We propose a novel Graph Continuous ThoughtMachine (Graph CTM) architecture that integrates a simulated prefrontal cortex to enable adaptive problem-solving and decision-making. The Graph CTM leverages graph neural networks to process complex data streams, while the simulatedprefrontal cortex modulates node activity to selectively focus on relevant information. Through reinforcement learning, the modelnavigates graph space to converge on optimal solutions, guided by the information contained in learnt node property vectors. The simulated prefrontal cortex regulates the flow of information by adjusting the disposition of nodes to lead to the next instantiationof the graph network. The Graph CTM incorporates an attention mechanism that integrates the internal state of the graph as input, which is modulated by outputs from the model’s neuralsynchronization matrix. This modulation enables the algorithm to selectively focus on specific subgraphs or node subsets,correlatingthem with the input, effectively emulating short-term and long-term memory mechanisms when attending to both the input and internal representation. By dynamically weighting the importance of different graph components, the model can adaptively process and retain relevant information, facilitating more accurate andcontext-dependent decision-making.