Instead of calling large language models for every fuzzy task, you can compile a natural-language specification once into a tiny reusable neural artifact that runs locally and cheaply—shifting from per-input problem solving to one-time function compilation.
This paper introduces Program-as-Weights (PAW), a method to compile natural-language function specifications into small, locally-executable neural adapters. A 4B compiler generates parameter-efficient adapters that run on a lightweight 0.6B interpreter, matching the performance of much larger models while using 50x less memory and running efficiently on consumer hardware like MacBook M3.