让人类与智能体在决策前先模拟未来，提升协作的预见性。
From Control to Foresight: Simulation as a New Paradigm for Human-Agent Collaboration
- 引入仿真回路机制，让用户预演决策后果
- 用户可提前发现潜在约束与偏好，避免错误决策
- 适合需要长期规划的复杂人机协作场景
大型语言模型（LLMs）正被广泛用于驱动自主智能体完成复杂、多步骤任务。然而，当前人机交互仍以逐点响应为主：用户只能对单个动作进行批准或修正，无法预知后续影响，不得不靠心理模拟推演长期后果，这一过程认知负担重且易出错。用户虽有操作控制权，却缺乏前瞻视野。本文认为有效协作需具备预见性而非仅限于控制。为此提出“仿真回路”交互范式，使用户与智能体能在实际执行前共同探索未来可能的发展轨迹。该机制将干预从被动猜测转变为有依据的探索，并帮助用户发现隐含的约束与偏好。本文剖析现有范式的局限，构建基于仿真的协作概念框架，并通过具体人机协作场景展示其潜力。
原文摘要 · Abstract (English)
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.
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