Compile by Training Converts Natural Language to Local Neural Functions
September 2, 2026
This method generates task-specific adapters for compact interpreters by using teacher models to produce training examples from natural language specs. It achieves 83.6% semantic accuracy on FuzzyBench-Hard, enabling reusable, versioned neural functions that eliminate remote API latency and cost.
HOW THIS AFFECTS YOU
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builderYou can replace expensive, high-latency LLM calls with small, local, specialized adapters.
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founderThis offers a path to significantly reduce inference COGS for text-processing features.