Compile English function descriptions into 22 MB neural programs that run locally [P]

We built a system, ProgramAsWeights (PAW), where a neural compiler takes a plain-English function description and produces a "neural program" (a combination of a continuous LoRA adapter and a discrete pseudo-program). At inference time, these adapt a fixed interpreter to perform the specified task. The target use case is fuzzy functions: tasks that are easy to describe in language but painful to implement with rigid rules.

A concrete fuzzy function: word-guessing

Consider the function "given a player's hint about a secret word, output the word." A keyword matcher will never bridge "fluffy thing that purrs" -> cat. A prompted LLM would work but is way too heavy to ship. We built this exact function as a single neural program adapting a 0.6B-parameter interpreter and turned it into a playable browser game called Alien Taboo:

https://programasweights.com/alien

Model Architecture

• The interpreter (Qwen3 0.6B or GPT-2 124M) weights are never modified. All task-specific behavior comes from the compiled program.
• The compiler itself is a finetuned Qwen3-4B that takes a spec as input and outputs the LoRA weights + pseudo-program in a single forward pass. No gradient descent at compile time.
• Data: 10M (English description, function input, function output) triples synthesised by GPT-5.2
• Training: The compiler is finetuned end-to-end so the compiler's outputs make the interpreter solve the spec. The interpreter is frozen.

Results on FuzzyBench (fuzzy function tasks described in natural language, huggingface.co/datasets/yuntian-deng/fuzzy_bench_verified):

The PAW-adapted 0.6B beats the 50× larger raw-prompted 32B on this benchmark. The GPT-2 interpreter is small enough for browser inference via WebAssembly (~134 MB base + ~5 MB per program).

Usage

pip install programasweights import programasweights as paw f = paw.compile_and_load("Classify if this is urgent or not.") f("Need your signature by EOD") # "urgent" 

Compilation is a hosted API call (the 4B compiler is ~16 GB on GPU); inference is fully local through llama-cpp-python.

Links

• Demo + docs: https://programasweights.com
• Github: https://github.com/programasweights/programasweights-python