Decompose, Don't Expand: Capability-Independent AI Systems by Construction

arXiv:2610.38144v1 Announce Type: new Abstract: A recurring design choice in building AI systems is whether, when a task is too hard for a model, to expand the model (make it larger, better-prompted, fine-tuned), or to decompose the task (extract the parts that admit a deterministic solution to a host, leaving the model only the irreducibly fuzzy residue). We make three claims. First, that many tasks commonly delegated whole to a model are in fact deterministic tasks with a small generative core. Second, and our central empirical result, that decomposing such a task yields a measurable and desirable property we call parity by construction: on the extracted sub-tasks the model-size performance curve becomes flat, because no model performs them at all.