Self-Evolving Neuromorphic Machines: Automated Hypothesis Generation and Program Synthesis

Neural networks are typically static after training, and architectural modifications require manual engineering. In this paper, we introduce a self-evolving neuromorphic machine designed for online hypothesis search and automated program synthesis. Operating under pure integer constraints, the machine continuously monitors its predictive performance using a sliding correctness window. If prediction accuracy drops below a dynamically adjusted threshold, the machine initiates an evolutionary search across a space of context configurations, decay coefficients, and hashing functions. New candidate architectures (hypotheses) are generated, compiled, and executed in sandboxed virtual slots. The optimal candidate is dynamically elected to replace the parent node.