This paper explores whether artificial agents can develop an understanding of their partners' abilities during collaborative tasks without being explicitly programmed to do so. Researchers trained recurrent neural network (RNN) agents to play a cooperative game called "Overcooked-AI" with a variety of partners having different skill levels. The study found that these agents developed structured internal representations of their partners' task abilities, allowing them to adapt and generalize to new collaborators. This emergent partner modeling was observed to be stronger when the agents could influence their partner's actions, suggesting that environmental pressure is key. Even "blind" agents with limited information were able to develop these representations, indicating the interaction structure is sufficient.