Program-as-Weights: A Programming Paradigm for Fuzzy Functions

Many practical programming tasks—like flagging important log entries or fixing malformed JSON—don't fit clean rules but are usually solved by expensive, non-reproducible calls to large LLM APIs. This paper proposes compiling such "fuzzy" natural-language specifications into small, locally-run neural adapters instead. Using a compact 4B compiler and a lightweight 0.6B interpreter, the resulting programs match much larger models' performance while using a fraction of the memory, running efficiently even on a laptop. This approach could let developers embed cheap, offline, reproducible "fuzzy logic" directly into applications, reducing dependency on cloud LLM APIs for narrowly scoped tasks. Authors: Wentao Zhang, Liliana Hotsko, Woojeong Kim, Pengyu Nie, Stuart Shieber, Yuntian Deng Paper: https://arxiv.org/abs/2607.02512v1