对比人类与语言智能体在反复对战中的行为演化，发现当前智能体进化不持久。

Large language model agents are increasingly evaluated through games, but most benchmarks emphasize final outcomes rather than how players learn from repeated interaction. We study experience-sensitive game learning: how gameplay experience changes the decision-making behavior of humans and language agents. We formulate experience-sensitive game learning as a framework for analyzing behavioral change across repeated gameplay, rather than only final score or win rate. We introduce a suite of interactive games with reusable strategic structure, together with cross-game greedy-to-global metrics and game-specific behavioral diagnostics that make experience-driven change observable from action traces. We also collect repeated-game trajectories from human players and evaluate recent self-evolving language agents in the same behavioral metric space. Our results show that human players exhibit interpretable and relatively stable shifts from locally greedy heuristics toward more global strategic decisions. In contrast, current self-evolving agents often show noisy and transient gains, suggesting that existing self-evolution methods remain limited in converting gameplay experience into durable changes in decision-making behavior.