arXiv:2605.00169v1 Announce Type: cross
Abstract: Network digital twins (NDTs) are transforming network management by offering precise virtual replicas of physical network systems. However, their reliance on diverse and sensitive data introduces signi…
arXiv:2507.09001v3 Announce Type: replace-cross
Abstract: Machine learning (ML) models for electronic structure typically rely on large datasets generated by computationally expensive Kohn-Sham density functional theory calculations, as it is not know…
arXiv:2605.00370v1 Announce Type: new
Abstract: Centralized multimodal learning commonly compresses language, acoustic, and visual signals into a single fused representation for prediction. While effective, this paradigm suffers from two limitations: …
arXiv:2512.04341v3 Announce Type: replace
Abstract: Popular offline reinforcement learning (RL) methods rely on explicit conservatism, penalizing out-of-dataset actions or restricting rollout horizons. We question the universality of this principle an…
arXiv:2605.00324v1 Announce Type: cross
Abstract: Large-scale ranking systems depend on thousands of features derived from user behavior across multiple time horizons. Typically requires model retraining — resulting in long iteration cycles (3–6 mon…
arXiv:2512.19927v2 Announce Type: replace
Abstract: Seismology faces fundamental challenges in state forecasting and reconstruction (e.g., earthquake early warning and ground motion prediction) and managing the parametric variability of source locatio…
arXiv:2602.02443v2 Announce Type: replace
Abstract: Test-time scaling improves LLM performance by generating multiple candidate solutions, yet token-level sampling requires temperature tuning that trades off diversity against stability. Fine-grained M…
arXiv:2604.27977v2 Announce Type: replace
Abstract: Despite recent progress in language models and agents for scientific data-driven discovery, further advancing their capabilities is held back by the absence of verifiable environments representing re…
arXiv:2605.00445v1 Announce Type: new
Abstract: Large Language Models have achieved remarkable success and are increasingly deployed in critical applications involving tabular data, such as Table Question Answering. However, their robustness to the st…
arXiv:2510.21141v2 Announce Type: replace-cross
Abstract: Internet speed tests are indispensable for users, ISPs, and policymakers, but their static flooding-based design imposes growing costs: a single high-speed test can transfer hundreds of MB, and…