最新模型
Compile by Training Compiles Natural-Language Specifications into Reusable Offline Neural Functions
A new method first uses a teacher model to synthesize task data, then fine-tunes adapters for a small interpreter, packaging text specifications as versionable `.paw` functions. It achieves 83.6% semantic accuracy on difficult, ambiguous text tasks, but compilation requires an external model and roughly 40GB of accelerator memory, and it cannot provide the formal guarantees of conventional programs.