Study Compares Human and AI Learning in Interactive Games

A new study from arXiv (paper 2608.07490) investigates how humans and large language model agents learn from repeated gameplay, introducing a framework called 'experience-sensitive game learning.' The research, conducted by an unnamed team, moves beyond traditional benchmarks that focus on final outcomes like scores or win rates, instead analyzing behavioral changes across repeated interactions. The authors developed a suite of interactive games with reusable strategic structures, along with cross-game greedy-to-global metrics and game-specific behavioral diagnostics to make experience-driven changes observable from action traces. They collected repeated-game trajectories from human players and evaluated recent self-evolving language agents within the same behavioral metric space. Preliminary results indicate that human players exhibit interpretable and relatively stable shifts in behavior, whereas language agents show different learning patterns. The study aims to provide a more nuanced understanding of how both humans and AI adapt through experience, with potential implications for AI evaluation and game design. The paper is available on arXiv under the identifier 2608.07490.