Compile by Training: Turning Natural-Language Specifications into Local Neural Functions
- 类型：arxiv
- 标识：2609.04199
- 链接：https://arxiv.org/abs/2609.04199
- 主分类：engineering
- 形态：method
- TLDR：Many recurring text functions are easy to describe but difficult to implement with rules, while calling a large remote model for every input introduces repeated cost, latency, and dependency on a provider. We present compile by training, which turns a natural-language specification into a reusable neural function. At compile time, teacher models generate task-specific examples that are used to train a small adapter for a compact interpreter. The resulting function runs without the teachers and can be stored, versioned, and composed like ordinary software. On FuzzyBench-Hard, a subset on which
- 待LLM分类：否
- 来源文件：
- /inbox/tom/_candidates/2026-09-04-agent-rag-longcontext-candidates.json