program-as-weights-fuzzy-functions

name	program-as-weights-fuzzy-functions
description	Program-as-Weights (PAW) paradigm for fuzzy function programming. Compile natural-language specifications into compact, locally-executable neural artifacts using parameter-efficient adapters for frozen interpreters. Achieves 32B-level performance at 1/50 inference memory.
trigger	program as weights, fuzzy functions, PAW, neural artifact compilation, parameter efficient adapters, tool builder pattern
category	ai_collection/collection/skills

Program-as-Weights: Fuzzy Function Programming

Overview

Many programming tasks resist clean rule-based implementation (alerting on log lines, repairing malformed JSON, ranking search results by intent). PAW reframes the foundation model from a per-input problem solver into a tool builder: invoked once per function definition, it produces a small reusable artifact.

Architecture

1. Compiler (4B model): Trained on FuzzyBench (10M examples), emits parameter-efficient adapters
2. Interpreter (0.6B frozen model): Executes PAW programs locally
3. FuzzyBench: 10M-example dataset for compiler training

Performance

• 0.6B Qwen3 interpreter executing PAW programs matches direct prompting of Qwen3-32B
• Uses ~1/50 of inference memory
• Runs at 30 tokens/s on MacBook M3
• Subsequent calls per function application are cheap and offline

Usage Pattern

# Step 1: Define function in natural language (once)
# "Alert on important log lines" → Compiler emits adapter

# Step 2: Execute adapter locally (many times, offline)
# Cheap inference with small interpreter

Benefits

• Locality: No API calls needed after compilation
• Reproducibility: Deterministic local execution
• Cost efficiency: 1/50 memory, 30 tok/s on consumer hardware
• Offline: No network dependency after compilation

Activation Keywords

program as weights, PAW, fuzzy functions, neural compilation, parameter efficient, tool builder, FuzzyBench

Source

arXiv: 2607.02512 (2026-07-02)