Reverse engineering mobile systems eventually teaches you that code behaves more like a set of polite guidelines than binding instructions. You instrument one unremarkable routine and the runtime responds by activating binder paths, scheduler branches, and JIT transitions that look suspiciously like improvisation. A minor permission check can light up peripheral services as if the platform wants to show you something it forgot to mention in the documentation. After enough traces, you start treating the whole system as a high-dimensional dynamical model that expresses itself through side effects. The logs insist the world is stable, yet every microstate transition quietly suggests that the system is participating in its own research project.

Machine learning only adds to the fun. Feed a transformer a few gigabytes of event graphs and it begins mapping out latent manifolds that correspond to drift in privilege boundaries or shifts in memory residency, often with more confidence than any sane model should have. Contrastive pipelines happily separate two execution states that appear identical to human inspection, which is the computational equivalent of raising an eyebrow. Graph encoders reveal state machines you were not entirely sure existed. Gradient-informed detectors nudge you toward transition points you would have missed on your fiftieth review of the trace. When the platform behaves like a cryptic puzzle and the ML pipeline behaves like a colleague who enjoys pointing out oddities, reverse engineering feels less like deciphering a machine and more like having an ongoing technical conversation with one.