An example of dynamic environment changes that challenge workflow action utilization. However, workflow actions do not always lead to task success. A representative example is shown in Figure 7. When booking flights, users often input a city name such as "New York," yet the system often pops up some nearby airports to support next-step search. While one can induce a book flight workflow that enters all required data via a pre-determined action sequence, the action to choose pop-up airports is executed without seeing the intermediate states with available pop-up options, and is not flexible enough to do so. More advanced techniques such as granting real-time state access or dynamic execution loops can be promising to solve this issue, and we encourage future work to leverage the AWM framework to explore these.

An example of dynamic environment changes that challenge workflow action utilization. However, workflow actions do not always lead to task success. A representative example is shown in Figure 7. When booking flights, users often input a city name such as "New York," yet the system often pops up some nearby airports to support next-step search. While one can induce a book flight workflow that enters all required data via a pre-determined action sequence, the action to choose pop-up airports is executed without seeing the intermediate states with available pop-up options, and is not flexible enough to do so. More advanced techniques such as granting real-time state access or dynamic execution loops can be promising to solve this issue, and we encourage future work to leverage the AWM framework to explore these.

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Despite the potential of language model-based agents to solve real-world tasks such as web navigation, current methods still struggle with long-horizon tasks with complex action trajectories. In contrast, humans can flexibly solve complex tasks by learning reusable task workflows from past experiences and using them to guide future actions. To buil...

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