Transformers Decode the Chaos: Neural Networks Outpace Classical Attacks on PCGs

This research demonstrates that Transformer models can effectively learn and predict complex pseudorandom sequences that were previously thought to be resistant to classical cryptanalysis. By revealing how these models represent mathematical structures through curriculum learning and internal clustering, it underscores a significant shift in how we evaluate the security of modern PRNGs against AI-driven pattern recognition.