Embodied agents are increasingly capable of perceiving, planning, and acting autonomously in dynamic environments, demonstrating strong performance on complex cooperative tasks. Recent advances typically assume that cooperative tasks are known in advance. In real-world applications, however, hidden-cooperation tasks are more common: the need for cooperation only becomes apparent after an agent observes and interacts with task-related targets. In this paper, we first present HiddenCoop, a benchmark for effectively evaluating agent cooperation in hidden-cooperation tasks. We further propose Discovery-Probability Graph Planning (DPGP), a method for enabling effective agent cooperation in such tasks. At the core of DPGP is a discovery-probability graph that encodes the probability distribution over the number and locations of task-related targets, facilitating effective and efficient information exchange among agents. A search algorithm is then used to derive action plans that minimize expected costs. Experiments show that DPGP achieves cooperative-task success rates of 99.3% on C-WAH and 58.3% on TDW-MAT, surpassing all compared baselines, while also reducing overall embodied execution time and substantially lowering LLM token usage. Our code is available at https://github.com/hnu-vis/DPGP.

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