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Compile by Training: Turning Natural-Language Specifications into Local Neural Functions

Paper recorded by Signals 4 on 2026-09-03 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-03 on arXiv · recorded by Signals 4 on 2026-09-04

Category: cs.AI · 人工智能 · first seen 2026-09-04

Abstract

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 tra

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#181 most recent of 300 cs.AI papers we have recorded · ↑ newer: How Does mHC Use Its Residual Streams? Selective Routing and Near-Iden · ↓ older: Clean Engineering, Unstable Measurement: A Preregistered Reliability F
Cite this page: Compile by Training: Turning Natural-Language Specifications into Local Neural Functions: the #181 most recent of 300 cs.AI papers we have recorded (as of 2026-09-03). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/compile-by-training-turning-natural-language-specifications-into-local-neural-fu.html
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