Most day-to-day text processing tasks—intent routing, JSON repair, log triage, fuzzy search—are "fuzzy": they resist brittle rule systems but don't need a 30B model call per input. The paper's core insight is to turn a foundation model from a per-input problem solver into a tool builder: compile once into a small neural artifact and run that artifact locally and cheaply.
Program-as-Weights: A Programming Paradigm for Fuzzy Functions
Compiles natural-language function specifications into compact, locally-executable neural programs (PAW) that run on a small frozen interpreter; a 4B compiler emits LoRA adapters for a 0.6B runtime to provide offline, low-memory fuzzy text functions.
Introduction
Information
- Websitearxiv.org
- OrganizationsUniversity of Waterloo, Cornell University, Harvard University
- AuthorsWentao Zhang, Liliana Hotsko, Woojeong Kim, Pengyu Nie, Stuart Shieber, Yuntian Deng
- Published date2026/07/02
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