What is a fuzzy function?
Define a function in natural language, compile it, and call it from ordinary code.
Reading list
Neural compilation
and fuzzy functions
How can a description become a reusable neural program?
These papers study how to turn descriptions into reusable neural programs: from hypernetworks and generated adapters to Program-as-Weights and Compile by Training.
Programming, compilation, and evaluation
What does a neural compiler produce?
Compare generated adapters, compressed instructions, and compilation through training.
How should neural functions be evaluated?
Test new tasks and noisy specifications, then measure compilation and execution costs.
Fuzzy-function programming
Program-as-Weights
First arXiv submission · July 2, 2026
Program-as-Weights: A Programming Paradigm for Fuzzy Functions
PAW proposes fuzzy-function programming: define a function in natural language, compile it into a reusable neural program, and execute it with a lightweight interpreter. The paper implements this compiler–interpreter interface with prefixes and LoRA adapters.
Compilation through training
Compile by Training
First arXiv submission · September 3, 2026
EMNLP 2026 System Demonstrations
Compile by Training: Turning Natural-Language Specifications into Local Neural Functions
Teacher models generate examples from a function specification. Training on those examples refines the program produced by PAW’s fast compiler. The resulting function uses PAW’s program format and interpreter, with more work at compile time for higher accuracy.
Browse by topic
Yuntian Deng’s June 2026 essay, From AI as Problem Solver to AI as Tool Builder, describes the long-term vision behind PAW.
- Programs and reusable artifacts — Learn programs that can be saved and run on new inputs.
- Generated weights and adapters — Generate model weights from a task description, examples, or context.
- Context compression — Compress an instruction or context once and reuse the result across calls.
- Parameter-efficient building blocks — Adapt a frozen model with a small set of learned parameters.
- Compilation through training — Generate examples from a function specification and train a reusable neural program.