Academic
arXiv:2602.21469v1 Announce Type: new Abstract: Data assimilation and scientific inverse problems require reconstructing high-dimensional physical states from sparse and noisy observations, ideally with uncertainty-aware posterior …
arXiv:2602.21492v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a central post-training paradigm for large language models (LLMs), but its performance is highly sensitive …
arXiv:2602.21498v1 Announce Type: new Abstract: Irregular Multivariate Time Series (IMTS) are characterized by uneven intervals between consecutive timestamps, which carry sampling pattern information valuable and …
arXiv:2602.21508v1 Announce Type: new Abstract: Robust watermarking is critical for intellectual property protection, whereas existing methods face a severe vulnerability against regeneration-based AIGC attacks. We …
arXiv:2602.21515v1 Announce Type: new Abstract: Many emerging agentic paradigms require agents to collaborate with one another (or people) to achieve shared goals. Unfortunately, existing approaches …
arXiv:2602.21545v1 Announce Type: new Abstract: The Muon optimizer has demonstrated promising performance in pre-training large language models through gradient (or momentum) orthogonalization. In this work, …
arXiv:2602.21546v1 Announce Type: new Abstract: The Flexible Job Shop Problem (FJSP) is a well-studied combinatorial optimization problem with extensive applications for manufacturing and production scheduling. …
arXiv:2602.21550v1 Announce Type: new Abstract: Gene expression prediction, which predicts mRNA expression levels from DNA sequences, presents significant challenges. Previous works often focus on extending …
arXiv:2602.21551v1 Announce Type: new Abstract: Learning PDE dynamics for fluids increasingly relies on neural operators and Transformer-based models, yet these approaches often lack interpretability and …
arXiv:2602.21565v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) learn to sample diverse candidates in proportion to a reward function, making them well-suited for scientific …
arXiv:2602.21585v1 Announce Type: new Abstract: Many applications seek to optimize LLM outputs at test time by iteratively proposing, scoring, and refining candidates over a discrete …
arXiv:2602.21588v1 Announce Type: new Abstract: Agent-based epidemic models (ABMs) encode behavioral and policy heterogeneity but are too slow for nightly hospital planning. We develop county-ready …
