Large Language Models (LLMs) are increasingly used to power autonomous agents for complex, multi-step tasks. However, human-agent interaction remains pointwise and reactive: users approve or correct individual actions to mitigate immediate risks, without visibility into subsequent consequences. This forces users to mentally simulate long-term effects, a cognitively demanding and often inaccurate process. Users have control over individual steps but lack the foresight to make informed decisions. We argue that effective collaboration requires foresight, not just control. We propose simulation-in-the-loop, an interaction paradigm that enables users and agents to explore simulated future trajectories before committing to decisions. Simulation transforms intervention from reactive guesswork into informed exploration, while helping users discover latent constraints and preferences along the way. This perspective paper characterizes the limitations of current paradigms, introduces a conceptual framework for simulation-based collaboration, and illustrates its potential through concrete human-agent collaboration scenarios.

翻译：大型语言模型（LLM）正日益被用于驱动自主智能体执行复杂的多步骤任务。然而，当前的人机交互仍然是点状且反应式的：用户通过批准或纠正单个动作来规避即时风险，却无法洞察后续后果。这迫使用户必须在脑海中模拟长期效应，这一过程不仅认知负荷高，且常常不够准确。用户虽能控制单个步骤，却缺乏做出明智决策所需的预见能力。我们认为，有效的协作需要预见，而不仅仅是控制。为此，我们提出“仿真在环”这一交互范式，使用户和智能体能够在做出决策前，共同探索模拟的未来轨迹。仿真将干预从被动的猜测转变为有依据的探索，同时帮助用户在过程中发现潜在的约束和偏好。本视角论文阐述了当前范式的局限性，提出了一个基于仿真的协作概念框架，并通过具体的人机协作场景阐明了其潜力。