Kernel Autoresearch for Open-Ended Model Discovery

Kernel Autoresearch (Kernaut) is a new approach that treats kernel design as open‑ended model discovery, using coding agents to write kernel programs while construction contracts guarantee validity. The method maintains a quality‑diversity archive and novelty screening to encourage functionally distinct kernels, which are shown to generalize to unseen tasks and outperform meta‑learned deep kernels on black‑box optimization families. Additionally, kernels discovered from enzyme‑kinetic rate laws achieve lower error than tuned ARD and deep kernel baselines, and a human‑refined version further improves predictive accuracy and optimization regret.

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