Abstract

A one-source kernel DSL exposes each operation's schedule — tile dimensions, buffering depth, vector width, per-shape selection — as compile-time knobs, so schedule search becomes a discrete search over monomorphized, bit-exact kernels. Building on an existing per-(device, op, shape) winner cache, this paper extracts the constants such a search must respect from the campaign's ~15 hand-evaluated configurations (free static rejection, a ±0.4% per-op fitness signal, strongly non-additive lever interaction, and hard device non-transfer), specifies a multi-fidelity evolutionary schedule search (~300 configurations/night against ~15 by hand), and embeds it in a continual-improvement loop where a calibrated judge flock — weighted by measured ground truth rather than preference — selects training signal under a Goodhart tripwire, framed as the small-N precursor of a mean-field game. The search is specified, not yet run; that boundary is marked throughout.