Program-as-Weights: A Programming Paradigm for Fuzzy Functions
- Published
- Source
- arXiv
- Paper number
- 551
- Field
- Machine Learning
- arXiv ID
- 2607.02512
Key points
- The paper proposes a new paradigm that compiles a natural-language specification into a hybrid neural program composed of pseudo-program text and LoRA weights.
- It releases FuzzyBench-10M, a fuzzy-function dataset with 29 topic variants, more than 800 categories, and 10 million examples.
- A 0.6B interpreter plus the PAW program outperforms direct prompting of Qwen3-32B in accuracy, 73.78 percent versus 68.70 percent.
- When quantized on a MacBook M3, it runs offline at 30 tokens per second with a 430 MB shared base and 23 MB per-function LoRA.
- By replacing only the compiler in a vision-language model, the approach can extend to image-conditioned fuzzy tasks.
- The compiled programs are reusable artifacts that support versioning, package-manager distribution, and two-line Python or JavaScript API calls.
Paper links
External research summaries. These are not HDATF publications or measured product results.