DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and Observability Parad

arXiv:2608.20799v1 Announce Type: new Abstract: We study dynamic context scheduling as a training instrument for contextual re- inforcement learning. Rather than treating

arXiv:2603.27628v1 Announce Type: new Abstract: In dynamic manufacturing environments, disruptions such as machine breakdowns and new order arrivals continuously shift the optimal

arXiv:2610.21008v1 Announce Type: new Abstract: Deep Reinforcement Learning (DRL) offers learned job selection for High-Performance Computing (HPC), but performance against

arXiv:2603.10651v2 Announce Type: replace-cross Abstract: Task and Motion Planning combines high-level task sequencing (what to do) with low-level motion planning (how to do it)

arXiv:2605.23957v1 Announce Type: new Abstract: Learning-assisted hyper-heuristics can select among dispatching rules while preserving the feasibility and interpretability of