We study the problem of human-AI collaboration for unseen human partners with evolving strategies. Existing zero-shot coordination algorithms enable agents to cooperate with human partners without prior adaptation. However, these methods typically assume that the human strategies remain fixed throughout the interaction. As a result, they are unable to accommodate shifts in human behavior. To address this limitation, we propose IB-ToM, a Theory of Mind module that leverages the Information Bottleneck principle to model the evolving behavioral patterns of human partners over time. IB-ToM learns a compact, behaviorally relevant latent representation from the human partner’s past observations and actions, which is subsequently incorporated into the agent’s policy to improve coordination performance. Furthermore, the Information Bottleneck constraint ensures that the ToM module extracts features most relevant to decision-making. Experimental results in the Overcooked environment demonstrate that IB-ToM significantly improves human-agent collaboration across a range of zero-shot coordination algorithms, yielding up to 89.68% higher rewards and demonstrating strong generalization to unseen partners and tasks.