The Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) is a popular artificial intelligence (AI) benchmark that tests fluid reasoning. Humans solve ARC-AGI with ease, but it is extremely difficult for all but the most advanced AI systems. In this paper, we propose a novel, neurosymbolic ARC-AGI solver with cognitive inspiration from dual process theory and vector symbolic algebra (VSA) research. Our solver structures solutions to ARC-AGI tasks as object-centric programs implemented in a bespoke DSL, and represents these programs using VSAs. Additionally, our solver synthesizes solutions to ARC-AGI tasks by integrating neural System 1 intuitions with procedural System 2 reasoning, and implements both using VSAs. Quantitatively, our solver scores 13.5 percent on ARC-AGI-1-Train and 3.5 percent on ARC-AGI-1-Eval, indicating success; qualitatively, our solver is efficient and interpretable, outperforming naive methods by 7 times and brute force methods by 235 times. Our code is available at: https://github.com/ijoffe/ARC-VSA-2026.