Game-playing AI research has evolved beyond simply teaching machines how to play at a high level. Instead, an in-context transformer processes a user’s historical decisions to build an internal model of how that specific individual operates. Automated scripts often rely on fixed parameters or repetitive patterns that stand out when analyzed across multiple sessions. Navigating High-Pressure, Low-Information Spots: Humans demonstrate varied, intuitive risk tolerances, whereas automated rules engines tend to fall into rigid statistical thresholds. Platform Integrity: For a network, analyzing patterns across all accounts generates signatures that distinguish real users from automated scripts.