arXiv:2603.25975v1 Announce Type: new Abstract: We show that they do. Schank’s conceptual dependency theory proposed that all events decompose into primitive operations — ATRANS, PTRANS, MTRANS, and others — hand-coded from linguistic intuition. Can the same primitives be discovered automatically…
arXiv:2508.14765v3 Announce Type: replace Abstract: Designing therapeutic peptides with tailored properties is hindered by the vastness of sequence space, limited experimental data, and poor interpretability of current generative models. To address these challenges, we introduce PepThink-R1, a generative framework that…
arXiv:2603.25976v1 Announce Type: new Abstract: Second-order methods promise improved stability and faster convergence, yet they remain underused due to implementation overhead, tuning brittleness, and the lack of composable APIs. We introduce Somax, a composable Optax-native stack that treats curvature-aware training…
arXiv:2511.10938v2 Announce Type: replace Abstract: Motivated by the challenges of edge inference, we study a variant of the cascade bandit model in which each arm corresponds to an inference model with an associated accuracy and error probability. We analyse four…
arXiv:2603.26017v1 Announce Type: new Abstract: Time series forecasting is critical across finance, healthcare, and cloud computing, yet progress is constrained by a fundamental bottleneck: the scarcity of large-scale, high-quality benchmarks. To address this gap, we introduce textsc{QuitoBench}, a regime-balanced benchmark…
arXiv:2512.14150v3 Announce Type: replace Abstract: Radio path loss prediction (RPP) is critical for optimizing 5G networks and enabling IoT, smart city, and similar applications. However, current deep learning-based RPP methods lack proactive environmental modeling, struggle with realistic multi-transmitter scenarios, and…
arXiv:2603.26023v1 Announce Type: new Abstract: Digital twins of complex physical systems are expected to infer unobserved states from sparse measurements and predict their evolution in time, yet these two functions are typically treated as separate tasks. Here we present GLU,…
arXiv:2602.20396v3 Announce Type: replace Abstract: Explainable artificial intelligence promises to yield insights into relevant features, thereby enabling humans to examine and scrutinize machine learning models or even facilitating scientific discovery. Considering the widespread technique of Shapley values, we find that…
arXiv:2603.26024v1 Announce Type: new Abstract: Identification of causal directionality in bivariate numerical data is a fundamental research problem with important practical implications. This paper presents two alternative methods to identify direction of causation by considering conditional distributions: (1) Anticipated Asymmetric…
arXiv:2603.21606v5 Announce Type: replace Abstract: Current language model training commonly applies multi-task Supervised Fine-Tuning (SFT) using a homogeneous compute budget across all sub-datasets. This approach is fundamentally sub-optimal: heterogeneous learning dynamics cause faster-learning tasks to overfit early while slower ones…
