arXiv:2603.28507v1 Announce Type: new
Abstract: Classical AI scaling laws, especially for pre-training, describe how training loss decreases with compute in a power-law form. Their effectiveness has a basic and very practical sense: they make progress…
arXiv:2603.20654v3 Announce Type: replace-cross
Abstract: Classical Amdahl’s Law quantifies the limit of speedup under a fixed serial-parallel decomposition and homogeneous replication. Modern systems instead allocate constrained resources across hete…
arXiv:2603.20654v2 Announce Type: replace-cross
Abstract: Classical Amdahl’s Law assumes a fixed decomposition between serial and parallel work and homogeneous replication; historically, it bounds how much parallel speedup is attainable. Modern system…