The Serial Scaling Hypothesis

ArXi:2507.12549v4 Announce Type: replace While machine learning has advanced through massive parallelization, we identify a critical blind spot: some problems are fundamentally sequential. These "inherently serial" problems-from mathematical reasoning to physical simulations to sequential decision-making-require sequentially dependent computational steps that cannot be efficiently parallelized. We formalize this distinction in complexity theory, and nstrate that current parallel-centric architectures face fundamental limitations on such tasks.