TimeArena: Shaping Efficient Multitasking Language Agents in a
  Time-Aware Simulation
Yikai Zhang Siyu Yuan Caiyu Hu Kyle Richardson Yanghua Xiao Jiangjie Chen
Despite remarkable advancements in emulating human-like behavior through
Large Language Models (LLMs), current textual simulations do not adequately
address the notion of time. To this end, we introduce TimeArena, a novel
textual simulated environment that incorporates complex temporal dynamics and
constraints that better reflect real-life planning scenarios. In TimeArena,
agents are asked to complete multiple tasks as soon as possible, allowing for
parallel processing to save time. We implement the dependency between actions,
the time duration for each action, and the occupancy of the agent and the
objects in the environment. TimeArena grounds to 30 real-world tasks in
cooking, household activities, and laboratory work. We conduct extensive
experiments with various state-of-the-art LLMs using TimeArena. Our findings
reveal that even the most powerful models, e.g., GPT-4, still lag behind humans
in effective multitasking, underscoring the need for enhanced temporal
awareness in the development of language agents.