Neuro-Genetic Structural Synthesis and Autonomous Discovery

Abstract

This repository presents an experimental framework for autonomous program synthesis and emergent mathematical concept discovery via neuro-genetic evolution. The system operates without human-defined objectives, synthesizing executable programs and inferring novel computational goals through temporal inversion of observed input/output patterns.

System Components

Module	Language	Role
neuro_genetic_synthesizer.py	Python	Hybrid evolutionary AST generation
self_purpose_engine.py	Python	Autonomous goal induction via temporal inversion
concept_transfer.py	Python	Cross-domain concept generalization
rs_machine/	Rust (PyO3)	High-performance stack-based execution VM
autonomous_rsi_loop.py	Python	Recursive self-improvement (RSI) orchestration

Architecture

graph TD
    subgraph "Decision & Logic (Python)"
        Orchestrator["Systemtest.py"]
        Brain["Neuro-Genetic Synthesizer"]
        Purpose["SelfPurposeEngine"]
    end
    subgraph "Execution Layer (Rust)"
        RustVM["rs_machine (PyO3 VM)"]
    end
    Purpose -->|Induce Goal| Orchestrator
    Orchestrator -->|Evolve Solution| Brain
    Brain -->|Compile & Evaluate| RustVM
    RustVM -->|Return Fitness| Brain

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Empirical Results

• Emergent Complexity (2026-01-11): The system autonomously evolved a 4.6 KB genetic program (triangular_g1662) approximating the triangular number sequence (T(n) = n(n+1)/2) using deeply nested control-flow primitives (if_gt, div, mod, mul), without hardcoded target logic.
• Autonomous Goal Discovery: Verified induction of arithmetic progressions from unlabeled expression outputs (EMERGENT PURPOSE: linear_d2).
• Persistence: 147+ evolutionary checkpoints persisted to disk autonomously.

Installation

Prerequisites: Python 3.8+, Rust toolchain, maturin

# Build Rust acceleration layer
cd rs_machine && maturin develop --release && cd ..

# Run autonomous discovery loop
python Systemtest.py hrm-life

The system gracefully degrades to a pure-Python interpreter if the Rust binary is unavailable.

Research Context

This system constitutes a preliminary empirical investigation into open-ended program synthesis at the intersection of genetic programming, meta-learning, and recursive self-improvement (RSI). The SelfPurposeEngine implements a form of intrinsic motivation through inverse problem generation—enabling the system to define and pursue novel computational objectives without external reward signals.