Decompose, Don't Expand: Capability-Independent AI Systems by Construction
Read the original on arXiv AI →The paper argues that many AI tasks can be split into a deterministic core and a fuzzy residue, and that decomposing tasks rather than expanding models yields a property called parity by construction—where model‑size no longer affects performance on the deterministic sub‑tasks. Across nine models ranging from 494 M to Opus‑class size, the authors show that once the deterministic context is resolved, operation selection and selector resolution scores become identical, while a non‑decomposable task (labelling) remains sensitive to model size. They present a general method for identifying decomposition points, constructing deterministic surfaces and grammars, and ensuring safe residues, and demonstrate the approach in physics, motion retargeting, object‑model editing, and a Web‑CLI tool. whyItMatters":"The study demonstrates that task decomposition can eliminate the need for model scaling or fine‑tuning, offering a systematic design discipline applicable across diverse AI domains."
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