared subspace can …